Data processing techniques to delete specified columns in DataFrame using Pandas

Data processing skills: Use Pandas to delete specific columns in the DataFrame

In the process of data analysis and processing, deleting unnecessary columns in the DataFrame is one of the common requirements. one. Pandas is a commonly used data analysis and processing library in Python, providing rich functions and flexible operation methods. This article will introduce how to use Pandas to delete specific columns in a DataFrame and provide specific code examples.

1. First, we need to import the Pandas library and create a DataFrame for demonstration:

import pandas as pd

# 创建示例DataFrame
data = {'姓名': ['张三', '李四', '王五', '赵六'],
        '性别': ['男', '女', '男', '女'],
        '年龄': [25, 30, 35, 28],
        '成绩': [80, 90, 85, 95]}
df = pd.DataFrame(data)

print(df)

In the above code, we created a DataFrame containing four columns: name, gender, age and grades. DataFrame, and print it out, the results are as follows:

  姓名 性别  年龄  成绩
0  张三  男  25  80
1  李四  女  30  90
2  王五  男  35  85
3  赵六  女  28  95

2. Next, we will demonstrate how to use Pandas to delete specific columns in the DataFrame.

1. Use the drop method to delete a single column

# 删除单个列
df_drop = df.drop('性别', axis=1)

print(df_drop)

In the above code, we use the drop method to delete a single column in the DataFrame 'Gender' column and save the results in a new DataFrame df_drop. axis=1 means that the column is deleted, the result is as follows:

  姓名  年龄  成绩
0  张三  25  80
1  李四  30  90
2  王五  35  85
3  赵六  28  95

2. Use the list to delete multiple columns

# 删除多个列
df_drop_multi = df.drop(['年龄', '成绩'], axis=1)

print(df_drop_multi)

In the above code, we use The drop method deletes the 'age' and 'grade' columns in the DataFrame and saves the results in a new DataFrame df_drop_multi. The results are as follows:

  姓名 性别
0  张三  男
1  李四  女
2  王五  男
3  赵六  女

3. Directly use list index to delete multiple columns

# 直接使用列表索引删除多个列
df_drop_iat = df[df.columns[[0, 2]]]

print(df_drop_iat)

In the above code, we use the DataFrame's columns attribute and list index to delete the 'name' in the DataFrame and 'age' columns, and save the result in a new DataFrame df_drop_iat, the result is as follows:

  姓名  年龄
0  张三  25
1  李四  30
2  王五  35
3  赵六  28

3. Through the above example, we learned how to delete the DataFrame in using Pandas Different methods and techniques for specific columns. The choice of these methods depends on practical needs as well as personal preference.

Summary:

1. Use the drop method to delete single or multiple columns. You need to specify axis=1 to indicate that the columns are being deleted.
2. Use list index to delete multiple columns. You can directly select the columns that need to be retained through the df.columns property.
3. When deleting a column, the original DataFrame is not modified, but a new DataFrame is returned.

Through the flexible operations and rich functions provided by Pandas, we can easily process and manage the data in DataFrame to meet different data analysis and processing needs.

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