How to quickly read CSV files using Python

Use the Pandas library to quickly read CSV files: First install Pandas. Use the read_csv() function to read the CSV file and store it in a data frame. Use the head() function to view the first few rows of the data frame. By grouping the data frame and using the sum() function, you can quickly calculate the total sales for each product.

How to quickly read CSV files using Python

CSV (Comma Separated Values) files are a simple, easy-to-parse Data storage and exchange format. In Python, we can use the powerful Pandas library to read and process CSV files quickly and efficiently.

Installing Pandas

Before you begin, make sure you have Pandas installed. Run the following command in the command line:

pip install pandas

Read CSV file

To read a CSV file using Pandas, we can use read_csv()function. This function accepts a filename or file path as an argument and returns a Pandas object called a data frame. A data frame is a table-like data structure that behaves like a spreadsheet.

Here is a sample code on how to read a CSV file:

import pandas as pd

# 读取CSV文件并将其存储在名为df的数据框中
df = pd.read_csv('my_data.csv')

View the data frame

You can use head() The function looks at the first few rows of the data frame:

# 查看数据框的前五行
df.head()

Practical case

Suppose we have a CSV file named sales.csv, where Contains the following data:

We can use Pandas to read this file and do some quick analysis:

import pandas as pd

# 读取CSV文件
df = pd.read_csv('sales.csv')

# 计算每种产品的总销售额
total_sales = df.groupby('产品').sum()['销售额']

# 打印每种产品的总销售额
print(total_sales)

This code will output the following results:

产品
笔记本    100
台式机    200
平板电脑    150
Name: 销售额, dtype: int64

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