diff --git a/environment.yml b/environment.yml
index 5db4b19e..ee00f91e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -6,6 +6,7 @@ dependencies:
   - anaconda=2025.12
   - pip
   - pip:
+    - pandas>=3
     - jupyter-book>=1.0.4post1,<2.0
     - quantecon-book-theme==0.15.1
     - sphinx-tojupyter==0.6.0
diff --git a/lectures/pandas.md b/lectures/pandas.md
index 3d2c809d..0a5fb258 100644
--- a/lectures/pandas.md
+++ b/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 rest 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
diff --git a/lectures/pandas_panel.md b/lectures/pandas_panel.md
index 8bb6b29e..8cb3b762 100644
--- a/lectures/pandas_panel.md
+++ b/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`.