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Python indexing from dataframe

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by vsasikalabe dot icon Updated: Feb 22, 2023

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To identify the rows an index on a Pandas data frame gives us away. Identifying rows by a "label" is better than identifying a row by number. You must remember the number for each row. If you only have the integer position to work with. 


Indexing is selecting values from rows and columns in a data frame. We can select all rows and some columns or rows and all columns by using Indexing. To better understand Indexing, let us create sample data in a series form. We must make a pandas data frame. We can also create a DataFrame from a CSV file or dict. To identify the columns to set as an index. This method is used to set a specific column or multiple columns as an index in pandas DataFrame. To index pandas dataframes, there are two main ways: label-based and position-based. Also, based on predefined conditions, it is possible to apply boolean data frame indexing or even mix several types of data frame indexing. 

Select rows and columns of data from the DataFrame Indexing used. Indexing refers to Subset Selection. It means selecting all the rows and some of the columns, some of the rows and columns, or some of the rows and columns. 

Another indexer is used in the early development of pandas, ix. We can select both by label and integer location using this method. It confused me because it needed to be more explicit while versatile. Sometimes the labels for rows or columns are integers. 


Here is an example of how to index the dataframe using Python: 

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.

Python indexing from dataframe with iloc

PythondotLines of Code : 18dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np
import pandas as pd

df = pd.DataFrame([10, 20, 0, 30, 50], columns = ['Values'])

df.Values[df['Values'] == 0] = np.nan

df['Values'] = df['Values'].interpolate(how='linear')

print(df)

   Values
0    10.0
1    20.0
2    25.0
3    30.0
4    50.0

Instructions

Follow the steps carefully to get the output easily.

  1. Install pandas on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add required dependencies and import them in Python file.
  4. Run the file to generate the output.


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


I found this code snippet by searching for ' Python indexing from dataframe with iloc' 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.
  4. numpy version 1.24.0.


Using this solution, we are able to do Python indexing from dataframe 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 do Python indexing from 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 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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