In programming, even small tips or tools can make a big difference.
For example, a shortcut key or a helpful package might simplify a lot of work and double your efficiency.
Here I’ll share a few small tricks I often use.

1. Use pandas_profiling to Inspect DataFrames

Understanding your data is essential before doing any analysis.
Although df.describe() and df.info() provide basic summaries, they’re limited for larger or more complex datasets.
The older pandas-profiling project is now distributed as ydata-profiling, and it still provides a quick way to generate a detailed report.

Installation

pip install ydata-profiling
# or
conda install -c conda-forge ydata-profiling

Usage

It’s very easy to use:

import pandas as pd
from ydata_profiling import ProfileReport

df = pd.read_csv("train.csv")
profile = ProfileReport(df, title="Titanic Profiling Report")
profile.to_notebook_iframe()

You can also export the report to HTML:

profile.to_file(output_file="titanic_Profiling_Report.html")

2. Interactive Plotting with cufflinks

Pandas has built-in plotting via .plot(), but it’s not interactive.
If you want interactivity, try the cufflinks package.

Installation

pip install plotly   # dependency
pip install cufflinks

Usage

import pandas as pd
import cufflinks as cf
import plotly.offline

cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)

df = pd.read_csv("train.csv")
df.iplot()

Left: Interactive chart by cufflinks
Right: Static chart by Pandas .plot()

3. Use Jupyter “Magic” Functions

Jupyter has many built-in “magic functions” that help with analysis.
You can list them all using:

%lsmagic

There are two kinds:

• Line magic: starts with %
• Cell magic: starts with %%

%pastebin

Uploads your code to Pastebin for sharing.
Example:

%pastebin prfile.ipynb

%matplotlib notebook

• %matplotlib inline: static plots
• %matplotlib notebook: interactive plots in Jupyter

%run

Runs a Python script directly from the notebook.

%run file.py

%%writefile

Writes everything in the current cell into a file.

%%latex

For rendering math formulas.

4. Debugging with %debug

This magic function is great for troubleshooting errors.

Use q to quit the debug console.

5. Pretty Printing with pprint

pprint makes dictionary or JSON output much more readable.

6. Highlight Comment Cells in Jupyter

Use different colors to highlight info, warnings, success, and danger.

Blue (info)

Yellow (warning)

Green (success)

Red (danger)

7. Show All Outputs in a Cell

By default, only the last output is shown in a cell.
To show all outputs:

Use IPython’s InteractiveShell:

8. Use the -i Flag When Running Python Scripts

Normally we run:

python hello.py

If there’s an error, we don’t get much insight.
Instead, use:

python -i hello.py

Now you can inspect variables after it runs, even if there’s an error.

9. Quick Commenting with Ctrl + /

Select the code and press Ctrl + / to comment or uncomment it.

10. Undo Cell Deletion with ESC + Z

If you accidentally delete a whole cell, press ESC + Z to undo it.

Summary

This article collects several practical tricks for Python data analysis in Jupyter Notebook, from quick dataset inspection to debugging and interactive plotting. Small workflow improvements like these usually save more time than large refactors.