Count values with relative frequencies

We are going to add normalize parameter to get the relative frequencies of the repeated data. It is set to True.

Syntax:

data[‘column_name’].value_counts(normalize=True)

Example: Count values with relative frequencies

Python3

# import pandas module
import pandas as pd
 
# create a dataframe
# with 5 rows and 4 columns
data = pd.DataFrame({
    'name': ['sravan', 'ojsawi', 'bobby',  'rohith', 
             'gnanesh', 'sravan', 'sravan', 'ojaswi'],
    'subjects': ['java', 'php', 'java', 'php', 'java',
                 'html/css', 'python', 'R'],
    'marks': [98, 90, 78, 91, 87, 78, 89, 90],
    'age': [11, 23, 23, 21, 21, 21, 23, 21]
})
 
# count all values in name  with relative frequencies
print(data['name'].value_counts(normalize=True))

Output:

sravan     0.375
ojaswi     0.125
ojsawi     0.125
bobby      0.125
rohith     0.125
gnanesh    0.125
Name: name, dtype: float64

How to Count Occurrences of Specific Value in Pandas Column?

In this article, we will discuss how to count occurrences of a specific column value in the pandas column.

Dataset in use:

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.

Syntax: data[‘column_name’].value_counts()[value]

where

data is the input dataframe

value is the string/integer value present in the column to be counted

column_name is the column in the dataframe

Example: To count occurrences of a specific value

Python3

# import pandas module
import pandas as pd
 
# create a dataframe
# with 5 rows and 4 columns
data = pd.DataFrame({
    'name': ['sravan', 'ojsawi', 'bobby',  'rohith', 
             'gnanesh', 'sravan', 'sravan', 'ojaswi'],
    'subjects': ['java', 'php', 'java', 'php', 'java',
                 'html/css', 'python', 'R'],
    'marks': [98, 90, 78, 91, 87, 78, 89, 90],
    'age': [11, 23, 23, 21, 21, 21, 23, 21]
})
 
# count values in name column
print(data['name'].value_counts()['sravan'])
 
# count values in subjects column
print(data['subjects'].value_counts()['php'])
 
# count values in marks column
print(data['marks'].value_counts()[89])

Output:

3
2
1

If we want to count all values in a particular column, then we do not need to mention the value.

Syntax:

data['column_name'].value_counts()

Example: To count the occurrence of a value in a particular column

Python3

# import pandas module
import pandas as pd
 
# create a dataframe
# with 5 rows and 4 columns
data = pd.DataFrame({
    'name': ['sravan', 'ojsawi', 'bobby',  'rohith', 
             'gnanesh', 'sravan', 'sravan', 'ojaswi'],
    'subjects': ['java', 'php', 'java', 'php', 'java',
                 'html/css', 'python', 'R'],
    'marks': [98, 90, 78, 91, 87, 78, 89, 90],
    'age': [11, 23, 23, 21, 21, 21, 23, 21]
})
 
# count all values in name column
print(data['name'].value_counts())
 
# count all values in subjects column
print(data['subjects'].value_counts())
 
# count all values in marks column
print(data['marks'].value_counts())
 
# count all values in age column
print(data['age'].value_counts())

Output:

If we want to get the results in order (like ascending and descending order), we have to specify the parameter

Syntax:

Ascending order:

data[‘column_name’].value_counts(ascending=True)

Descending Order:

data[‘column_name’].value_counts(ascending=False)

Example: To get results in an ordered fashion

Python3

# import pandas module
import pandas as pd
 
# create a dataframe
# with 5 rows and 4 columns
data = pd.DataFrame({
    'name': ['sravan', 'ojsawi', 'bobby',  'rohith',
             'gnanesh', 'sravan', 'sravan', 'ojaswi'],
    'subjects': ['java', 'php', 'java', 'php', 'java',
                 'html/css', 'python', 'R'],
    'marks': [98, 90, 78, 91, 87, 78, 89, 90],
    'age': [11, 23, 23, 21, 21, 21, 23, 21]
})
 
# count all values in name column in ascending order
print(data['name'].value_counts(ascending=True))
 
# count all values in subjects column in ascending order
print(data['subjects'].value_counts(ascending=True))
 
# count all values in marks column in descending order
print(data['marks'].value_counts(ascending=False))
 
# count all values in age column in descending order
print(data['age'].value_counts(ascending=False))

Output:

Tags:

#pandas-dataframe-program #Python pandas-dataFrame #Python-pandas #Python #python

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Count values with relative frequencies

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How to Count Occurrences of Specific Value in Pandas Column?

Dataset in use:

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