From f18a50b2c02ed16bed459821de83b57dd819f364 Mon Sep 17 00:00:00 2001
From: Brian Rose <brose@albany.edu>
Date: Fri, 28 May 2021 13:56:35 -0400
Subject: [PATCH 1/5] First draft of self-generating cover art on landing page

---
 landing-page.ipynb | 90 ++++++++++++++++++++++++++++++++++++++++++++++
 landing-page.md    |  7 ----
 2 files changed, 90 insertions(+), 7 deletions(-)
 create mode 100644 landing-page.ipynb
 delete mode 100644 landing-page.md

diff --git a/landing-page.ipynb b/landing-page.ipynb
new file mode 100644
index 000000000..fa81ae8dd
--- /dev/null
+++ b/landing-page.ipynb
@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "24611359",
+   "metadata": {},
+   "source": [
+    "# Project Pythia Foundations\n",
+    "\n",
+    "## A community learning resource for Python-based computing in the geosciences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "356d3fba",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x612 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from cartopy import crs as ccrs, feature as cfeature\n",
+    "\n",
+    "fig = plt.figure(figsize=(11, 8.5))\n",
+    "projMoll = ccrs.Mollweide(central_longitude=0)\n",
+    "ax = plt.subplot(1, 1, 1, projection=projMoll)\n",
+    "ax.set_title(\"A placeholder cover image for the Foundations book!\", fontsize=18)\n",
+    "\n",
+    "# Add in the cartographic shapefiles\n",
+    "ax.coastlines()\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue')\n",
+    "\n",
+    "ax.stock_img();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa6980e8",
+   "metadata": {},
+   "source": [
+    "Brought to you by [Project Pythia](https://projectpythia.org), this growing collection covers the foundational skills everyone needs to get started with scientific computing in the open-source Python ecosystem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c986b6d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/landing-page.md b/landing-page.md
deleted file mode 100644
index dc2dc9c54..000000000
--- a/landing-page.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Project Pythia Foundations
-
-Brought to you by [Project Pythia](https://projectpythia.org), this collection will cover the foundational skills everyone needs to get started with scientific computing in the open-source Python ecosystem.
-
-```{note}
-This content is under construction!
-```

From 76dc9f242d0f7fd5bbf44809a9d0e4ef5e4eade2 Mon Sep 17 00:00:00 2001
From: Brian Rose <brose@albany.edu>
Date: Fri, 28 May 2021 14:48:55 -0400
Subject: [PATCH 2/5] Suppress cartopy warnings in landing-page notebook

---
 landing-page.ipynb | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/landing-page.ipynb b/landing-page.ipynb
index fa81ae8dd..6c848e253 100644
--- a/landing-page.ipynb
+++ b/landing-page.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "356d3fba",
    "metadata": {
     "tags": [
@@ -34,6 +34,11 @@
     }
    ],
    "source": [
+    "#  Suppress warnings issued by Cartopy when downloading data files\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "from cartopy import crs as ccrs, feature as cfeature\n",
     "\n",
@@ -46,7 +51,7 @@
     "ax.coastlines()\n",
     "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue')\n",
     "\n",
-    "ax.stock_img();\n"
+    "ax.stock_img();"
    ]
   },
   {

From ab66288094365ece55a7bfd0dce766c7f2f9e2f4 Mon Sep 17 00:00:00 2001
From: Brian Rose <brose@albany.edu>
Date: Mon, 14 Jun 2021 15:43:04 -0400
Subject: [PATCH 3/5] Use pythia-datasets in pandas notebook

---
 core/pandas/enso_data.csv             |  473 ---
 core/pandas/pandas_fullNotebook.ipynb | 4181 ++-----------------------
 environment.yml                       |    4 +
 3 files changed, 263 insertions(+), 4395 deletions(-)
 delete mode 100644 core/pandas/enso_data.csv

diff --git a/core/pandas/enso_data.csv b/core/pandas/enso_data.csv
deleted file mode 100644
index 1ada455af..000000000
--- a/core/pandas/enso_data.csv
+++ /dev/null
@@ -1,473 +0,0 @@
-datetime,Nino12,Nino12anom,Nino3,Nino3anom,Nino4,Nino4anom,Nino34,Nino34anom
-1982-01-01,24.29,-0.17,25.87,0.24,28.3,0.0,26.72,0.15
-1982-02-01,25.49,-0.58,26.38,0.01,28.21,0.11,26.7,-0.02
-1982-03-01,25.21,-1.31,26.98,-0.16,28.41,0.22,27.2,-0.02
-1982-04-01,24.5,-0.97,27.68,0.18,28.92,0.42,28.02,0.24
-1982-05-01,23.97,-0.23,27.79,0.71,29.49,0.7,28.54,0.69
-1982-06-01,22.89,0.07,27.46,1.03,29.76,0.92,28.75,1.1
-1982-07-01,22.47,0.87,26.44,0.82,29.38,0.58,28.1,0.88
-1982-08-01,21.75,1.1,26.15,1.16,29.04,0.36,27.93,1.11
-1982-09-01,21.8,1.44,26.52,1.67,29.16,0.47,28.11,1.39
-1982-10-01,22.94,2.12,27.11,2.19,29.38,0.72,28.64,1.95
-1982-11-01,24.59,3.0,27.62,2.64,29.23,0.6,28.81,2.16
-1982-12-01,26.13,3.34,28.39,3.25,29.15,0.66,29.21,2.64
-1983-01-01,27.42,2.96,28.92,3.29,29.0,0.7,29.36,2.79
-1983-02-01,28.09,2.02,28.92,2.55,28.79,0.69,29.13,2.41
-1983-03-01,28.68,2.16,29.1,1.96,28.76,0.57,29.03,1.81
-1983-04-01,28.56,3.09,29.12,1.62,28.85,0.35,28.91,1.13
-1983-05-01,28.19,3.99,28.97,1.89,29.08,0.29,28.89,1.04
-1983-06-01,27.44,4.62,28.15,1.72,28.88,0.04,28.24,0.59
-1983-07-01,25.95,4.35,26.62,1.0,28.65,-0.15,27.07,-0.15
-1983-08-01,23.78,3.13,25.87,0.88,28.38,-0.3,26.53,-0.29
-1983-09-01,22.24,1.88,25.24,0.39,28.23,-0.46,26.44,-0.28
-1983-10-01,21.86,1.04,24.61,-0.31,27.75,-0.91,25.87,-0.82
-1983-11-01,21.9,0.31,24.17,-0.81,27.76,-0.87,25.58,-1.07
-1983-12-01,23.01,0.22,24.44,-0.7,27.82,-0.67,25.59,-0.98
-1984-01-01,24.18,-0.28,24.82,-0.81,27.64,-0.66,25.64,-0.93
-1984-02-01,25.18,-0.89,26.22,-0.15,27.25,-0.85,26.39,-0.33
-1984-03-01,26.0,-0.52,27.12,-0.02,27.21,-0.98,26.86,-0.36
-1984-04-01,25.16,-0.31,27.34,-0.16,27.7,-0.8,27.39,-0.39
-1984-05-01,23.23,-0.97,26.46,-0.62,27.95,-0.84,27.39,-0.46
-1984-06-01,21.96,-0.86,25.38,-1.05,28.13,-0.71,26.86,-0.79
-1984-07-01,21.24,-0.36,24.96,-0.66,28.35,-0.45,26.74,-0.48
-1984-08-01,20.17,-0.48,24.5,-0.49,28.17,-0.51,26.34,-0.48
-1984-09-01,20.37,0.01,24.35,-0.5,28.61,-0.08,26.43,-0.29
-1984-10-01,20.52,-0.3,23.95,-0.97,28.28,-0.38,25.93,-0.76
-1984-11-01,21.5,-0.09,24.03,-0.95,27.99,-0.64,25.41,-1.24
-1984-12-01,22.58,-0.21,23.7,-1.44,27.44,-1.05,25.0,-1.57
-1985-01-01,23.59,-0.87,24.51,-1.12,27.71,-0.59,25.43,-1.14
-1985-02-01,24.87,-1.2,25.19,-1.18,27.55,-0.55,25.67,-1.05
-1985-03-01,25.74,-0.78,26.11,-1.03,27.38,-0.81,26.23,-0.99
-1985-04-01,24.25,-1.22,26.52,-0.98,27.72,-0.78,26.8,-0.98
-1985-05-01,22.29,-1.91,26.12,-0.96,28.06,-0.73,27.11,-0.74
-1985-06-01,21.75,-1.07,25.6,-0.83,28.08,-0.76,26.86,-0.79
-1985-07-01,20.44,-1.16,24.74,-0.88,28.28,-0.52,26.69,-0.53
-1985-08-01,19.29,-1.36,24.4,-0.59,28.32,-0.36,26.5,-0.32
-1985-09-01,19.44,-0.92,24.15,-0.7,28.33,-0.36,26.25,-0.47
-1985-10-01,19.9,-0.92,24.15,-0.77,28.28,-0.38,26.19,-0.5
-1985-11-01,20.69,-0.9,24.28,-0.7,28.52,-0.11,26.19,-0.46
-1985-12-01,22.4,-0.39,24.29,-0.85,28.53,0.04,26.11,-0.46
-1986-01-01,24.61,0.15,24.73,-0.9,28.11,-0.19,25.79,-0.78
-1986-02-01,26.06,-0.01,25.81,-0.56,27.93,-0.17,25.94,-0.78
-1986-03-01,25.91,-0.61,26.84,-0.3,27.97,-0.22,26.65,-0.57
-1986-04-01,24.58,-0.89,27.17,-0.33,28.21,-0.29,27.44,-0.34
-1986-05-01,23.38,-0.82,26.68,-0.4,28.58,-0.21,27.5,-0.35
-1986-06-01,21.98,-0.84,26.3,-0.13,28.84,0.0,27.69,0.04
-1986-07-01,21.12,-0.48,25.7,0.08,28.9,0.1,27.37,0.15
-1986-08-01,20.97,0.32,25.02,0.03,29.04,0.36,27.15,0.33
-1986-09-01,20.44,0.08,25.25,0.4,29.18,0.49,27.33,0.61
-1986-10-01,21.07,0.25,25.62,0.7,29.38,0.72,27.57,0.88
-1986-11-01,22.03,0.44,25.92,0.94,29.4,0.77,27.73,1.08
-1986-12-01,23.0,0.21,25.86,0.72,29.19,0.7,27.7,1.13
-1987-01-01,25.3,0.84,26.69,1.06,29.02,0.72,27.91,1.34
-1987-02-01,27.14,1.07,27.42,1.05,28.93,0.83,28.02,1.3
-1987-03-01,28.01,1.49,28.2,1.06,29.04,0.85,28.47,1.25
-1987-04-01,27.17,1.7,28.49,0.99,29.21,0.71,28.8,1.02
-1987-05-01,25.58,1.38,28.22,1.14,29.25,0.46,28.75,0.9
-1987-06-01,24.06,1.24,27.71,1.28,29.53,0.69,29.03,1.38
-1987-07-01,22.78,1.18,27.07,1.45,29.47,0.67,28.8,1.58
-1987-08-01,21.73,1.08,26.52,1.53,29.41,0.73,28.58,1.76
-1987-09-01,21.45,1.09,26.57,1.72,29.51,0.82,28.39,1.67
-1987-10-01,22.39,1.57,26.2,1.28,29.61,0.95,28.07,1.38
-1987-11-01,22.63,1.04,26.13,1.15,29.8,1.17,27.99,1.34
-1987-12-01,23.47,0.68,26.2,1.06,29.44,0.95,27.6,1.03
-1988-01-01,24.64,0.18,26.12,0.49,29.13,0.83,27.32,0.75
-1988-02-01,25.74,-0.33,26.55,0.18,28.69,0.59,27.22,0.5
-1988-03-01,25.78,-0.74,27.14,0.0,28.2,0.01,27.31,0.09
-1988-04-01,24.54,-0.93,26.73,-0.77,28.15,-0.35,27.32,-0.46
-1988-05-01,23.6,-0.6,25.22,-1.86,28.36,-0.43,26.48,-1.37
-1988-06-01,21.27,-1.55,24.46,-1.97,28.13,-0.71,26.11,-1.54
-1988-07-01,20.26,-1.34,23.71,-1.91,27.88,-0.92,25.57,-1.65
-1988-08-01,19.12,-1.53,23.37,-1.62,27.68,-1.0,25.24,-1.58
-1988-09-01,19.19,-1.17,23.61,-1.24,27.63,-1.06,25.43,-1.29
-1988-10-01,19.5,-1.32,23.17,-1.75,27.06,-1.6,24.62,-2.07
-1988-11-01,20.55,-1.04,23.03,-1.95,26.76,-1.87,24.27,-2.38
-1988-12-01,21.8,-0.99,23.07,-2.07,26.75,-1.74,24.33,-2.24
-1989-01-01,24.09,-0.37,24.15,-1.48,26.54,-1.76,24.53,-2.04
-1989-02-01,26.26,0.19,25.61,-0.76,26.55,-1.55,25.33,-1.39
-1989-03-01,26.66,0.14,26.02,-1.12,27.0,-1.19,25.9,-1.32
-1989-04-01,25.63,0.16,26.67,-0.83,27.54,-0.96,26.69,-1.09
-1989-05-01,23.18,-1.02,26.37,-0.71,28.14,-0.65,27.09,-0.76
-1989-06-01,22.0,-0.82,26.08,-0.35,27.94,-0.9,26.98,-0.67
-1989-07-01,21.12,-0.48,25.28,-0.34,28.2,-0.6,26.74,-0.48
-1989-08-01,20.32,-0.33,24.56,-0.43,28.14,-0.54,26.33,-0.49
-1989-09-01,19.87,-0.49,24.45,-0.4,28.25,-0.44,26.25,-0.47
-1989-10-01,20.33,-0.49,24.49,-0.43,28.39,-0.27,26.26,-0.43
-1989-11-01,21.31,-0.28,24.56,-0.42,28.23,-0.4,26.24,-0.41
-1989-12-01,22.19,-0.6,24.71,-0.43,28.52,0.03,26.38,-0.19
-1990-01-01,24.02,-0.44,25.34,-0.29,28.56,0.26,26.55,-0.02
-1990-02-01,25.88,-0.19,26.37,0.0,28.62,0.52,26.95,0.23
-1990-03-01,26.16,-0.36,27.03,-0.11,28.78,0.59,27.46,0.24
-1990-04-01,25.22,-0.25,27.67,0.17,28.93,0.43,28.02,0.24
-1990-05-01,24.05,-0.15,27.35,0.27,28.96,0.17,28.06,0.21
-1990-06-01,22.68,-0.14,26.45,0.02,28.94,0.1,27.58,-0.07
-1990-07-01,21.0,-0.6,25.45,-0.17,28.98,0.18,27.25,0.03
-1990-08-01,20.25,-0.4,25.06,0.07,29.17,0.49,27.05,0.23
-1990-09-01,20.13,-0.23,24.85,0.0,29.04,0.35,26.75,0.03
-1990-10-01,20.28,-0.54,24.9,-0.02,29.23,0.57,26.98,0.29
-1990-11-01,20.84,-0.75,24.82,-0.16,29.06,0.43,26.72,0.07
-1990-12-01,22.45,-0.34,25.08,-0.06,29.14,0.65,26.91,0.34
-1991-01-01,23.86,-0.6,25.65,0.02,29.0,0.7,27.01,0.44
-1991-02-01,25.97,-0.1,26.27,-0.1,28.73,0.63,26.93,0.21
-1991-03-01,26.51,-0.01,26.99,-0.15,28.64,0.45,27.25,0.03
-1991-04-01,24.99,-0.48,27.32,-0.18,29.13,0.63,27.98,0.2
-1991-05-01,24.37,0.17,27.58,0.5,29.42,0.63,28.35,0.5
-1991-06-01,23.05,0.23,27.34,0.91,29.35,0.51,28.36,0.71
-1991-07-01,22.05,0.45,26.57,0.95,29.26,0.46,27.92,0.7
-1991-08-01,21.08,0.43,25.47,0.48,29.25,0.57,27.44,0.62
-1991-09-01,20.75,0.39,25.05,0.2,29.19,0.5,27.07,0.35
-1991-10-01,21.13,0.31,25.6,0.68,29.44,0.78,27.63,0.94
-1991-11-01,22.18,0.59,25.98,1.0,29.45,0.82,27.86,1.21
-1991-12-01,23.43,0.64,26.52,1.38,29.45,0.96,28.37,1.8
-1992-01-01,24.83,0.37,27.0,1.37,29.06,0.76,28.41,1.84
-1992-02-01,26.68,0.61,27.67,1.3,29.02,0.92,28.63,1.91
-1992-03-01,27.76,1.24,28.33,1.19,29.08,0.89,28.83,1.61
-1992-04-01,27.68,2.21,28.72,1.22,29.42,0.92,29.14,1.36
-1992-05-01,26.31,2.11,28.43,1.35,29.46,0.67,28.99,1.14
-1992-06-01,23.82,1.0,26.66,0.23,29.31,0.47,28.02,0.37
-1992-07-01,21.95,0.35,25.53,-0.09,29.35,0.55,27.53,0.31
-1992-08-01,20.55,-0.1,24.7,-0.29,28.9,0.22,26.64,-0.18
-1992-09-01,20.06,-0.3,24.52,-0.33,28.79,0.1,26.48,-0.24
-1992-10-01,20.82,0.0,24.62,-0.3,28.69,0.03,26.34,-0.35
-1992-11-01,21.49,-0.1,24.79,-0.19,28.7,0.07,26.51,-0.14
-1992-12-01,22.48,-0.31,25.01,-0.13,28.76,0.27,26.73,0.16
-1993-01-01,24.43,-0.03,25.56,-0.07,28.6,0.3,26.69,0.12
-1993-02-01,26.49,0.42,26.61,0.24,28.41,0.31,26.97,0.25
-1993-03-01,27.17,0.65,27.54,0.4,28.6,0.41,27.66,0.44
-1993-04-01,26.44,0.97,28.45,0.95,28.93,0.43,28.59,0.81
-1993-05-01,25.15,0.95,28.16,1.08,29.13,0.34,28.82,0.97
-1993-06-01,23.76,0.94,27.11,0.68,29.17,0.33,28.28,0.63
-1993-07-01,22.06,0.46,25.77,0.15,29.2,0.4,27.55,0.33
-1993-08-01,21.05,0.4,24.93,-0.06,28.94,0.26,26.84,0.02
-1993-09-01,20.83,0.47,24.97,0.12,29.07,0.38,26.92,0.2
-1993-10-01,20.99,0.17,25.21,0.29,28.9,0.24,26.93,0.24
-1993-11-01,21.64,0.05,25.17,0.19,28.97,0.34,26.91,0.26
-1993-12-01,22.75,-0.04,25.32,0.18,28.9,0.41,26.76,0.19
-1994-01-01,24.32,-0.14,25.71,0.08,28.47,0.17,26.6,0.03
-1994-02-01,25.79,-0.28,26.07,-0.3,28.07,-0.03,26.59,-0.13
-1994-03-01,25.43,-1.09,26.89,-0.25,28.26,0.07,27.27,0.05
-1994-04-01,24.32,-1.15,27.06,-0.44,28.62,0.12,27.9,0.12
-1994-05-01,23.22,-0.98,26.97,-0.11,29.0,0.21,28.04,0.19
-1994-06-01,22.43,-0.39,26.5,0.07,29.18,0.34,27.99,0.34
-1994-07-01,21.21,-0.39,25.19,-0.43,29.4,0.6,27.35,0.13
-1994-08-01,19.7,-0.95,24.71,-0.28,29.46,0.78,27.35,0.53
-1994-09-01,20.16,-0.2,24.81,-0.04,29.23,0.54,27.0,0.28
-1994-10-01,21.53,0.71,25.53,0.61,29.45,0.79,27.49,0.8
-1994-11-01,22.41,0.82,25.87,0.89,29.63,1.0,27.87,1.22
-1994-12-01,23.61,0.82,26.07,0.93,29.5,1.01,27.87,1.3
-1995-01-01,25.33,0.87,26.34,0.71,29.2,0.9,27.55,0.98
-1995-02-01,26.43,0.36,26.87,0.5,29.01,0.91,27.45,0.73
-1995-03-01,26.12,-0.4,27.08,-0.06,28.96,0.77,27.63,0.41
-1995-04-01,24.47,-1.0,27.1,-0.4,28.89,0.39,27.93,0.15
-1995-05-01,23.1,-1.1,26.4,-0.68,29.15,0.36,27.73,-0.12
-1995-06-01,22.45,-0.37,26.2,-0.23,29.01,0.17,27.59,-0.06
-1995-07-01,21.23,-0.37,25.42,-0.2,28.78,-0.02,27.01,-0.21
-1995-08-01,20.01,-0.64,24.33,-0.66,28.43,-0.25,26.33,-0.49
-1995-09-01,20.17,-0.19,24.02,-0.83,28.25,-0.44,25.96,-0.76
-1995-10-01,20.15,-0.67,24.01,-0.91,28.07,-0.59,25.67,-1.02
-1995-11-01,21.2,-0.39,24.03,-0.95,27.97,-0.66,25.66,-0.99
-1995-12-01,22.02,-0.77,24.19,-0.95,28.07,-0.42,25.57,-1.0
-1996-01-01,23.84,-0.62,24.96,-0.67,27.92,-0.38,25.74,-0.83
-1996-02-01,25.71,-0.36,25.72,-0.65,27.57,-0.53,25.85,-0.87
-1996-03-01,26.09,-0.43,26.71,-0.43,27.71,-0.48,26.62,-0.6
-1996-04-01,23.85,-1.62,26.72,-0.78,28.07,-0.43,27.36,-0.42
-1996-05-01,22.89,-1.31,26.33,-0.75,28.43,-0.36,27.37,-0.48
-1996-06-01,21.56,-1.26,25.89,-0.54,28.55,-0.29,27.32,-0.33
-1996-07-01,20.02,-1.58,25.35,-0.27,28.5,-0.3,27.09,-0.13
-1996-08-01,19.53,-1.12,24.6,-0.39,28.54,-0.14,26.56,-0.26
-1996-09-01,19.24,-1.12,24.37,-0.48,28.43,-0.26,26.35,-0.37
-1996-10-01,19.95,-0.87,24.37,-0.55,28.42,-0.24,26.24,-0.45
-1996-11-01,20.26,-1.33,24.38,-0.6,28.33,-0.3,26.19,-0.46
-1996-12-01,21.61,-1.18,24.2,-0.94,28.44,-0.05,26.02,-0.55
-1997-01-01,23.67,-0.79,24.7,-0.93,28.41,0.11,25.96,-0.61
-1997-02-01,25.74,-0.33,25.75,-0.62,28.33,0.23,26.36,-0.36
-1997-03-01,26.95,0.43,26.98,-0.16,28.52,0.33,27.03,-0.19
-1997-04-01,26.64,1.17,27.59,0.09,29.32,0.82,28.03,0.25
