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Table of Contents
1. Read CSV using pandas (recommended)
2. Read using csv module (standard library)
3. Handle FAQs
Home Backend Development Python Tutorial python read csv file example

python read csv file example

Jul 24, 2025 am 01:02 AM
java programming

Reading CSV files is commonly implemented in Python using pandas library or csv module. 1. Use pandas to read through pd.read_csv(), return DataFrame, supports specifying parameters such as sep, header, index_col, encoding, na_values, etc., suitable for data analysis; 2. Use csv module to read line by line through csv.reader or csv.DictReader, the former returns a list, and the latter returns a dictionary, suitable for lightweight or no dependencies of third-party libraries; 3. Frequently asked questions: Use a complete path to avoid path errors, set encoding='gbk' or 'utf-8' to solve Chinese garbled code, and skiprows parameter skips specific rows. It is recommended to use pandas for daily analysis, and the script can handle optional csv modules, which are complete and flexible in operation.

python read csv file example

Reading CSV files is a very common operation in Python. It is usually done using the pandas library, or the csv module in the standard library can be used. Here are a few practical examples suitable for different scenarios.

python read csv file example
 import pandas as pd

# Read CSV file df = pd.read_csv('data.csv')

# Show the first few lines of data print(df.head())

illustrate:

  • pd.read_csv() is the most commonly used method.
  • Supports automatic parsing of column names, processing missing values, specifying data types, etc.
  • Returns a DataFrame for subsequent data analysis.

Common parameters:

python read csv file example
  • sep=',' : Specify the delimiter (default is a comma).
  • header=0 : Use the first row as the column name.
  • index_col=None : Do not specify an index column, and a column can also be set as an index.
  • encoding='utf-8' : Specify the encoding, commonly used when processing Chinese.
  • na_values=['N/A', ''] : Customize missing value identification.

example:

 df = pd.read_csv('data.csv', encoding='utf-8', na_values='NULL')

2. Read using csv module (standard library)

If you don't want to rely on third-party libraries, you can use the built-in csv module in Python.

python read csv file example
 import csv

with open('data.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row) # Each row is a list

If the CSV has headers, you can use DictReader:

 import csv

with open('data.csv', mode='r', encoding='utf-8') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row) # Each row is a dictionary, the key is the column name

3. Handle FAQs

  • File path error?
    Make sure the file is in the current working directory, or use the full path:

     df = pd.read_csv(r'C:\path\to\your\data.csv')
  • Chinese garbled?
    Try different encodings:

     pd.read_csv('data.csv', encoding='gbk') # Commonly used in Chinese Windows systems
  • Skip certain lines?

     pd.read_csv('data.csv', skiprows=1) # Skip the first line

    Basically these common methods. pandas is recommended for daily analysis, which is simple and efficient; csv modules are available for scripts or lightweight scenarios.

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