In Python’s Pandas DataFrame, What Is The Method To Tally The Number Of NaN Values In A Column?

To count the NaN values in a column in a pandas DataFrame, you can use the isna() function to create a boolean mask and then use the sum() function to count the occurrences of True values.

Here’s an example:

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, np.nan, 4, np.nan],
        'B': [np.nan, 2, 3, np.nan, 5]}
df = pd.DataFrame(data)

# Count the NaN values in column 'A'
nan_count = df['A'].isna().sum()

print(nan_count)

Output:

2

In this example, we first create a DataFrame with two columns (A and B) where some values are NaN. We then use df['A'].isna() to create a boolean mask that identifies the NaN values in column 'A'. Finally, we use sum() on the boolean mask to count the number of True values, which corresponds to the number of NaN values in column 'A'.

About the Author Rex

I'm a passionate tech blogger with an insatiable love for programming! From my early days tinkering with code, I've delved into web dev, mobile apps, and AI. Sharing insights and tutorials with the world is my joy, connecting me to a global community of like-minded tech enthusiasts. Python holds a special place in my heart, but I embrace all challenges. Constantly learning, I attend tech conferences, contribute to open-source projects, and engage in code review sessions. My ultimate goal is to inspire the next generation of developers and contribute positively to the ever-evolving tech landscape. Let's code together!