-1997-05-01,26.71,2.51,28.06,0.98,29.45,0.66,28.6,0.75
-1997-06-01,26.27,3.45,28.14,1.71,29.4,0.56,28.94,1.29
-1997-07-01,25.59,3.99,28.01,2.39,29.5,0.7,28.92,1.7
-1997-08-01,24.8,4.15,27.84,2.85,29.26,0.58,28.84,2.02
-1997-09-01,24.4,4.04,27.84,2.99,29.32,0.63,28.93,2.21
-1997-10-01,24.58,3.76,28.17,3.25,29.32,0.66,29.23,2.54
-1997-11-01,25.63,4.04,28.55,3.57,29.49,0.86,29.32,2.67
-1997-12-01,26.92,4.13,28.76,3.62,29.32,0.83,29.26,2.69
-1998-01-01,28.22,3.76,28.94,3.31,29.01,0.71,29.1,2.53
-1998-02-01,28.98,2.91,28.93,2.56,28.87,0.77,28.86,2.14
-1998-03-01,29.15,2.63,29.14,2.0,28.65,0.46,28.67,1.45
-1998-04-01,28.61,3.14,29.09,1.59,28.53,0.03,28.56,0.78
-1998-05-01,27.69,3.49,28.17,1.09,28.71,-0.08,28.47,0.62
-1998-06-01,25.18,2.36,26.0,-0.43,28.61,-0.23,26.72,-0.93
-1998-07-01,23.43,1.83,25.24,-0.38,28.07,-0.73,25.94,-1.28
-1998-08-01,21.77,1.12,24.63,-0.36,27.77,-0.91,25.49,-1.33
-1998-09-01,20.87,0.51,24.19,-0.66,27.88,-0.81,25.61,-1.11
-1998-10-01,21.16,0.34,24.06,-0.86,27.33,-1.33,25.34,-1.35
-1998-11-01,21.43,-0.16,24.11,-0.87,27.23,-1.4,25.18,-1.47
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-2006-08-01,21.59,0.94,25.45,0.46,29.22,0.54,27.22,0.4
-2006-09-01,21.43,1.07,25.74,0.89,29.4,0.71,27.34,0.62
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-2006-11-01,22.69,1.1,26.07,1.09,29.63,1.0,27.73,1.08
-2006-12-01,23.45,0.66,26.36,1.22,29.47,0.98,27.76,1.19
-2007-01-01,24.99,0.53,26.5,0.87,28.93,0.63,27.26,0.69
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-2008-08-01,21.79,1.14,25.63,0.64,28.18,-0.5,26.85,0.03
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-2009-05-01,24.83,0.56,27.4,0.32,28.99,0.2,28.03,0.18
-2009-06-01,23.73,0.85,27.12,0.69,29.2,0.36,28.11,0.47
-2009-07-01,22.63,1.02,26.56,0.94,29.21,0.4,27.94,0.72
-2009-08-01,21.64,1.0,25.94,0.95,29.21,0.53,27.53,0.71
-2009-09-01,20.82,0.47,25.66,0.8,29.28,0.58,27.47,0.75
-2009-10-01,20.96,0.17,25.73,0.81,29.65,0.99,27.63,0.94
-2009-11-01,22.11,0.51,26.23,1.26,29.88,1.25,28.19,1.54
-2009-12-01,23.16,0.35,26.67,1.53,29.67,1.18,28.3,1.72
-2010-01-01,24.82,0.3,26.63,1.0,29.51,1.21,28.07,1.5
-2010-02-01,26.08,-0.06,27.12,0.75,29.1,1.0,27.94,1.22
-2010-03-01,26.24,-0.4,27.73,0.6,29.21,1.02,28.29,1.08
-2010-04-01,26.05,0.45,28.05,0.55,29.25,0.74,28.36,0.59
-2010-05-01,24.28,0.0,26.97,-0.11,29.03,0.24,27.68,-0.17
-2010-06-01,22.6,-0.27,25.75,-0.68,28.64,-0.21,27.0,-0.65
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-2010-08-01,19.27,-1.37,23.87,-1.12,27.47,-1.2,25.5,-1.32
-2010-09-01,18.9,-1.44,23.59,-1.26,27.13,-1.56,25.07,-1.65
-2010-10-01,19.06,-1.73,23.25,-1.66,27.06,-1.6,25.01,-1.68
-2010-11-01,20.03,-1.56,23.4,-1.58,27.07,-1.57,25.07,-1.58
-2010-12-01,21.48,-1.34,23.5,-1.64,26.89,-1.6,24.95,-1.62
-2011-01-01,24.08,-0.44,24.31,-1.32,26.72,-1.58,24.93,-1.64
-2011-02-01,26.22,0.08,25.55,-0.82,26.95,-1.15,25.46,-1.27
-2011-03-01,26.21,-0.43,26.39,-0.75,27.42,-0.77,26.23,-0.98
-2011-04-01,25.76,0.16,27.18,-0.32,27.86,-0.64,27.02,-0.76
-2011-05-01,24.89,0.62,26.94,-0.14,28.28,-0.51,27.42,-0.43
-2011-06-01,23.72,0.85,26.54,0.1,28.47,-0.37,27.46,-0.18
-2011-07-01,22.07,0.45,25.61,-0.01,28.47,-0.33,26.96,-0.26
-2011-08-01,20.64,0.0,24.58,-0.42,28.32,-0.36,26.19,-0.64
-2011-09-01,19.75,-0.59,24.22,-0.63,28.05,-0.64,25.98,-0.74
-2011-10-01,20.19,-0.6,23.97,-0.95,27.94,-0.72,25.72,-0.97
-2011-11-01,20.79,-0.8,23.89,-1.09,27.87,-0.77,25.6,-1.05
-2011-12-01,21.85,-0.96,24.2,-0.94,27.39,-1.1,25.53,-1.04
-2012-01-01,23.88,-0.64,24.9,-0.73,27.09,-1.21,25.49,-1.08
-2012-02-01,26.3,0.16,26.19,-0.18,27.2,-0.9,26.03,-0.69
-2012-03-01,26.91,0.27,26.92,-0.21,27.53,-0.66,26.63,-0.58
-2012-04-01,26.9,1.3,27.58,0.08,28.16,-0.34,27.38,-0.39
-2012-05-01,25.48,1.2,27.23,0.15,28.53,-0.26,27.8,-0.05
-2012-06-01,24.47,1.59,27.09,0.66,28.73,-0.11,27.95,0.31
-2012-07-01,22.61,0.99,26.54,0.92,28.86,0.06,27.75,0.53
-2012-08-01,20.99,0.35,25.72,0.73,29.1,0.42,27.55,0.73
-2012-09-01,20.83,0.49,25.28,0.43,29.12,0.43,27.24,0.51
-2012-10-01,20.68,-0.11,24.93,0.01,29.16,0.5,26.98,0.29
-2012-11-01,21.21,-0.38,25.11,0.14,29.17,0.54,27.01,0.36
-2012-12-01,22.13,-0.68,24.91,-0.23,28.71,0.23,26.46,-0.11
-2013-01-01,24.0,-0.52,25.06,-0.57,28.28,-0.02,26.16,-0.41
-2013-02-01,25.74,-0.41,25.9,-0.46,28.06,-0.04,26.32,-0.4
-2013-03-01,26.71,0.07,27.21,0.07,27.95,-0.24,27.0,-0.22
-2013-04-01,24.74,-0.86,27.35,-0.15,28.47,-0.03,27.68,-0.1
-2013-05-01,22.89,-1.38,26.39,-0.69,28.71,-0.08,27.57,-0.27
-2013-06-01,21.48,-1.4,25.8,-0.64,28.76,-0.08,27.43,-0.21
-2013-07-01,20.29,-1.33,24.97,-0.66,28.76,-0.04,26.91,-0.31
-2013-08-01,19.66,-0.98,24.44,-0.55,28.71,0.03,26.54,-0.28
-2013-09-01,19.78,-0.57,24.72,-0.13,28.7,0.01,26.65,-0.07
-2013-10-01,20.16,-0.63,24.7,-0.21,28.7,0.04,26.36,-0.33
-2013-11-01,21.06,-0.54,24.81,-0.17,28.91,0.27,26.65,0.01
-2013-12-01,22.61,-0.2,25.1,-0.04,28.64,0.15,26.53,-0.04
-2014-01-01,24.79,0.27,25.26,-0.37,28.14,-0.17,26.06,-0.51
-2014-02-01,25.4,-0.75,25.56,-0.81,28.37,0.27,26.18,-0.55
-2014-03-01,25.86,-0.78,26.9,-0.24,28.71,0.52,26.99,-0.22
-2014-04-01,25.23,-0.37,27.73,0.23,29.13,0.63,28.01,0.24
-2014-05-01,25.57,1.3,27.69,0.61,29.56,0.77,28.31,0.46
-2014-06-01,24.51,1.64,27.32,0.89,29.43,0.59,28.11,0.46
-2014-07-01,22.98,1.36,26.27,0.65,29.09,0.29,27.4,0.18
-2014-08-01,21.91,1.27,25.51,0.52,29.14,0.46,27.02,0.2
-2014-09-01,21.3,0.96,25.31,0.45,29.34,0.65,27.17,0.45
-2014-10-01,21.54,0.75,25.58,0.66,29.31,0.64,27.17,0.49
-2014-11-01,22.33,0.74,25.88,0.91,29.52,0.88,27.5,0.85
-2014-12-01,22.9,0.08,25.94,0.8,29.4,0.91,27.35,0.78
-2015-01-01,24.13,-0.39,25.99,0.36,29.16,0.86,27.1,0.53
-2015-02-01,25.59,-0.55,26.55,0.18,29.12,1.02,27.29,0.56
-2015-03-01,26.69,0.06,27.29,0.15,29.32,1.13,27.79,0.58
-2015-04-01,26.95,1.35,28.17,0.67,29.73,1.23,28.56,0.78
-2015-05-01,26.71,2.43,28.28,1.19,29.88,1.09,28.88,1.03
-2015-06-01,25.42,2.54,28.1,1.66,29.93,1.09,28.96,1.32
-2015-07-01,24.48,2.87,27.79,2.17,29.8,1.0,28.82,1.6
-2015-08-01,22.88,2.24,27.33,2.34,29.66,0.98,28.89,2.07
-2015-09-01,22.91,2.57,27.48,2.63,29.73,1.04,29.0,2.28
-2015-10-01,23.31,2.52,27.58,2.66,29.79,1.12,29.15,2.46
-2015-11-01,23.83,2.24,27.91,2.93,30.3,1.67,29.6,2.95
-2015-12-01,25.01,2.19,27.99,2.85,30.11,1.63,29.39,2.82
-2016-01-01,25.93,1.41,28.21,2.58,29.65,1.35,29.17,2.6
-2016-02-01,26.81,0.67,28.36,1.99,29.55,1.45,29.12,2.4
-2016-03-01,27.57,0.93,28.7,1.57,29.53,1.34,28.9,1.68
-2016-04-01,25.83,0.23,28.34,0.84,29.39,0.89,28.87,1.09
-2016-05-01,24.55,0.27,27.11,0.03,29.39,0.6,28.15,0.3
-2016-06-01,23.17,0.29,26.31,-0.12,29.36,0.52,27.53,-0.12
-2016-07-01,21.79,0.17,25.14,-0.48,29.06,0.26,26.73,-0.49
-2016-08-01,21.03,0.39,24.53,-0.46,28.68,-0.0,26.28,-0.54
-2016-09-01,20.87,0.53,24.67,-0.18,28.48,-0.21,26.11,-0.61
-2016-10-01,21.18,0.39,24.47,-0.45,28.26,-0.4,25.96,-0.73
-2016-11-01,21.68,0.09,24.58,-0.4,28.27,-0.37,26.1,-0.55
-2016-12-01,23.35,0.53,24.78,-0.36,28.35,-0.14,26.16,-0.41
-2017-01-01,25.75,1.23,25.61,-0.02,28.18,-0.12,26.25,-0.32
-2017-02-01,27.76,1.62,27.0,0.63,28.03,-0.07,26.87,0.14
-2017-03-01,28.52,1.89,27.7,0.57,28.13,-0.06,27.34,0.13
-2017-04-01,26.53,0.93,28.09,0.59,28.65,0.15,28.1,0.32
-2017-05-01,25.06,0.78,27.6,0.51,29.08,0.29,28.3,0.46
-2017-06-01,22.98,0.1,26.73,0.3,29.39,0.55,28.19,0.55
-2017-07-01,21.54,-0.07,25.85,0.23,29.21,0.4,27.61,0.39
-2017-08-01,20.19,-0.45,24.82,-0.17,28.87,0.19,26.67,-0.15
-2017-09-01,19.67,-0.67,24.17,-0.68,28.69,0.0,26.29,-0.43
-2017-10-01,19.45,-1.34,24.28,-0.64,28.55,-0.11,26.23,-0.46
-2017-11-01,20.44,-1.15,23.92,-1.05,28.46,-0.18,25.79,-0.86
-2017-12-01,21.44,-1.38,24.05,-1.09,28.24,-0.25,25.8,-0.77
-2018-01-01,23.71,-0.81,24.48,-1.14,28.03,-0.27,25.82,-0.75
-2018-02-01,25.57,-0.57,25.36,-1.01,27.86,-0.24,25.83,-0.9
-2018-03-01,25.83,-0.8,26.37,-0.76,28.14,-0.05,26.48,-0.73
-2018-04-01,24.58,-1.02,27.12,-0.38,28.63,0.12,27.42,-0.36
-2018-05-01,23.73,-0.54,26.94,-0.15,29.01,0.22,27.72,-0.13
-2018-06-01,22.19,-0.69,26.72,0.29,29.16,0.32,27.85,0.2
-2018-07-01,21.43,-0.19,26.05,0.43,29.1,0.3,27.52,0.3
-2018-08-01,20.66,0.02,25.14,0.15,29.19,0.51,27.11,0.29
-2018-09-01,20.31,-0.03,25.2,0.35,29.17,0.48,27.1,0.38
-2018-10-01,21.23,0.43,25.78,0.86,29.61,0.95,27.55,0.86
-2018-11-01,22.27,0.68,26.02,1.05,29.59,0.95,27.64,0.99
-2018-12-01,23.6,0.78,26.12,0.98,29.52,1.03,27.53,0.96
-2019-01-01,25.1,0.57,26.17,0.55,29.0,0.65,27.08,0.52
-2019-02-01,26.45,0.24,26.91,0.55,29.06,0.86,27.41,0.68
-2019-03-01,26.8,0.07,27.89,0.71,29.1,0.77,28.22,0.95
-2019-04-01,25.68,-0.01,28.17,0.56,29.24,0.56,28.6,0.71
-2019-05-01,24.38,-0.08,27.69,0.51,29.58,0.63,28.57,0.62
-2019-06-01,22.62,-0.38,26.81,0.27,29.62,0.6,28.24,0.47
-2019-07-01,21.34,-0.33,25.68,-0.07,29.73,0.77,27.63,0.3
-2019-08-01,20.2,-0.46,24.89,-0.15,29.5,0.68,26.97,0.1
-2019-09-01,19.5,-0.83,24.61,-0.23,29.33,0.55,26.7,-0.03
-2019-10-01,20.02,-0.71,25.12,0.17,29.64,0.88,27.31,0.59
-2019-11-01,21.32,-0.22,25.46,0.41,29.47,0.7,27.26,0.51
-2019-12-01,23.16,0.4,25.47,0.26,29.5,0.91,27.07,0.43
-2020-01-01,24.55,0.02,25.81,0.2,29.28,0.93,27.09,0.53
-2020-02-01,26.56,0.35,26.61,0.24,29.17,0.98,27.14,0.41
-2020-03-01,27.11,0.38,27.43,0.24,29.22,0.89,27.82,0.55
-2020-04-01,26.0,0.32,28.01,0.4,29.29,0.61,28.32,0.44
-2020-05-01,24.24,-0.22,26.82,-0.36,28.94,-0.01,27.59,-0.36
-2020-06-01,22.13,-0.87,25.75,-0.79,29.07,0.05,27.3,-0.47
-2020-07-01,20.44,-1.23,25.08,-0.66,28.83,-0.13,26.89,-0.44
-2020-08-01,19.69,-0.97,24.42,-0.62,28.47,-0.35,26.18,-0.69
-2020-09-01,19.48,-0.85,23.58,-1.26,28.29,-0.49,25.77,-0.96
-2020-10-01,19.67,-1.07,23.61,-1.34,27.89,-0.87,25.3,-1.42
-2020-11-01,20.94,-0.61,23.82,-1.23,27.91,-0.86,25.34,-1.42
-2020-12-01,22.16,-0.6,24.38,-0.83,27.65,-0.95,25.53,-1.12
-2021-01-01,23.89,-0.64,25.06,-0.55,27.1,-1.25,25.58,-0.99
-2021-02-01,25.55,-0.66,25.8,-0.57,27.2,-1.0,25.81,-0.92
-2021-03-01,26.48,-0.26,26.8,-0.39,27.79,-0.55,26.75,-0.51
-2021-04-01,24.89,-0.8,26.96,-0.65,28.47,-0.21,27.4,-0.49
diff --git a/core/pandas/pandas_fullNotebook.ipynb b/core/pandas/pandas_fullNotebook.ipynb
index 1781edbdc..666e1c0c4 100644
--- a/core/pandas/pandas_fullNotebook.ipynb
+++ b/core/pandas/pandas_fullNotebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1d7850d7",
+   "id": "9f59d3f1",
    "metadata": {},
    "source": [
     "<center><img src=\"https://github.com/pandas-dev/pandas/raw/master/web/pandas/static/img/pandas.svg\" alt=\"pandas Logo\" style=\"width: 800px;\"/></center>\n",
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "449d3086-2fa1-4520-a390-8ba043db7258",
+   "id": "b8627bb7",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\">\n",
@@ -33,17 +33,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0912eaec",
+   "execution_count": null,
+   "id": "daf58736",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "from pythia_datasets import DATASETS  # Project Pythia's custom store of example data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "85f7c6a5",
+   "id": "dab3da7f",
    "metadata": {},
    "source": [
     "## The pandas [`DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe)...\n",
@@ -56,19 +57,47 @@
     "Let's take a look by reading in some `.csv` data, which comes from the NCDC teleconnections database, including various El Niño Southern Oscillation (ENSO) indices! [[ref](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/)]."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1f2329c8",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "    <b>Tip:</b> Here we're getting the data from Project Pythia's custom library of example data, which we already imported above with <code>from pythia_datasets import DATASETS</code>. The <code>DATASETS.fetch()</code> method will automatically download and cache our example data file <code>enso_data.csv</code> locally.\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "ed076336-6bb7-4008-ba1c-451f08b9b31f",
+   "execution_count": null,
+   "id": "0be820cd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv('enso_data.csv')"
+    "filepath = DATASETS.fetch('enso_data.csv')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ecd8e8f4-2857-43e7-a6c3-8484f36e2d8f",
+   "id": "68316c6c",
+   "metadata": {},
+   "source": [
+    "Once we have a valid path to a data file that Pandas knows how to read, we can open it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e99652d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(filepath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae9dcbbd",
    "metadata": {},
    "source": [
     "If we print out our dataframe, you will notice that is text based, which is okay, but not the \"best\" looking output"
@@ -76,51 +105,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "fd65e146-f213-4156-859c-1f3b30c5ea24",
+   "execution_count": null,
+   "id": "25a23571",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       datetime  Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  \\\n",
-      "0    1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   \n",
-      "1    1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   \n",
-      "2    1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   \n",
-      "3    1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   \n",
-      "4    1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   \n",
-      "..          ...     ...         ...    ...        ...    ...        ...   \n",
-      "467  2020-12-01   22.16       -0.60  24.38      -0.83  27.65      -0.95   \n",
-      "468  2021-01-01   23.89       -0.64  25.06      -0.55  27.10      -1.25   \n",
-      "469  2021-02-01   25.55       -0.66  25.80      -0.57  27.20      -1.00   \n",
-      "470  2021-03-01   26.48       -0.26  26.80      -0.39  27.79      -0.55   \n",
-      "471  2021-04-01   24.89       -0.80  26.96      -0.65  28.47      -0.21   \n",
-      "\n",
-      "     Nino34  Nino34anom  \n",
-      "0     26.72        0.15  \n",
-      "1     26.70       -0.02  \n",
-      "2     27.20       -0.02  \n",
-      "3     28.02        0.24  \n",
-      "4     28.54        0.69  \n",
-      "..      ...         ...  \n",
-      "467   25.53       -1.12  \n",
-      "468   25.58       -0.99  \n",
-      "469   25.81       -0.92  \n",
-      "470   26.75       -0.51  \n",
-      "471   27.40       -0.49  \n",
-      "\n",
-      "[472 rows x 9 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "11e7d697-f5ae-4487-a74c-2406afcdfafb",
+   "id": "f22bb442",
    "metadata": {},
    "source": [
     "Instead, if we just use the pandas dataframe itself (without wrapping it in `print`), we have a nicely rendered table which is native to pandas and Jupyter Notebooks. See how much nicer that looks?"
