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Summarize using multiple columns in python pandas dataframe

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by vsasikalabe dot icon Updated: Feb 28, 2023

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The ability to summarize data using multiple columns in Pandas DataFrame is an essential technique for real-world data analysis. Here are a few ways this technique can be helpful: Grouping and summarizing data by multiple columns allows us to explore relationships between different variables. For example, in market research, we can group data by multiple columns, such as age and gender, and then calculate summary statistics, such as each group's average income or spending. This can help us identify patterns and trends in consumer behavior and preferences. 


A lambda function is one of the most flexible ways to apply custom aggregation functions. A lambda function is a tiny, anonymous function that can take any number of arguments and only have a single expression. We can use lambda functions with groupby() to define custom aggregation functions specific to our needs. In Pandas, groupby() is a powerful method that allows us to group a DataFrame by one or more columns and apply an aggregation function to each group.

 

The ability to summarize data using multiple columns in Pandas DataFrame is a powerful technique that can help us gain insights, identify patterns, and make data-driven decisions in various real-world applications. 


Here is an example of how to Summarize using multiple columns in the python pandas dataframe: 

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

Code

In this solution we used pandas and numpy library of python.

Summarize using multiple columns in python pandas dataframe

PythondotLines of Code : 39dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
df = pd.DataFrame({'col1': list('aaabbb'), 
                   'col2': list('xyxyxy'), 
                   'col3': np.random.randn(6), 
                   'col4': np.random.randn(6)})

df
Out: 
  col1 col2      col3      col4
0    a    x -2.276155  0.323778
1    a    y -0.367525 -2.570142
2    a    x -0.672530  2.265560
3    b    y  0.588741  0.193499
4    b    x -1.368829  0.717997
5    b    y  1.012271  1.354408

(df.groupby(['col1','col2'])
   .agg({'col3': 'sum','col4':'sum'})
   .assign(newcol=lambda x: x['col3']/x['col4']))
Out: 
               col4      col3    newcol
col1 col2                              
a    x     2.589338 -2.948686 -1.138780
     y    -2.570142 -0.367525  0.142998
b    x     0.717997 -1.368829 -1.906453
     y     1.547907  1.601012  1.034308

df.groupby(['col1','col2']).apply(lambda x: x['col3'].sum() / x['col4'].sum())
Out: 
col1  col2
a     x      -1.138780
      y       0.142998
b     x      -1.906453
      y       1.034308
dtype: float64

(df.groupby(['col1','col2'])
   .agg({'col3': 'sum','col4':'sum'})
   .eval('col3 / col4'))

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 (don't take the out statements) it in your python file.
  5. import the pandas and numpy library.Add print statement to the end.
  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 ' Summarize using multiple columns in python pandas dataframe ' 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
  4. numpy 1.24.1 version


Using this solution, we can do Summarize using multiple columns in python pandas dataframe.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 Summarize using multiple columns in python pandas dataframe.

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
      Security
        License
          Reuse

            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

                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

                      Support
                        Quality
                          Security
                            License
                              Reuse

                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
                                    Security
                                      License
                                        Reuse

                                          If you do not have pandas and numpy 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 and numpy.

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