Improve efficiency: a quick way to change data frame column names

Pandas Tips: Quickly modify the column names of the data frame

Introduction:
In the process of data processing and analysis, we often encounter the need to modify the data frame Listing status. Pandas is a powerful data processing library that provides rich functionality to manipulate and process data frames. This article will introduce several methods to quickly modify the column names of data frames and give specific code examples.

1. Use the rename() function
Pandas provides the rename() function, which can easily modify the column names of the data frame. This function accepts a dictionary as a parameter, the keys of the dictionary represent the original column names, and the values ​​of the dictionary represent the new column names. The following is an example:

import pandas as pd

# 创建一个数据框
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Gender': ['Female', 'Male', 'Male']}
df = pd.DataFrame(data)

# 使用rename()函数修改列名
df.rename(columns={'Name': '姓名', 'Age': '年龄', 'Gender': '性别'}, inplace=True)

# 打印修改后的数据框
print(df)

Run the above code, the output result is as follows:

        姓名  年龄      性别
0    Alice  25  Female
1      Bob  30    Male
2  Charlie  35    Male

2. Directly assign values ​​to the columns attribute
In addition to using the rename() function, we can also directly modify The final list of column names is assigned to the columns property of the data frame, thereby achieving the effect of quickly modifying column names. The following is an example:

import pandas as pd

# 创建一个数据框
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Gender': ['Female', 'Male', 'Male']}
df = pd.DataFrame(data)

# 直接赋值给columns属性修改列名
df.columns = ['姓名', '年龄', '性别']

# 打印修改后的数据框
print(df)

Run the above code, the output result is the same as the previous example:

        姓名  年龄      性别
0    Alice  25  Female
1      Bob  30    Male
2  Charlie  35    Male

3. Modify the column name to lowercase or uppercase
Sometimes, we need to The column names of the data frame are uniformly lowercase or uppercase. Pandas provides str.lower() and str.upper() functions to achieve this goal. The following is an example:

import pandas as pd

# 创建一个数据框
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Gender': ['Female', 'Male', 'Male']}
df = pd.DataFrame(data)

# 将列名修改为小写
df.columns = df.columns.str.lower()

# 打印修改后的数据框
print(df)

Run the above code, the output is as follows:

     name  age  gender
0    Alice   25  Female
1      Bob   30    Male
2  Charlie   35    Male

Through the above code, we change the column name to lowercase.

4. Use the str.replace() function
If you want to modify the column name according to certain rules, we can use the str.replace() function. This function accepts two parameters, the first parameter is the character or character pattern to be replaced, and the second parameter is the replaced character or character pattern. The following is an example:

import pandas as pd

# 创建一个数据框
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Gender': ['Female', 'Male', 'Male']}
df = pd.DataFrame(data)

# 使用str.replace()函数修改列名
df.columns = df.columns.str.replace('Name', '姓名')

# 打印修改后的数据框
print(df)

Run the above code, the output is as follows:

        姓名  Age  Gender
0    Alice   25  Female
1      Bob   30    Male
2  Charlie   35    Male

With the above code, we replace the "Name" contained in the column name with "Name".

Summary:
This article introduces several methods to quickly modify the column names of data frames, and gives specific code examples. By using the rename() function, direct assignment to the columns attribute, str.lower() function, and str.replace() function, we can easily modify the column names of the data frame to adapt to different needs.

The above is the detailed content of Improve efficiency: a quick way to change data frame column names. For more information, please follow other related articles on the PHP Chinese website!