Data Input/Output
One of the greatest strengths of Pandas is its ability to effortlessly read data into and write data out of a DataFrame from various file formats. This is achieved primarily through the functions prefixed with pd.read_ and the methods prefixed with df.to_.
Reading Data into a DataFrame
To load data into a Pandas DataFrame, you use the appropriate pd.read_...() function. The most common input format is CSV.
| Function | File Type | Example Usage |
|---|---|---|
pd.read_csv() | Comma-Separated Values (Text files) | df = pd.read_csv('data.csv') |
pd.read_excel() | Microsoft Excel files | df = pd.read_excel('data.xlsx') |
pd.read_json() | JavaScript Object Notation | df = pd.read_json('data.json') |
pd.read_sql() | SQL database tables | df = pd.read_sql(query, connection) |
Example: Reading a CSV File
The read_csv() function is highly flexible, supporting parameters to handle delimiters, missing values, and specific column selection.
# Load data from a CSV file into a DataFrame
df_sales = pd.read_csv('sales_data.csv')
Writing Data from a DataFrame
After you've cleaned, transformed, or analyzed your data, you'll use a .to_...() method on the DataFrame object to save the results.
| Method | File Type | Example Usage |
|---|---|---|
df.to_csv() | Comma-Separated Values | df.to_csv('cleaned_data.csv', index=False) |
df.to_excel() | Microsoft Excel files | df.to_excel('analysis.xlsx', sheet_name='Summary') |
df.to_json() | JavaScript Object Notation | df.to_json('data_output.json') |
Example: Writing to a CSV File
When writing to a CSV, it is best practice to use index=False to prevent the DataFrame's row indices (the 0, 1, 2, ... numbers) from being saved as an unnecessary extra column in the file.
# index=False ensures the row index is NOT included in the file
df_sales.to_csv('processed_sales.csv', index=False)