Methods and techniques for reading CSV files in Python

Read data from CSV files using the CSV module or Pandas. The CSV module provides a basic interface, while Pandas provides more advanced functions. Tips include: using Sniffer to determine delimiters, specifying delimiters, handling missing values, and reading in chunks. Practical case: reading temperature data and drawing charts, demonstrating the power of Python in processing CSV files.

Methods and techniques for reading CSV files with Python

Overview

CSV A (comma-separated values) file is a structured text file in which data is organized into rows, with each row consisting of comma-separated fields. In Python, there are several ways to read CSV files.

Using the CSV module

The CSV module provides a convenient interface for reading and writing CSV files. Here is a simple example of reading data from a CSV file using the csv module:

import csv

with open('data.csv', 'r') as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

Using Pandas

Pandas is a library for data manipulation and A powerful library for analysis. It provides more advanced CSV file processing functions, such as:

import pandas as pd

df = pd.read_csv('data.csv')
print(df.head())  # 显示数据的前五行

Practical case: reading temperature data

The following is a method to read and analyze CSV files using Python Practical case of temperature data:

import csv

# 从CSV文件读取气温数据
with open('temp_data.csv', 'r') as f:
    reader = csv.reader(f)
    data = list(reader)

# 创建日期和气温列表
dates = [row[0] for row in data[1:]]
temps = [float(row[1]) for row in data[1:]]

# 绘制气温随时间的变化图
import matplotlib.pyplot as plt

plt.plot(dates, temps)
plt.xlabel('日期')
plt.ylabel('气温')
plt.title('气温变化图')
plt.show()

Tips

• Use Sniffer to determine the separator:Sniffer# in the csv module ## class can detect delimiters in files.
• Specify the delimiter: You can use the delimiter parameter to specify the delimiter for the CSV file to avoid errors.
• Handling missing values: If the CSV file contains missing values, you can use the na_values parameter to specify how they are handled.
• Read in chunks: Use the chunksize parameter to read large CSV files in chunks to save memory.

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