How to Count the Frequency of Duplicate Rows in a Pandas DataFrame Based on Multiple Columns?

Getting a Frequency Count Based on Multiple Dataframe Columns

In a given dataframe, where each row consists of multiple columns, it is often necessary to determine how frequently duplicate rows appear. This task can be achieved using Python's pandas library.

Solution

The pandas groupby() function allows for grouping rows based on specific columns. To count the frequency of duplicate rows, we can group by the desired columns and utilize the size() function:

<code class="python">dfg = df.groupby(by=["Group", "Size"]).size()</code>

This code will generate a pandas.Series object with the group keys as index and the frequency count as values. To convert it into a dataframe, we can use the reset_index() function:

<code class="python">dfg = df.groupby(by=["Group", "Size"]).size().reset_index(name="Time")</code>

In this example, the resulting dataframe will have columns for "Group," "Size," and "Time," where "Time" represents the frequency count.

An alternative approach is to use the as_index=False argument in groupby():

<code class="python">dfg = df.groupby(by=["Group", "Size"], as_index=False).size()</code>

This will directly generate a dataframe without the need for further index manipulation.

By utilizing these techniques, you can easily obtain a frequency count based on multiple columns in a dataframe and gain valuable insights into the distribution of data.

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