[pandas, pandas_panel] Update lectures for pandas 3.0 compatibility (#470)

* [pandas, pandas_panel] Update lectures for pandas 3.0 compatibility

anaconda 2026.06 (merged in #562) ships pandas 3.0.3, so the interim
pandas>=3 pip pin and anaconda=2025.12 are no longer needed. This branch
now carries only the lecture updates:

- pandas_panel: drop redundant future_stack=True from .stack() calls
  (the new stacking layout is the default in pandas 3.0)
- pandas_panel: text now says axis=1 in groupby is "removed" not
  "deprecated" (it is fully removed in 3.0)
- pandas: df.where(df.POP >= 20000, False) -> df.where(df.POP >= 20000)
  so the example fills non-matching rows with NaN instead of scattering
  False through the string columns

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* Address Copilot grammar feedback

- pandas: "replace the rest rows" -> "replace the remaining rows"
- pandas_panel: add commas around the introductory phrase / before "as"
  to fix the run-on sentence about groupby axis=1 removal

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

Author: mmcky

lectures/pandas.md

@@ -349,10 +349,10 @@ df.loc[complexCondition]
 The ability to make changes in dataframes is important to generate a clean dataset for future analysis.
 
 
-**1.** We can use `df.where()` conveniently to "keep" the rows we have selected and replace the rest rows with any other values
+**1.** We can use `df.where()` conveniently to "keep" the rows we have selected and replace the remaining rows with `NaN`
 
 ```{code-cell} ipython3
-df.where(df.POP >= 20000, False)
+df.where(df.POP >= 20000)
 ```
 
 **2.** We can simply use `.loc[]` to specify the column that we want to modify, and assign values

lectures/pandas_panel.md

@@ -150,14 +150,14 @@ the row index (`.unstack()` works in the opposite direction - try it
 out)
 
 ```{code-cell} ipython3
-realwage.stack(future_stack=True).head()
+realwage.stack().head()
 ```
 
 We can also pass in an argument to select the level we would like to
 stack
 
 ```{code-cell} ipython3
-realwage.stack(level='Country', future_stack=True).head()  # future_stack=True is required until pandas>3.0
+realwage.stack(level='Country').head()
 ```
 
 Using a `DatetimeIndex` makes it easy to select a particular time
@@ -167,7 +167,7 @@ Selecting one year and stacking the two lower levels of the
 `MultiIndex` creates a cross-section of our panel data
 
 ```{code-cell} ipython3
-realwage.loc['2015'].stack(level=(1, 2), future_stack=True).transpose().head() # future_stack=True is required until pandas>3.0
+realwage.loc['2015'].stack(level=(1, 2)).transpose().head()
 ```
 
 For the rest of lecture, we will work with a dataframe of the hourly
@@ -401,7 +401,7 @@ plt.show()
 We can also specify a level of the `MultiIndex` (in the column axis)
 to aggregate over. 
 
-In the case of `groupby` we need to use `.T` to transpose the columns into rows as `pandas` has deprecated the use of `axis=1` in the `groupby` method.
+In the case of `groupby`, we need to use `.T` to transpose the columns into rows, as `pandas` has removed support for `axis=1` in the `groupby` method.
 
 ```{code-cell} ipython3
 merged.T.groupby(level='Continent').mean().head()
@@ -432,7 +432,7 @@ plt.show()
 summary statistics
 
 ```{code-cell} ipython3
-merged.stack(future_stack=True).describe()
+merged.stack().describe()
 ```
 
 This is a simplified way to use `groupby`.