Operating on the Pandas DataFrame in Python

05 Jun 2023 Balmiki Mandal 0 AI/ML

Pandas is a powerful Python library for data analysis. It provides a DataFrame object, which is a tabular data structure that can be used to store and manipulate data.

In this tutorial, you will learn how to operate on Pandas DataFrames in Python. You will learn about basic operations such as accessing, filtering, and sorting, as well as more advanced operations such as grouping, aggregating, and plotting.

Basic Operations

The following are some basic operations that you can perform on Pandas DataFrames:

  • Accessing data: You can access data in a DataFrame using its index or column names.
  • Filtering data: You can filter data in a DataFrame using Boolean expressions.
  • Sorting data: You can sort data in a DataFrame by its index or column values.

Advanced Operations

The following are some advanced operations that you can perform on Pandas DataFrames:

  • Grouping data: You can group data in a DataFrame by its index or column values.
  • Aggregating data: You can aggregate data in a DataFrame by using functions such as mean, max, min, and sum.
  • Plotting data: You can plot data in a DataFrame using Matplotlib or Seaborn.

 

Operations that can be performed on Pandas DataFrames in Python:

  • Row and column selection: You can select specific rows or columns from a DataFrame using the [] operator. For example, to select the first row of a DataFrame called df, you would use the following code:
     df.iloc[0]
 
  • Filter data: You can filter a DataFrame using Boolean expressions. For example, to filter a DataFrame called df to only include rows where the Name column is equal to "John Doe", you would use the following code:
    df[df['Name'] == 'John Doe']
  • Null values: Pandas DataFrames can contain null values, which are represented by the NaN value. You can check for null values in a DataFrame using the isnull() or notnull() methods. For example, to check if there are any null values in the Age column of a DataFrame called df, you would use the following code:
    df['Age'].isnull()
  • Drop null values: You can drop null values from a DataFrame using the dropna() method. For example, to drop all rows from a DataFrame called df that contain any null values, you would use the following code:
     df = df.dropna()
  • Re-ordering the variables: You can re-order the variables in a DataFrame using the reindex() method. For example, to re-order the columns of a DataFrame called df so that the Name column is first and the Age column is last, you would use the following code:
    df = df.reindex(columns=['Name', 'Age'])

 

These are just a few of the many operations that can be performed on Pandas DataFrames in Python. For more information, please refer to the Pandas documentation.

For more information on operating on Pandas DataFrames, please refer to the Pandas documentation: <https://pandas.pydata.org/pandas-docs/stable/index.html>

BY: Balmiki Mandal

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