Given a Pandas dataframe, we need to find the frequency counts of each item in one or more columns of this dataframe. This can be achieved in multiple ways:
Method #1: Using Series.value_counts()
This method is applicable to pandas.Series object. Since each DataFrame object is a collection of Series object, we can apply this method to get the frequency counts of values in one column.
# importing pandas as pd import pandas as pd # sample dataframe df = pd.DataFrame({'A': ['foo', 'bar', 'g2g', 'g2g', 'g2g', 'bar', 'bar', 'foo', 'bar'], 'B': ['a', 'b', 'a', 'b', 'b', 'b', 'a', 'a', 'b'] }) # frequency count of column A count = df['A'].value_counts() print(count) |
Output:
Method #2: Using GroupBy.count()
This method can be used to count frequencies of objects over single columns. After grouping a DataFrame object on one column, we can apply count() method on the resulting groupby object to get a DataFrame object containing frequency count.
# importing pandas as pd import pandas as pd # sample dataframe df = pd.DataFrame({ 'A': ['foo', 'bar', 'g2g', 'g2g', 'g2g', 'bar', 'bar', 'foo', 'bar'], 'B': ['a', 'b', 'a', 'b', 'b', 'b', 'a', 'a', 'b'] }) # Multi-column frequency count count = df.groupby(['A']).count() print(count) |
Output:
Method #3: Using GroupBy.size()
This method can be used to count frequencies of objects over single or multiple columns. After grouping a DataFrame object on one or more columns, we can apply size() method on the resulting groupby object to get a Series object containing frequency count.
# importing pandas as pd import pandas as pd # sample dataframe df = pd.DataFrame({ 'A': ['foo', 'bar', 'g2g', 'g2g', 'g2g', 'bar', 'bar', 'foo', 'bar'], 'B': ['a', 'b', 'a', 'b', 'b', 'b', 'a', 'a', 'b'] }) # Multi-column frequency count count = df.groupby(['A', 'B']).size() print(count) |
Output:
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