@@ -128,222 +123,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "4ff69abc",
+   "execution_count": null,
+   "id": "b8942e69",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1982-01-01</td>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1982-02-01</td>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>26.70</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1982-03-01</td>\n",
-       "      <td>25.21</td>\n",
-       "      <td>-1.31</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1982-04-01</td>\n",
-       "      <td>24.50</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.02</td>\n",
-       "      <td>0.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1982-05-01</td>\n",
-       "      <td>23.97</td>\n",
-       "      <td>-0.23</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>28.54</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>467</th>\n",
-       "      <td>2020-12-01</td>\n",
-       "      <td>22.16</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>24.38</td>\n",
-       "      <td>-0.83</td>\n",
-       "      <td>27.65</td>\n",
-       "      <td>-0.95</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>-1.12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>468</th>\n",
-       "      <td>2021-01-01</td>\n",
-       "      <td>23.89</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>25.06</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>-1.25</td>\n",
-       "      <td>25.58</td>\n",
-       "      <td>-0.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>469</th>\n",
-       "      <td>2021-02-01</td>\n",
-       "      <td>25.55</td>\n",
-       "      <td>-0.66</td>\n",
-       "      <td>25.80</td>\n",
-       "      <td>-0.57</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-1.00</td>\n",
-       "      <td>25.81</td>\n",
-       "      <td>-0.92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>470</th>\n",
-       "      <td>2021-03-01</td>\n",
-       "      <td>26.48</td>\n",
-       "      <td>-0.26</td>\n",
-       "      <td>26.80</td>\n",
-       "      <td>-0.39</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>-0.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>2021-04-01</td>\n",
-       "      <td>24.89</td>\n",
-       "      <td>-0.80</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-0.65</td>\n",
-       "      <td>28.47</td>\n",
-       "      <td>-0.21</td>\n",
-       "      <td>27.40</td>\n",
-       "      <td>-0.49</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>472 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       datetime  Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  \\\n",
-       "0    1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   \n",
-       "1    1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   \n",
-       "2    1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   \n",
-       "3    1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   \n",
-       "4    1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   \n",
-       "..          ...     ...         ...    ...        ...    ...        ...   \n",
-       "467  2020-12-01   22.16       -0.60  24.38      -0.83  27.65      -0.95   \n",
-       "468  2021-01-01   23.89       -0.64  25.06      -0.55  27.10      -1.25   \n",
-       "469  2021-02-01   25.55       -0.66  25.80      -0.57  27.20      -1.00   \n",
-       "470  2021-03-01   26.48       -0.26  26.80      -0.39  27.79      -0.55   \n",
-       "471  2021-04-01   24.89       -0.80  26.96      -0.65  28.47      -0.21   \n",
-       "\n",
-       "     Nino34  Nino34anom  \n",
-       "0     26.72        0.15  \n",
-       "1     26.70       -0.02  \n",
-       "2     27.20       -0.02  \n",
-       "3     28.02        0.24  \n",
-       "4     28.54        0.69  \n",
-       "..      ...         ...  \n",
-       "467   25.53       -1.12  \n",
-       "468   25.58       -0.99  \n",
-       "469   25.81       -0.92  \n",
-       "470   26.75       -0.51  \n",
-       "471   27.40       -0.49  \n",
-       "\n",
-       "[472 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "03aa6062-ce17-4e9e-97e7-afd4930faabe",
+   "id": "377d4803",
    "metadata": {},
    "source": [
     "The `index` within pandas is essentially a list of the unique row IDs, which by default, is a list of sequential integers which start at 0"
@@ -351,28 +141,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "36fff562",
+   "execution_count": null,
+   "id": "cde6999b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=472, step=1)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.index"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "407c2b57",
+   "id": "8af49ac9",
    "metadata": {},
    "source": [
     "Our indexing column isn't particularly helpful currently. Pandas is clever! A few optional keyword arguments later, and..."
@@ -380,253 +159,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "a8b10c82",
+   "execution_count": null,
+   "id": "b4657f7e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-02-01</th>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>26.70</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-03-01</th>\n",
-       "      <td>25.21</td>\n",
-       "      <td>-1.31</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-04-01</th>\n",
-       "      <td>24.50</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.02</td>\n",
-       "      <td>0.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-05-01</th>\n",
-       "      <td>23.97</td>\n",
-       "      <td>-0.23</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>28.54</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-12-01</th>\n",
-       "      <td>22.16</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>24.38</td>\n",
-       "      <td>-0.83</td>\n",
-       "      <td>27.65</td>\n",
-       "      <td>-0.95</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>-1.12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-01-01</th>\n",
-       "      <td>23.89</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>25.06</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>-1.25</td>\n",
-       "      <td>25.58</td>\n",
-       "      <td>-0.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-02-01</th>\n",
-       "      <td>25.55</td>\n",
-       "      <td>-0.66</td>\n",
-       "      <td>25.80</td>\n",
-       "      <td>-0.57</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-1.00</td>\n",
-       "      <td>25.81</td>\n",
-       "      <td>-0.92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-01</th>\n",
-       "      <td>26.48</td>\n",
-       "      <td>-0.26</td>\n",
-       "      <td>26.80</td>\n",
-       "      <td>-0.39</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>-0.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-04-01</th>\n",
-       "      <td>24.89</td>\n",
-       "      <td>-0.80</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-0.65</td>\n",
-       "      <td>28.47</td>\n",
-       "      <td>-0.21</td>\n",
-       "      <td>27.40</td>\n",
-       "      <td>-0.49</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>472 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   26.72   \n",
-       "1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   26.70   \n",
-       "1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   27.20   \n",
-       "1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   28.02   \n",
-       "1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   28.54   \n",
-       "...            ...         ...    ...        ...    ...        ...     ...   \n",
-       "2020-12-01   22.16       -0.60  24.38      -0.83  27.65      -0.95   25.53   \n",
-       "2021-01-01   23.89       -0.64  25.06      -0.55  27.10      -1.25   25.58   \n",
-       "2021-02-01   25.55       -0.66  25.80      -0.57  27.20      -1.00   25.81   \n",
-       "2021-03-01   26.48       -0.26  26.80      -0.39  27.79      -0.55   26.75   \n",
-       "2021-04-01   24.89       -0.80  26.96      -0.65  28.47      -0.21   27.40   \n",
-       "\n",
-       "            Nino34anom  \n",
-       "datetime                \n",
-       "1982-01-01        0.15  \n",
-       "1982-02-01       -0.02  \n",
-       "1982-03-01       -0.02  \n",
-       "1982-04-01        0.24  \n",
-       "1982-05-01        0.69  \n",
-       "...                ...  \n",
-       "2020-12-01       -1.12  \n",
-       "2021-01-01       -0.99  \n",
-       "2021-02-01       -0.92  \n",
-       "2021-03-01       -0.51  \n",
-       "2021-04-01       -0.49  \n",
-       "\n",
-       "[472 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df = pd.read_csv('enso_data.csv', index_col=0, parse_dates=True)\n",
+    "df = pd.read_csv(filepath, index_col=0, parse_dates=True)\n",
     "\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "d648b36a",
+   "execution_count": null,
+   "id": "b0d3ae28",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatetimeIndex(['1982-01-01', '1982-02-01', '1982-03-01', '1982-04-01',\n",
-       "               '1982-05-01', '1982-06-01', '1982-07-01', '1982-08-01',\n",
-       "               '1982-09-01', '1982-10-01',\n",
-       "               ...\n",
-       "               '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01',\n",
-       "               '2020-11-01', '2020-12-01', '2021-01-01', '2021-02-01',\n",
-       "               '2021-03-01', '2021-04-01'],\n",
-       "              dtype='datetime64[ns]', name='datetime', length=472, freq=None)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.index"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1757f632",
+   "id": "8d26ed71",
    "metadata": {},
    "source": [
     "... now we have our data helpfully organized by a proper `datetime`-like object. Each of our multiple columns of data can now be referenced by their date! This sneak preview at the pandas `DatetimeIndex` also unlocks for us much of pandas most useful time series functionality. Don't worry, we'll get there. What are the actual columns of data we've read in here?"
@@ -634,30 +189,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "33a95505",
+   "execution_count": null,
+   "id": "847347f8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Nino12', 'Nino12anom', 'Nino3', 'Nino3anom', 'Nino4', 'Nino4anom',\n",
-       "       'Nino34', 'Nino34anom'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.columns"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c3ab938b",
+   "id": "08d3b2fb",
    "metadata": {},
    "source": [
     "## The pandas [`Series`](https://pandas.pydata.org/docs/user_guide/dsintro.html#series)...\n",
@@ -671,40 +213,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "dc4d61cf",
+   "execution_count": null,
+   "id": "ee085815",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "              ...  \n",
-       "2020-12-01    25.53\n",
-       "2021-01-01    25.58\n",
-       "2021-02-01    25.81\n",
-       "2021-03-01    26.75\n",
-       "2021-04-01    27.40\n",
-       "Name: Nino34, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[\"Nino34\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b73e16d-f921-48c4-91f7-6eb2c8bd7b08",
+   "id": "2fcc97fa",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\">\n",
@@ -713,40 +232,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "c4849ee1",
+   "execution_count": null,
+   "id": "7d46cbb9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "              ...  \n",
-       "2020-12-01    25.53\n",
-       "2021-01-01    25.58\n",
-       "2021-02-01    25.81\n",
-       "2021-03-01    26.75\n",
-       "2021-04-01    27.40\n",
-       "Name: Nino34, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3bed7402",
+   "id": "c2a10249",
    "metadata": {},
    "source": [
     "## Slicing and Dicing the `DataFrame` and `Series`"
@@ -754,7 +250,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b0bdcaa",
+   "id": "e38faac9",
    "metadata": {},
    "source": [
     "We will expand on what you just saw, soon! Importantly,\n",
@@ -768,33 +264,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "bdb5ebff",
+   "execution_count": null,
+   "id": "ec9ed333",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "              ...  \n",
-       "2020-12-01    25.53\n",
-       "2021-01-01    25.58\n",
-       "2021-02-01    25.81\n",
-       "2021-03-01    26.75\n",
-       "2021-04-01    27.40\n",
-       "Name: Nino34, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series = df[\"Nino34\"]\n",
     "\n",
@@ -803,7 +276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75bc2403",
+   "id": "c84a81b9",
    "metadata": {},
    "source": [
     "`Series` can be indexed, selected, and subset as both `ndarray`-like,"
@@ -811,28 +284,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "37d20e44",
+   "execution_count": null,
+   "id": "39bb0ae3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series[3]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6e7ac685",
+   "id": "a1a55a7c",
    "metadata": {},
    "source": [
     "and `dict`-like, using labels"
@@ -840,28 +302,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "f64e5695",
+   "execution_count": null,
+   "id": "62006988",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series[\"1982-04-01\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "686a3ecf",
+   "id": "cb17f245",
    "metadata": {},
    "source": [
     "and these can be extended in ways that you might expect, but perhaps also in unexpected ways:"
@@ -869,7 +320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "251cb40a-06cd-4bfa-b959-a434e0e601eb",
+   "id": "ae740b50",
    "metadata": {},
    "source": [
     "### Numpy-like Interval Slices"
@@ -877,7 +328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee4a8bfd-e63c-4bf6-9b43-addfeca9b4b2",
+   "id": "85123793",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\">\n",
@@ -887,41 +338,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "2ddc17a0",
+   "execution_count": null,
+   "id": "221e798d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "1982-06-01    28.75\n",
-       "1982-07-01    28.10\n",
-       "1982-08-01    27.93\n",
-       "1982-09-01    28.11\n",
-       "1982-10-01    28.64\n",
-       "1982-11-01    28.81\n",
-       "1982-12-01    29.21\n",
-       "Name: Nino34, dtype: float64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series[0:12]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "606352a3-6596-4683-ab9c-c72a0bf609d1",
+   "id": "6281e3a6",
    "metadata": {},
    "source": [
     "### Label-based Slicing"
@@ -929,7 +356,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "849645a7-d3a2-4a61-9cf9-52a765086049",
+   "id": "b6dceb40",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\">\n",
@@ -938,41 +365,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "1c31803c",
+   "execution_count": null,
+   "id": "f7a06967",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "1982-06-01    28.75\n",
-       "1982-07-01    28.10\n",
-       "1982-08-01    27.93\n",
-       "1982-09-01    28.11\n",
-       "1982-10-01    28.64\n",
-       "1982-11-01    28.81\n",
-       "1982-12-01    29.21\n",
-       "Name: Nino34, dtype: float64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series[\"1982-01-01\":\"1982-12-01\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "057795dd-ac38-4812-a04c-25a9516931bc",
+   "id": "84edfd6f",
    "metadata": {},
    "source": [
     "### Another Way of Slicing"
@@ -980,41 +383,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "a629daf5",
+   "execution_count": null,
+   "id": "771d6f04",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "1982-06-01    28.75\n",
-       "1982-07-01    28.10\n",
-       "1982-08-01    27.93\n",
-       "1982-09-01    28.11\n",
-       "1982-10-01    28.64\n",
-       "1982-11-01    28.81\n",
-       "1982-12-01    29.21\n",
-       "Name: Nino34, dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series[slice(\"1982-01-01\", \"1982-12-01\")]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fab92608",
+   "id": "9798abf4",
    "metadata": {},
    "source": [
     "### Using `.iloc` and `.loc` to index\n",
@@ -1024,117 +403,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "8828ebae",
+   "execution_count": null,
+   "id": "d5eb9de2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series.iloc[3]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "d37ca35a",
+   "execution_count": null,
+   "id": "8d0bc3e8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "1982-06-01    28.75\n",
-       "1982-07-01    28.10\n",
-       "1982-08-01    27.93\n",
-       "1982-09-01    28.11\n",
-       "1982-10-01    28.64\n",
-       "1982-11-01    28.81\n",
-       "1982-12-01    29.21\n",
-       "Name: Nino34, dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series.iloc[0:12]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "1cd4b99d",
+   "execution_count": null,
+   "id": "59a10070",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series.loc[\"1982-04-01\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "df53360c",
+   "execution_count": null,
+   "id": "3e2b3fc1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "1982-06-01    28.75\n",
-       "1982-07-01    28.10\n",
-       "1982-08-01    27.93\n",
-       "1982-09-01    28.11\n",
-       "1982-10-01    28.64\n",
-       "1982-11-01    28.81\n",
-       "1982-12-01    29.21\n",
-       "Name: Nino34, dtype: float64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series.loc[\"1982-01-01\":\"1982-12-01\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "67be0464",
+   "id": "44824b21",
    "metadata": {},
    "source": [
     "These capabilities extend back to our original `DataFrame`, as well! :"
@@ -1142,7 +451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "183e94a7-36dd-4f93-a8fe-e51b58ca0c15",
+   "id": "481cde44",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-danger\">\n",
@@ -1152,114 +461,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "6d0c9bd7",
+   "execution_count": null,
+   "id": "b8971371",
    "metadata": {
     "tags": [
      "raises-exception"
     ]
    },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'1982-01-01'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: '1982-01-01'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-865480cd374e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1982-01-01\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: '1982-01-01'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[\"1982-01-01\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "006ba58a",
+   "execution_count": null,
+   "id": "3c147c06",
    "metadata": {
     "tags": [
      "raises-exception"
     ]
    },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-ad11118bc8f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 0"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "94bff713",
+   "execution_count": null,
+   "id": "0b05470d",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "              ...  \n",
-       "2020-12-01    25.53\n",
-       "2021-01-01    25.58\n",
-       "2021-02-01    25.81\n",
-       "2021-03-01    26.75\n",
-       "2021-04-01    27.40\n",
-       "Name: Nino34, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[\"Nino34\"]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0d7f203e",
+   "id": "d4e6213d",
    "metadata": {},
    "source": [
     "With this plus our knowledge of the `Series` `df[\"Nino34\"]` gives us, we can chain our brackets to pull out any value from any of our columns in `df`."
@@ -1267,49 +509,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "e8b04193",
+   "execution_count": null,
+   "id": "c61fa6d4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[\"Nino34\"][\"1982-04-01\"]  # selecting the Series, then selecting a label within"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "7ca8eadc",
+   "execution_count": null,
+   "id": "3bd7cf95",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[\"Nino34\"][3]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "09d33515",
+   "id": "afb0d6ef",
    "metadata": {},
    "source": [
     "However, this is not a pandas-preferred way to index and subset our data, and has limited capabilities for us. As we touched on before, `.loc` and `.iloc` give us more to work with, and their functionality grows further for `df`."
