How to Count the Frequency of Identical Rows in a Pandas DataFrame?

Get a Frequency Count Based on Multiple Dataframe Columns

To determine how often identical rows appear in a dataframe, we can employ Pandas' groupby function. Consider the following example:

data = {'Group': ['Short', 'Short', 'Moderate', 'Moderate', 'Tall'], 'Size': ['Small', 'Small', 'Medium', 'Small', 'Large']}
df = pd.DataFrame(data)

We can calculate the frequency count in three ways:

Option 1:

dfg = df.groupby(by=["Group", "Size"]).size()

This produces a Series with the following output:

Group     Size
Moderate  Medium    1
          Small     1
Short     Small     2
Tall      Large     1
dtype: int64

Option 2:

dfg = df.groupby(by=["Group", "Size"]).size().reset_index(name="Time")

This results in a DataFrame with an added "Time" column:

      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1

Option 3:

dfg = df.groupby(by=["Group", "Size"], as_index=False).size()

This also produces a DataFrame, equivalent to the output of Option 2.

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