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Data Extraction in Pandas column Operations

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

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In Pandas, data extraction is nothing but where data is analyzed and crawled through to retrieve relevant information or data from a data source like a database in a specific pattern. Further, data processing involves adding metadata and other data integration, another process in the data workflow. 


To extract data from a column in Pandas, we can extract data from a column of pandas DataFrame depending on another value by using the DataFrame.query() method. This method is used to query the columns of a DataFrame with a boolean expression. One of the essential features of ScrapingBee is the ability to extract exact data without the need to post-process the request's content using external libraries. We can also use this feature by specifying an additional parameter with the name extract_rules, then manipulate and extract data using column headings and index locations in Pandas. Employ slicing to select sets of data from a DataFrame and employ label and integer-based indexing to select ranges of data in a dataframe, then re-assign values within subsets of a DataFrame. 


Here is an example of how to extract data in Pandas column operations:

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.

data extraction: pandas column operations

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

Dependent Libraries : 
data = [['tom', 10,'M'], ['nick', 15,'M'], ['juli', 14,'F']]
df = pd.DataFrame(data, columns=['Name', 'Age','Gender'])

json_records = df.to_dict('records')

req_json = {}
male_list = []
female_list = []

for item in json_records:
    if item['Gender'] == 'M':
        male_list.append(item['Name'])
    if item['Gender'] == 'F':
        female_list.append(item['Name'])
    req_json['males'] = male_list
    req_json['females'] = female_list
print(req_json)

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(import pandas).
  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 'data extraction in pandas column operations' 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 data extraction in pandas column operations 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 data extraction in pandas column operations.

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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