Split Pandas Dataframe by column value

Last Updated : 20 Apr, 2022

Sometimes in order to analyze the Dataframe more accurately, we need to split it into 2 or more parts. The Pandas provide the feature to split Dataframe according to column index, row index, and column values, etc.

Let' see how to Split Pandas Dataframe by column value in Python?

Now, let's create a Dataframe:

villiers

Python3

# importing pandas library
import pandas as pd

# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]

# creating a pandas dataframe
df = pd.DataFrame(player_list, 
                  columns = ['Name', 'Age', 
                             'Weight', 'Salary'])

# show the dataframe
df

Output:

dataframe

Method 1: Using boolean masking approach.

This method is used to print only that part of dataframe in which we pass a boolean value True.

Example 1:

Python3

# importing pandas library
import pandas as pd

# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]

# creating a pandas dataframe
df = pd.DataFrame(player_list, 
                  columns = ['Name', 'Age', 
                             'Weight', 'Salary'])

# splitting the dataframe into 2 parts
# on basis of 'Age' column values
# using Relational operator
df1 = df[df['Age'] >= 37]

# printing df1
df1

Output:

Python3

df2 = df[df['Age'] < 37]

# printing df2
df2

Output:

In the above example, the data frame 'df' is split into 2 parts 'df1' and 'df2' on the basis of values of column 'Age'.

Example 2:

Python3

# importing pandas library
import pandas as pd

# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]

# creating a pandas dataframe
df = pd.DataFrame(player_list, 
                  columns = ['Name', 'Age', 
                             'Weight', 'Salary'])

# splitting the dataframe into 2 parts
# on basis of 'Weight' column values
mask = df['Weight'] >= 80

df1 = df[mask]

# invert the boolean values
df2 = df[~mask]

# printing df1
df1

Output:

Python3

# printing df2
df2

Output:

In the above example, the data frame 'df' is split into 2 parts 'df1' and 'df2' on the basis of values of column 'Weight'.

Method 2: Using Dataframe.groupby().

This method is used to split the data into groups based on some criteria.

Example:

Python3

# importing pandas library
import pandas as pd

# Initializing the nested list with Data-set
player_list = [['M.S.Dhoni', 36, 75, 5428000],
               ['A.B.D Villiers', 38, 74, 3428000],
               ['V.Kholi', 31, 70, 8428000],
               ['S.Smith', 34, 80, 4428000],
               ['C.Gayle', 40, 100, 4528000],
               ['J.Root', 33, 72, 7028000],
               ['K.Peterson', 42, 85, 2528000]]

# creating a pandas dataframe
df = pd.DataFrame(player_list, 
                  columns = ['Name', 'Age', 
                             'Weight', 'Salary'])

# splitting the dataframe into 2 parts
# on basis of 'Salary' column values
# using dataframe.groupby() function
df1, df2 = [x for _, x in df.groupby(df['Salary'] < 4528000)]

# printing df1
df1