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Pandas DataFrame merging with aggregation

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by vsasikalabe dot icon Updated: Mar 1, 2023

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Create two data frames with different values(df1 &df2). Assign df1 column values by indexing. We can join both data frame values using the append() method. The result is stored in df_both. We have to drop duplicate values by using the drop_duplicates() method. The same indexing value is added using sum() and resetting the value into the data frame. Finally, sort all the elements in the data frame. 


We have three main ways of combining Data Frames, i.e., merge(), join(), and append(). We can use the append or merge method to join two data frames. In the append method, we can add a single element without changing the whole values in the data frame. The merge method allows combining two data frames with the common column. The drop_duplicates() method drops all duplicate rows or values. When looking for duplicates, use the subset parameter if only some specified columns should be considered. You'll use sort_values() to sort the Data Frame based on the values in a single column. This will return a new Data Frame sorted in ascending order by default. DataFrame.groupby() function can help collect identical data into groups. It performs aggregate functions. 


Here is an example of how to do Pandas Data frame merging with aggregation: 

Preview of the output that you will get on running this code from your IDE.

Code

In this solution we used pandas library of python.

Pandas DataFrame merging with aggregation

PythondotLines of Code : 24dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import pandas as pd
df1 = pd.DataFrame([["A", "A", "B", "B", "C"], ["a1", "a2", "b1", "b2", "c1"], [10, 10, 20, 20, 30], [1, 5, 6, 3, 4]]).T
df2 = pd.DataFrame([["B", "B", "C", "F"], ["b1", "b3", "c1", "f2"], [30, 30, 40, 40], [8, 3, 5, 2]]).T
df1.columns = df2.columns = ["label1", "label2", "total", "count"]

df_both = df1.assign(df=1).append(df2.assign(df=2))

df_lable1 = df_both.drop_duplicates(['label1', 'df']).groupby('label1')['total'].sum().reset_index()
df_lable2 = df_both.groupby('label2')['count'].sum().reset_index()

df3 = df_both[['label1', 'label2']].drop_duplicates(["label1", "label2"]).sort_values(["label1", "label2"])

df3 = df3.merge(df_lable1, on=['label1'], how='left')
df3 = df3.merge(df_lable2, on=['label2'], how='left')

#   label1 label2 total count
# 0      A     a1    10     1
# 1      A     a2    10     5
# 2      B     b1    50    14
# 3      B     b2    50     3
# 4      B     b3    50     3
# 5      C     c1    70     9
# 6      F     f2    40     2

Instructions

Follow the steps carefully to get the output easily.

  1. Download and Install the PyCharm Community Edition on your desktop.
  2. Install pandas on your IDE from python interpreter in setting options.
  3. Create new python file on your IDE.
  4. Copy the snippet using the 'copy' button and paste it in your python file.
  5. Add print statement to print the value.
  6. Run the current file to generate the output.


I hope you found this useful. I have added the link to dependent library, version information in the following sections.


I found this code snippet by searching for ' Pandas DataFrame merging with aggregation' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2022.3.1
  2. The solution is created in Python 3.11.1 Version
  3. pandas 1.5.2 Version


Using this solution, we can do Pandas DataFrame merging with aggregation.This process also facilities an easy to use, hassle free method to create a hands-on working version of code in python which would help us to do Pandas DataFrame merging with aggregation.

Dependent Libraries

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
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            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have pandas library that is required to run this code, you can install it by clicking on the above link.

                      You can search for any dependent library on kandi like pandas .

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