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Creating a Pandas DataFrame from a Nested List:

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by vigneshchennai74 dot icon Updated: Feb 20, 2023

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Pandas provide a versatile set of data analysis, cleaning, and visualization tools and are widely used in data science and machine learning. Creating a Pandas DataFrame from a nested list can be useful in various data analysis and manipulation tasks.  


With the help of the Pandas library, you can perform various operations on the data, such as filtering, sorting, grouping, merging, and aggregating, among others. These operations can be used to gain insights from the data and make data-driven decisions. Creating a Pandas DataFrame from a nested list can be a crucial step in data analysis and is a widely used technique in data science and machine learning. It allows you to easily store and manipulate large amounts of data in a structured way and provides powerful tools for data analysis, cleaning, and visualization. 


Pandas is a popular open-source data analysis library for Python that provides fast, flexible, and expressive data structures for working with structured data. Some of the key benefits of using Pandas are: 

  • Data handling: Pandas provides data structures like Series (1-dimensional labeled arrays) and DataFrame (2-dimensional labeled arrays), allowing you to store and manipulate large datasets easily. It can handle various data formats like CSV, Excel, SQL databases, and JSON. 
  • Data cleaning and preprocessing: Pandas provides a rich set of tools for cleaning, preprocessing, and transforming data. You can handle missing values, perform data imputation, reshape data, and do other manipulations with just a few lines of code. 
  • Data visualization: Pandas provides an interface to the popular visualization library Matplotlib, making it easy to create plots, histograms, and other visualizations. This allows you to gain insights from the data and communicate the results effectively. 
  • Data analysis and statistics: Pandas provide a wide range of data analysis and statistics functions. You can perform grouping and aggregation, pivot tables, time series analysis, and statistical operations like mean, median, mode, standard deviation, and correlation. 


Here is the example of Creating a Pandas DataFrame from a Nested List: 

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

Code

In this code we have used Pandas Library

How to create a Pandas DataFrame from a list of lists?

PythondotLines of Code : 21dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
L = [[], ['1', 'Anthony Taylor', '6', '11.17', '0.68', '0.17', '1.83', '11', '0.17', '1'], ['2', 'Jonathan Moss', '4', '12.75', '0.73', '0.00', '2.00', '8', '0.00', '0'], ['3', 'Martin Atkinson', '3', '11.00', '0.69', '0.67', '1.67', '5', '0.00', '0'], ['4', 'Graham Scott', '3', '10.00', '0.67', '0.00', '0.33', '1', '0.00', '0'], ['5', 'Chris Kavanagh', '3', '10.33', '0.74', '0.00', '1.67', '5', '0.00', '0'], ['6', 'Michael Oliver', '2', '12.00', '0.62', '1.00', '2.00', '4', '0.00', '0'], ['7', 'Paul Tierney', '2', '12.50', '0.66', '0.00', '2.50', '5', '0.00', '0'], ['8', 'Mike Dean', '2', '14.50', '0.73', '0.50', '3.00', '6', '0.00', '0'], ['9', 'Andre Marriner', '2', '8.50', '0.53', '0.00', '2.00', '4', '0.00', '0'], ['10', 'Kevin Friend', '2', '9.50', '0.50', '0.00', '3.00', '6', '0.00', '0'], ['11', 'Craig Pawson', '1', '9.00', '0.45', '0.00', '1.00', '1', '0.00', '0'], ['12', 'Stuart Attwell', '1', '9.00', '0.75', '0.00', '2.00', '2', '0.00', '0'], ['13', 'Lee Probert', '1', '11.00', '0.73', '0.00', '0.00', '0', '0.00', '0'], ['', 'Total / Average', '32', '11.09', '0.66', '0.19', '1.81', '58', '0.03', '1']]

cols = ['id','name','a','b','c','d','e','f','g','h']
df = pd.DataFrame(L[1:], columns=cols)
print(df)
    id             name   a      b     c     d     e   f     g  h
0    1   Anthony Taylor   6  11.17  0.68  0.17  1.83  11  0.17  1
1    2    Jonathan Moss   4  12.75  0.73  0.00  2.00   8  0.00  0
2    3  Martin Atkinson   3  11.00  0.69  0.67  1.67   5  0.00  0
3    4     Graham Scott   3  10.00  0.67  0.00  0.33   1  0.00  0
4    5   Chris Kavanagh   3  10.33  0.74  0.00  1.67   5  0.00  0
5    6   Michael Oliver   2  12.00  0.62  1.00  2.00   4  0.00  0
6    7     Paul Tierney   2  12.50  0.66  0.00  2.50   5  0.00  0
7    8        Mike Dean   2  14.50  0.73  0.50  3.00   6  0.00  0
8    9   Andre Marriner   2   8.50  0.53  0.00  2.00   4  0.00  0
9   10     Kevin Friend   2   9.50  0.50  0.00  3.00   6  0.00  0
10  11     Craig Pawson   1   9.00  0.45  0.00  1.00   1  0.00  0
11  12   Stuart Attwell   1   9.00  0.75  0.00  2.00   2  0.00  0
12  13      Lee Probert   1  11.00  0.73  0.00  0.00   0  0.00  0
13      Total / Average  32  11.09  0.66  0.19  1.81  58  0.03  1
  1. Copy this code using "COPY" button above and paste it in your python ide
  2. Import Pandas Library
  3. Run this code to Create a Data frame


I hope you have found this useful. I have added the dependent Library and version information in following sections.


I found this code snippet by searching "How to create a pandas DataFrame from a list of lists"in Kandi .you can try any use case.

Environment Tested

In this solution we have used the following versions. Be mindful to change when working with other versions.


  1. This solution is created using Python version 3.7.15
  2. This solution is Tested using Pandas 1.5.2


Using this solution we can able to Create Dataframe using List of list with Pandas library in python with simple Steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us Create Dataframe in Python.

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 that is required to run this code you can install it by clicking on th above link and copying the pip install command from the pandas page in Kandi.

                      You can search for any dependent library in Kandi like Pandas.

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