Reading a CSV File
Load real-world data from CSV, Excel, and other formats
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Explanation
The most common way to get data into pandas is pd.read_csv().
df = pd.read_csv('data.csv')Useful parameters:
python # Specify which column to use as index df = pd.read_csv('data.csv', index_col='id') # Parse dates automatically df = pd.read_csv('data.csv', parse_dates=['date']) # Read only certain columns df = pd.read_csv('data.csv', usecols=['name', 'age', 'salary']) # Handle missing value markers df = pd.read_csv('data.csv', na_values=['N/A', 'none', '-']) # Limit rows read (useful for large files) df = pd.read_csv('data.csv', nrows=1000)
Other formats:
pd.read_excel('file.xlsx')pd.read_json('file.json')pd.read_sql(query, connection)
Saving back:
python df.to_csv('output.csv', index=False) # index=False avoids writing the row numbers
Examples
Load, peek, and check
Always check shape and info() after loading
import pandas as pd
df = pd.read_csv('titanic.csv')
print(df.shape) # (891, 12)
print(df.head())
print(df.info()) # shows nulls per column
# Save a cleaned version
df.to_csv('titanic_clean.csv', index=False)How well did you understand this?
Next in pandas
Selecting Columns