diff --git a/w1/conda.md b/w1/conda.md new file mode 100644 index 0000000..4cfe0e6 --- /dev/null +++ b/w1/conda.md @@ -0,0 +1,30 @@ +# BasisOfLearning +Week 1 + +# Working with conda environments + +Firstly install anaconda from https://www.anaconda.com/products/distribution#windows + +Open a terminal window and type +#### conda --version +Conda displays the number of the version that you have installed. + +Update conda to the current version. Type the following: +#### conda update conda + +# Managing Environments + +Open conda command prompt- + +Create a new environment and install a package in it. + +We will create a new anaconda package say 'deeplearning'. +Using the following command in conda command promp +#### conda create --name deeplearning + +To use, or "activate" the new environment, type the following: +#### conda activate deeplearning + +To install package to given environment we will type: + +#### conda install -n deeplearning python diff --git a/w1/jupyter.md b/w1/jupyter.md new file mode 100644 index 0000000..a635a3e --- /dev/null +++ b/w1/jupyter.md @@ -0,0 +1,41 @@ +# Jupyter Notebook Installation + +This page contains information and links about installing and using tools across the Jupyter ecosystem. Generally speaking, the documentation of each tool is the place to learn about the best-practices for how to install and use the tool. + +Use the following installation steps: + +1.Download Anaconda. We recommend downloading Anaconda’s latest Python 3 version (currently Python 3.9). + +2.Install the version of Anaconda which you downloaded, following the instructions on the download page. + +3.Congratulations, you have installed Jupyter Notebook. To run the notebook type in conda command prompt: + +#### jupyter notebook + +or directly use anaconda navigator to access it. + +# Creating a new Notebook + +A new notebook may be created at any time, either from the dashboard, or using the File → New menu option from +within an active notebook. The new notebook is created within the same directory and will open in a new browser tab. +It will also be reflected as a new entry in the notebook list on the dashboard. + +When you create a new notebook document, you will be presented with the notebook name, a menu bar, a toolbar +and an empty code cell. + +# Structure of Notebook + +The notebook consists of a sequence of cells. A cell is a multiline text input field, and its contents can be executed by +using Shift-Enter, or by clicking either the “Play” button the toolbar, or Cell, Run in the menu bar. The execution +behavior of a cell is determined by the cell’s type. There are three types of cells: code cells, markdown cells, and raw +cells. Every cell starts off being a code cell, but its type can be changed by using a drop-down on the toolbar (which +will be “Code”, initially), or via keyboard shortcuts + +# Code Cells + +A code cell allows you to edit and write new code, with full syntax highlighting and tab completion. The programming +language you use depends on the kernel, and the default kernel (IPython) runs Python code. +When a code cell is executed, code that it contains is sent to the kernel associated with the notebook. The results that +are returned from this computation are then displayed in the notebook as the cell’s output. The output is not limited to +text, with many other possible forms of output are also possible, including matplotlib figures and HTML tables (as +used, for example, in the pandas data analysis package). This is known as IPython’s rich display capability. diff --git a/w1/matplotlib.ipynb b/w1/matplotlib.ipynb new file mode 100644 index 0000000..d81b229 --- /dev/null +++ b/w1/matplotlib.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee377870", + "metadata": {}, + "source": [ + "Matplotlib is a low level graph plotting library in python that serves as a visualization utility.