<\/span><\/h2>\nTo find missing values in a column of a Pandas DataFrame, you can use the `isnull()` function. This function returns a Boolean mask indicating where the missing values are located. By applying this function to a specific column, you can determine which rows have missing values in that column. Let’s walk through an example:<\/p>\n
“`python
\nimport pandas as pd<\/p>\n
# Create a sample DataFrame
\ndata = {‘Name’: [‘John’, ‘Alice’, ‘Bob’, ‘Chris’],
\n ‘Age’: [25, None, 30, 35],
\n ‘Location’: [‘New York’, ‘Paris’, None, ‘London’]}
\ndf = pd.DataFrame(data)<\/p>\n
# Check for missing values in the ‘Age’ column
\nmissing_values = df[‘Age’].isnull()<\/p>\n
# Print the rows with missing values in the ‘Age’ column
\nprint(df[missing_values])
\n“`<\/p>\n
In this example, we have a DataFrame with three columns: ‘Name’, ‘Age’, and ‘Location’. We check for missing values in the ‘Age’ column using `df[‘Age’].isnull()`, which returns a Boolean mask. Finally, by indexing the DataFrame `df` with the Boolean mask `missing_values`, we obtain the rows that contain missing values in the ‘Age’ column.<\/p>\n
**<\/p>\n