bigframes.pandas.DataFrame.value_counts#

DataFrame.value_counts(subset:Hashable|Sequence[Hashable]=None,normalize:bool=False,sort:bool=True,ascending:bool=False,dropna:bool=True)[source]#

Return a Series containing counts of unique rows in the DataFrame.

Examples:

>>>df=bpd.DataFrame({'num_legs':[2,4,4,6,7],...'num_wings':[2,0,0,0,pd.NA]},...index=['falcon','dog','cat','ant','octopus'],...dtype='Int64')>>>df         num_legs  num_wingsfalcon          2          2dog             4          0cat             4          0ant             6          0octopus         7       <NA>[5 rows x 2 columns]

value_counts sorts the result by counts in a descending order by default:

>>>df.value_counts()num_legs  num_wings4         0          22         2          16         0          1Name: count, dtype: Int64

You can normalize the counts to return relative frequencies by settingnormalize=True:

>>>df.value_counts(normalize=True)num_legs  num_wings4         0             0.52         2            0.256         0            0.25Name: proportion, dtype: Float64

You can get the rows in the ascending order of the counts by settingascending=True:

>>>df.value_counts(ascending=True)num_legs  num_wings2         2          16         0          14         0          2Name: count, dtype: Int64

You can include the counts of the rows withNA values by settingdropna=False:

>>>df.value_counts(dropna=False)num_legs  num_wings4         0            22         2            16         0            17         <NA>         1Name: count, dtype: Int64
Parameters:

  • subset (label orlist oflabels,optional) – Columns to use when counting unique combinations.

  • normalize (bool,default False) – Return proportions rather than frequencies.

  • sort (bool,default True) – Sort by frequencies.

  • ascending (bool,default False) – Sort in ascending order.

  • dropna (bool,default True) – Don’t include counts of rows that contain NA values.

Returns:

Series containing counts of unique rows in the DataFrame

Return type:

bigframes.pandas.Series