\n", + "\n", + "Matplotlib was created by John D. Hunter.\n", + "\n", + "Matplotlib is open source and we can use it freely.\n", + "\n", + "Matplotlib is mostly written in python, a few segments are written in C, Objective-C and Javascript for Platform compatibility." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e6004226", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "plt.plot([1,2,3],[3,4,5])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "386934e2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "plt.plot([1,2,3],[3,4,5])\n", + "plt.title(\"data\")\n", + "plt.ylabel(\"Y\")\n", + "plt.xlabel(\"X\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0aab2771", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib import style\n", + "style.use('ggplot')\n", + "plt.plot([1,2,3],[3,4,5])\n", + "plt.title(\"data\")\n", + "plt.ylabel(\"Y\")\n", + "plt.xlabel(\"X\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/w1/numpy.ipynb b/w1/numpy.ipynb new file mode 100644 index 0000000..0e303b4 --- /dev/null +++ b/w1/numpy.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6437d32a", + "metadata": {}, + "source": [ + "NumPy is a Python library used for working with arrays.\n", + "\n", + "It also has functions for working in domain of linear algebra, fourier transform, and matrices.\n", + "\n", + "NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.\n", + "\n", + "NumPy stands for Numerical Python." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d74f7966", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "data=np.array([1,2,3,4])\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "id": "89c51eeb", + "metadata": {}, + "source": [ + "To create an ndarray, we can pass a list,\n", + "tuple or any array-like object into the array() method, and it will be converted into an ndarray" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "566350a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "#check dimension of array\n", + "data=np.array([[1,2,3],[1,2,3]])\n", + "print(data.ndim)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "81827eac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5th element on 2nd row: 10\n" + ] + } + ], + "source": [ + "#accessing array element\n", + "import numpy as np\n", + "\n", + "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n", + "\n", + "print('5th element on 2nd row: ', arr[1, 4])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6969ec8f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 3 4 5]\n" + ] + } + ], + "source": [ + "#Slicing Arrays\n", + "import numpy as np\n", + "\n", + "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n", + "\n", + "print(arr[1:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cd5c2253", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3]\n", + "int32\n" + ] + } + ], + "source": [ + "#changing data types\n", + "import numpy as np\n", + "\n", + "arr = np.array([1.1, 2.1, 3.1])\n", + "\n", + "newarr = arr.astype('i')\n", + "\n", + "print(newarr)\n", + "print(newarr.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f589833c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[42 2 3 4 5]\n", + "[1 2 3 4 5]\n" + ] + } + ], + "source": [ + "#array copy \n", + "#Make a copy, change the original array, and display both arrays:\n", + "import numpy as np\n", + "\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "x = arr.copy()\n", + "arr[0] = 42\n", + "\n", + "print(arr)\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ef77a16f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[42 2 3 4 5]\n", + "[42 2 3 4 5]\n" + ] + } + ], + "source": [ + "#array view\n", + "#Make a view, change the original array, and display both arrays:\n", + "import numpy as np\n", + "\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "x = arr.view()\n", + "arr[0] = 42\n", + "\n", + "print(arr)\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe48d67f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/w1/pandas.ipynb b/w1/pandas.ipynb new file mode 100644 index 0000000..220ea80 --- /dev/null +++ b/w1/pandas.