Pandas groupby count nulls

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Pandas groupby count nulls

Using the pandas library in Python, you can easily perform a groupby operation to count null values in a dataframe.

Here’s an example to demonstrate this:

# Import the necessary libraries
import pandas as pd

# Create a sample dataframe
data = {'Name': ['John', 'Alice', 'Bob', 'John', 'Alice'],
        'Age': [25, 30, None, 35, 40],
        'Salary': [50000, 60000, 70000, None, 90000]}
df = pd.DataFrame(data)

# Use the groupby function to group data by 'Name' column
grouped_data = df.groupby('Name')

# Count the number of null values in each group
null_counts = grouped_data.isnull().sum()

# Print the result
print(null_counts)

In this example, we have a sample dataframe that contains information about individuals’ names, ages, and salaries. Some of the entries have missing values indicated by “None” or “NaN”.

We start by importing the pandas library and creating the dataframe. Next, we use the groupby function to group the data based on the ‘Name’ column.

To count the number of null values in each group, we apply the isnull function to the grouped data and then call the sum function. This will give us the count of null values for each column in each group.

Finally, we print the result, which will show the count of null values for each group. For example, the result might look like:

Name
Alice    0
Bob      1
John     1
dtype: int64

This output indicates that in the ‘Alice’ group, there are no null values, in the ‘Bob’ group there is 1 null value, and in the ‘John’ group there is also 1 null value.

This is how you can use the groupby function in pandas to count null values in a dataframe.

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