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Pandas Indexing

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by Abdul Rawoof A R dot icon Updated: Mar 3, 2023

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A two-dimensional, immutable, and heterogeneous tabular data structure with labeled axis rows and columns is a DataFrame. An immutable sequence used for indexing DataFrame and series is the Pandas Index. This index is a basic object that stores axis labels for all pandas objects in the dataframe. 


Indexing is nothing but broadly refers to using some benchmark indicator or measure as a reference or yardstick. Indexing is a statistical measure for tracking economic data such as inflation, unemployment, gross domestic product (GDP) growth, productivity, and market returns in finance and economics. Selecting subsets of data such as rows, columns, and individual cells from that dataframe is the indexing in Pandas DataFrame and to get the index of a Pandas DataFrame from to call DataFrame.index property. This property returns an Index object representing the index of this DataFrame, and an index on a Pandas DataFrame provides us a way to identify rows. 


Types of index: 

  • Expression-based indexes. 
  • Bidirectional indexes. 
  • Partitioned and non-partitioned indexes. 
  • Clustered and non-clustered indexes. 
  • Unique and non-unique indexes. 


Here is an example of Pandas indexing: 

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

Code

In this solution we're using Pandas library.

python pandas | index

PythondotLines of Code : 19dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import pandas as pd
data = [
[100,   3,  '2021-09-29 04:00:00-04:00'],
[300,   2,  '2021-09-29 04:30:00-04:00'],
[700,   5,  '2021-09-29 05:30:00-04:00'],
[500,   9,  '2021-09-29 06:00:00-04:00'],
[900,   22, '2021-09-29 06:30:00-04:00'],
[400,   29, '2021-09-29 07:00:00-04:00']
]

df = pd.DataFrame(data,columns=["volume","price","datetime"])
i = df["volume"].idxmax()
print(df.iloc[i])

volume                            900
price                              22
datetime    2021-09-29 06:30:00-04:00
Name: 4, dtype: object

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 'pandas index' 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 2021.3.
  2. The solution is tested on Python 3.9.7.
  3. Pandas version-v1.5.2.


Using this solution, we are able to implement indexing in 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 implement indexing in 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

                      You can also search for any dependent libraries on kandi like 'pandas'.

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