Split Pandas DataFrame

Split Pandas DataFrame

Suraj Joshi Nov-26, 2021 Jan-16, 2021 Pandas Pandas DataFrame
  1. Split DataFrame Using the Row Indexing
  2. Split DataFrame Using the groupby() Method
  3. Split DataFrame Using the sample() Method

This tutorial explains how we can split a DataFrame into multiple smaller DataFrames using row indexing, the DataFrame.groupby() method, and DataFrame.sample() method.

We will use the apprix_df DataFrame below to explain how we can split a DataFrame into multiple smaller DataFrames.

import pandas as pd

apprix_df = pd.DataFrame({
    'Name': ["Anish","Rabindra","Manish","Samir","Binam"],
    'Post': ["CEO","CTO","System Admin","Consultant","Engineer"],
    'Qualification':["MBA","MS","MCA","PhD","BE"]
})

print("Apprix Team DataFrame:")
print(apprix_df,"\n")

Output:

Apprix Team DataFrame:
       Name          Post Qualification
0     Anish           CEO           MBA
1  Rabindra           CTO            MS
2    Manish  System Admin           MCA
3     Samir    Consultant           PhD
4     Binam      Engineer            BE

Split DataFrame Using the Row Indexing

import pandas as pd

apprix_df = pd.DataFrame({
    'Name': ["Anish","Rabindra","Manish","Samir","Binam"],
    'Post': ["CEO","CTO","System Admin","Consultant","Engineer"],
    'Qualification':["MBA","MS","MCA","PhD","BE"]
})

print("Apprix Team DataFrame:")
print(apprix_df,"\n")

apprix_1 = apprix_df.iloc[:2,:]
apprix_2 = apprix_df.iloc[2:,:]

print("The DataFrames formed by splitting of Apprix Team DataFrame are: ","\n")
print(apprix_1,"\n")
print(apprix_2,"\n")

Output:

Apprix Team DataFrame:
       Name          Post Qualification
0     Anish           CEO           MBA
1  Rabindra           CTO            MS
2    Manish  System Admin           MCA
3     Samir    Consultant           PhD
4     Binam      Engineer            BE

The DataFrames formed by splitting the Apprix Team DataFrame are:

       Name Post Qualification
0     Anish  CEO           MBA
1  Rabindra  CTO            MS

     Name          Post Qualification
2  Manish  System Admin           MCA
3   Samir    Consultant           PhD
4   Binam      Engineer            BE

It splits the DataFrame apprix_df into two parts using the row indexing. The first part contains the first two rows from the apprix_df DataFrame, while the second part contains the last three rows.

We can specify the rows to be included in each split in the iloc property. [:2,:] represents select the rows up to row with index 2 exclusive (the row with index 2 is not included) and all the columns from the DataFrame. Hence, apprix_df.iloc[:2,:] selects first two rows from the DataFrame apprix_df with index 0 and 1.

Split DataFrame Using the groupby() Method

import pandas as pd

apprix_df = pd.DataFrame({
    'Name': ["Anish","Rabindra","Manish","Samir","Binam"],
    'Post': ["CEO","CTO","System Admin","Consultant","Engineer"],
    'Qualification':["MBA","MS","MS","PhD","MS"]
})

print("Apprix Team DataFrame:")
print(apprix_df,"\n")

groups = apprix_df.groupby(apprix_df.Qualification)
ms_df = groups.get_group("MS")
mba_df=groups.get_group("MBA")
phd_df=groups.get_group("PhD")

print("Group with Qualification MS:")
print(ms_df,"\n")

print("Group with Qualification MBA:")
print(mba_df,"\n")

print("Group with Qualification PhD:")
print(phd_df,"\n")

Output:

Apprix Team DataFrame:
       Name          Post Qualification
0     Anish           CEO           MBA
1  Rabindra           CTO            MS
2    Manish  System Admin            MS
3     Samir    Consultant           PhD
4     Binam      Engineer            MS

Group with Qualification MS:
       Name          Post Qualification
1  Rabindra           CTO            MS
2    Manish  System Admin            MS
4     Binam      Engineer            MS

Group with Qualification MBA:
    Name Post Qualification
0  Anish  CEO           MBA

Group with Qualification PhD:
    Name        Post Qualification
3  Samir  Consultant           PhD

It splits the DataFrame apprix_df into three parts based on the value of the Qualification column. The rows with the same value of the Qualification column will be placed in the same group.

The groupby() function will form groups based on the Qualification column’s value. We then extract the rows grouped by groupby() method using the get_group() method.

Split DataFrame Using the sample() Method

We can form a DataFrame by sampling rows randomly from a DataFrame using the sample() method. We can set the ratio of rows to be sampled from the parent DataFrame.

import pandas as pd

apprix_df = pd.DataFrame({
    'Name': ["Anish","Rabindra","Manish","Samir","Binam"],
    'Post': ["CEO","CTO","System Admin","Consultant","Engineer"],
    'Qualification':["MBA","MS","MS","PhD","MS"]
})

print("Apprix Team DataFrame:")
print(apprix_df,"\n")

random_df = apprix_df.sample(frac=0.4,random_state=60)

print("Random split from the Apprix Team DataFrame:")
print(random_df)

Output:

Apprix Team DataFrame:
       Name          Post Qualification
0     Anish           CEO           MBA
1  Rabindra           CTO            MS
2    Manish  System Admin            MS
3     Samir    Consultant           PhD
4     Binam      Engineer            MS

Random split from the Apprix Team DataFrame:
    Name      Post Qualification
0  Anish       CEO           MBA
4  Binam  Engineer            MS

It randomly samples 40% of the rows from the apprix_df DataFrame and then displays the DataFrame formed from the sampled rows. The random_state is set to ensure that we get the same random samples on sampling every time.

Related Article - Pandas DataFrame

  • Get Pandas DataFrame Column Headers as a List
  • Delete Pandas DataFrame Column
  • Convert Pandas Column to Datetime
  • Convert a Float to an Integer in Pandas DataFrame
  • Sort Pandas DataFrame by One Column's Values
  • Get the Aggregate of Pandas Group-By and Sum