@@ -1317,28 +537,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "6c9ac325",
+   "execution_count": null,
+   "id": "9fb5df7b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.02"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[\"1982-04-01\", \"Nino34\"]  # note [<row>, <column>] ordering"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e7cfdd38",
+   "id": "e710252f",
    "metadata": {},
    "source": [
     "These allow us to pull out entire rows of `df`,"
@@ -1346,517 +555,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "f51c7741",
+   "execution_count": null,
+   "id": "aad4fde6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Nino12        24.50\n",
-       "Nino12anom    -0.97\n",
-       "Nino3         27.68\n",
-       "Nino3anom      0.18\n",
-       "Nino4         28.92\n",
-       "Nino4anom      0.42\n",
-       "Nino34        28.02\n",
-       "Nino34anom     0.24\n",
-       "Name: 1982-04-01 00:00:00, dtype: float64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[\"1982-04-01\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "e42c3508",
+   "execution_count": null,
+   "id": "f93737ba",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-02-01</th>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>26.70</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-03-01</th>\n",
-       "      <td>25.21</td>\n",
-       "      <td>-1.31</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-04-01</th>\n",
-       "      <td>24.50</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.02</td>\n",
-       "      <td>0.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-05-01</th>\n",
-       "      <td>23.97</td>\n",
-       "      <td>-0.23</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>28.54</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-06-01</th>\n",
-       "      <td>22.89</td>\n",
-       "      <td>0.07</td>\n",
-       "      <td>27.46</td>\n",
-       "      <td>1.03</td>\n",
-       "      <td>29.76</td>\n",
-       "      <td>0.92</td>\n",
-       "      <td>28.75</td>\n",
-       "      <td>1.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-07-01</th>\n",
-       "      <td>22.47</td>\n",
-       "      <td>0.87</td>\n",
-       "      <td>26.44</td>\n",
-       "      <td>0.82</td>\n",
-       "      <td>29.38</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>28.10</td>\n",
-       "      <td>0.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-08-01</th>\n",
-       "      <td>21.75</td>\n",
-       "      <td>1.10</td>\n",
-       "      <td>26.15</td>\n",
-       "      <td>1.16</td>\n",
-       "      <td>29.04</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>27.93</td>\n",
-       "      <td>1.11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-09-01</th>\n",
-       "      <td>21.80</td>\n",
-       "      <td>1.44</td>\n",
-       "      <td>26.52</td>\n",
-       "      <td>1.67</td>\n",
-       "      <td>29.16</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>28.11</td>\n",
-       "      <td>1.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-10-01</th>\n",
-       "      <td>22.94</td>\n",
-       "      <td>2.12</td>\n",
-       "      <td>27.11</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>29.38</td>\n",
-       "      <td>0.72</td>\n",
-       "      <td>28.64</td>\n",
-       "      <td>1.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-11-01</th>\n",
-       "      <td>24.59</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>27.62</td>\n",
-       "      <td>2.64</td>\n",
-       "      <td>29.23</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>28.81</td>\n",
-       "      <td>2.16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-12-01</th>\n",
-       "      <td>26.13</td>\n",
-       "      <td>3.34</td>\n",
-       "      <td>28.39</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>0.66</td>\n",
-       "      <td>29.21</td>\n",
-       "      <td>2.64</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   26.72   \n",
-       "1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   26.70   \n",
-       "1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   27.20   \n",
-       "1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   28.02   \n",
-       "1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   28.54   \n",
-       "1982-06-01   22.89        0.07  27.46       1.03  29.76       0.92   28.75   \n",
-       "1982-07-01   22.47        0.87  26.44       0.82  29.38       0.58   28.10   \n",
-       "1982-08-01   21.75        1.10  26.15       1.16  29.04       0.36   27.93   \n",
-       "1982-09-01   21.80        1.44  26.52       1.67  29.16       0.47   28.11   \n",
-       "1982-10-01   22.94        2.12  27.11       2.19  29.38       0.72   28.64   \n",
-       "1982-11-01   24.59        3.00  27.62       2.64  29.23       0.60   28.81   \n",
-       "1982-12-01   26.13        3.34  28.39       3.25  29.15       0.66   29.21   \n",
-       "\n",
-       "            Nino34anom  \n",
-       "datetime                \n",
-       "1982-01-01        0.15  \n",
-       "1982-02-01       -0.02  \n",
-       "1982-03-01       -0.02  \n",
-       "1982-04-01        0.24  \n",
-       "1982-05-01        0.69  \n",
-       "1982-06-01        1.10  \n",
-       "1982-07-01        0.88  \n",
-       "1982-08-01        1.11  \n",
-       "1982-09-01        1.39  \n",
-       "1982-10-01        1.95  \n",
-       "1982-11-01        2.16  \n",
-       "1982-12-01        2.64  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[\"1982-01-01\":\"1982-12-01\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "dc66c1d6",
+   "execution_count": null,
+   "id": "6c23cbca",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Nino12        24.50\n",
-       "Nino12anom    -0.97\n",
-       "Nino3         27.68\n",
-       "Nino3anom      0.18\n",
-       "Nino4         28.92\n",
-       "Nino4anom      0.42\n",
-       "Nino34        28.02\n",
-       "Nino34anom     0.24\n",
-       "Name: 1982-04-01 00:00:00, dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.iloc[3]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "9e1fc528",
+   "execution_count": null,
+   "id": "22c07d7d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-02-01</th>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>26.70</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-03-01</th>\n",
-       "      <td>25.21</td>\n",
-       "      <td>-1.31</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-04-01</th>\n",
-       "      <td>24.50</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.02</td>\n",
-       "      <td>0.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-05-01</th>\n",
-       "      <td>23.97</td>\n",
-       "      <td>-0.23</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>28.54</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-06-01</th>\n",
-       "      <td>22.89</td>\n",
-       "      <td>0.07</td>\n",
-       "      <td>27.46</td>\n",
-       "      <td>1.03</td>\n",
-       "      <td>29.76</td>\n",
-       "      <td>0.92</td>\n",
-       "      <td>28.75</td>\n",
-       "      <td>1.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-07-01</th>\n",
-       "      <td>22.47</td>\n",
-       "      <td>0.87</td>\n",
-       "      <td>26.44</td>\n",
-       "      <td>0.82</td>\n",
-       "      <td>29.38</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>28.10</td>\n",
-       "      <td>0.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-08-01</th>\n",
-       "      <td>21.75</td>\n",
-       "      <td>1.10</td>\n",
-       "      <td>26.15</td>\n",
-       "      <td>1.16</td>\n",
-       "      <td>29.04</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>27.93</td>\n",
-       "      <td>1.11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-09-01</th>\n",
-       "      <td>21.80</td>\n",
-       "      <td>1.44</td>\n",
-       "      <td>26.52</td>\n",
-       "      <td>1.67</td>\n",
-       "      <td>29.16</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>28.11</td>\n",
-       "      <td>1.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-10-01</th>\n",
-       "      <td>22.94</td>\n",
-       "      <td>2.12</td>\n",
-       "      <td>27.11</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>29.38</td>\n",
-       "      <td>0.72</td>\n",
-       "      <td>28.64</td>\n",
-       "      <td>1.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-11-01</th>\n",
-       "      <td>24.59</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>27.62</td>\n",
-       "      <td>2.64</td>\n",
-       "      <td>29.23</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>28.81</td>\n",
-       "      <td>2.16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-12-01</th>\n",
-       "      <td>26.13</td>\n",
-       "      <td>3.34</td>\n",
-       "      <td>28.39</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>0.66</td>\n",
-       "      <td>29.21</td>\n",
-       "      <td>2.64</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   26.72   \n",
-       "1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   26.70   \n",
-       "1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   27.20   \n",
-       "1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   28.02   \n",
-       "1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   28.54   \n",
-       "1982-06-01   22.89        0.07  27.46       1.03  29.76       0.92   28.75   \n",
-       "1982-07-01   22.47        0.87  26.44       0.82  29.38       0.58   28.10   \n",
-       "1982-08-01   21.75        1.10  26.15       1.16  29.04       0.36   27.93   \n",
-       "1982-09-01   21.80        1.44  26.52       1.67  29.16       0.47   28.11   \n",
-       "1982-10-01   22.94        2.12  27.11       2.19  29.38       0.72   28.64   \n",
-       "1982-11-01   24.59        3.00  27.62       2.64  29.23       0.60   28.81   \n",
-       "1982-12-01   26.13        3.34  28.39       3.25  29.15       0.66   29.21   \n",
-       "\n",
-       "            Nino34anom  \n",
-       "datetime                \n",
-       "1982-01-01        0.15  \n",
-       "1982-02-01       -0.02  \n",
-       "1982-03-01       -0.02  \n",
-       "1982-04-01        0.24  \n",
-       "1982-05-01        0.69  \n",
-       "1982-06-01        1.10  \n",
-       "1982-07-01        0.88  \n",
-       "1982-08-01        1.11  \n",
-       "1982-09-01        1.39  \n",
-       "1982-10-01        1.95  \n",
-       "1982-11-01        2.16  \n",
-       "1982-12-01        2.64  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.iloc[0:12]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c1aa8b6a",
+   "id": "4c2ed15e",
    "metadata": {},
    "source": [
     "Even further,"
@@ -1864,155 +603,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "abb48564",
+   "execution_count": null,
+   "id": "8390a35b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino34</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>26.72</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-02-01</th>\n",
-       "      <td>25.49</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>26.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-03-01</th>\n",
-       "      <td>25.21</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>27.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-04-01</th>\n",
-       "      <td>24.50</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>28.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-05-01</th>\n",
-       "      <td>23.97</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>28.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-06-01</th>\n",
-       "      <td>22.89</td>\n",
-       "      <td>27.46</td>\n",
-       "      <td>29.76</td>\n",
-       "      <td>28.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-07-01</th>\n",
-       "      <td>22.47</td>\n",
-       "      <td>26.44</td>\n",
-       "      <td>29.38</td>\n",
-       "      <td>28.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-08-01</th>\n",
-       "      <td>21.75</td>\n",
-       "      <td>26.15</td>\n",
-       "      <td>29.04</td>\n",
-       "      <td>27.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-09-01</th>\n",
-       "      <td>21.80</td>\n",
-       "      <td>26.52</td>\n",
-       "      <td>29.16</td>\n",
-       "      <td>28.11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-10-01</th>\n",
-       "      <td>22.94</td>\n",
-       "      <td>27.11</td>\n",
-       "      <td>29.38</td>\n",
-       "      <td>28.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-11-01</th>\n",
-       "      <td>24.59</td>\n",
-       "      <td>27.62</td>\n",
-       "      <td>29.23</td>\n",
-       "      <td>28.81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-12-01</th>\n",
-       "      <td>26.13</td>\n",
-       "      <td>28.39</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>29.21</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino3  Nino4  Nino34\n",
-       "datetime                                \n",
-       "1982-01-01   24.29  25.87  28.30   26.72\n",
-       "1982-02-01   25.49  26.38  28.21   26.70\n",
-       "1982-03-01   25.21  26.98  28.41   27.20\n",
-       "1982-04-01   24.50  27.68  28.92   28.02\n",
-       "1982-05-01   23.97  27.79  29.49   28.54\n",
-       "1982-06-01   22.89  27.46  29.76   28.75\n",
-       "1982-07-01   22.47  26.44  29.38   28.10\n",
-       "1982-08-01   21.75  26.15  29.04   27.93\n",
-       "1982-09-01   21.80  26.52  29.16   28.11\n",
-       "1982-10-01   22.94  27.11  29.38   28.64\n",
-       "1982-11-01   24.59  27.62  29.23   28.81\n",
-       "1982-12-01   26.13  28.39  29.15   29.21"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[\n",
     "    \"1982-01-01\":\"1982-12-01\",  # slice of rows\n",
@@ -2022,7 +616,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "317de007-32dd-435b-be58-7af6c5739f1a",
+   "id": "e65349af",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\">\n",
@@ -2031,7 +625,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "848ba67d-490d-4d6a-8538-a9f59ff92b9c",
+   "id": "2e2739dc",
    "metadata": {},
    "source": [
     "## Exploratory Data Analysis\n",
@@ -2042,277 +636,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "d14272a0-fba2-426c-85f3-84640df66003",
+   "execution_count": null,
+   "id": "3c11b92a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-02-01</th>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>26.70</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-03-01</th>\n",
-       "      <td>25.21</td>\n",
-       "      <td>-1.31</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-04-01</th>\n",
-       "      <td>24.50</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.02</td>\n",
-       "      <td>0.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-05-01</th>\n",
-       "      <td>23.97</td>\n",
-       "      <td>-0.23</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>28.54</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   26.72   \n",
-       "1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   26.70   \n",
-       "1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   27.20   \n",
-       "1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   28.02   \n",
-       "1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   28.54   \n",
-       "\n",
-       "            Nino34anom  \n",
-       "datetime                \n",
-       "1982-01-01        0.15  \n",
-       "1982-02-01       -0.02  \n",
-       "1982-03-01       -0.02  \n",
-       "1982-04-01        0.24  \n",
-       "1982-05-01        0.69  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "64cb13f6-8f70-4e37-9c4c-16b5d9b6112f",
+   "execution_count": null,
+   "id": "3bf87294",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2020-12-01</th>\n",
-       "      <td>22.16</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>24.38</td>\n",
-       "      <td>-0.83</td>\n",
-       "      <td>27.65</td>\n",
-       "      <td>-0.95</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>-1.12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-01-01</th>\n",
-       "      <td>23.89</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>25.06</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>-1.25</td>\n",
-       "      <td>25.58</td>\n",
-       "      <td>-0.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-02-01</th>\n",
-       "      <td>25.55</td>\n",
-       "      <td>-0.66</td>\n",
-       "      <td>25.80</td>\n",
-       "      <td>-0.57</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-1.00</td>\n",
-       "      <td>25.81</td>\n",
-       "      <td>-0.92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-01</th>\n",
-       "      <td>26.48</td>\n",
-       "      <td>-0.26</td>\n",
-       "      <td>26.80</td>\n",
-       "      <td>-0.39</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>-0.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-04-01</th>\n",
-       "      <td>24.89</td>\n",
-       "      <td>-0.80</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-0.65</td>\n",
-       "      <td>28.47</td>\n",
-       "      <td>-0.21</td>\n",
-       "      <td>27.40</td>\n",
-       "      <td>-0.49</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "2020-12-01   22.16       -0.60  24.38      -0.83  27.65      -0.95   25.53   \n",
-       "2021-01-01   23.89       -0.64  25.06      -0.55  27.10      -1.25   25.58   \n",
-       "2021-02-01   25.55       -0.66  25.80      -0.57  27.20      -1.00   25.81   \n",
-       "2021-03-01   26.48       -0.26  26.80      -0.39  27.79      -0.55   26.75   \n",
-       "2021-04-01   24.89       -0.80  26.96      -0.65  28.47      -0.21   27.40   \n",
-       "\n",
-       "            Nino34anom  \n",
-       "datetime                \n",
-       "2020-12-01       -1.12  \n",
-       "2021-01-01       -0.99  \n",
-       "2021-02-01       -0.92  \n",
-       "2021-03-01       -0.51  \n",
-       "2021-04-01       -0.49  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.tail()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0da2b376-8964-4b7b-b313-fa2ffb8ef372",
+   "id": "cba9f221",
    "metadata": {},
    "source": [
     "### Quick Plots of Your Data\n",
@@ -2321,30 +665,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "7b09fdb4",
+   "execution_count": null,
+   "id": "bf317171",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34.plot();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "92800a25",
+   "id": "99c2c7a3",
    "metadata": {},
    "source": [
     "Before, we called `.plot()` which generated a single line plot. This is helpful, but there are other plots which can also help with understanding your data! Let's try using a histogram to understand distributions...\n",
@@ -2354,30 +685,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "c4db0c16",
+   "execution_count": null,
+   "id": "5f85e2dd",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[['Nino12', 'Nino34']].plot.hist();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "db891ecb",
+   "id": "a4e07618",
    "metadata": {},
    "source": [
     "We can see some clear differences in the distributions, which is helpful! Another plot one might like to use would be a `boxplot`. Here, we replace `hist` with `box`"
@@ -2385,30 +703,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "b353ed8b",
+   "execution_count": null,
+   "id": "6329d231",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[['Nino12', 'Nino34']].plot.box();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6346ecd3",
+   "id": "c338385b",
    "metadata": {},
    "source": [
     "Here, we again see a clear difference in the distributions. These are not the only plots you can use within pandas! For more examples of plotting choices, check out [the pandas plot documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)"
@@ -2416,7 +721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd10d71d-63df-44d5-bca3-6eb436cfd68d",
+   "id": "69fc4078",
    "metadata": {},
    "source": [
     "#### Customize your Plot\n",
@@ -2425,23 +730,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "cadb301b-2f22-4cff-9c58-bd8394e9e5fc",
+   "execution_count": null,
+   "id": "da22f990",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34.plot(\n",
     "    color='black',\n",
