Pandas: How to Use dropna() with Specific Columns

---

You can use the dropna() function with the subset argument to drop rows from a pandas DataFrame which contain missing values in specific columns.

Here are the most common ways to use this function in practice:

Method 1: Drop Rows with Missing Values in One Specific Column

df.dropna(subset = ['column1'], inplace=True)

Method 2: Drop Rows with Missing Values in One of Several Specific Columns

df.dropna(subset = ['column1', 'column2', 'column3'], inplace=True)

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [18, np.nan, 19, 14, 14, 11, 20, 28],
                   'assists': [5, np.nan, np.nan, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, np.nan]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A    18.0      5.0      11.0
1    B     NaN      NaN       8.0
2    C    19.0      NaN      10.0
3    D    14.0      9.0       6.0
4    E    14.0     12.0       6.0
5    F    11.0      9.0       5.0
6    G    20.0      9.0       9.0
7    H    28.0      4.0       NaN

Example 1: Drop Rows with Missing Values in One Specific Column

We can use the following syntax to drop rows with missing values in the ‘assists’ column:

#drop rows with missing values in 'assists' column
df.dropna(subset = ['assists'], inplace=True)

#view updated DataFrame
print(df)

  team  points  assists  rebounds
0    A    18.0      5.0      11.0
3    D    14.0      9.0       6.0
4    E    14.0     12.0       6.0
5    F    11.0      9.0       5.0
6    G    20.0      9.0       9.0
7    H    28.0      4.0       NaN

Notice that the two rows with missing values in the ‘assists’ column have both been removed from the DataFrame.

Also note that the last row in the DataFrame is kept even though it has a missing value because the missing value is not located in the ‘assists’ column.

Example 2: Drop Rows with Missing Values in One of Several Specific Columns

We can use the following syntax to drop rows with missing values in the ‘points’ or ‘rebounds’ columns:

#drop rows with missing values in 'points' or 'rebounds' column
df.dropna(subset = ['points', 'rebounds'], inplace=True)

#view updated DataFrame
print(df)

  team  points  assists  rebounds
0    A    18.0      5.0      11.0
2    C    19.0      NaN      10.0
3    D    14.0      9.0       6.0
4    E    14.0     12.0       6.0
5    F    11.0      9.0       5.0
6    G    20.0      9.0       9.0

Notice that the two rows with missing values in the ‘points’ or ‘rebounds’ columns have been removed from the DataFrame.