How can I count values in a column in pandas?

You can count values in a column in a pandas DataFrame using several methods, depending on what exactly you want to achieve.

✅ 1. Using value_counts() (Most Common Method)

import pandas as pd

# Example DataFrame
df = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Orange', 'Banana', 'Banana']
})

# Count occurrences of each unique value in the 'Fruit' column
count = df['Fruit'].value_counts()
print(count)

Output:

Banana    3
Apple     2
Orange    1
Name: Fruit, dtype: int64

• Sorted by default (descending order).
• Use normalize=True to get proportions instead of counts.

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✅ 2. Using groupby() with size()

# Group by unique values and count occurrences
count = df.groupby('Fruit').size()
print(count)

Output:

Fruit
Apple     2
Banana    3
Orange    1
dtype: int64

• Similar to value_counts() but useful when working with multiple columns.

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✅ 3. Using count() (Counts Non-Null Values Only)

# Count non-null values in the 'Fruit' column
non_null_count = df['Fruit'].count()
print(non_null_count)

Output:

6

• Note: This counts all non-null values, not unique occurrences.

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✅ 4. Using collections.Counter (For More Flexibility)

from collections import Counter

# Count occurrences using Counter
count = Counter(df['Fruit'])
print(count)

Output:

Counter({'Banana': 3, 'Apple': 2, 'Orange': 1})

• Useful if you need to perform additional operations with the counts.

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📊 Summary:

• value_counts() → Best for quick frequency counts of unique values.
• groupby().size() → Ideal when grouping by multiple columns.
• count() → Counts non-null values (not specific to unique occurrences).
• Counter → Flexible for advanced counting operations.