Reading CSV Files
Load CSV files with pd.read_csv, control delimiters, header rows, and index columns, and preview the result.
Why CSV Is the Universal Format
CSV (Comma-Separated Values) is the most widely used format for exchanging tabular data. It is human-readable, supported by every database, spreadsheet, and analytics tool, and requires no schema definition. pd.read_csv() is the most frequently called Pandas function in data analysis because virtually every public dataset, API export, or database dump is available in CSV.
import pandas as pd
# Read a CSV file from disk
df = pd.read_csv('data/sales.csv')
print(df.shape)
print(df.head())Basic read_csv() Call
The only required argument is the file path (string or Path object). By default Pandas assumes the first row is the header (column names), the delimiter is a comma, and values are inferred to appropriate dtypes. The resulting DataFrame has a default integer RangeIndex. Always call df.info() immediately after loading to spot type inference issues.
import pandas as pd
df = pd.read_csv('products.csv')
# Equivalent with explicit defaults:
df = pd.read_csv('products.csv',
sep=',',
header=0,
index_col=None)