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Slicing multiple columns using Pandas

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by vigneshchennai74 dot icon Updated: Feb 27, 2023

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Pandas DataFrame Slicing with Multiple Column Ranges refers to selecting specific columns from a DataFrame based on their column index. This is typically done using the iloc method of the DataFrame and specifying the column indices to be selected. 


iloc is a method in the Pandas library that allows for integer-location based indexing and selection of rows and columns in a DataFrame. The iloc method takes one or two arguments. The first argument specifies the rows to select, and the second argument (if present) specifies the columns to select. The rows and columns can be selected using integer indices, ranges of indices, or boolean masks. 


Slicing the DataFrame in this way is helpful because it allows for creating a new DataFrame that contains only the columns relevant to a particular analysis. This can simplify the analysis by reducing the amount of data to be processed and visualized. It also allows for creating a smaller DataFrame that can be used for training machine learning models, leading to faster and more efficient training times. In the given code, the columns of interest are selected using two ranges of column indices concatenated together. This is done using the np.r_ function, which creates a concatenated array of the specified ranges. 


Here is the example of how to Slice multiple columns using pandas: 

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

Code

In this code we have used Dataframe.iloc[] Function . This function is used for positions or integer based

Pandas dataframe slicing with multiple column ranges

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

Dependent Libraries : 
loc = df.columns.get_loc
df.iloc[:, np.r_[loc('lat'):loc('long')+1, loc('year'):loc('day')+1]] 

        lat      long      year     month       day
0  0.218559  0.418508  0.345499  0.166776  0.878559
1  0.572760  0.898007  0.702427  0.386477  0.694439
2  0.803740  0.983359  0.945517  0.649540  0.860832
3  0.873401  0.906277  0.463535  0.610538  0.496282
4  0.187359  0.687674  0.039455  0.647117  0.638054
5  0.169531  0.794548  0.352917  0.484498  0.697736
6  0.022867  0.375123  0.444112  0.498140  0.414346
7  0.729086  0.415919  0.430047  0.734766  0.556216
8  0.138769  0.614932  0.109311  0.539576  0.289299
9  0.037969  0.500108  0.758036  0.262273  0.100859

ranges = [('lat', 'long'), ('year', 'day')]
pd.concat([df.loc[:, i:j] for i, j in ranges], axis=1)

        lat      long      year     month       day
0  0.218559  0.418508  0.345499  0.166776  0.878559
1  0.572760  0.898007  0.702427  0.386477  0.694439
2  0.803740  0.983359  0.945517  0.649540  0.860832
3  0.873401  0.906277  0.463535  0.610538  0.496282
4  0.187359  0.687674  0.039455  0.647117  0.638054
5  0.169531  0.794548  0.352917  0.484498  0.697736
6  0.022867  0.375123  0.444112  0.498140  0.414346
7  0.729086  0.415919  0.430047  0.734766  0.556216
8  0.138769  0.614932  0.109311  0.539576  0.289299
9  0.037969  0.500108  0.758036  0.262273  0.100859
  1. Copy this code using "Copy" button above and paste it in your Python ide
  2. Import Pandas library of python.
  3. Run the code to get a Slicing columns


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


I found this code snippet by searching "Pandas Dataframe slicing with multiple column ranges" in kandi. you can try any use case.


Note:-


  • Import pandas and numpy and pd and np
  • Mark the selected subset of columns from the original DataFrame using 'T' Variable.
  • print the original DataFrame df and the new DataFrame T using print(df) and print(T). This is for you refrence

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

                      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 that is required to run this code you can install it by clicking on the 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. and numpy

                                          Environment Tested


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


                                          • This solution is created and tested using Vscode version 1.75.1
                                          • This solution is created using Python version 3.7.15
                                          • This solution is Tested using Pandas version 1.5.2
                                          • This solution is Tested using numpy version 1.21.6


                                          Using this solution we can able to Slicing using 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 select a column in Dataframe Index from column in Python.

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