@@ -2454,7 +746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14ef06ef",
+   "id": "e7145ef6",
    "metadata": {},
    "source": [
     "This can be a great way to take a quick look at your data, but what if you wanted a more ***quantitative*** perspective? We can use the `describe` method on our `DataFrame`; this returns a table of summary statistics for all columns in the `DataFrame`\n",
@@ -2466,168 +758,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "0b7011a6",
+   "execution_count": null,
+   "id": "3b5c27a8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "      <td>472.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>23.209619</td>\n",
-       "      <td>0.059725</td>\n",
-       "      <td>25.936568</td>\n",
-       "      <td>0.039428</td>\n",
-       "      <td>28.625064</td>\n",
-       "      <td>0.063814</td>\n",
-       "      <td>27.076780</td>\n",
-       "      <td>0.034894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.431522</td>\n",
-       "      <td>1.157590</td>\n",
-       "      <td>1.349621</td>\n",
-       "      <td>0.965464</td>\n",
-       "      <td>0.755422</td>\n",
-       "      <td>0.709401</td>\n",
-       "      <td>1.063004</td>\n",
-       "      <td>0.947936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>18.570000</td>\n",
-       "      <td>-2.100000</td>\n",
-       "      <td>23.030000</td>\n",
-       "      <td>-2.070000</td>\n",
-       "      <td>26.430000</td>\n",
-       "      <td>-1.870000</td>\n",
-       "      <td>24.270000</td>\n",
-       "      <td>-2.380000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>21.152500</td>\n",
-       "      <td>-0.712500</td>\n",
-       "      <td>24.850000</td>\n",
-       "      <td>-0.600000</td>\n",
-       "      <td>28.140000</td>\n",
-       "      <td>-0.430000</td>\n",
-       "      <td>26.330000</td>\n",
-       "      <td>-0.572500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>22.980000</td>\n",
-       "      <td>-0.160000</td>\n",
-       "      <td>25.885000</td>\n",
-       "      <td>-0.115000</td>\n",
-       "      <td>28.760000</td>\n",
-       "      <td>0.205000</td>\n",
-       "      <td>27.100000</td>\n",
-       "      <td>0.015000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>25.322500</td>\n",
-       "      <td>0.515000</td>\n",
-       "      <td>26.962500</td>\n",
-       "      <td>0.512500</td>\n",
-       "      <td>29.190000</td>\n",
-       "      <td>0.630000</td>\n",
-       "      <td>27.792500</td>\n",
-       "      <td>0.565000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>29.150000</td>\n",
-       "      <td>4.620000</td>\n",
-       "      <td>29.140000</td>\n",
-       "      <td>3.620000</td>\n",
-       "      <td>30.300000</td>\n",
-       "      <td>1.670000</td>\n",
-       "      <td>29.600000</td>\n",
-       "      <td>2.950000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Nino12  Nino12anom       Nino3   Nino3anom       Nino4   Nino4anom  \\\n",
-       "count  472.000000  472.000000  472.000000  472.000000  472.000000  472.000000   \n",
-       "mean    23.209619    0.059725   25.936568    0.039428   28.625064    0.063814   \n",
-       "std      2.431522    1.157590    1.349621    0.965464    0.755422    0.709401   \n",
-       "min     18.570000   -2.100000   23.030000   -2.070000   26.430000   -1.870000   \n",
-       "25%     21.152500   -0.712500   24.850000   -0.600000   28.140000   -0.430000   \n",
-       "50%     22.980000   -0.160000   25.885000   -0.115000   28.760000    0.205000   \n",
-       "75%     25.322500    0.515000   26.962500    0.512500   29.190000    0.630000   \n",
-       "max     29.150000    4.620000   29.140000    3.620000   30.300000    1.670000   \n",
-       "\n",
-       "           Nino34  Nino34anom  \n",
-       "count  472.000000  472.000000  \n",
-       "mean    27.076780    0.034894  \n",
-       "std      1.063004    0.947936  \n",
-       "min     24.270000   -2.380000  \n",
-       "25%     26.330000   -0.572500  \n",
-       "50%     27.100000    0.015000  \n",
-       "75%     27.792500    0.565000  \n",
-       "max     29.600000    2.950000  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.describe()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a09a3ca9",
+   "id": "a92bb8b3",
    "metadata": {},
    "source": [
     "You can look at specific statistics too, such as mean! Notice how the output is a `Series` (column) now"
@@ -2635,36 +776,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "2a4b8b6a",
+   "execution_count": null,
+   "id": "db9e4a16",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Nino12        23.209619\n",
-       "Nino12anom     0.059725\n",
-       "Nino3         25.936568\n",
-       "Nino3anom      0.039428\n",
-       "Nino4         28.625064\n",
-       "Nino4anom      0.063814\n",
-       "Nino34        27.076780\n",
-       "Nino34anom     0.034894\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.mean()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c3ead9af",
+   "id": "1ff5aec7",
    "metadata": {},
    "source": [
     "If you are interested in a single column mean, subset for that and use `.mean`"
@@ -2672,28 +794,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "2123223e",
+   "execution_count": null,
+   "id": "9aa38e59",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27.07677966101695"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34.mean()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "678fde2f",
+   "id": "7295e7b0",
    "metadata": {},
    "source": [
     "### Subsetting Using the Datetime Column\n",
@@ -2705,590 +816,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "8a82e067",
+   "execution_count": null,
+   "id": "a506724a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1983-01-01</th>\n",
-       "      <td>27.42</td>\n",
-       "      <td>2.96</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>29.36</td>\n",
-       "      <td>2.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1984-01-01</th>\n",
-       "      <td>24.18</td>\n",
-       "      <td>-0.28</td>\n",
-       "      <td>24.82</td>\n",
-       "      <td>-0.81</td>\n",
-       "      <td>27.64</td>\n",
-       "      <td>-0.66</td>\n",
-       "      <td>25.64</td>\n",
-       "      <td>-0.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985-01-01</th>\n",
-       "      <td>23.59</td>\n",
-       "      <td>-0.87</td>\n",
-       "      <td>24.51</td>\n",
-       "      <td>-1.12</td>\n",
-       "      <td>27.71</td>\n",
-       "      <td>-0.59</td>\n",
-       "      <td>25.43</td>\n",
-       "      <td>-1.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1986-01-01</th>\n",
-       "      <td>24.61</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>24.73</td>\n",
-       "      <td>-0.90</td>\n",
-       "      <td>28.11</td>\n",
-       "      <td>-0.19</td>\n",
-       "      <td>25.79</td>\n",
-       "      <td>-0.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1987-01-01</th>\n",
-       "      <td>25.30</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>26.69</td>\n",
-       "      <td>1.06</td>\n",
-       "      <td>29.02</td>\n",
-       "      <td>0.72</td>\n",
-       "      <td>27.91</td>\n",
-       "      <td>1.34</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988-01-01</th>\n",
-       "      <td>24.64</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>26.12</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>29.13</td>\n",
-       "      <td>0.83</td>\n",
-       "      <td>27.32</td>\n",
-       "      <td>0.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1989-01-01</th>\n",
-       "      <td>24.09</td>\n",
-       "      <td>-0.37</td>\n",
-       "      <td>24.15</td>\n",
-       "      <td>-1.48</td>\n",
-       "      <td>26.54</td>\n",
-       "      <td>-1.76</td>\n",
-       "      <td>24.53</td>\n",
-       "      <td>-2.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-01</th>\n",
-       "      <td>24.02</td>\n",
-       "      <td>-0.44</td>\n",
-       "      <td>25.34</td>\n",
-       "      <td>-0.29</td>\n",
-       "      <td>28.56</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>26.55</td>\n",
-       "      <td>-0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1991-01-01</th>\n",
-       "      <td>23.86</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>25.65</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>27.01</td>\n",
-       "      <td>0.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1992-01-01</th>\n",
-       "      <td>24.83</td>\n",
-       "      <td>0.37</td>\n",
-       "      <td>27.00</td>\n",
-       "      <td>1.37</td>\n",
-       "      <td>29.06</td>\n",
-       "      <td>0.76</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>1.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1993-01-01</th>\n",
-       "      <td>24.43</td>\n",
-       "      <td>-0.03</td>\n",
-       "      <td>25.56</td>\n",
-       "      <td>-0.07</td>\n",
-       "      <td>28.60</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>26.69</td>\n",
-       "      <td>0.12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1994-01-01</th>\n",
-       "      <td>24.32</td>\n",
-       "      <td>-0.14</td>\n",
-       "      <td>25.71</td>\n",
-       "      <td>0.08</td>\n",
-       "      <td>28.47</td>\n",
-       "      <td>0.17</td>\n",
-       "      <td>26.60</td>\n",
-       "      <td>0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1995-01-01</th>\n",
-       "      <td>25.33</td>\n",
-       "      <td>0.87</td>\n",
-       "      <td>26.34</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.20</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>27.55</td>\n",
-       "      <td>0.98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1996-01-01</th>\n",
-       "      <td>23.84</td>\n",
-       "      <td>-0.62</td>\n",
-       "      <td>24.96</td>\n",
-       "      <td>-0.67</td>\n",
-       "      <td>27.92</td>\n",
-       "      <td>-0.38</td>\n",
-       "      <td>25.74</td>\n",
-       "      <td>-0.83</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-01-01</th>\n",
-       "      <td>23.67</td>\n",
-       "      <td>-0.79</td>\n",
-       "      <td>24.70</td>\n",
-       "      <td>-0.93</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>25.96</td>\n",
-       "      <td>-0.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998-01-01</th>\n",
-       "      <td>28.22</td>\n",
-       "      <td>3.76</td>\n",
-       "      <td>28.94</td>\n",
-       "      <td>3.31</td>\n",
-       "      <td>29.01</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.10</td>\n",
-       "      <td>2.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999-01-01</th>\n",
-       "      <td>23.73</td>\n",
-       "      <td>-0.73</td>\n",
-       "      <td>24.41</td>\n",
-       "      <td>-1.22</td>\n",
-       "      <td>26.59</td>\n",
-       "      <td>-1.71</td>\n",
-       "      <td>24.90</td>\n",
-       "      <td>-1.67</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-01</th>\n",
-       "      <td>23.86</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>23.88</td>\n",
-       "      <td>-1.75</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-1.34</td>\n",
-       "      <td>24.65</td>\n",
-       "      <td>-1.92</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001-01-01</th>\n",
-       "      <td>23.88</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>24.99</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>27.50</td>\n",
-       "      <td>-0.80</td>\n",
-       "      <td>25.74</td>\n",
-       "      <td>-0.83</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2002-01-01</th>\n",
-       "      <td>23.64</td>\n",
-       "      <td>-0.82</td>\n",
-       "      <td>25.09</td>\n",
-       "      <td>-0.54</td>\n",
-       "      <td>28.81</td>\n",
-       "      <td>0.51</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>-0.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2003-01-01</th>\n",
-       "      <td>24.38</td>\n",
-       "      <td>-0.08</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>29.25</td>\n",
-       "      <td>0.95</td>\n",
-       "      <td>27.76</td>\n",
-       "      <td>1.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2004-01-01</th>\n",
-       "      <td>24.60</td>\n",
-       "      <td>0.14</td>\n",
-       "      <td>25.92</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>28.83</td>\n",
-       "      <td>0.53</td>\n",
-       "      <td>26.74</td>\n",
-       "      <td>0.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2005-01-01</th>\n",
-       "      <td>24.47</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>25.89</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>29.21</td>\n",
-       "      <td>0.91</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>0.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2006-01-01</th>\n",
-       "      <td>24.33</td>\n",
-       "      <td>-0.13</td>\n",
-       "      <td>25.00</td>\n",
-       "      <td>-0.63</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>-0.62</td>\n",
-       "      <td>25.64</td>\n",
-       "      <td>-0.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2007-01-01</th>\n",
-       "      <td>24.99</td>\n",
-       "      <td>0.53</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>0.87</td>\n",
-       "      <td>28.93</td>\n",
-       "      <td>0.63</td>\n",
-       "      <td>27.26</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2008-01-01</th>\n",
-       "      <td>23.86</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>24.13</td>\n",
-       "      <td>-1.50</td>\n",
-       "      <td>26.62</td>\n",
-       "      <td>-1.68</td>\n",
-       "      <td>24.71</td>\n",
-       "      <td>-1.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2009-01-01</th>\n",
-       "      <td>24.42</td>\n",
-       "      <td>-0.10</td>\n",
-       "      <td>25.03</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>27.42</td>\n",
-       "      <td>-0.88</td>\n",
-       "      <td>25.54</td>\n",
-       "      <td>-1.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2010-01-01</th>\n",
-       "      <td>24.82</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>26.63</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>29.51</td>\n",
-       "      <td>1.21</td>\n",
-       "      <td>28.07</td>\n",
-       "      <td>1.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2011-01-01</th>\n",
-       "      <td>24.08</td>\n",
-       "      <td>-0.44</td>\n",
-       "      <td>24.31</td>\n",
-       "      <td>-1.32</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>-1.58</td>\n",
-       "      <td>24.93</td>\n",
-       "      <td>-1.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012-01-01</th>\n",
-       "      <td>23.88</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>24.90</td>\n",
-       "      <td>-0.73</td>\n",
-       "      <td>27.09</td>\n",
-       "      <td>-1.21</td>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-1.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2013-01-01</th>\n",
-       "      <td>24.00</td>\n",
-       "      <td>-0.52</td>\n",
-       "      <td>25.06</td>\n",
-       "      <td>-0.57</td>\n",
-       "      <td>28.28</td>\n",
-       "      <td>-0.02</td>\n",
-       "      <td>26.16</td>\n",
-       "      <td>-0.41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-01-01</th>\n",
-       "      <td>24.79</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>25.26</td>\n",
-       "      <td>-0.37</td>\n",
-       "      <td>28.14</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>26.06</td>\n",
-       "      <td>-0.51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-01-01</th>\n",
-       "      <td>24.13</td>\n",
-       "      <td>-0.39</td>\n",
-       "      <td>25.99</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>29.16</td>\n",
-       "      <td>0.86</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>0.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-01-01</th>\n",
-       "      <td>25.93</td>\n",
-       "      <td>1.41</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>2.58</td>\n",
-       "      <td>29.65</td>\n",
-       "      <td>1.35</td>\n",
-       "      <td>29.17</td>\n",
-       "      <td>2.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-01-01</th>\n",
-       "      <td>25.75</td>\n",
-       "      <td>1.23</td>\n",
-       "      <td>25.61</td>\n",
-       "      <td>-0.02</td>\n",
-       "      <td>28.18</td>\n",
-       "      <td>-0.12</td>\n",
-       "      <td>26.25</td>\n",
-       "      <td>-0.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01</th>\n",
-       "      <td>23.71</td>\n",
-       "      <td>-0.81</td>\n",
-       "      <td>24.48</td>\n",
-       "      <td>-1.14</td>\n",
-       "      <td>28.03</td>\n",
-       "      <td>-0.27</td>\n",
-       "      <td>25.82</td>\n",
-       "      <td>-0.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-01-01</th>\n",
-       "      <td>25.10</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>26.17</td>\n",
-       "      <td>0.55</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>27.08</td>\n",
-       "      <td>0.52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-01</th>\n",
-       "      <td>24.55</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>25.81</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>29.28</td>\n",
-       "      <td>0.93</td>\n",
-       "      <td>27.09</td>\n",
-       "      <td>0.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-01-01</th>\n",
-       "      <td>23.89</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>25.06</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>-1.25</td>\n",
-       "      <td>25.58</td>\n",
-       "      <td>-0.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   26.72   \n",
-       "1983-01-01   27.42        2.96  28.92       3.29  29.00       0.70   29.36   \n",
-       "1984-01-01   24.18       -0.28  24.82      -0.81  27.64      -0.66   25.64   \n",
-       "1985-01-01   23.59       -0.87  24.51      -1.12  27.71      -0.59   25.43   \n",
-       "1986-01-01   24.61        0.15  24.73      -0.90  28.11      -0.19   25.79   \n",
-       "1987-01-01   25.30        0.84  26.69       1.06  29.02       0.72   27.91   \n",
-       "1988-01-01   24.64        0.18  26.12       0.49  29.13       0.83   27.32   \n",
-       "1989-01-01   24.09       -0.37  24.15      -1.48  26.54      -1.76   24.53   \n",
-       "1990-01-01   24.02       -0.44  25.34      -0.29  28.56       0.26   26.55   \n",
-       "1991-01-01   23.86       -0.60  25.65       0.02  29.00       0.70   27.01   \n",
-       "1992-01-01   24.83        0.37  27.00       1.37  29.06       0.76   28.41   \n",
-       "1993-01-01   24.43       -0.03  25.56      -0.07  28.60       0.30   26.69   \n",
-       "1994-01-01   24.32       -0.14  25.71       0.08  28.47       0.17   26.60   \n",
-       "1995-01-01   25.33        0.87  26.34       0.71  29.20       0.90   27.55   \n",
-       "1996-01-01   23.84       -0.62  24.96      -0.67  27.92      -0.38   25.74   \n",
-       "1997-01-01   23.67       -0.79  24.70      -0.93  28.41       0.11   25.96   \n",
-       "1998-01-01   28.22        3.76  28.94       3.31  29.01       0.71   29.10   \n",
-       "1999-01-01   23.73       -0.73  24.41      -1.22  26.59      -1.71   24.90   \n",
-       "2000-01-01   23.86       -0.60  23.88      -1.75  26.96      -1.34   24.65   \n",
-       "2001-01-01   23.88       -0.58  24.99      -0.64  27.50      -0.80   25.74   \n",
-       "2002-01-01   23.64       -0.82  25.09      -0.54  28.81       0.51   26.50   \n",
-       "2003-01-01   24.38       -0.08  26.38       0.75  29.25       0.95   27.76   \n",
-       "2004-01-01   24.60        0.14  25.92       0.29  28.83       0.53   26.74   \n",
-       "2005-01-01   24.47        0.01  25.89       0.26  29.21       0.91   27.10   \n",
-       "2006-01-01   24.33       -0.13  25.00      -0.63  27.68      -0.62   25.64   \n",
-       "2007-01-01   24.99        0.53  26.50       0.87  28.93       0.63   27.26   \n",
-       "2008-01-01   23.86       -0.60  24.13      -1.50  26.62      -1.68   24.71   \n",
-       "2009-01-01   24.42       -0.10  25.03      -0.60  27.42      -0.88   25.54   \n",
-       "2010-01-01   24.82        0.30  26.63       1.00  29.51       1.21   28.07   \n",
