Post final QA

Author: bzaczynski

how-to-drop-null-values-in-pandas/README.md

@@ -1,15 +1,25 @@
+# How to Drop Null Values in pandas
+
 The materials contained in this download are designed to complement the Real Python tutorial [How to Drop Null Values in pandas](https://realpython.com/how-to-drop-null-values-in-pandas/).
 
-You should create a new folder named `pandas_nulls` on your computer and place each file inside it. You may also consider creating a [Python virtual environment](https://realpython.com/python-virtual-environments-a-primer/) within this folder.
+Consider creating a [Python virtual environment](https://realpython.com/python-virtual-environments-a-primer/) before installing the dependencies:
+
+```shell
+$ python3 -m venv .venv/ --prompt pandas-nulls
+$ source .venv/bin/activate
+(pandas-nulls) $ python -m pip install -r requirements.txt
+```
+
+Your download bundle contains the following four files:
 
-Your download bundle contains the following four files. The first three files contain the code from different tutorial sections, while the fourth contains the solutions to the exercise.
+1. `drop_null_rows.py`
+2. `drop_null_columns.py`
+3. `drop_a_subset.py`
+4. `exercise_solutions.py`
 
-`drop_null_rows.py`        
-`drop_null_columns.py`   
-`drop_a_subset.py`         
-`exercise_solutions.py` 
+The first three files contain the code from different tutorial sections, while the fourth contains the solutions to the exercise.
 
 There are also two data files containing the data used throughout the tutorial:
 
-`sales_data_with_missing_values.csv`
-`grades.csv`
+1. `sales_data_with_missing_values.csv`
+2. `grades.csv`

how-to-drop-null-values-in-pandas/drop_a_subset.py

@@ -8,11 +8,7 @@
     date_format="%d/%m/%Y",
 ).convert_dtypes(dtype_backend="pyarrow")
 
-
-sales_data.dropna(axis=0, subset=(["discount", "sale_price"]))
-
-sales_data.dropna(how="all")
-
-sales_data.dropna(thresh=5)
-
-sales_data.dropna(thresh=5, ignore_index=True)
+print(sales_data.dropna(axis=0, subset=(["discount", "sale_price"])))
+print(sales_data.dropna(how="all"))
+print(sales_data.dropna(thresh=5))
+print(sales_data.dropna(thresh=5, ignore_index=True))

how-to-drop-null-values-in-pandas/drop_null_columns.py

@@ -6,4 +6,4 @@
     date_format="%d/%m/%Y",
 ).convert_dtypes(dtype_backend="pyarrow")
 
-sales_data.dropna(axis="columns")
+print(sales_data.dropna(axis="columns"))

how-to-drop-null-values-in-pandas/drop_null_rows.py

@@ -8,12 +8,12 @@
     date_format="%d/%m/%Y",
 ).convert_dtypes(dtype_backend="pyarrow")
 
-sales_data
-
-sales_data.isna().sum()
-
-sales_data.dropna()
+print(sales_data)
+print(sales_data.isna().sum())
+print(sales_data.dropna())
 
 clean_sales_data = sales_data.dropna()
+print(clean_sales_data)
 
-clean_sales_data = sales_data.dropna(inplace=True)
+sales_data.dropna(inplace=True)
+print(sales_data)

how-to-drop-null-values-in-pandas/exercise_solutions.py

@@ -5,21 +5,16 @@
 ).convert_dtypes(dtype_backend="pyarrow")
 
 # 1. Use `.dropna()` in such a way that it permanently drops the row in the dataframe containing only null values.
-
 grades.dropna(how="all", inplace=True)
 
 # 2. Display the rows for the exams that all students have completed.
-
 grades.dropna()
 
 # 3. Display any columns with no missing data.
-
 grades.dropna(axis=1)
 
 # 4. Display the exams sat by at least five students.
-
 grades.dropna(axis=0, thresh=6)  # Remember there are seven columns.
 
 # 5. Who else was in in every exam that both S2 and S4 sat?
-
 grades.dropna(subset=["S2", "S4"]).dropna(axis=1, ignore_index=True)

how-to-drop-null-values-in-pandas/requirements.txt

@@ -0,0 +1,7 @@
+numpy==2.3.2
+pandas==2.3.2
+pyarrow==21.0.0
+python-dateutil==2.9.0.post0
+pytz==2025.2
+six==1.17.0
+tzdata==2025.2