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I have a dataset
category cat a cat b cat a I'd like to be able to return something like (showing unique values and frequency)
category freq cat a 2 cat b 1 
863
To count the frequency of a value in a DataFrame column in Pandas, we can use df. groupby(column name). size() method.
We can count by using the value_counts() method. This function is used to count the values present in the entire dataframe and also count values in a particular column.
Use Sum Function to Count Specific Values in a Column in a Dataframe. We can use the sum() function on a specified column to count values equal to a set condition, in this case we use == to get just rows equal to our specific data point. If we wanted to count specific values that match another boolean operation we can.
You can use the nunique() function to count the number of unique values in a pandas DataFrame.
Use groupby and count:
In [37]: df = pd.DataFrame({'a':list('abssbab')}) df.groupby('a').count() Out[37]: a a a 2 b 3 s 2 [3 rows x 1 columns] See the online docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html
Also value_counts() as @DSM has commented, many ways to skin a cat here
In [38]: df['a'].value_counts() Out[38]: b 3 a 2 s 2 dtype: int64 If you wanted to add frequency back to the original dataframe use transform to return an aligned index:
In [41]: df['freq'] = df.groupby('a')['a'].transform('count') df Out[41]: a freq 0 a 2 1 b 3 2 s 2 3 s 2 4 b 3 5 a 2 6 b 3 [7 rows x 2 columns] If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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