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Applying Function to every other row using Pandas

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by vsasikalabe dot icon Updated: Feb 28, 2023

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Pandas is one of the most familiar libraries in Python. We have to install it into your file. Next, we will create a data frame with some data. Print this data frame using the print statement to apply any function to that data frame.  


In Pandas, the main purpose of using .loc and iloc is to slice data frames. Function .loc is primarily used for label indexing, and .iloc function is mainly applied for integer indexing. A pivot table is created and used to store multiple values in Python. We can also perform some calculations or operations using this method. A pivot table is used for multi-dimensional operations. The alternative rows are dropped and sorted with the help of sort(). Here the ascending conditions are set to be false. As a result, the four rows are converted into two rows. 


Here is an example of how to apply a function to every row using Pandas: 

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 - Applying Function to every other row

PythondotLines of Code : 34dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import pandas as pd

raw_df = pd.DataFrame({"GameId": [1, 1, 2, 2],
                       "Team": ["Spirit", "Rockets", "Lighting", "Flames"],
                       "Home": [1, 0, 1, 0],
                       "Score": [81, 66, 73, 82]})
print(raw_df)

   GameId      Team  Home  Score
0       1    Spirit     1     81
1       1   Rockets     0     66
2       2  Lighting     1     73
3       2    Flames     0     82

raw_df.loc[:, "Home"] = raw_df.Home.map({
        1: "Home",
        0: "Away"
    })

result = raw_df.pivot_table(index=["GameId"],
                            columns=["Home"],
                            values=["Team", "Score"],
                            aggfunc={"Team": lambda team: " ".join(team.tolist()),
                                     "Score": lambda score: score})

result = result.sort_index(axis="columns", level=[0, "Home"], ascending=False)
result.columns = [' '.join(reversed(col)) for col in result.columns]
print(result)

       Home Team Away Team  Home Score  Away Score
GameId                                            
1         Spirit   Rockets          81          66
2       Lighting    Flames          73          82

Instructions

Follow the steps carefully to get the output easily.

  1. Download and Install PyCharm on your Computer.
  2. Create a New Python file on your IDE.
  3. Copy the snippet using the 'copy' and paste it into your Python file.
  4. Install pandas on your IDE.
  5. Run the file to generate the output.


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


I found this code snippet by searching for ' Applying Function to every other row using Pandas '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. The solution is created in PyCharm 2022.3.
  2. The solution is tested on Python 3.11.1
  3. Pandas version-1.5.2.


Using this solution, we are able to apply function to every other row using Pandas with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to apply function to every other row using Pandas.

Dependent Library

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

                      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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