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Pandas : join/merge/concat two dataframes

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by vsasikalabe dot icon Updated: Mar 2, 2023

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Create a data frame with some values to perform the merge function. Here the values are like id, name, and published papers. We can print any sub-values using a print statement. We can join all the sub-values using the concat() method. Also, we will split the values by using commas. 


Pandas melt() function merges or adds the data frame's two values. This function adds the id and name value into paper_id. The dropna() method always removes the rows or columns which contain NULL values. In our code, this method allows dropping all subset values of the paper_id. We can perform this operation on all rows or columns. This can also be done using merge(), groupby(), and np.sum(). 


A DataFrame object has two axes: "axis 0" and "axis 1": 

  • "axis 0" represents rows  
  • "axis 1" represents columns. 


Here is an example of how to join/merge/concat two dataframes using Pandas in Python: 

  • merge() - joining together data on common columns. 
  • join() - joining the data on an index. 
  • concat() for combining Data Frames across rows or columns. 


These methods help you to combine data from various sources or split a single DataFrame into multiple parts. Using these methods, you can create a more comprehensive dataset that includes information from various sources or organize the data more meaningfully. You can use these methods to perform data cleaning, transformation, and analysis operations. Combining data in this way helps you to gain insights from the data and make better decisions based on the insights. 


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.

Pandas : join/merge/concat two dataframes

PythondotLines of Code : 41dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
authors_df = pd.DataFrame({
  'id': [100, 101],
  'name': ['Amy', 'Bla'],
  'has_published_papers': ['pp3524172', 'pp0120888,pp0343660,pp0151738,pp0120631']
})
authors_df.has_published_papers.str.split(',', expand=True)

           0          1          2          3
0  pp3524172       None       None       None
1  pp0120888  pp0343660  pp0151738  pp0120631

authors_papers_df = (
    pd.concat([
        authors_df.drop(columns=['has_published_papers']),
        authors_df.has_published_papers.str.split(',', expand=True)
    ], axis=1)
    .melt(['id', 'name'], value_name='paper_id')
    .dropna(subset=['paper_id']))

    id name variable   paper_id
0  100  Amy        0  pp3524172
1  101  Bla        0  pp0120888
3  101  Bla        1  pp0343660
5  101  Bla        2  pp0151738
7  101  Bla        3  pp0120631

papers_topics_df = (
    pd.concat([
        papers_df.drop(columns=['topics']),
        papers_df.topics.str.split(', ', expand=True)
    ], axis=1)
    .melt(['paper_id', 'name'], value_name='topic')
    .dropna(subset=['topic'])
)

authors_papers_topics_df = authors_papers_df.merge(papers_topics_df, on='paper_id')

authors_papers_topics_df.groupby('id')['topics'].nunique()

np.sum(authors_papers_topics_df.groupby('id')['topics'].nunique() > 1)

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 : join/merge/concat two dataframes' 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.


Using this solution, we are able to merge the two dataframes using 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 merge the two dataframes using 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

                      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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