Sample DataFrame context
A sales.csv file with columns order_id, order_date, region, product, quantity, revenue.
Syntax or pattern
pd.read_csv("file.csv")5 practical examples
Load a CSV file
Read a CSV file into a DataFrame.
import pandas as pd
sales = pd.read_csv("sales.csv")
sales.head()The file is loaded and the first rows are previewed.
Parse dates while importing
Convert order_date into a datetime column during import.
sales = pd.read_csv("sales.csv", parse_dates=["order_date"])The date column is ready for date filters and monthly summaries.
Read selected columns
Load only the columns needed for the report.
cols = ["order_id", "order_date", "region", "revenue"]
sales = pd.read_csv("sales.csv", usecols=cols)The DataFrame is smaller and easier to work with.
Handle a semicolon-delimited file
Read a file that does not use commas as separators.
sales = pd.read_csv("sales_export.csv", sep=";")Pandas splits fields using semicolons instead of commas.
Fix encoding issues
Read a file that contains special characters.
sales = pd.read_csv("sales.csv", encoding="utf-8")Product and customer names keep their characters correctly.
Common mistakes to avoid
- Changing the original DataFrame before checking the result.
- Forgetting to inspect row counts before and after the operation.
- Using a pattern without confirming column names and data types.
FAQ
What is the main use of read_csv Examples?
Load CSV files into DataFrames, handle delimiters, select columns, parse dates and clean common import issues.
Should I use this in a notebook or a script?
Both work. Use a notebook while exploring the data, then move the final workflow into a repeatable script when it is stable.
How do I avoid breaking my source data?
Create a copy of the DataFrame before transforming it and check row counts, missing values and sample rows after each important step.