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Create new column in Pandas DataFrame based on other columns

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by vsasikalabe dot icon Updated: Feb 28, 2023

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In Pandas, a DataFrame is a two-dimensional table-like data structure where the data is organized into rows and columns. The columns represent the variables or features of the data, and the rows represent the observations or instances. 


Sometimes, you can add a new column to a DataFrame based on the values of one or more existing columns. You can calculate a new value based on a formula or function, transform the data somehow, or add metadata or labels to the data. In a Pandas DataFrame, to create a new column depending on other columns, you can use the assign() method for adding a new column to the DataFrame with a value calculated from one or more existing columns. This allows you to perform data manipulations that involve calculations or transformations on the existing data and store the results in a new column. The new column can then be used for further analysis and visualization or to make the data more informative and useful for downstream tasks. 


Concatenation combines two or more data structures, such as lists, arrays, or DataFrames, into a single data structure. In the context of Pandas, concatenation refers to combining two or more Pandas objects, such as Series or DataFrames, into a single DataFrame. Creating new columns in a Pandas DataFrame based on other columns is a useful data manipulation technique that can help you explore, clean, and analyze your data in more detail. 


Here is an example of how to find Create a new column in Pandas DataFrame based on other columns: 

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.

Create new column in Pandas DataFrame based on other columns

PythondotLines of Code : 30dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
df = pd.DataFrame({
    "NAME": [ "N1", "N2", "N3" ],
    "I1": [ 1, 4, 4 ],
    "I2": [ 2, 5, 2 ],
    "I3": [ 3, 6, 6 ]
})

>>> df
  NAME  I1  I2  I3
0   N1   1   2   3
1   N2   4   5   6
2   N3   4   2   6

cond1 = df["I1"] == 1
cond2 = df["I2"] == 2
cond3 = df["I3"] == 3

result = pd.concat([
    df[cond1].assign(V=1),
    df[cond2].assign(V=2),
    df[cond3].assign(V=3)
])

>>> result
  NAME  I1  I2  I3  V
0   N1   1   2   3  1
0   N1   1   2   3  2
2   N3   4   2   6  2
0   N1   1   2   3  3

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 (line no 1 to 6 and line no 12 to 19) it in your python file.
  5. import the pandas 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 ' Create new column in Pandas DataFrame based on other columns ' 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 create new column in Pandas DataFrame based on other columns in python.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 create new column in Pandas DataFrame based on other columns.

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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