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "6739c4c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Duration Pulse Maxpulse Calories\n", + "0 60 110 130 409.1\n", + "1 60 117 145 479.0\n", + "2 60 103 135 340.0\n", + "3 45 109 175 282.4\n", + "4 45 117 148 406.0\n", + "5 60 102 127 300.0\n", + "6 60 110 136 374.0\n", + "7 45 104 134 253.3\n", + "8 30 109 133 195.1\n", + "9 60 98 124 269.0\n", + "10 60 103 147 329.3\n", + "11 60 100 120 250.7\n", + "12 60 106 128 345.3\n", + "13 60 104 132 379.3\n", + "14 60 98 123 275.0\n", + "15 60 98 120 215.2\n", + "16 60 100 120 300.0\n", + "17 45 90 112 NaN\n", + "18 60 103 123 323.0\n", + "19 45 97 125 243.0\n", + "20 60 108 131 364.2\n", + "21 45 100 119 282.0\n", + "22 60 130 101 300.0\n", + "23 45 105 132 246.0\n", + "24 60 102 126 334.5\n", + "25 60 100 120 250.0\n", + "26 60 92 118 241.0\n", + "27 60 103 132 NaN\n", + "28 60 100 132 280.0\n", + "29 60 102 129 380.3\n", + "30 60 92 115 243.0\n", + "31 45 90 112 180.1\n", + "32 60 101 124 299.0\n", + "33 60 93 113 223.0\n", + "34 60 107 136 361.0\n", + "35 60 114 140 415.0\n", + "36 60 102 127 300.0\n", + "37 60 100 120 300.0\n", + "38 60 100 120 300.0\n", + "39 45 104 129 266.0\n", + "40 45 90 112 180.1\n", + "41 60 98 126 286.0\n", + "42 60 100 122 329.4\n", + "43 60 111 138 400.0\n", + "44 60 111 131 397.0\n", + "45 60 99 119 273.0\n", + "46 60 109 153 387.6\n", + "47 45 111 136 300.0\n", + "48 45 108 129 298.0\n", + "49 60 111 139 397.6\n", + "50 60 107 136 380.2\n", + "51 80 123 146 643.1\n", + "52 60 106 130 263.0\n", + "53 60 118 151 486.0\n", + "54 30 136 175 238.0\n", + "55 60 121 146 450.7\n", + "56 60 118 121 413.0\n", + "57 45 115 144 305.0\n", + "58 20 153 172 226.4\n", + "59 45 123 152 321.0\n", + "60 210 108 160 1376.0\n", + "61 160 110 137 1034.4\n", + "62 160 109 135 853.0\n", + "63 45 118 141 341.0\n", + "64 20 110 130 131.4\n", + "65 180 90 130 800.4\n", + "66 150 105 135 873.4\n", + "67 150 107 130 816.0\n", + "68 20 106 136 110.4\n", + "69 300 108 143 1500.2\n", + "70 150 97 129 1115.0\n", + "71 60 109 153 387.6\n", + "72 90 100 127 700.0\n", + "73 150 97 127 953.2\n", + "74 45 114 146 304.0\n", + "75 90 98 125 563.2\n", + "76 45 105 134 251.0\n", + "77 45 110 141 300.0\n", + "78 120 100 130 500.4\n", + "79 270 100 131 1729.0\n", + "80 30 159 182 319.2\n", + "81 45 149 169 344.0\n", + "82 30 103 139 151.1\n", + "83 120 100 130 500.0\n", + "84 45 100 120 225.3\n", + "85 30 151 170 300.0\n", + "86 45 102 136 234.0\n", + "87 120 100 157 1000.1\n", + "88 45 129 103 242.0\n", + "89 20 83 107 50.3\n", + "90 180 101 127 600.1\n", + "91 45 107 137 NaN\n", + "92 30 90 107 105.3\n", + "93 15 80 100 50.5\n", + "94 20 150 171 127.4\n", + "95 20 151 168 229.4\n", + "96 30 95 128 128.2\n", + "97 25 152 168 244.2\n", + "98 30 109 131 188.2\n", + "99 90 93 124 604.1\n", + "100 20 95 112 77.7\n", + "101 90 90 110 500.0\n", + "102 90 90 100 500.0\n", + "103 90 90 100 500.4\n", + "104 30 92 108 92.7\n", + "105 30 93 128 124.0\n", + "106 180 90 120 800.3\n", + "107 30 90 120 86.2\n", + "108 90 90 120 500.3\n", + "109 210 137 184 1860.4\n", + "110 60 102 124 325.2\n", + "111 45 107 124 275.0\n", + "112 15 124 139 124.2\n", + "113 45 100 120 225.3\n", + "114 60 108 131 367.6\n", + "115 60 108 151 351.7\n", + "116 60 116 141 443.0\n", + "117 60 97 122 277.4\n", + "118 60 105 125 NaN\n", + "119 60 103 124 332.7\n", + "120 30 112 137 193.9\n", + "121 45 100 120 