How to drop rows of Pandas DataFrame whose value in a certain column is NaN?

 

You can drop rows of a Pandas DataFrame whose value in a certain column is NaN using the dropna() method. Here's an example:

python

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, None, 30, 22]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Drop rows where the 'Age' column has NaN values
df_cleaned = df.dropna(subset=['Age'])

print("\nDataFrame after dropping rows with NaN values in 'Age' column:")
print(df_cleaned)

Output:

sql

Original DataFrame:
      Name   Age
0    Alice  25.0
1      Bob   NaN
2  Charlie  30.0
3    David  22.0

DataFrame after dropping rows with NaN values in 'Age' column:
      Name   Age
0    Alice  25.0
2  Charlie  30.0
3    David  22.0

In this example:

• We create a sample DataFrame with the 'Name' and 'Age' columns.
• The dropna() method is used to drop rows where the value in the 'Age' column is NaN. The subset parameter specifies the column(s) to consider for NaN checking.
• The resulting DataFrame df_cleaned contains only the rows where the 'Age' column does not have NaN values.

Remember that the dropna() method by default returns a new DataFrame with the NaN rows dropped, leaving the original DataFrame unchanged. If you want to modify the original DataFrame in-place, you can use the inplace=True parameter:

python

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