-       "2011-01-01   24.08       -0.44  24.31      -1.32  26.72      -1.58   24.93   \n",
-       "2012-01-01   23.88       -0.64  24.90      -0.73  27.09      -1.21   25.49   \n",
-       "2013-01-01   24.00       -0.52  25.06      -0.57  28.28      -0.02   26.16   \n",
-       "2014-01-01   24.79        0.27  25.26      -0.37  28.14      -0.17   26.06   \n",
-       "2015-01-01   24.13       -0.39  25.99       0.36  29.16       0.86   27.10   \n",
-       "2016-01-01   25.93        1.41  28.21       2.58  29.65       1.35   29.17   \n",
-       "2017-01-01   25.75        1.23  25.61      -0.02  28.18      -0.12   26.25   \n",
-       "2018-01-01   23.71       -0.81  24.48      -1.14  28.03      -0.27   25.82   \n",
-       "2019-01-01   25.10        0.57  26.17       0.55  29.00       0.65   27.08   \n",
-       "2020-01-01   24.55        0.02  25.81       0.20  29.28       0.93   27.09   \n",
-       "2021-01-01   23.89       -0.64  25.06      -0.55  27.10      -1.25   25.58   \n",
-       "\n",
-       "            Nino34anom  \n",
-       "datetime                \n",
-       "1982-01-01        0.15  \n",
-       "1983-01-01        2.79  \n",
-       "1984-01-01       -0.93  \n",
-       "1985-01-01       -1.14  \n",
-       "1986-01-01       -0.78  \n",
-       "1987-01-01        1.34  \n",
-       "1988-01-01        0.75  \n",
-       "1989-01-01       -2.04  \n",
-       "1990-01-01       -0.02  \n",
-       "1991-01-01        0.44  \n",
-       "1992-01-01        1.84  \n",
-       "1993-01-01        0.12  \n",
-       "1994-01-01        0.03  \n",
-       "1995-01-01        0.98  \n",
-       "1996-01-01       -0.83  \n",
-       "1997-01-01       -0.61  \n",
-       "1998-01-01        2.53  \n",
-       "1999-01-01       -1.67  \n",
-       "2000-01-01       -1.92  \n",
-       "2001-01-01       -0.83  \n",
-       "2002-01-01       -0.07  \n",
-       "2003-01-01        1.19  \n",
-       "2004-01-01        0.17  \n",
-       "2005-01-01        0.53  \n",
-       "2006-01-01       -0.93  \n",
-       "2007-01-01        0.69  \n",
-       "2008-01-01       -1.86  \n",
-       "2009-01-01       -1.03  \n",
-       "2010-01-01        1.50  \n",
-       "2011-01-01       -1.64  \n",
-       "2012-01-01       -1.08  \n",
-       "2013-01-01       -0.41  \n",
-       "2014-01-01       -0.51  \n",
-       "2015-01-01        0.53  \n",
-       "2016-01-01        2.60  \n",
-       "2017-01-01       -0.32  \n",
-       "2018-01-01       -0.75  \n",
-       "2019-01-01        0.52  \n",
-       "2020-01-01        0.53  \n",
-       "2021-01-01       -0.99  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Uses the datetime column\n",
     "df[df.index.month == 1]"
@@ -3296,7 +827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "373000f5",
+   "id": "16d4e0e7",
    "metadata": {},
    "source": [
     "You could even assign this month to a new column!"
@@ -3304,8 +835,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "2d694ebc",
+   "execution_count": null,
+   "id": "fe5d5ee4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3314,7 +845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49100623",
+   "id": "8750443f",
    "metadata": {},
    "source": [
     "Now that it is its own column (`Series`), we can use `groupby` to group by the month, then taking the average, to determine average monthly values over the dataset"
@@ -3322,30 +853,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "035b2a37",
+   "execution_count": null,
+   "id": "f94c91f9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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KcEtbHG47TM2pGmqaaqg5ZQd2Y1djpMykjEnMzJlJWW4ZM3Nn4g5N5rUd3azfXke7P4TXZTBxXE9ATxqXxuScnnAen+3Dcx77bO7oDrHt0Cn+sr+Rd/c38eGxViwNKW6DS4tzuXxaHpdPy2POpGzc0pd0v3qHenVjNXtO7YmEeqYnk/K8cgl1kXDGbID7Q372ntobCema5hr2nNoTOS/qVm6mjZvGzFw7rMtyy5iRM4PslGw6AyH+9MFxfrXlMNuPtOB1G3xq9nhuWTiFS4tzE/rHaVu7gmw52Mxf9jfy1/1N1JxoByAjxc3CklwuL7UDfdaErIQ+jtEWDvXqxuqelvqpjwha9s06vUO9Is8+pz4WvygViWtMBHizv7nn9IczPNh2MHIKJMOTwczcmZTnlkcCuzS7FK8r9qqO6rpWfr3lMOv/Vsfp7hAXF2bwhYVTuGn+JMalJecVIE2nu3n3QE+gH2i0v3Adl+bhshI7zD82LY+LCzMkfAYQNIPsa9kXe/rlDKFellvGtHHTKM4qHjOXNorEk9QB/u87/p3f1PyGhq6GyLwJ6RN6WtU59mmQSRmTzhhOHd0hXvqgjl9vOcwHR1tJcRtcP2cCX1g4hUum5oy5UDvR6uevBxr5y74m/rK/iWMt9ieS/IwUrrw4j2vKi/jEjAKyU+UKl8EYKNQVikkZk5g2bhql2aWUjiu1h9mlZHiHdumYEGFDDnCl1EXAc8B47FtZntJa/4tSai3wv4CTTtFvaa3/fLZtDTXAf7/n97xf/37MaZDslMFdjvPh0VZ+teUwG7YfoyNgMrMok1sWXsTKqslkp1044XWkuZO/OF+IvrO3keaOAG5DsbAkl2vLi7i2vIgpecl1S/toC5pBDrYdtK+AaTnI/tb9HGg9QG1rbSTYAQrTCpmWPS0m1EvHlZLrG/qPlIgLy3ACfAIwQWv9vlIqE3gPuBH4HHBaa71usJU4X19itvuDbHBa2zuPteHzGHx6zkRuWTiF+VOS45b1kWRamu1HTvHargbe2F3P3ga735IZRRksccJ83kXjcMm58yEJWSGOnT7G/hY70A+0HLCHrQdirlnPScmhJLukT6s9UbsQEKMnbqdQlFJ/BJ4AriCBAlxrzY6j9rntDR/U0RkwKRufyRcWTeEz8ybJqYKzONTUweu7G3h9Vz1bapsxLU1eupdrygpZUl7E4un5pKfItefDZWmL+o56u6UeFer7W/ZH7ugFSPekx7TUp2XbAT8xYyIuQy4ZHUlaa0JWiC6zi+5QN/6Qny6zC3/IT7fZTVfIHvebfnsYNd4V6sJv+u31TH+kbHi9f7z8H6kqrBpSveIS4EqpYuBtYDbwIHA70AZsA1ZrrU+dbf2RCPA2f5A/bq/j15sPs+t4G6keFzfMncAtC6cw7yJpbZ+r1s4gb+1p4I3dDWz8qMG+pNJt8LFpeVxbXsSS8kImZKeOdjXHFK01Tf4mDrYe7NNqP9l1MlIuxZVCji+HVHcqPpePVHcqqZ5UUl2p9rg7FZ/bFzNMc6f1mZfqdtbxpEa2Fe83Bq01lrYwtUnICmFqE9MyCelQn2nTMiPlQlaIoBWMGfaeHzOu7fGgGYxsO3r8jOtZIbrN7tgwdoam7tsn/EBcyoXP7cPn8sUO3T3Tq+asYlberCE9nsMOcKVUBvDfwCNa6xeUUkVAI/YNqv+EfZrlzn7WWwWsApgyZcolhw4dGtIBRNNas/1IC7/ecpiXPjhOV9Bk1oQsblk0hc/Mmyi3nsdJ0LTYWtvM67saeH13PYebOwGYPSmLJWVFfHJWERUTs+RNcgS1Bdp6WustB2jpbom09MItvK5QV8yfP+RHn2PnJV7DGxvw7lRSXClodCRwo4O3v2AOzw9Pn0+GMnArN27DjcfliYy7DTcewxMZhsfdhhuvyxt5A4sO2j4B7IyHy6a4U0h1pcYsdxsj+4MewwpwpZQH+BPwX1rrx/tZXgz8SWs9+2zbGW4LvLUryPq/HePXWw5Tc6KdNK+Lz8yzz21XTsqWIBlBWmv2NZy2T7Xsruf9w6fQ2r4bdUl5IdfOKuLy0rxh3X0q4kNrHfNxv0/AO+HfFbTHO0OdMWWjTwcYGLgNNy7DhVvZQ5dy2fOUq9/pfsudYVnv7USHbXh57xDus9zZ5lg2nC8xFfAs0Ky1fiBq/gSt9XFn/GvAIq31351tW0MN8O1HWvjlXw/x8od1+IMWlZOyuWXhFFbMm0iGnJsdFY2nu9lYY59qeXvvSToDJmleF4un57OkvIhrygrJz0gZ7WoKMSYM5wcdrgC+BHyolNruzPsWcItSah72KZRa4H/Hpab9WP+3Y/znzuPcNH8yt1w6hcrJQ+vRS8RPfkYKNy+4iJsXXIQ/aPLugSZe313PG7sb+K/qepSCqovGsaS8iOWzx1NaINdCCxFvSXEjT3NHgBS3IVdCJAGtNdV1bbzhnGr58FgrYJ83XzF3Ip+eM5GJ4+RLUCHORVLfiSmS1/HWLl7ecZyXPqjjg6N2mC8szuWGeRP51Ozx5MlpFiEGJAEuRl1tYwcvfVDHhg/q2NtwGpehuPLifFbMncjSiiIy5eohIfolAS4ShtaamhPtbPigjpc+qOPoqS68boNrZhayYt5ErikrlKtZhIgiAS4Sktaa9w+38NIHdfxpx3EaT3eTkeJm6awibpg3kSsvzj+v/a4LkYgkwEXCC5kW7x5oZsMHx3hl5wna/SFy070snz2eFXMnJnwf7UKMFAlwkVS6Qyb//dFJNnxQx+u76/EHLSZk+/j0nAmsmDuJ2ZPkDlBx4UjYAA8Ggxw9ehS/33/e6iH65/P5mDx5Mh5PYn2Z2NEd4vXd9WzYXsfbe08SNDWl+el8eu5EVsydyMWFco25GNsSNsAPHjxIZmYmeXl50qIaRVprmpqaaG9vp6SkZLSrc0YtnQFe2XmCDdvrePdgE1rDrAlZrJg3kRvmTmSSXGMuxqCEDfDdu3dTVlYm4Z0AtNbU1NRQXl4+2lUZlPo2P3/acZwNH9TxwZEWABZMzWHZ7PEsnTVefqBCjBnDuZV+xEl4J4Zkex6Ksnx8+coSvnxlCYeaOiJXsnz35d189+XdlI3PZOmsIpZWjJdeE8WoON7axRu77R9O+ccbKijJT4/r9uX6LOzgWr16dWR63bp1rF27FoCf/vSnPPfcc0PablNTE1dffTUZGRncd999kfmdnZ1cf/31lJWVUVFRwcMPPzys+guYmpfOfddM5z8f+Dhv/z9X8+3ry8lK9fDExn18+v9s4opH32Tthmr+sq+RoGmNdnXFGGVZmg+OtPD4qx9x/b++w+Xff5Nvr9/J/pMdnGiN//d8CdECH20pKSm88MILfPOb3yQ/Pz9m2d133z3k7fp8Pv7pn/6JnTt3snPnzphlX//617n66qsJBAIsWbKEV155heXLlw95X6LHlLw07lpcyl2LS2k63c0bNQ28Wl3Pr7cc5pm/1JKd6mFJWSFLK4r4+IwC0rzyMhBD1xkI8T/7mnhjdz1v1DRwsr0bQ8ElU3N4eHkZS8oKubgwY0Q+Acr/XMDtdrNq1Sr++Z//mUceeSRm2dq1a8nIyODrX/86V111FYsWLWLjxo20tLTws5/9jMWLF+P3+7nnnnvYtm0bbrebxx9/nKuvvpr09HSuvPJK9u3bF7PNtLQ0rr76agC8Xi/z58/n6NGj5+14LyR5GSl8bsFFfG7BRXQGQryzt5FXq+t5o6aeF/52jBS3weLp+SydNZ4l5YXSN4sYlOhTI3/Z30R3yCIzxc3HZxSwpLyQq2YWkpvuHfF6JFSAf+elanbVtQ1c8BzMmpjFmhsqBiz393//98yZM4eHHnrorOVCoRBbtmzhz3/+M9/5znd4/fXX+clPfgLAhx9+SE1NDUuXLmXPnj34fL4B99vS0sJLL73EV7/61cEdkBiyNK+b6yrGc13FeEKmxdbaU7y66wSvVtfz+u4GDAULpuaytML+taGpefE9XymSl2VpPjzWGmllVzs5NSU3jS8smsK15UVcWpyL131+z0onVICPpqysLG699Vb+9V//ldTUM1+KdtNNNwFwySWXUFtbC8CmTZu4//77ASgrK2Pq1Kns2bOHOXPmnHWfoVCIW265ha985SuUlpbG50DEoLhdBpdPy+PyaXn846dnset4G69W1/PqrvrIl6AzizJZWlHE0lnj5cahC9BonhoZrIQK8MG0lEfSAw88wPz587njjjvOWCYlxf6I7XK5CIXs3/0b6qWYq1atYvr06TzwwANDWl/Eh1KKionZVEzM5mufnMGR5k5e3VXPq9Un+MnGffyfN/cxMdvHJ50rWhaW5Er/LGNUopwaGayECvDRlpuby+c+9zl+9rOfceedfX6f+Yw+/vGP8/zzz3PNNdewZ88eDh8+zMyZM8+6zre//W1aW1t5+umnh1ttEWcX5aZFLk9s7gjwZk0Dr1af4D+2HeHZvx4iy+dmSXkRS2fZX4LKD40kr8ipkRo7tBPl1Mhgyf+8XlavXs0TTzxxTuvce++93H333VRWVuJ2u3nmmWciLfXi4mLa2toIBAKsX7+eV199laysLB555BHKysqYP38+APfddx933XVX3I9HDE9uupfPXjKZz14yma6AyTt7T/Lqrnre2F3Pi387httQzJmczaLSPBaV5LKgOFd+pzWBaa05eqqLD4+18vaekwl7amSwEuJOzGS58+9CIM/H4IS/BH1n70nePdDEjqOthCyNy1DMnpgVE+jZqYnVt8yFQmvNoaZOdta18uGxVqqPtbGzrpWWziBAQp8a6S2h78QUItlEfwkK9hde7x9qYfPBJjYfaOaZ/6nlqbcPYCj7SqhFJXagLyzJZVxa4gZFsrIszcGmDnYea2XnMSew69po99vfU3lcipnjM1k+ezwVE7OpnJRN+YSshD01MlgS4ELEQZrXzZXT87lyun0jmD9o8v7hU2w+0Mzmg0388t1D/GzTQZSCmUWZXFaax2WluSwsyUvoll8iMi3N/pOne4L6WBvVda10BEwAvG6D8gn2j2hXTspm9qRsZhRlJn1Y90cCXIgR4PO4+Ni0fD42zQ707pDJB0da2XygiXcPNvGbrfZdoQAzijLsFnppLotK8ijIlJuJwkKmxd6G0zEt693H2+kK2mHt8xjMmpDF/3XJZGZPymb2xGymF2VcMFcJSYALcR6kuF0sdE6h3M90AiGLD4+18O6BZjYfbOYP7x/ll+8eAmBaQXrkHPplpXkUZQ18Q9hYEAhZ7Klvt8O6rpUPj7VRc7yN7pDdd02a10XFxCz+buFFzJ6YTeXkbErz03FfIGHdHwlwIUaB121wydRcLpmay99fDUHTYuexVjYfbGbzgSZe2l7HrzYfBqA4Ly3SQr+4MIOcNC+56V7SvK6kuFICoCtgUt/mt//au2kIj7d1U9/mp6G9m2Onugg4HY1lpripmJTFly6bSuVk+xr9kvx0XPKTejEkwIVIAB6XQdWUHKqm5HD3J6ZhWppddW1sPtjEuweaeGXncf5j25GYdbxug9w0L+PSPOSme8lJ95KbFh567GG6NxL4OWleUr2uuNa7O2Rysr2b+raoUG53QrmtmxPOvPCXidF8HoOiLB9FmT4qJmaxtKKI2RPtc9ZTc9Pk908HYcAAV0pdBDwHjAcs4Cmt9b8opXKB/wCKgVrgc1rrUyNX1ZGjlOLBBx/kRz/6EWB3J3v69GnWrl3LT3/6U9LS0rj11lvPebtNTU189rOfZevWrdx+++2R68s7Ozu5+eab2b9/Py6XixtuuIFHH300rsckkpvLUFROtk8T3LW4FNPSfHSinWMtXZzqDHCqI0BzeNgR5FRngN11bTR3BmjtCnKmq4N9HqMn5HuFe266h3FR05k+N6c6Az2t5Da/E8g9rebmjkCffXhcisJMH0VZKUwvzODKi/MpzEqhKNNnB3ZWCoVZPrJ87qT5BJGoBtMCDwGrtdbvK6UygfeUUq8BtwNvaK0fVUo9DDwMfGPkqjpypDtZkehchmLWxCxmTcwasKxpaVq7gjR3BDjVGbCHvQK/pdOePtLcSXNHgLZ+Wsi9GQoKMlMoyvIxOSeNS6bmUJTlY3yWzw7oLDugx6V6pPV8ngwY4Frr48BxZ7xdKbUbmAR8BrjKKfYs8BZJGuCJ1J3sSy+9xHe/+10CgQB5eXk8//zzFBUVsXbtWg4fPsyBAwc4fPgwDzzwAF/5ylcAePzxx/n5z38OwF133cUDDzxAbW0ty5Yt48orr+Tdd99l7ty53HHHHaxZs4aGhgaef/55Fi5cONIPrRgFLkOR67SwBytoWrR0BiOB39Jph3pumjfSas7LSJFz0AnmnM6BK6WKgSpgM1DkhDta6+NKqcJh1+aVh+HEh8PeTIzxlbB84NMTidKdbDhwlVI8/fTTPPbYY5FTOzU1NWzcuJH29nZmzpzJPffcw44dO/jFL37B5s2b0VqzaNEiPvGJT5CTk8O+ffv43e9+x1NPPcWll17Kr371KzZt2sSGDRv43ve+x/r16wesn7gweFwGBZkpcgljkhn09TdKqQzgD8ADWutBd9qtlFqllNqmlNp28uTJodTxvIjuTvZsztSd7Je+9CUgtjvZgfTXnezRo0e57rrrqKys5Ic//CHV1dWR8tdffz0pKSnk5+dTWFhIfX09mzZtYuXKlaSnp5ORkcFNN93EO++8A0BJSQmVlZUYhkFFRQVLlixBKUVlZWWk7kKI5DWoFrhSyoMd3s9rrV9wZtcrpSY4re8JQEN/62qtnwKeArsvlLPuaBAt5ZGUCN3J3n///Tz44IOsWLGCt956K/LbnNH7jt7/2fYdXd4wjMi0YRiRugshkteALXBlf038M2C31vrxqEUbgNuc8duAP8a/eudXdHey5yLcnSxwzt3J/vjHP46Z39rayqRJkwB49tlnB7Xv9evX09nZSUdHBy+++CKLFy8+p/oLIZLTYE6hXAF8CbhGKbXd+fsU8CjwSaXUXuCTznTSW716NY2Njee0zr333otpmlRWVvL5z3++T3eyDz74IM888wyTJ09m165dHD16lEceeYRdu3Yxf/585s2bF+kXfO3atdx8880sXry4zxUx/Zk/fz633347CxcuZNGiRdx1111UVVWd+4ELIZKOdCcrYsjzIUTiOVN3shduJwJCCJHkJMCFECJJSYALIUSSkgAXQogkJQEuhBBJSgJcCCGSlAQ4dneyq1evjkyvW7cucgfkT3/6U5577rkhbXfLli3MmzePefPmMXfuXF588cV4VFcIIQD5QQdg5LqTnT17dqSHwuPHjzN37lxuuOEG3G552IUQwyctcGK7k+1t7dq1rFu3DoCrrrqKb3zjGyxcuJAZM2ZEOo3y+/3ccccdVFZWUlVVxcaNGwG729hwWPv9fum8XggRVwnVFPzBlh9Q01wT122W5ZbxjYUDd1M+Ut3Jbt68mTvvvJNDhw7xy1/+UlrfQoi4kRa4Y6S6k120aBHV1dVs3bqV73//+/j9/pE7CCHEBSWhmoODaSmPpJHsTra8vJz09HR27tzJggV9ujQQQohzJi3wKPHuTvbgwYORkD906BAfffQRxcXF8a62EOICJQHeSzy7k920aRNz585l3rx5rFy5kieffHJQXcQKIcRgSHeyIoY8H0IkHulOVgghxhgJcCGESFIS4EIIkaQkwIUQIklJgAshRJKSABdCiCQlAY50JyuESE4JdSv9aJHuZIUQyUha4IxOd7L33HMPCxYsoKKigjVr1kTmFxcXs2bNGubPn09lZSU1NXbvjM3Nzdx4443MmTOHyy67jB07dkTqd9ttt7F06VKKi4t54YUXeOihh6isrGTZsmUEg8EReMSEEIkgoZqCJ773Pbp3x7c72ZTyMsZ/61sDljvf3ck+8sgj5ObmYpomS5YsYceOHcyZMweA/Px83n//fZ588knWrVvH008/zZo1a6iqqmL9+vW8+eab3HrrrWzfvh2A/fv3s3HjRnbt2sXll1/OH/7wBx577DFWrlzJyy+/zI033jj0B1AIkbCkBe44393J/va3v2X+/PlUVVVRXV3Nrl27Br2Pa665hqamJlpbWwFYvnw5Ho+HyspKTNNk2bJlAFRWVkbWF0KMPQO2wJVSPwc+DTRorWc789YC/ws46RT7ltb6z8OtzGBayiPpfHUnm5eXx7p169i6dSs5OTncfvvtMf2ED3Yf4VMy4fKGYeDxeCLzDcOIrC+EGHsG0wJ/BljWz/x/1lrPc/6GHd6J4Hx1J9vW1kZ6ejrZ2dnU19fzyiuvnNM+3nrrLfLz88nKyjrHIxRCjCUDtsC11m8rpYrPQ10SwurVq3niiSfOaZ17772Xu+++m8rKStxud0x3so8++igejwfDMCLdyebn51NVVUVFRQWlpaVcccUVA+5j7dq13HHHHcyZM4e0tDSeffbZoR6iEGKMGFR3sk6A/6nXKZTbgTZgG7Baa33qDOuuAlYBTJky5ZJDhw7FLJfuSxOLPB9CJJ54dyf7b8A0YB5wHPjRmQpqrZ/SWi/QWi8oKCgY4u6EEEL0NqQA11rXa61NrbUF/DuwML7VEkIIMZAhBbhSakLU5EpgZ3yqI4QQYrAGcxnhr4GrgHyl1FFgDXCVUmoeoIFa4H+PXBWFEEL0ZzBXodzSz+xzu85OCCFE3MmdmEIIkaQkwBm57mTDDh8+TEZGRqRTLCGEiAcJcHq6k21sbOyz7O677+bWW28d1va/9rWvsXz58mFtQwghepMAZ+S6kwVYv349paWlVFRUnJ+DEUJcMBKqO9l3fruHxiOn47rN/IsyWPy5GQOWG4nuZE3T5Ac/+AGvvfaanD4RQsRdQgX4aIruTjY1NfWM5c7U1ev9998PxHYn+9xzz/G1r32NjIyMEa+/EOLCk1ABPpiW8kiKd3eymzdv5ve//z0PPfQQLS0tGIaBz+fjvvvui3/lhRAXHDkHHiXe3cm+88471NbWUltbywMPPMC3vvUtCW8hRNxIgPeyevXqfq9GOZt7770X0zSprKzk85//fKQ7WSGEGEmD6k42XhYsWKC3bdsWM0+6L00s8nwIkXji3Z2sEEKIUSYBLoQQSUoCXAghkpQEuBBCJCkJcCGESFIS4EIIkaQkwJHuZIUQyUkCHOlOVgiRnCTAGZ3uZO+55x4WLFhARUUFa9asicwvLi5mzZo1zJ8/n8rKSmpqagBobm7mxhtvZM6cOVx22WXs2LEjUr/bbruNpUuXUlxczAsvvMBDDz1EZWUly5YtIxgMxvfBEkIkjITqzGrjM0/RcOhAXLdZOLWUq29fNWC5892d7COPPEJubi6mabJkyRJ27NjBnDlzAMjPz+f999/nySefZN26dTz99NOsWbOGqqoq1q9fz5tvvsmtt97K9u3bAdi/fz8bN25k165dXH755fzhD3/gscceY+XKlbz88svceOON5/7ACSESnrTAHdHdyZ7NmbqT/dKXvgTEdie7Zs2aM3Yn+9vf/pb58+dTVVVFdXU1u3btGvQ+rrnmGpqammhtbQVg+fLleDweKisrMU2TZcuWAVBZWRlZXwgx9iRUC3wwLeWRdL66k73++utZt24dW7duJScnh9tvvx2/33/O+1BKxZQ3DAOPxxOZbxhGZH0hxNgjLfAo56s72ba2NtLT08nOzqa+vp5XXnnlnPbx1ltvkZ+fT1ZW1rkfpBBizEioFngiWL16NU888cQ5rXPvvfdy9913U1lZidvtHrA72blz51JVVUVFRQWlpaVcccUVA+5j7dq13HHHHcyZM4e0tDSeffbZc6qjEGLske5kRQx5PoRIPEPuTlYp9XOlVINSamfUvFyl1GtKqb3OMCfeFRZCCHF2gzkH/gywrNe8h4E3tNbTgTecaSGEEOfRgAGutX4baO41+zNA+CTss8CN8a2WEEKIgQz1KpQirfVxAGdYeKaCSqlVSqltSqltJ0+eHOLuhBBC9DbilxFqrZ/SWi/QWi8oKCgY6d0JIcQFY6gBXq+UmgDgDBviVyUhhBCDMdQA3wDc5ozfBvwxPtUZHSPVneyWLVuYN28e8+bNY+7cubz44ot9yqxYsYLZs2cPaftCiAvbgDfyKKV+DVwF5CuljgJrgEeB3yqlvgwcBm4eyUqOtHB3st/85jfJz8+PWXb33XcPebuzZ89m27ZtuN1ujh8/zty5c7nhhhtwu+2H/YUXXui3nxQhhBiMwVyFcovWeoLW2qO1nqy1/pnWuklrvURrPd0Z9r5KJamMVHeyaWlpkbD2+/2RPkoATp8+zeOPP863v/3tkT48IcQYlVC30re8tJ9AXUdct+mdmM64G6YNWG4kupP1+Xxs3ryZO++8k0OHDvHLX/4yEuj/8A//wOrVq0lLSxv+QQohLkjSmZVjJLqTBVi0aBHV1dVs3bqV73//+/j9frZv386+fftYuXLlyB2QEGLMS6gW+GBayiMp3t3JRisvLyc9PZ2dO3eydetW3nvvPYqLiwmFQjQ0NHDVVVfx1ltvxeU4hBAXBmmBR4l3d7IHDx6MhPyhQ4f46KOPKC4u5p577qGuro7a2lo2bdrEjBkzJLyFEOdMAryX1atX9/vjxmdz7733YpomlZWVfP7zn490J7tp0ybmzp3LvHnzWLlyJU8++WSfq1yEEGKopDtZEUOeDyESz5C7kxVCCJGYJMCFECJJSYALIUSSkgAXQogkJQEuhBBJSgJcCCGSlAQ40p2sECI5JdSt9KNFupMVQiQjaYGTWN3JvvTSSyxatIiqqiquvfZa6uvrI/W48847ueqqqygtLY3pdOvxxx9n9uzZzJ49mx//+McA1NbWUlZWxl133cXs2bP54he/yOuvv84VV1zB9OnT2bJlS5wePSHEaEmoFvgrr7zCiRMn4rrN8ePHs3z58gHLJUp3sldeeSXvvvsuSimefvppHnvsMX70ox8BUFNTw8aNG2lvb2fmzJncc8897Nixg1/84hds3rwZrTWLFi3iE5/4BDk5Oezbt4/f/e53PPXUU1x66aX86le/YtOmTWzYsIHvfe97rF+/fgiPqBAiUUgL3JEo3ckePXqU6667jsrKSn74wx9SXV0dWXb99deTkpJCfn4+hYWF1NfXs2nTJlauXEl6ejoZGRncdNNNkU8GJSUlVFZWYhgGFRUVLFmyBKUUlZWVkboLIZJXQrXAB9NSHkmJ0J3s/fffz4MPPsiKFSt46623Il+mRu87ev9n23d0ecMwItOGYUTqLoRIXtICj5II3cm2trYyadIkAJ599tlB7Xv9+vV0dnbS0dHBiy++yOLFi8+p/kKI5CQB3stodye7du1abr75ZhYvXjyormfnz5/P7bffzsKFC1m0aBF33XUXVVVV51R/IURyku5kRQx5PoRIPNKdrBBCjDES4EIIkaQkwIUQIkklRICfz/Pw4szkeRAiuQzrOnClVC3QDphAqL+T7APx+Xw0NTWRl5cXc6u5OL+01jQ1NeHz+Ua7KkKIQYrHjTxXa63P7bq7KJMnT+bo0aOcPHkyDlURw+Hz+Zg8efJoV0MIMUijfiemx+OhpKRktKshhBBJZ7jnwDXwqlLqPaXUqnhUSAghxOAMtwV+hda6TilVCLymlKrRWr8dXcAJ9lUAU6ZMGebuhBBChA2rBa61rnOGDcCLwMJ+yjyltV6gtV5QUFAwnN0JIYSIMuQAV0qlK6Uyw+PAUmBnvComhBDi7IZzCqUIeNG59M8N/Epr/Z9xqZUQQogBDTnAtdYHgLlxrIsQQohzkBB3YgohhDh3EuBCCJGkJMCFECJJSYALIUSSkgAXQogkJQEuhBBJSgJcCCGSlAS4EEIkKQlwIYRIUhLgQgiRpCTAhRAiSUmACyFEkpIAF0KIJCUBLoQQSUoCXAghkpQEuBBCJCkJcCGESFIS4EIIkaQkwIUQIklJgAshRJKSABdCiCQlAS6EEElKAlwIIZKUBLgQQiQpCXAhhEhSwwpwpdQypdRHSql9SqmH41UpIYQQAxtygCulXMBPgOXALOAWpdSseFVMCCHE2bmHse5CYJ/W+gCAUuo3wGeAXfGoWIxDf4GG3bHzlEJrjdagtcY0NdqysCwLbdnzTdNCO9OWpSPLLNOyp7XGDI87f6apsbSFNu3l2jLt9Zx5WtMzbVl2HSxnHQt7vtZY2h7H6hnX4aGl0YClNVig0c6xKDT2NNo5PsACcI5T2wffc+wKtD3TnraL2uXD4yhnH8ouq3TP4+isrJyVwnsIrxnZGD07UeE6hp8K3WudqO3a5VTUUoUOTysV3kLUXlXU80vsuqpn3Ziy2I8DUdPh7cbuub8y9Jmr1JmWxM7pKadilvWdrSJlI3VQseORZVGPiVLOESh7fuy6zjad+UoplLOuPW4Pe5YbKKOnhoahevbhMuztocBQzvpGZH0ADKPngJQBCjRGpC7Oxp3yKnaooqbDf/084Pb/6Z7XL1pjYb++0Jb9etHh14r9h/N6i7y2osrElAe7LMp+Xeo+u488CSry/yZ6XuRZ6XkYwo939NOtotZVyv6f7Wzz0us+xZRLLiOehhPgk4AjUdNHgUXDq07//v1HP+d4Wl74Eep5sUa/aKOehehgiC0Tntf/i7gvxfAeol6bCu/WFZ9NCjFkVtS4OSIrjLKYt9JBL+qXHmB6kMZt/HNCBXh/D0GfQ1NKrQJWAUyZMmVIOyqYMp3OxkaU7nl3VNAzrZzx8Lundloh9JQ3Iq2W8HRPK0WhnGlll1PhaQNl2ENUbMvEMOx5hqFQhguUgTIMez3DcNZ1oQy7rHKWq8g6BigDw2VgoMBlYITLGKqnfHgdl8uugzOtlIKo8XBZouvdz3SkVXaWNzGlopr1fRf2jOozNWOi/nPo8BPTUx8jXPfo6f7mRU33PP49rczoYe/xM9FnOq5hLA/Pi7QKo8e1/cmQyKe1qFZiKIS2TLQZAmeoLQvMoF3GDGGFl5sm2jRBm/Z2TMtex/mUqC0rsi+tnWlLY5lmz6dFq+cTqdYm2uypU/SnSjSRT57OEYKzffsjo3bGtf1/INKiNZ1PlM562kI5rWrA3oY90rMNbdHzcVFHXn/KCL8endexK2o6/BpSUf//nRe5oQz7v0vU//Xo1yQGMa+h2KfT/oSneyad59KZsD++Rn/2DFc7/CjZD1XkUXNa+852i6781Fn/bw3FcAL8KHBR1PRkoK53Ia31U8BTAAsWLBjSe9eNX//GUFYTQogxbThXoWwFpiulSpRSXuDvgA3xqZYQQoiBDLkFrrUOKaXuA/4L+6zuz7XW1XGrmRBCiLMa1jd0Wus/A3+OU12EEEKcA7kTUwghkpQEuBBCJCkJcCGESFIS4EIIkaQkwIUQIkmpge48i+vOlDoJHDpvOxyefKBxtCsxQsbyscHYPj45tuQ1nOObqrUu6D3zvAZ4MlFKbdNaLxjteoyEsXxsMLaPT44teY3E8ckpFCGESFIS4EIIkaQkwM/sqdGuwAgay8cGY/v45NiSV9yPT86BCyFEkpIWuBBCJCkJ8ChKqYuUUhuVUruVUtVKqa+Odp3iTSnlUkr9TSn1p9GuS7wppcYppX6vlKpxnsPLR7tO8aKU+przf3KnUurXSinfaNdpOJRSP1dKNSildkbNy1VKvaaU2usMc0azjkN1hmP7ofP/codS6kWl1Lh47EsCPFYIWK21LgcuA/5+DP5Q81eB3QOWSk7/Avyn1roMmMsYOU6l1CTgK8ACrfVs7O6b/250azVszwDLes17GHhDaz0deMOZTkbP0PfYXgNma63nAHuAb8ZjRxLgUbTWx7XW7zvj7dgBMGl0axU/SqnJwPXA06Ndl3hTSmUBHwd+BqC1DmitW0a1UvHlBlKVUm4gjX5+/SqZaK3fBpp7zf4M8Kwz/ixw4/msU7z0d2xa61e11iFn8l3sXzAbNgnwM1BKFQNVwOZRrko8/Rh4iNhfqB0rSoGTwC+cU0RPK6XSR7tS8aC1PgasAw4Dx4FWrfWro1urEVGktT4OdmMKKBzl+oyUO4FX4rEhCfB+KKUygD8AD2it20a7PvGglPo00KC1fm+06zJC3MB84N+01lVAB8n7ETyGcy74M0AJMBFIV0r936NbKzEUSqn/F/tU7fPx2J4EeC9KKQ92eD+vtX5htOsTR1cAK5RStcBvgGuUUv/f6FYpro4CR7XW4U9Mv8cO9LHgWuCg1vqk1joIvAB8bJTrNBLqlVITAJxhwyjXJ66UUrcBnwa+qON0/bYEeBSllMI+h7pba/34aNcnnrTW39RaT9ZaF2N/Afam1nrMtOK01ieAI0qpmc6sJcCuUaxSPB0GLlNKpTn/R5cwRr6g7WUDcJszfhvwx1GsS1wppZYB3wBWaK0747VdCfBYVwBfwm6dbnf+PjXalRKDdj/wvFJqBzAP+N7oVic+nE8VvwfeBz7Eft0m9V2LSqlfA38FZiqljiqlvgw8CnxSKbUX+KQznXTOcGxPAJnAa06u/DQu+5I7MYUQIjlJC1wIIZKUBLgQQiQpCXAhhEhSEuBCCJGkJMCFECJJSYALcRZOD4f3Rk1fNRZ7chTJSQJciLMbB9w7UCEhRoMEuBgzlFLFTp/LTzv9Zj+vlLpWKfU/Th/TC50+p9c7/TK/q5Sa46y71unH+S2l1AGl1FeczT4KTHNuvvihMy8jqt/x5527I4U479yjXQEh4uxi4GZgFbAV+AJwJbAC+BZwBPib1vpGpdQ1wHPYd20ClAFXY98x95FS6t+wO8SarbWeB/YpFOxeKiuwu3T9H+w7eDeN+JEJ0Yu0wMVYc1Br/aHW2gKqsX8gQGPfgl6MHea/BNBavwnkKaWynXVf1lp3a60bsTtSKjrDPrZorY86+9jubFeI804CXIw13VHjVtS0hf2Js7/THeH+JKLXNTnzJ9TBlhNiREmAiwvN28AXIXI6pHGAPt/bsU+pCJFwpOUgLjRrsX+1ZwfQSU/3pf3SWjc5X4LuxP4VlZdHvopCDI70RiiEEElKTqEIIUSSkgAXQogkJQEuhBBJSgJcCCGSlAS4EEIkKQlwIYRIUhLgQgiRpCTAhRAiSf3/dWXeMdQDaYYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.groupby('month').mean().plot();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b04c22fc",
+   "id": "a0c9481b",
    "metadata": {},
    "source": [
     "### Investigating Extreme Values"
@@ -3353,7 +871,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3aea3cc",
+   "id": "fec15b77",
    "metadata": {},
    "source": [
     "You can also use ***conditional indexing***, such that you can search where rows meet a certain criteria. In this case, we are interested in where the Nino34 anomaly is greater than 2"
@@ -3361,331 +879,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "4e723968",
+   "execution_count": null,
+   "id": "098fc88d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "      <th>month</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-11-01</th>\n",
-       "      <td>24.59</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>27.62</td>\n",
-       "      <td>2.64</td>\n",
-       "      <td>29.23</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>28.81</td>\n",
-       "      <td>2.16</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-12-01</th>\n",
-       "      <td>26.13</td>\n",
-       "      <td>3.34</td>\n",
-       "      <td>28.39</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>0.66</td>\n",
-       "      <td>29.21</td>\n",
-       "      <td>2.64</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1983-01-01</th>\n",
-       "      <td>27.42</td>\n",
-       "      <td>2.96</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>29.36</td>\n",
-       "      <td>2.79</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1983-02-01</th>\n",
-       "      <td>28.09</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>2.55</td>\n",
-       "      <td>28.79</td>\n",
-       "      <td>0.69</td>\n",
-       "      <td>29.13</td>\n",
-       "      <td>2.41</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-08-01</th>\n",