100.7\n", + "122 60 119 169 336.7\n", + "123 60 107 127 344.9\n", + "124 60 111 151 368.5\n", + "125 60 98 122 271.0\n", + "126 60 97 124 275.3\n", + "127 60 109 127 382.0\n", + "128 90 99 125 466.4\n", + "129 60 114 151 384.0\n", + "130 60 104 134 342.5\n", + "131 60 107 138 357.5\n", + "132 60 103 133 335.0\n", + "133 60 106 132 327.5\n", + "134 60 103 136 339.0\n", + "135 20 136 156 189.0\n", + "136 45 117 143 317.7\n", + "137 45 115 137 318.0\n", + "138 45 113 138 308.0\n", + "139 20 141 162 222.4\n", + "140 60 108 135 390.0\n", + "141 60 97 127 NaN\n", + "142 45 100 120 250.4\n", + "143 45 122 149 335.4\n", + "144 60 136 170 470.2\n", + "145 45 106 126 270.8\n", + "146 60 107 136 400.0\n", + "147 60 112 146 361.9\n", + "148 30 103 127 185.0\n", + "149 60 110 150 409.4\n", + "150 60 106 134 343.0\n", + "151 60 109 129 353.2\n", + "152 60 109 138 374.0\n", + "153 30 150 167 275.8\n", + "154 60 105 128 328.0\n", + "155 60 111 151 368.5\n", + "156 60 97 131 270.4\n", + "157 60 100 120 270.4\n", + "158 60 114 150 382.8\n", + "159 30 80 120 240.9\n", + "160 30 85 120 250.4\n", + "161 45 90 130 260.4\n", + "162 45 95 130 270.0\n", + "163 45 100 140 280.9\n", + "164 60 105 140 290.8\n", + "165 60 110 145 300.0\n", + "166 60 115 145 310.2\n", + "167 75 120 150 320.4\n", + "168 75 125 150 330.4\n" + ] + } + ], + "source": [ + "#loading files in a data frame\n", + "import pandas as pd\n", + "df=pd.read_csv('data.csv') #to read data \n", + "print(df.to_string()) # to print data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c1298dd3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " cars color\n", + "0 BMW RED\n", + "1 AUDI BLUE\n", + "2 ROLLS ROYCE BLACK\n" + ] + } + ], + "source": [ + "# to form a dataframe\n", + "mydataset={\n", + " 'cars':[\"BMW\",\"AUDI\",\"ROLLS ROYCE\"],\n", + " 'color':[\"RED\",\"BLUE\",\"BLACK\"]\n", + "}\n", + "myvar=pd.DataFrame(mydataset)\n", + "print(myvar)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "fe8dcb9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "1 2\n", + "2 3\n", + "3 4\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# printing data as a series\n", + "ser=[1,2,3,4]\n", + "myvar=pd.Series(ser)\n", + "print(myvar)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "79b6adf5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cars BMW\n", + "color RED\n", + "Name: 0, dtype: object\n" + ] + } + ], + "source": [ + "#locating a data in data set\n", + "\n", + "mydataset={\n", + " 'cars':[\"BMW\",\"AUDI\",\"ROLLS ROYCE\"],\n", + " 'color':[\"RED\",\"BLUE\",\"BLACK\"]\n", + "}\n", + "myvar=pd.DataFrame(mydataset)\n", + "print(myvar.loc[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c7d64a71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Duration Pulse Maxpulse Calories\n", + "0 60 110 130 409.1\n", + "1 60 117 145 479.0\n", + "2 60 103 135 340.0\n", + "3 45 109 175 282.4\n", + "4 45 117 148 406.0\n", + "5 60 102 127 300.0\n", + "6 60 110 136 374.0\n", + "7 45 104 134 253.3\n", + "8 30 109 133 195.1\n", + "9 60 98 124 269.0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " Duration Pulse Maxpulse Calories\n", + "159 30 80 120 240.9\n", + "160 30 85 120 250.4\n", + "161 45 90 130 260.4\n", + "162 45 95 130 270.0\n", + "163 45 100 140 280.9\n", + "164 60 105 140 290.8\n", + "165 60 110 145 300.0\n", + "166 60 115 145 310.2\n", + "167 75 120 150 320.4\n", + "168 75 125 150 330.4\n" + ] + } + ], + "source": [ + "# viewing the data \n", + "import pandas as pd\n", + "df=pd.read_csv('data.csv') #to read data \n", + "print(df.head(10))# returns the headers and a specified number of rows, starting from the top\n", + "print(\"\\n\\n\\n\\n\")\n", + "print(df.tail(10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cba3c839", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +}