What are the commonly used functions in the pandas library?

Commonly used functions in the pandas library include: 1. read_csv() and read_excel() functions; 2. head() and tail() functions; 3. info() function; 4. describe() function, etc. Detailed introduction: 1. read_csv() and read_excel() functions. These two functions are used to read data from CSV and Excel files. They can read the data into data frame objects to facilitate further data analysis; 2. head () and tail() functions, etc.

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

Pandas is a powerful Python data analysis library that provides many commonly used functions. The following are some common functions of the Pandas library:

1, read_csv() and read_excel() functions

These two functions are used to read data from CSV and Excel files. They can read data into DataFrame objects to facilitate further data analysis.

Sample code:

import pandas as pd  
  
df = pd.read_csv('file_path.csv')  # 从CSV文件中读取数据  
df = pd.read_excel('file_path.xlsx', sheet_name='Sheet1')  # 从名为'Sheet1'的Excel工作表中读取数据

2, head() and tail() functions

These two functions are used to obtain the first n rows or last n rows of data of the data frame . They make it easy to view the first or last few rows of a data set without loading the entire data set into memory.

Sample code:

import pandas as pd  
  
df = pd.read_csv('file_path.csv')  
df.head(5)  # 获取前5行数据  
df.tail(3)  # 获取后3行数据

3. info() function

info() function can provide basic information of the data frame, including the shape of the data frame, column names, and each Column data type, etc. It helps us quickly understand the structure of the data frame.

Sample code:

import pandas as pd  
  
df = pd.read_csv('file_path.csv')  
df.info()  # 查看数据框的基本信息

4. describe() function

describe() function can provide descriptive statistics of each column of data in the data frame, including count, average value, standard deviation, minimum value, maximum value, etc. It can help us quickly understand the distribution of data in each column of the data frame.

Sample code:

import pandas as pd  
  
df = pd.read_csv('file_path.csv')  
df.describe()  # 查看数据框中每列数据的描述性统计信息

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