# Calculate the frequency counts of each unique value of a Pandas series

• Difficulty Level : Basic
• Last Updated : 15 Sep, 2022

Let us see how to find the frequency counts of each unique value of a Pandas series. We will use these methods to calculate the frequency counts of each unique value of a Pandas series.

## Using values_counts() to calculate the frequency of unique value

Here values_counts() function is used to find the frequency of unique value in a Pandas series.

Example 1 : Here we are creating a series and then with the help of values_counts() function we are calculating the frequency of unique values.

## Python3

 `# importing the module``import` `pandas as pd` `# creating the series``s ``=` `pd.Series(data ``=` `[``2``, ``3``, ``4``, ``5``, ``5``, ``6``,``                      ``7``, ``8``, ``9``, ``5``, ``3``])` `# displaying the series``print``(s)` `# finding the unique count``print``(s.value_counts())`

Output : Calculate the frequency counts of each unique value

Example 2 : Here we are randomly generating integer values and then finally calculating the counts for each value.

## Python3

 `# importing the module``import` `pandas as pd` `# creating the series``s ``=` `pd.Series(np.take(``list``(``'0123456789'``),``              ``np.random.randint(``10``, size ``=` `40``)))` `# finding the unique count``s.value_counts()` `# displaying the series``print``(s)`

Output : Calculate the frequency counts of each unique value

Example 3 : Here we have a series where each value is a string and we are counting the occurrence of each element in the series.

## Python3

 `# importing pandas as pd``import` `pandas as pd``  ` `# creating the Series``sr ``=` `pd.Series([``'Mumbai'``, ``'Pune'``, ``'Agra'``, ``'Pune'``,``                ``'Goa'``, ``'Shimla'``, ``'Goa'``, ``'Pune'``])` `# finding the unique count``sr.value_counts()` `# displaying the series` `print``(sr)`

Output : Calculate the frequency counts of each unique value

## Using groupby() to calculate the frequency of unique value

Here we are using the groupby() function to group all the same values and then calculate their frequencies.

## Python3

 `import` `pandas as pd` `technologies ``=` `{``    ``"data"``:[``2``, ``3``, ``4``, ``5``, ``5``, ``6``,``            ``7``, ``8``, ``9``, ``5``, ``3``]``               ``}``df ``=` `pd.DataFrame(technologies)` `df[``'frequency'``] ``=` `df.groupby(``'data'``)``                  ``[``'data'``].transform(``'count'``)``print``(df)`

Output: Calculate the frequency counts of each unique value

## Using apply().fillna() function to calculate the frequency of unique value

Here we are filling NAN or 0 for the None values in the series and apply function to apply the same, and then calculate their frequencies.

## Python3

 `import` `pandas as pd` `technologies ``=` `{``    ``"data"``:[``2``, ``3``, ``4``, ``5``, ``5``, ``6``,``            ``7``, ``8``, ``9``, ``5``, ``3``]``               ``}``df ``=` `pd.DataFrame(technologies)` `df1 ``=` `df.``apply``(pd.value_counts).fillna(``0``)` `print``(df1)`

Output: Calculate the frequency counts of each unique value

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