In this short guide, I'll show you how to count occurrences of a specific value in Pandas. Whether you want to count values in a single column or across the whole DataFrame, this guide has you covered.

(1) Count occurrences in a single column

df['column_name'].value_counts().get('target_value', 0)

(2) Count occurrences across the entire DataFrame

(df == 'target_value').sum().sum()

(3) Count occurrences using .shape

df[df['column_name'] == 'target_value'].shape[0]

(4) Count occurrences using len()

len(df[df['column_name'] == 'target_value'])

(5) Count occurrences using .query()

df.query('column_name == "target_value"').column_name.count()

(6) Count occurrences using boolean mask with .sum()

(df['column_name'] == 'target_value').sum()

(7) Count occurrences using NumPy array

(df['column_name'].values == 'target_value').sum()

1: Example DataFrame

Let's create a sample DataFrame:

import pandas as pd

data = {
    'A': ['apple', 'banana', 'apple', 'orange', 'banana'],
    'B': ['apple', 'apple', 'banana', 'banana', 'apple']
}

df = pd.DataFrame(data)

Output:

A B
0 apple apple
1 banana apple
2 apple banana
3 orange banana
4 banana apple

2: Count Occurrences in a Column

To count occurrences of "apple" in column A:

df['A'].value_counts().get('apple', 0)

Output:

2

3: Count Occurrences Across the Entire DataFrame

To count "apple" occurrences in all columns:

(df == 'apple').sum().sum()

Output:

4

4: Count Multiple Values

If you want to count multiple values in a column:

df['A'].value_counts()

Output:

banana    2  
apple     2  
orange    1  
Name: A, dtype: int64

Conclusion

In this guide, we covered:

  • Counting occurrences of a value in a specific column
  • Counting occurrences across the entire DataFrame
  • Using .value_counts() for frequency analysis

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