Pandas is a popular data manipulation library in Python. It allows users to easily manipulate and analyze tabular data, also known as DataFrames.
Within this library, users can drop columns from their DataFrame. In this article, we will explore how to do this, as well as additional resources for working with Pandas DataFrames.
Dropping Columns from a Pandas DataFrame
When working with DataFrames in Pandas, it is often necessary to remove unwanted columns. The basic syntax for this operation is:
This will drop the specified columns from the DataFrame, which will return a modified DataFrame.
Note that the original DataFrame will remain unchanged. To make any changes permanent, you will need to assign the output to a variable that overwrites the original DataFrame.
The `drop` method can also take additional arguments, such as `axis` and `inplace`. The `axis` argument specifies whether you want to drop columns or rows.
The value `0` specifies rows, and `1` specifies columns. The default value is `0`.
The `inplace` argument specifies whether to modify the original DataFrame or to return a modified copy. The default value is `False`.
Example of Dropping Columns
Let’s consider an example of dropping columns from a basketball player dataset. Suppose we have a DataFrame containing the following information:
| Player Name | Points per Game | Rebounds per Game | Assists per Game |
| ———– | ————– | —————- | —————- |
| LeBron James | 25.2 | 7.8 | 10.6 |
| Stephen Curry | 24.6 | 4.6 | 6.5 |
| Kevin Durant | 26.0 | 6.4 | 5.0 |
| Giannis Antetokounmpo | 29.5 | 13.6 | 5.6 |
| James Harden | 25.1 | 5.6 | 8.7 |
Suppose we only want to keep the `Player Name` and `Points per Game` columns.
We can do this by using the following code:
keep_cols = [‘Player Name’, ‘Points per Game’]
new_df = df[keep_cols]
This code will create a new DataFrame `new_df`, which only contains the columns specified in `keep_cols`. Note that the original DataFrame `df` remains unchanged.
Pandas is a powerful library, with many more methods and capabilities beyond what we have covered here. To learn more, I recommend checking out the official Pandas documentation.
The DataFrame methods section provides an overview of all the methods available to DataFrames, along with examples of how to use them.
Dropping columns from a Pandas DataFrame is a simple task that can be accomplished with the `drop` method. Remember to assign the output to a variable to make any changes permanent.
Pandas also has a wide range of other methods and capabilities for data manipulation and analysis, which can be explored further in the official documentation. In summary, Pandas is a powerful data manipulation library in Python, and the ability to drop columns from a DataFrame is a basic but essential task for data analysis.
By using the `drop` method, it is easy to remove unwanted data columns, and the modified DataFrame can be assigned to a new variable for further analysis. For those seeking to learn more about working with Pandas DataFrames, the official Pandas documentation provides comprehensive resources and examples.
Overall, dropping columns from a Pandas DataFrame is a valuable tool for organizing and analyzing data, and is an important technique to master for effective data manipulation and analysis.