How to drop columns in a Pandas DataFrame - Mastering DataFrame Manipulation
Before going into “How to drop columns in Pandas DataFrame” we can little bit talk about the data manipulation capabilities. Data manipulation is a crucial aspect of any data analysis or machine learning project, and Pandas is a powerful Python library that excels in handling tabular data. In this guide, we’ll delve into the art of dropping columns from a Pandas DataFrame.
How to drop columns in a Pandas DataFrame - Understanding the Basics
In short, a Pandas DataFrame is a two-dimensional, tabular data structure where you can store and manipulate data easily. Columns are the vertical slices of this table, and sometimes, you may need to remove certain columns to streamline your analysis or reduce unnecessary information. If your data source has unnecessary columns that you do not required in the analysis, it is always recommended to drop those. This will make your DataFrame lighter thus will enhance the performance.
How to drop columns in a Pandas DataFrame - using drop method
The primary and most popular method for dropping columns in Pandas is the drop method. It allows you to remove one or more columns from a DataFrame by specifying their names or indices.
Let’s start with a simple example. Consider a DataFrame named df:
Syntax: df = df.drop('column name', axis=1) 
Here, axis=1 indicates that we are dropping a column (since columns are along the vertical axis). In general in pandas dataframe axis 0 denotes rows and 1 denotes columns. The resulting DataFrame will now exclude the ‘City’ column.
How to drop columns in a Pandas DataFrame - Dropping Multiple Columns
To drop multiple columns simultaneously, you can pass a list of column names in place of single column.
Syntax: df = df.drop(list of columns, axis=1)
inplace=True . How to drop columns in a Pandas DataFrame - using inplace
By default, the drop method returns a new DataFrame with the specified columns removed, leaving the original DataFrame unchanged. If you want to modify the original DataFrame in-place, you can use the inplace=True parameter.
However, be cautious when using inplace=True, as it can lead to unexpected behavior and make your code harder to understand. It’s generally recommended to create a new DataFrame unless memory constraints are a concern.
How to drop columns in a Pandas DataFrame - using del
Del is not as used as drop. However, you can use del to drop or delete a column from a Pandas DataFrame.
Syntax: del df['City']
The del can not be used for multiple column at once. One has to use the iterative approach to use del. This could be one major drawback using del over drop method.
for col in ['Age', 'City']:
del df[col]
How to drop columns in a Pandas DataFrame - Conclusion
Dropping columns in a Pandas DataFrame is a fundamental skill for any data analyst or data scientist. The drop method provides a flexible and powerful way to achieve this. Whether you’re removing irrelevant information, dealing with missing values, or simply restructuring your data, mastering column dropping in Pandas is a key step towards efficient data manipulation.
Remember, always double-check your code and keep an eye on the modified DataFrame to ensure it meets your analysis requirements. With these techniques, you’re now equipped to confidently handle column removal in Pandas, making your data manipulation tasks more efficient and effective. Happy coding!
Read more
Refer back to pandas official documentation – https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html
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