-       "      <td>24.80</td>\n",
-       "      <td>4.15</td>\n",
-       "      <td>27.84</td>\n",
-       "      <td>2.85</td>\n",
-       "      <td>29.26</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>28.84</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-09-01</th>\n",
-       "      <td>24.40</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>27.84</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>0.63</td>\n",
-       "      <td>28.93</td>\n",
-       "      <td>2.21</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-10-01</th>\n",
-       "      <td>24.58</td>\n",
-       "      <td>3.76</td>\n",
-       "      <td>28.17</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>0.66</td>\n",
-       "      <td>29.23</td>\n",
-       "      <td>2.54</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-11-01</th>\n",
-       "      <td>25.63</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>28.55</td>\n",
-       "      <td>3.57</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.86</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>2.67</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-12-01</th>\n",
-       "      <td>26.92</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>28.76</td>\n",
-       "      <td>3.62</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>0.83</td>\n",
-       "      <td>29.26</td>\n",
-       "      <td>2.69</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998-01-01</th>\n",
-       "      <td>28.22</td>\n",
-       "      <td>3.76</td>\n",
-       "      <td>28.94</td>\n",
-       "      <td>3.31</td>\n",
-       "      <td>29.01</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.10</td>\n",
-       "      <td>2.53</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998-02-01</th>\n",
-       "      <td>28.98</td>\n",
-       "      <td>2.91</td>\n",
-       "      <td>28.93</td>\n",
-       "      <td>2.56</td>\n",
-       "      <td>28.87</td>\n",
-       "      <td>0.77</td>\n",
-       "      <td>28.86</td>\n",
-       "      <td>2.14</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-08-01</th>\n",
-       "      <td>22.88</td>\n",
-       "      <td>2.24</td>\n",
-       "      <td>27.33</td>\n",
-       "      <td>2.34</td>\n",
-       "      <td>29.66</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>28.89</td>\n",
-       "      <td>2.07</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-09-01</th>\n",
-       "      <td>22.91</td>\n",
-       "      <td>2.57</td>\n",
-       "      <td>27.48</td>\n",
-       "      <td>2.63</td>\n",
-       "      <td>29.73</td>\n",
-       "      <td>1.04</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>2.28</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-01</th>\n",
-       "      <td>23.31</td>\n",
-       "      <td>2.52</td>\n",
-       "      <td>27.58</td>\n",
-       "      <td>2.66</td>\n",
-       "      <td>29.79</td>\n",
-       "      <td>1.12</td>\n",
-       "      <td>29.15</td>\n",
-       "      <td>2.46</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-11-01</th>\n",
-       "      <td>23.83</td>\n",
-       "      <td>2.24</td>\n",
-       "      <td>27.91</td>\n",
-       "      <td>2.93</td>\n",
-       "      <td>30.30</td>\n",
-       "      <td>1.67</td>\n",
-       "      <td>29.60</td>\n",
-       "      <td>2.95</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-12-01</th>\n",
-       "      <td>25.01</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>27.99</td>\n",
-       "      <td>2.85</td>\n",
-       "      <td>30.11</td>\n",
-       "      <td>1.63</td>\n",
-       "      <td>29.39</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-01-01</th>\n",
-       "      <td>25.93</td>\n",
-       "      <td>1.41</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>2.58</td>\n",
-       "      <td>29.65</td>\n",
-       "      <td>1.35</td>\n",
-       "      <td>29.17</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-02-01</th>\n",
-       "      <td>26.81</td>\n",
-       "      <td>0.67</td>\n",
-       "      <td>28.36</td>\n",
-       "      <td>1.99</td>\n",
-       "      <td>29.55</td>\n",
-       "      <td>1.45</td>\n",
-       "      <td>29.12</td>\n",
-       "      <td>2.40</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-11-01   24.59        3.00  27.62       2.64  29.23       0.60   28.81   \n",
-       "1982-12-01   26.13        3.34  28.39       3.25  29.15       0.66   29.21   \n",
-       "1983-01-01   27.42        2.96  28.92       3.29  29.00       0.70   29.36   \n",
-       "1983-02-01   28.09        2.02  28.92       2.55  28.79       0.69   29.13   \n",
-       "1997-08-01   24.80        4.15  27.84       2.85  29.26       0.58   28.84   \n",
-       "1997-09-01   24.40        4.04  27.84       2.99  29.32       0.63   28.93   \n",
-       "1997-10-01   24.58        3.76  28.17       3.25  29.32       0.66   29.23   \n",
-       "1997-11-01   25.63        4.04  28.55       3.57  29.49       0.86   29.32   \n",
-       "1997-12-01   26.92        4.13  28.76       3.62  29.32       0.83   29.26   \n",
-       "1998-01-01   28.22        3.76  28.94       3.31  29.01       0.71   29.10   \n",
-       "1998-02-01   28.98        2.91  28.93       2.56  28.87       0.77   28.86   \n",
-       "2015-08-01   22.88        2.24  27.33       2.34  29.66       0.98   28.89   \n",
-       "2015-09-01   22.91        2.57  27.48       2.63  29.73       1.04   29.00   \n",
-       "2015-10-01   23.31        2.52  27.58       2.66  29.79       1.12   29.15   \n",
-       "2015-11-01   23.83        2.24  27.91       2.93  30.30       1.67   29.60   \n",
-       "2015-12-01   25.01        2.19  27.99       2.85  30.11       1.63   29.39   \n",
-       "2016-01-01   25.93        1.41  28.21       2.58  29.65       1.35   29.17   \n",
-       "2016-02-01   26.81        0.67  28.36       1.99  29.55       1.45   29.12   \n",
-       "\n",
-       "            Nino34anom  month  \n",
-       "datetime                       \n",
-       "1982-11-01        2.16     11  \n",
-       "1982-12-01        2.64     12  \n",
-       "1983-01-01        2.79      1  \n",
-       "1983-02-01        2.41      2  \n",
-       "1997-08-01        2.02      8  \n",
-       "1997-09-01        2.21      9  \n",
-       "1997-10-01        2.54     10  \n",
-       "1997-11-01        2.67     11  \n",
-       "1997-12-01        2.69     12  \n",
-       "1998-01-01        2.53      1  \n",
-       "1998-02-01        2.14      2  \n",
-       "2015-08-01        2.07      8  \n",
-       "2015-09-01        2.28      9  \n",
-       "2015-10-01        2.46     10  \n",
-       "2015-11-01        2.95     11  \n",
-       "2015-12-01        2.82     12  \n",
-       "2016-01-01        2.60      1  \n",
-       "2016-02-01        2.40      2  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[df.Nino34anom > 2]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2bc0275e",
+   "id": "f26bc439",
    "metadata": {},
    "source": [
     "You can also sort columns based on the values!"
@@ -3693,236 +897,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "a3cd6935",
+   "execution_count": null,
+   "id": "8051c4f6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "      <th>month</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1988-11-01</th>\n",
-       "      <td>20.55</td>\n",
-       "      <td>-1.04</td>\n",
-       "      <td>23.03</td>\n",
-       "      <td>-1.95</td>\n",
-       "      <td>26.76</td>\n",
-       "      <td>-1.87</td>\n",
-       "      <td>24.27</td>\n",
-       "      <td>-2.38</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988-12-01</th>\n",
-       "      <td>21.80</td>\n",
-       "      <td>-0.99</td>\n",
-       "      <td>23.07</td>\n",
-       "      <td>-2.07</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>-1.74</td>\n",
-       "      <td>24.33</td>\n",
-       "      <td>-2.24</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988-10-01</th>\n",
-       "      <td>19.50</td>\n",
-       "      <td>-1.32</td>\n",
-       "      <td>23.17</td>\n",
-       "      <td>-1.75</td>\n",
-       "      <td>27.06</td>\n",
-       "      <td>-1.60</td>\n",
-       "      <td>24.62</td>\n",
-       "      <td>-2.07</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1989-01-01</th>\n",
-       "      <td>24.09</td>\n",
-       "      <td>-0.37</td>\n",
-       "      <td>24.15</td>\n",
-       "      <td>-1.48</td>\n",
-       "      <td>26.54</td>\n",
-       "      <td>-1.76</td>\n",
-       "      <td>24.53</td>\n",
-       "      <td>-2.04</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-01</th>\n",
-       "      <td>23.86</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>23.88</td>\n",
-       "      <td>-1.75</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-1.34</td>\n",
-       "      <td>24.65</td>\n",
-       "      <td>-1.92</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-11-01</th>\n",
-       "      <td>25.63</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>28.55</td>\n",
-       "      <td>3.57</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.86</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>2.67</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-12-01</th>\n",
-       "      <td>26.92</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>28.76</td>\n",
-       "      <td>3.62</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>0.83</td>\n",
-       "      <td>29.26</td>\n",
-       "      <td>2.69</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1983-01-01</th>\n",
-       "      <td>27.42</td>\n",
-       "      <td>2.96</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>29.36</td>\n",
-       "      <td>2.79</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-12-01</th>\n",
-       "      <td>25.01</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>27.99</td>\n",
-       "      <td>2.85</td>\n",
-       "      <td>30.11</td>\n",
-       "      <td>1.63</td>\n",
-       "      <td>29.39</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-11-01</th>\n",
-       "      <td>23.83</td>\n",
-       "      <td>2.24</td>\n",
-       "      <td>27.91</td>\n",
-       "      <td>2.93</td>\n",
-       "      <td>30.30</td>\n",
-       "      <td>1.67</td>\n",
-       "      <td>29.60</td>\n",
-       "      <td>2.95</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>472 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1988-11-01   20.55       -1.04  23.03      -1.95  26.76      -1.87   24.27   \n",
-       "1988-12-01   21.80       -0.99  23.07      -2.07  26.75      -1.74   24.33   \n",
-       "1988-10-01   19.50       -1.32  23.17      -1.75  27.06      -1.60   24.62   \n",
-       "1989-01-01   24.09       -0.37  24.15      -1.48  26.54      -1.76   24.53   \n",
-       "2000-01-01   23.86       -0.60  23.88      -1.75  26.96      -1.34   24.65   \n",
-       "...            ...         ...    ...        ...    ...        ...     ...   \n",
-       "1997-11-01   25.63        4.04  28.55       3.57  29.49       0.86   29.32   \n",
-       "1997-12-01   26.92        4.13  28.76       3.62  29.32       0.83   29.26   \n",
-       "1983-01-01   27.42        2.96  28.92       3.29  29.00       0.70   29.36   \n",
-       "2015-12-01   25.01        2.19  27.99       2.85  30.11       1.63   29.39   \n",
-       "2015-11-01   23.83        2.24  27.91       2.93  30.30       1.67   29.60   \n",
-       "\n",
-       "            Nino34anom  month  \n",
-       "datetime                       \n",
-       "1988-11-01       -2.38     11  \n",
-       "1988-12-01       -2.24     12  \n",
-       "1988-10-01       -2.07     10  \n",
-       "1989-01-01       -2.04      1  \n",
-       "2000-01-01       -1.92      1  \n",
-       "...                ...    ...  \n",
-       "1997-11-01        2.67     11  \n",
-       "1997-12-01        2.69     12  \n",
-       "1983-01-01        2.79      1  \n",
-       "2015-12-01        2.82     12  \n",
-       "2015-11-01        2.95     11  \n",
-       "\n",
-       "[472 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.sort_values('Nino34anom')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9af53b83",
+   "id": "a293de79",
    "metadata": {},
    "source": [
     "Let's change the way that is ordered..."
@@ -3930,236 +915,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "d89c47e8",
+   "execution_count": null,
+   "id": "be7ff8ce",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "      <th>month</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2015-11-01</th>\n",
-       "      <td>23.83</td>\n",
-       "      <td>2.24</td>\n",
-       "      <td>27.91</td>\n",
-       "      <td>2.93</td>\n",
-       "      <td>30.30</td>\n",
-       "      <td>1.67</td>\n",
-       "      <td>29.60</td>\n",
-       "      <td>2.95</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-12-01</th>\n",
-       "      <td>25.01</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>27.99</td>\n",
-       "      <td>2.85</td>\n",
-       "      <td>30.11</td>\n",
-       "      <td>1.63</td>\n",
-       "      <td>29.39</td>\n",
-       "      <td>2.82</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1983-01-01</th>\n",
-       "      <td>27.42</td>\n",
-       "      <td>2.96</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>29.00</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>29.36</td>\n",
-       "      <td>2.79</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-12-01</th>\n",
-       "      <td>26.92</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>28.76</td>\n",
-       "      <td>3.62</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>0.83</td>\n",
-       "      <td>29.26</td>\n",
-       "      <td>2.69</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997-11-01</th>\n",
-       "      <td>25.63</td>\n",
-       "      <td>4.04</td>\n",
-       "      <td>28.55</td>\n",
-       "      <td>3.57</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.86</td>\n",
-       "      <td>29.32</td>\n",
-       "      <td>2.67</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-01</th>\n",
-       "      <td>23.86</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>23.88</td>\n",
-       "      <td>-1.75</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-1.34</td>\n",
-       "      <td>24.65</td>\n",
-       "      <td>-1.92</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1989-01-01</th>\n",
-       "      <td>24.09</td>\n",
-       "      <td>-0.37</td>\n",
-       "      <td>24.15</td>\n",
-       "      <td>-1.48</td>\n",
-       "      <td>26.54</td>\n",
-       "      <td>-1.76</td>\n",
-       "      <td>24.53</td>\n",
-       "      <td>-2.04</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988-10-01</th>\n",
-       "      <td>19.50</td>\n",
-       "      <td>-1.32</td>\n",
-       "      <td>23.17</td>\n",
-       "      <td>-1.75</td>\n",
-       "      <td>27.06</td>\n",
-       "      <td>-1.60</td>\n",
-       "      <td>24.62</td>\n",
-       "      <td>-2.07</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988-12-01</th>\n",
-       "      <td>21.80</td>\n",
-       "      <td>-0.99</td>\n",
-       "      <td>23.07</td>\n",
-       "      <td>-2.07</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>-1.74</td>\n",
-       "      <td>24.33</td>\n",
-       "      <td>-2.24</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988-11-01</th>\n",
-       "      <td>20.55</td>\n",
-       "      <td>-1.04</td>\n",
-       "      <td>23.03</td>\n",
-       "      <td>-1.95</td>\n",
-       "      <td>26.76</td>\n",
-       "      <td>-1.87</td>\n",
-       "      <td>24.27</td>\n",
-       "      <td>-2.38</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>472 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "2015-11-01   23.83        2.24  27.91       2.93  30.30       1.67   29.60   \n",
-       "2015-12-01   25.01        2.19  27.99       2.85  30.11       1.63   29.39   \n",
-       "1983-01-01   27.42        2.96  28.92       3.29  29.00       0.70   29.36   \n",
-       "1997-12-01   26.92        4.13  28.76       3.62  29.32       0.83   29.26   \n",
-       "1997-11-01   25.63        4.04  28.55       3.57  29.49       0.86   29.32   \n",
-       "...            ...         ...    ...        ...    ...        ...     ...   \n",
-       "2000-01-01   23.86       -0.60  23.88      -1.75  26.96      -1.34   24.65   \n",
-       "1989-01-01   24.09       -0.37  24.15      -1.48  26.54      -1.76   24.53   \n",
-       "1988-10-01   19.50       -1.32  23.17      -1.75  27.06      -1.60   24.62   \n",
-       "1988-12-01   21.80       -0.99  23.07      -2.07  26.75      -1.74   24.33   \n",
-       "1988-11-01   20.55       -1.04  23.03      -1.95  26.76      -1.87   24.27   \n",
-       "\n",
-       "            Nino34anom  month  \n",
-       "datetime                       \n",
-       "2015-11-01        2.95     11  \n",
-       "2015-12-01        2.82     12  \n",
-       "1983-01-01        2.79      1  \n",
-       "1997-12-01        2.69     12  \n",
-       "1997-11-01        2.67     11  \n",
-       "...                ...    ...  \n",
-       "2000-01-01       -1.92      1  \n",
-       "1989-01-01       -2.04      1  \n",
-       "1988-10-01       -2.07     10  \n",
-       "1988-12-01       -2.24     12  \n",
-       "1988-11-01       -2.38     11  \n",
-       "\n",
-       "[472 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.sort_values('Nino34anom', ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "089d75db",
+   "id": "5504a0da",
    "metadata": {},
    "source": [
     "### Resampling\n",
@@ -4168,53 +934,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "1a5c0bf6",
+   "execution_count": null,
+   "id": "597cfeac",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34.plot();"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "376714eb",
+   "execution_count": null,
+   "id": "9e3ee506",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34.resample('1Y').mean().plot();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f000116f",
+   "id": "16c80788",
    "metadata": {},
    "source": [
     "### Applying operations to a dataframe\n",
@@ -4224,8 +964,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "dcee58a6",
+   "execution_count": null,
+   "id": "c8afa857",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4239,7 +979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c38ec17",
+   "id": "4fbef115",
    "metadata": {},
    "source": [
     "Now, this function accepts and returns a single value"
@@ -4247,21 +987,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "735d982b",
+   "execution_count": null,
+   "id": "34892381",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "273.15"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Convert a single value\n",
     "convert_degc_to_kelvin(0)"
@@ -4269,7 +998,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "434e497a",
+   "id": "384a0bdb",
    "metadata": {},
    "source": [
     "But what if we want to apply this to our dataframe? We can subset for Nino34, which is in degrees Celsius"
@@ -4277,40 +1006,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "e406e427",
+   "execution_count": null,
+   "id": "f09ee7c6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    26.72\n",
-       "1982-02-01    26.70\n",
-       "1982-03-01    27.20\n",
-       "1982-04-01    28.02\n",
-       "1982-05-01    28.54\n",
-       "              ...  \n",
-       "2020-12-01    25.53\n",
-       "2021-01-01    25.58\n",
-       "2021-02-01    25.81\n",
-       "2021-03-01    26.75\n",
-       "2021-04-01    27.40\n",
-       "Name: Nino34, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nino34_series"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f42069a5",
+   "id": "28ac04e8",
    "metadata": {},
    "source": [
     "Notice how the object type is a pandas series"
@@ -4318,28 +1024,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "7b000088",
+   "execution_count": null,
+   "id": "8718a43f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(df.Nino12[0:10])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "68a8f033",
+   "id": "ff1f569f",
    "metadata": {},
    "source": [
     "If you call `.values`, the object type is now a numpy array. Pandas `Series` values include numpy arrays, and calling `.values` returns the series as a numpy array!"
