Pandas DataFrame value_count() Method.
Table Of Contents:
- Syntax ‘value_count( )’ Method In Pandas.
- Examples ‘value_count( )’ Method.
(1) Syntax:
DataFrame.value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=TrueDescription:
- Return a Series containing counts of unique rows in the DataFrame.
df['your_column'].value_counts()– this will return the count of unique occurences in the specified column.- It is important to note that
value_countsonly works on pandas series, not Pandas dataframes. As a result, we only include one bracket df[‘your_column’] and not two brackets df[[‘your_column’]].
Parameters:
- subset: list-like, 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.
(2) Examples Of value_count() Method:
Example-1
import pandas as pd
student = {'Name':['Subrat','Abhispa','Arpita','Anuradha','Namita'],
'Roll_No':[100,101,102,103,104],
'Subject':['Math','English','Science','History','Commerce'],
'Mark':[95,88,76,73,93],
'Gender':['Male','Female','Female','Female','Female']}
student_object = pd.DataFrame(student)
student_objectOutput:
# Value Counts On DataFrame.
student_object.value_counts()Output:
Name Roll_No Subject Mark Gender
Abhispa 101 English 88 Female 1
Anuradha 103 History 73 Female 1
Arpita 102 Science 76 Female 1
Namita 104 Commerce 93 Female 1
Subrat 100 Math 95 Male 1
dtype: int64Note:
- value_count() on a DataFrame will result you the count of unique rows of the DataFrame.
# Value Counts On DataFrame Column.
student_object['Gender'].value_counts()Output:
Female 4
Male 1
Name: Gender, dtype: int64