How to read and manipulate CSV data using Python's pandas library

How to read CSV files and perform data processing using pandas

pandas is a powerful data processing and analysis tool that provides reading, operation and analysis Functionality for data in various different formats. In this article, we will introduce how to use pandas to read CSV files and perform data processing.

First, make sure you have installed the pandas library. If it is not installed yet, you can install it by running the following command in the terminal:

pip install pandas

Next, we will demonstrate using the following sample CSV file:

name,age,city
John,30,New York
Alice,25,Los Angeles
Bob,35,Chicago

Now, let’s start writing the code to Read files and process data.

First, import the pandas library:

import pandas as pd

Then, use the read_csv() function to read the CSV file:

df = pd.read_csv('data.csv')

This will create a file called df pandas DataFrame object to store the contents of the CSV file.

If you want to view the read data, you can use the head() function to display the first few lines of data:

print(df.head())

Next, let us introduce some commonly used Data processing operations.

1. Select columns:
   To select specific columns, you can use the column name as an index:

name_column = df['name']
age_column = df['age']

2. Select rows:
   To select specific For rows, you can use the loc or iloc function:

row_0 = df.loc[0]  # 使用索引选择第一行数据
row_1 = df.iloc[1]  # 使用位置选择第二行数据

3. to filter data:
   You can use conditions to filter those that meet specific conditions Data:

filtered_data = df[df['age'] > 30]  # 筛选年龄大于30的数据

4. Add columns:
   You can use the insert() function to add new columns:

df.insert(3, 'country', ['USA', 'USA', 'USA'])  # 添加一个名为'country'的列，所有行的值都是'USA'

5. Delete columns:
   To delete columns, use drop()Function:

df = df.drop('city', axis=1)  # 删除名为'city'的列

6. Modify data:
   To modify data, you can use index or Conditional selection and reassignment:

df.loc[0, 'age'] = 31  # 修改第一行'age'列的值为31
df['age'] = df['age'] + 1  # 将'age'列的所有值加1

These are just some of the many data processing operations provided by pandas. Depending on your specific needs, you can also perform other operations such as sorting data, merging data, and calculating statistics.

Finally, to save the data to a new CSV file, you can use the to_csv() function:

df.to_csv('new_data.csv', index=False)  # 将数据保存到名为'new_data.csv'的文件中，不包含行索引

This is using pandas to read the CSV file and perform data processing Basic methods and some common operations. With these operations, you can easily process and analyze data in a variety of different formats.

I hope this article is helpful to you, and I wish you success in your journey of data processing and analysis!

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