Pandas: Setting the number of maximum rows

Quick modifications: Adjusting display settings

Pandas provides a convenient way to temporarily adjust display options within a specified context using pd.option_context. The changes apply only to the specific block of code.

with pd.option_context('display.max_rows', None):
    print(df)  # Long DataFrame? Challenge accepted.
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Beautifying your DataFrame: width and precision

Set the specific display width and decimal precision for your DataFrame. Customize to fit your preference with 'display.width' and 'display.precision'.

pd.set_option('display.width', 150)  # Let's stretch this DataFrame out.
pd.set_option('display.precision', 2)  # In the case of doubt, round it to the nearest cent.
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Tailoring your environment: contextual adaptations

Environment-specific display options

Different environments will have varied display behaviors. For example, Jupyter notebooks may render DataFrames using df.head(500) instead of set_option.

Back to defaults

It's crucial to remember how to revert to default settings when needed. Use pd.reset_option to revert a single option and pd.reset_option('all') for all.

Discover options with tab-completion

In iPython environments like Jupyter, you can explore options by typing pd.options. and pressing Tab to see all the settings within your reach.

Going beyond: Exploring additional options

Explore more display options

To look into all available options, use pd.describe_option('display') to discover and tweak your data display.

Jupyter Notebook specifications

Jupyter allows more extensive DataFrame display adjustments like:

• Converting the DataFrame to HTML with a static display using: HTML(df.to_html(max_rows=10)).
• Adding custom CSS styles to such output with df.style.set_table_styles.