How Can I Efficiently Find Value Frequencies in a Pandas Dataframe Column?

Finding Value Frequencies in a Dataframe Column

In many data manipulation scenarios, it is crucial to determine the frequency of each unique value within a dataframe column. To address this need, consider the following dataset:

category
cat a
cat b
cat a

The goal is to generate a table displaying each unique value and its corresponding frequency:

category   freq 
cat a       2
cat b       1

To achieve this outcome, the value_counts() method offers a straightforward solution:

df['category'].value_counts()

Alternatively, you can employ the groupby() method in tandem with count():

df.groupby('category').count()

Both techniques effectively address the problem of finding value frequencies in a dataframe column, providing a clear understanding of the distribution within the data.

For further insights and documentation, refer to the official Pandas documentation. Additionally, if desired, you can use the transform() method to add the frequency column back to the original dataframe:

df['freq'] = df.groupby('a')['a'].transform('count')

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