Python provides an efficient csv module to process CSV files. You can do this by following these steps: Install the csv module. Use csv.reader() to read CSV files. Efficiently process large files line by line. Read and write CSV files in memory for faster processing. Use csv.writer() to write out a CSV file. Process CSV files in different formats through dialects. Customize delimiters and quotes to read non-standard CSV files.
Efficient methods and techniques for processing CSV files in Python
Practical case: reading and analyzing sales data CSV files
1. Install the csv
module
Use Python's pip
Install csv
Module:
pip install csv
2. Read CSV file
Use csv.reader()
Read CSV file:
import csv with open('sales_data.csv', 'r') as csv_file: csv_reader = csv.reader(csv_file) for row in csv_reader: print(row) # 打印每一行数据
3. Line-by-line processing
Use the line-by-line processing generator to process large files more efficiently:
import csv with open('sales_data.csv', 'r') as csv_file: csv_reader = csv.reader(csv_file) for row in csv_reader: process_row(row) # 调用自定义函数处理每一行
4. Read and write CSV in memory
Use the StringIO
class to load CSV data into memory for quick processing:
import csv from io import StringIO csv_data = """ 1,John Doe,100 2,Jane Smith,200 """ csv_file = StringIO(csv_data) csv_reader = csv.reader(csv_file) for row in csv_reader: print(row)
5. Write out the CSV file
Use csv.writer()
to write out the CSV file:
import csv with open('output.csv', 'w', newline='') as csv_file: csv_writer = csv.writer(csv_file) csv_writer.writerow(['ID', 'Name', 'Sales']) csv_writer.writerow([1, 'John Doe', 100])
6. Use dialects
to handle different dialects (such as Excel or Google Sheets) CSV file:
import csv with open('sales_data.csv', 'r') as csv_file: csv_reader = csv.reader(csv_file, dialect='excel') for row in csv_reader: print(row)
7. Set delimiter and quote character
Custom delimiter and quote character:
import csv with open('custom_data.csv', 'r') as csv_file: csv_reader = csv.reader(csv_file, delimiter=';', quotechar='"') for row in csv_reader: print(row)
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