5 Best Ways to Count NaN Occurrences in a Pandas Dataframe Column

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💡 Problem Formulation: When working with datasets in Python’s pandas library, it’s common to encounter missing values represented as NaN (Not a Number). Efficiently counting these NaN values in a specific column is crucial for data cleaning and analysis. Suppose we have a dataframe with a ‘sales’ column containing NaN entries. We wish to count these NaN occurrences to assess data completeness. The desired output is an integer reflecting the number of NaN values in the ‘sales’ column.

Method 1: Using isna() and sum() methods

This method combines the power of isna(), which returns a boolean mask indicating missing values, with the sum() method to count the True values representing NaNs. It’s efficient, easy-to-read, and well-suited for beginners.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'sales': [100, 200, None, 400, None]
})

# Count NaN occurrences
nan_count = df['sales'].isna().sum()

print(nan_count)

Output: 2

This code snippet creates a DataFrame with a ‘sales’ column and uses isna() to return a Series of boolean values, which is then summed using sum(), resulting in a count of the NaN occurrences in the ‘sales’ column.

Method 2: Using the isnull() method

Similar to isna(), the isnull() method in pandas detects missing values and can be used in combination with sum() to count NaN values. isnull() can be more intuitive for readers familiar with SQL’s IS NULL syntax.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'sales': [150, None, 250, None, 350]
})

# Count NaN occurrences
nan_count = df['sales'].isnull().sum()

print(nan_count)

Output: 2

After constructing a DataFrame with the ‘sales’ column, isnull() is called to get a boolean Series, with the sum() method then used to count all True values corresponding to NaN entries.

Method 3: Using value_counts() with the dropna parameter

The value_counts() method in pandas can be used to count all unique values including NaN by setting the dropna parameter to False. This method provides a frequency distribution of all values, including NaN counts.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'sales': [None, 250, None, 250, 500]
})

# Get the count of unique values including NaN
value_counts = df['sales'].value_counts(dropna=False)

print(value_counts)

Output:

NaN      2
250.0    2
500.0    1
Name: sales, dtype: int64

This code snippet generates a DataFrame and utilizes value_counts() to produce a frequency distribution of the ‘sales’ column with NaN included, extracting the NaN count from the resulting Series.

Method 4: Using info() method for an Overall Summary

The info() method provides a concise summary of the dataframe, including the number of non-null entries per column. Subtracting the non-null count from the total number of entries gives the NaN count.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'sales': [100, None, None, 300, None, 500]
})

# Display dataframe summary
df.info()

This won’t display a direct output of the NaN count, but it shows a summary with the count of non-null values. You can calculate NaN occurrences by subtracting the ‘Non-Null Count’ from the total number of entries in the ‘sales’ column.

Bonus One-Liner Method 5: Using list comprehension with isna()

A more Pythonic approach might involve a one-liner using list comprehension in combination with isna(). This manual method can be less efficient but more flexible if you want to extend the counting logic or integrate conditionals.

Here’s an example:

import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'sales': [100, None, 300, None, 500]
})

# Count NaN occurrences using list comprehension
nan_count = sum(1 for i in df['sales'] if pd.isna(i))

print(nan_count)

Output: 2

The list comprehension iterates through each element in the ‘sales’ column and uses pd.isna() to check for NaN values, tallying up the count.

Summary/Discussion

  • Method 1: isna() and sum(). Straightforward and efficient. Best suited for direct counting of NaNs. Does not provide additional context or a value distribution.
  • Method 2: isnull() method. Essentially identical to Method 1. The choice between isnull() and isna() may depend on the user’s coding background or preference.
  • Method 3: value_counts() and dropna=False. Offers additional insight into value distribution, which can be useful beyond NaN counting. However, slightly more resource-intensive if only the NaN count is needed.
  • Method 4: info() method. Provides a high-level overview of the DataFrame’s contents, including NaN count as calculated from non-null values. Not direct, as it requires additional computation, but useful for broader exploratory data analysis.
  • Bonus Method 5: List comprehension with isna(). Pythonic and flexible. Can integrate additional logic, but it is typically less efficient for large datasets than built-in pandas methods.