@@ -4347,28 +1042,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "1b6f337d",
+   "execution_count": null,
+   "id": "61a8255f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(df.Nino12.values[0:10])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fc00faa7",
+   "id": "2a5693fe",
    "metadata": {},
    "source": [
     "Let's apply this calculation to this `Series`; this returns another `Series` object."
@@ -4376,40 +1060,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "c721039d",
+   "execution_count": null,
+   "id": "ae197a92",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    299.87\n",
-       "1982-02-01    299.85\n",
-       "1982-03-01    300.35\n",
-       "1982-04-01    301.17\n",
-       "1982-05-01    301.69\n",
-       "               ...  \n",
-       "2020-12-01    298.68\n",
-       "2021-01-01    298.73\n",
-       "2021-02-01    298.96\n",
-       "2021-03-01    299.90\n",
-       "2021-04-01    300.55\n",
-       "Name: Nino34, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "convert_degc_to_kelvin(nino34_series)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4435c540",
+   "id": "87871b82",
    "metadata": {},
    "source": [
     "If we include `.values`, it returns a `numpy array`"
@@ -4417,7 +1078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c7ea806-7efe-4798-bb9b-f8a395797e24",
+   "id": "a256f852",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-warning\">\n",
@@ -4426,86 +1087,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "id": "9120d05e",
+   "execution_count": null,
+   "id": "52ae68ee",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([299.87, 299.85, 300.35, 301.17, 301.69, 301.9 , 301.25, 301.08,\n",
-       "       301.26, 301.79, 301.96, 302.36, 302.51, 302.28, 302.18, 302.06,\n",
-       "       302.04, 301.39, 300.22, 299.68, 299.59, 299.02, 298.73, 298.74,\n",
-       "       298.79, 299.54, 300.01, 300.54, 300.54, 300.01, 299.89, 299.49,\n",
-       "       299.58, 299.08, 298.56, 298.15, 298.58, 298.82, 299.38, 299.95,\n",
-       "       300.26, 300.01, 299.84, 299.65, 299.4 , 299.34, 299.34, 299.26,\n",
-       "       298.94, 299.09, 299.8 , 300.59, 300.65, 300.84, 300.52, 300.3 ,\n",
-       "       300.48, 300.72, 300.88, 300.85, 301.06, 301.17, 301.62, 301.95,\n",
-       "       301.9 , 302.18, 301.95, 301.73, 301.54, 301.22, 301.14, 300.75,\n",
-       "       300.47, 300.37, 300.46, 300.47, 299.63, 299.26, 298.72, 298.39,\n",
-       "       298.58, 297.77, 297.42, 297.48, 297.68, 298.48, 299.05, 299.84,\n",
-       "       300.24, 300.13, 299.89, 299.48, 299.4 , 299.41, 299.39, 299.53,\n",
-       "       299.7 , 300.1 , 300.61, 301.17, 301.21, 300.73, 300.4 , 300.2 ,\n",
-       "       299.9 , 300.13, 299.87, 300.06, 300.16, 300.08, 300.4 , 301.13,\n",
-       "       301.5 , 301.51, 301.07, 300.59, 300.22, 300.78, 301.01, 301.52,\n",
-       "       301.56, 301.78, 301.98, 302.29, 302.14, 301.17, 300.68, 299.79,\n",
-       "       299.63, 299.49, 299.66, 299.88, 299.84, 300.12, 300.81, 301.74,\n",
-       "       301.97, 301.43, 300.7 , 299.99, 300.07, 300.08, 300.06, 299.91,\n",
-       "       299.75, 299.74, 300.42, 301.05, 301.19, 301.14, 300.5 , 300.5 ,\n",
-       "       300.15, 300.64, 301.02, 301.02, 300.7 , 300.6 , 300.78, 301.08,\n",
-       "       300.88, 300.74, 300.16, 299.48, 299.11, 298.82, 298.81, 298.72,\n",
-       "       298.89, 299.  , 299.77, 300.51, 300.52, 300.47, 300.24, 299.71,\n",
-       "       299.5 , 299.39, 299.34, 299.17, 299.11, 299.51, 300.18, 301.18,\n",
-       "       301.75, 302.09, 302.07, 301.99, 302.08, 302.38, 302.47, 302.41,\n",
-       "       302.25, 302.01, 301.82, 301.71, 301.62, 299.87, 299.09, 298.64,\n",
-       "       298.76, 298.49, 298.33, 297.94, 298.05, 298.56, 299.4 , 299.99,\n",
-       "       300.12, 299.75, 299.5 , 298.74, 298.86, 298.79, 298.27, 298.05,\n",
-       "       297.8 , 298.34, 299.23, 300.16, 300.27, 300.18, 299.87, 299.6 ,\n",
-       "       299.36, 299.11, 298.93, 298.74, 298.89, 299.26, 299.99, 300.67,\n",
-       "       300.75, 300.83, 300.47, 300.02, 299.7 , 299.74, 299.6 , 299.32,\n",
-       "       299.65, 300.1 , 300.47, 301.09, 301.3 , 301.58, 301.13, 300.94,\n",
-       "       300.98, 301.2 , 301.42, 301.24, 300.91, 300.64, 300.96, 300.96,\n",
-       "       300.52, 300.63, 300.58, 300.  , 300.11, 300.34, 300.2 , 300.04,\n",
-       "       299.89, 300.01, 300.25, 300.99, 301.21, 300.91, 300.84, 300.69,\n",
-       "       300.62, 300.53, 300.46, 300.46, 300.25, 300.11, 300.7 , 301.22,\n",
-       "       301.35, 301.2 , 300.62, 300.03, 299.78, 299.9 , 299.49, 299.04,\n",
-       "       298.79, 299.23, 299.72, 300.74, 301.06, 301.  , 300.5 , 300.37,\n",
-       "       300.49, 300.62, 300.88, 300.91, 300.41, 299.96, 300.33, 300.93,\n",
-       "       300.72, 300.7 , 299.94, 299.35, 298.92, 298.37, 298.21, 298.12,\n",
-       "       297.86, 297.98, 299.22, 299.98, 300.33, 300.32, 300.34, 300.  ,\n",
-       "       299.59, 299.48, 299.45, 298.89, 298.69, 299.19, 299.82, 300.65,\n",
-       "       301.18, 301.26, 301.09, 300.68, 300.62, 300.78, 301.34, 301.45,\n",
-       "       301.22, 301.09, 301.44, 301.51, 300.83, 300.15, 299.24, 298.65,\n",
-       "       298.22, 298.16, 298.22, 298.1 , 298.08, 298.61, 299.38, 300.17,\n",
-       "       300.57, 300.61, 300.11, 299.34, 299.13, 298.87, 298.75, 298.68,\n",
-       "       298.64, 299.18, 299.78, 300.53, 300.95, 301.1 , 300.9 , 300.7 ,\n",
-       "       300.39, 300.13, 300.16, 299.61, 299.31, 299.47, 300.15, 300.83,\n",
-       "       300.72, 300.58, 300.06, 299.69, 299.8 , 299.51, 299.8 , 299.68,\n",
-       "       299.21, 299.33, 300.14, 301.16, 301.46, 301.26, 300.55, 300.17,\n",
-       "       300.32, 300.32, 300.65, 300.5 , 300.25, 300.44, 300.94, 301.71,\n",
-       "       302.03, 302.11, 301.97, 302.04, 302.15, 302.3 , 302.75, 302.54,\n",
-       "       302.32, 302.27, 302.05, 302.02, 301.3 , 300.68, 299.88, 299.43,\n",
-       "       299.26, 299.11, 299.25, 299.31, 299.4 , 300.02, 300.49, 301.25,\n",
-       "       301.45, 301.34, 300.76, 299.82, 299.44, 299.38, 298.94, 298.95,\n",
-       "       298.97, 298.98, 299.63, 300.57, 300.87, 301.  , 300.67, 300.26,\n",
-       "       300.25, 300.7 , 300.79, 300.68, 300.23, 300.56, 301.37, 301.75,\n",
-       "       301.72, 301.39, 300.78, 300.12, 299.85, 300.46, 300.41, 300.22,\n",
-       "       300.24, 300.29, 300.97, 301.47, 300.74, 300.45, 300.04, 299.33,\n",
-       "       298.92, 298.45, 298.49, 298.68, 298.73, 298.96, 299.9 , 300.55])"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "convert_degc_to_kelvin(nino34_series.values)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "de2e5b57",
+   "id": "65b3cd56",
    "metadata": {},
    "source": [
     "We can now assign our pandas `Series` with the converted temperatures to a new column in our dataframe!"
@@ -4513,8 +1105,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "id": "1ca04877",
+   "execution_count": null,
+   "id": "2d84dfe1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4523,40 +1115,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "1d997829",
+   "execution_count": null,
+   "id": "dd9a0811",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime\n",
-       "1982-01-01    299.87\n",
-       "1982-02-01    299.85\n",
-       "1982-03-01    300.35\n",
-       "1982-04-01    301.17\n",
-       "1982-05-01    301.69\n",
-       "               ...  \n",
-       "2020-12-01    298.68\n",
-       "2021-01-01    298.73\n",
-       "2021-02-01    298.96\n",
-       "2021-03-01    299.90\n",
-       "2021-04-01    300.55\n",
-       "Name: Nino34_degK, Length: 472, dtype: float64"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Nino34_degK"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1bcaf1cd",
+   "id": "a8c6dba3",
    "metadata": {},
    "source": [
     "Now that our analysis is done, we can save our data to a `csv` for later - or share with others!"
@@ -4564,8 +1133,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "id": "81b8d46b",
+   "execution_count": null,
+   "id": "054428db",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4574,249 +1143,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "id": "cd87abc5",
+   "execution_count": null,
+   "id": "3f7d378f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Nino12</th>\n",
-       "      <th>Nino12anom</th>\n",
-       "      <th>Nino3</th>\n",
-       "      <th>Nino3anom</th>\n",
-       "      <th>Nino4</th>\n",
-       "      <th>Nino4anom</th>\n",
-       "      <th>Nino34</th>\n",
-       "      <th>Nino34anom</th>\n",
-       "      <th>month</th>\n",
-       "      <th>Nino34_degK</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>datetime</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1982-01-01</th>\n",
-       "      <td>24.29</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>25.87</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>28.30</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>26.72</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>1</td>\n",
-       "      <td>299.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-02-01</th>\n",
-       "      <td>25.49</td>\n",
-       "      <td>-0.58</td>\n",
-       "      <td>26.38</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>28.21</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>26.70</td>\n",
-       "      <td>-0.02</td>\n",
-       "      <td>2</td>\n",
-       "      <td>299.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-03-01</th>\n",
-       "      <td>25.21</td>\n",
-       "      <td>-1.31</td>\n",
-       "      <td>26.98</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>28.41</td>\n",
-       "      <td>0.22</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-0.02</td>\n",
-       "      <td>3</td>\n",
-       "      <td>300.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-04-01</th>\n",
-       "      <td>24.50</td>\n",
-       "      <td>-0.97</td>\n",
-       "      <td>27.68</td>\n",
-       "      <td>0.18</td>\n",
-       "      <td>28.92</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.02</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>4</td>\n",
-       "      <td>301.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1982-05-01</th>\n",
-       "      <td>23.97</td>\n",
-       "      <td>-0.23</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>0.71</td>\n",
-       "      <td>29.49</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>28.54</td>\n",
-       "      <td>0.69</td>\n",
-       "      <td>5</td>\n",
-       "      <td>301.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-12-01</th>\n",
-       "      <td>22.16</td>\n",
-       "      <td>-0.60</td>\n",
-       "      <td>24.38</td>\n",
-       "      <td>-0.83</td>\n",
-       "      <td>27.65</td>\n",
-       "      <td>-0.95</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>-1.12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>298.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-01-01</th>\n",
-       "      <td>23.89</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>25.06</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>27.10</td>\n",
-       "      <td>-1.25</td>\n",
-       "      <td>25.58</td>\n",
-       "      <td>-0.99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>298.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-02-01</th>\n",
-       "      <td>25.55</td>\n",
-       "      <td>-0.66</td>\n",
-       "      <td>25.80</td>\n",
-       "      <td>-0.57</td>\n",
-       "      <td>27.20</td>\n",
-       "      <td>-1.00</td>\n",
-       "      <td>25.81</td>\n",
-       "      <td>-0.92</td>\n",
-       "      <td>2</td>\n",
-       "      <td>298.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-03-01</th>\n",
-       "      <td>26.48</td>\n",
-       "      <td>-0.26</td>\n",
-       "      <td>26.80</td>\n",
-       "      <td>-0.39</td>\n",
-       "      <td>27.79</td>\n",
-       "      <td>-0.55</td>\n",
-       "      <td>26.75</td>\n",
-       "      <td>-0.51</td>\n",
-       "      <td>3</td>\n",
-       "      <td>299.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-04-01</th>\n",
-       "      <td>24.89</td>\n",
-       "      <td>-0.80</td>\n",
-       "      <td>26.96</td>\n",
-       "      <td>-0.65</td>\n",
-       "      <td>28.47</td>\n",
-       "      <td>-0.21</td>\n",
-       "      <td>27.40</td>\n",
-       "      <td>-0.49</td>\n",
-       "      <td>4</td>\n",
-       "      <td>300.55</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>472 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Nino12  Nino12anom  Nino3  Nino3anom  Nino4  Nino4anom  Nino34  \\\n",
-       "datetime                                                                     \n",
-       "1982-01-01   24.29       -0.17  25.87       0.24  28.30       0.00   26.72   \n",
-       "1982-02-01   25.49       -0.58  26.38       0.01  28.21       0.11   26.70   \n",
-       "1982-03-01   25.21       -1.31  26.98      -0.16  28.41       0.22   27.20   \n",
-       "1982-04-01   24.50       -0.97  27.68       0.18  28.92       0.42   28.02   \n",
-       "1982-05-01   23.97       -0.23  27.79       0.71  29.49       0.70   28.54   \n",
-       "...            ...         ...    ...        ...    ...        ...     ...   \n",
-       "2020-12-01   22.16       -0.60  24.38      -0.83  27.65      -0.95   25.53   \n",
-       "2021-01-01   23.89       -0.64  25.06      -0.55  27.10      -1.25   25.58   \n",
-       "2021-02-01   25.55       -0.66  25.80      -0.57  27.20      -1.00   25.81   \n",
-       "2021-03-01   26.48       -0.26  26.80      -0.39  27.79      -0.55   26.75   \n",
-       "2021-04-01   24.89       -0.80  26.96      -0.65  28.47      -0.21   27.40   \n",
-       "\n",
-       "            Nino34anom  month  Nino34_degK  \n",
-       "datetime                                    \n",
-       "1982-01-01        0.15      1       299.87  \n",
-       "1982-02-01       -0.02      2       299.85  \n",
-       "1982-03-01       -0.02      3       300.35  \n",
-       "1982-04-01        0.24      4       301.17  \n",
-       "1982-05-01        0.69      5       301.69  \n",
-       "...                ...    ...          ...  \n",
-       "2020-12-01       -1.12     12       298.68  \n",
-       "2021-01-01       -0.99      1       298.73  \n",
-       "2021-02-01       -0.92      2       298.96  \n",
-       "2021-03-01       -0.51      3       299.90  \n",
-       "2021-04-01       -0.49      4       300.55  \n",
-       "\n",
-       "[472 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pd.read_csv('nino_analyzed_output.csv', index_col=0, parse_dates=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "33f433ed",
+   "id": "9327e958",
    "metadata": {},
    "source": [
     "---\n",
diff --git a/environment.yml b/environment.yml
index e632eff59..dd2831ec2 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,3 +10,7 @@ dependencies:
   - sqlalchemy<1.4
   - cartopy
   - xarray
+  - pip
+  - pip:
+      # works for regular pip packages
+      - pythia-datasets

From 370da9541687a62423163e496f6a161d218bcfed Mon Sep 17 00:00:00 2001
From: Brian Rose <brose@albany.edu>
Date: Fri, 28 May 2021 13:56:35 -0400
Subject: [PATCH 4/5] First draft of self-generating cover art on landing page

---
 landing-page.ipynb | 90 ++++++++++++++++++++++++++++++++++++++++++++++
 landing-page.md    |  7 ----
 2 files changed, 90 insertions(+), 7 deletions(-)
 create mode 100644 landing-page.ipynb
 delete mode 100644 landing-page.md

diff --git a/landing-page.ipynb b/landing-page.ipynb
new file mode 100644
index 000000000..fa81ae8dd
--- /dev/null
+++ b/landing-page.ipynb
@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "24611359",
+   "metadata": {},
+   "source": [
+    "# Project Pythia Foundations\n",
+    "\n",
+    "## A community learning resource for Python-based computing in the geosciences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "356d3fba",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x612 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from cartopy import crs as ccrs, feature as cfeature\n",
+    "\n",
+    "fig = plt.figure(figsize=(11, 8.5))\n",
+    "projMoll = ccrs.Mollweide(central_longitude=0)\n",
+    "ax = plt.subplot(1, 1, 1, projection=projMoll)\n",
+    "ax.set_title(\"A placeholder cover image for the Foundations book!\", fontsize=18)\n",
+    "\n",
+    "# Add in the cartographic shapefiles\n",
+    "ax.coastlines()\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue')\n",
+    "\n",
+    "ax.stock_img();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa6980e8",
+   "metadata": {},
+   "source": [
+    "Brought to you by [Project Pythia](https://projectpythia.org), this growing collection covers the foundational skills everyone needs to get started with scientific computing in the open-source Python ecosystem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c986b6d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/landing-page.md b/landing-page.md
deleted file mode 100644
index dc2dc9c54..000000000
--- a/landing-page.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Project Pythia Foundations
-
-Brought to you by [Project Pythia](https://projectpythia.org), this collection will cover the foundational skills everyone needs to get started with scientific computing in the open-source Python ecosystem.
-
-```{note}
-This content is under construction!
-```

From 3c22650f4ef84834c9f412b7e69c4baba9e28705 Mon Sep 17 00:00:00 2001
From: Brian Rose <brose@albany.edu>
Date: Fri, 28 May 2021 14:48:55 -0400
Subject: [PATCH 5/5] Suppress cartopy warnings in landing-page notebook

---
 landing-page.ipynb | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/landing-page.ipynb b/landing-page.ipynb
index fa81ae8dd..6c848e253 100644
--- a/landing-page.ipynb
+++ b/landing-page.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "356d3fba",
    "metadata": {
     "tags": [
@@ -34,6 +34,11 @@
     }
    ],
    "source": [
+    "#  Suppress warnings issued by Cartopy when downloading data files\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "from cartopy import crs as ccrs, feature as cfeature\n",
     "\n",
@@ -46,7 +51,7 @@
     "ax.coastlines()\n",
     "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue')\n",
     "\n",
-    "ax.stock_img();\n"
+    "ax.stock_img();"
    ]
   },
   {