How to delete a column in pandas

pandas can delete a column by using the drop method and the del operator. Detailed introduction: 1. Use the drop method to create a sample DataFrame and determine the column names to be deleted; 2. Use the del operator to determine the columns to be deleted and use the del operator to delete the columns.

The operating system for this tutorial: Windows 10 system, Python version 3.11.4, DELL G3 computer.

To delete a column in a Pandas data frame (DataFrame), you can use the drop method or del operator. Below I’ll discuss both methods in detail so you can choose the one that suits your needs.

Method 1: Use the drop method

The drop method is a common method in Pandas for deleting rows or columns. Here are the steps to delete a column:

• Step 1: Determine the column you want to delete

First, you need to determine the name of the column you want to delete , can be a string or a list.

import pandas as pd
# 创建一个示例DataFrame
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
# 确定要删除的列名称
column_to_drop = 'B'

Alternatively, if you want to delete multiple columns, you can store the column names in a list:

columns_to_drop = ['B', 'C']

• Step 2: Use the drop method to delete columns

Use the drop method and directly specify the axis=1 parameter to delete the column. In the following example, we will delete single and multiple columns.

# 删除单列
df = df.drop(column_to_drop, axis=1)
# 删除多列
df = df.drop(columns_to_drop, axis=1)

Method 2: Use the del operator

Another way to delete a column is to use Python’s del operator. This method does not require reassignment, it will directly modify the DataFrame.

• Step 1: Determine the column to be deleted

Same as using the drop method, you first need to ensure the name of the column to be deleted.

• Step 2: Use del operator to delete columns

# 删除单列
del df[column_to_drop]
# 删除多列
for col in columns_to_drop:
del df[col]

Notes

Whether you Whether you choose to use the drop method or the del operator, please note that Pandas data operations are based on labels, so you must ensure that the column names to be deleted are actual column names in the DataFrame. If the column name is misspelled, an error will result.

Before deleting a column, it is recommended to make a backup before performing larger operations, because deletion operations are irreversible.

Summary

With these two methods, you can delete columns in Pandas as needed. Choosing to use the drop method or the del operator depends on your personal preference and workflow. The drop method provides more flexible options and can directly generate the deleted DataFrame. The del operator is more direct and suitable for simple column deletion operations. Depending on the specific scenario, choosing the appropriate method is very important to improve work efficiency.

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