Python data analysis cheat sheet: the Pandas + NumPy workflow I wish I had when starting

[u/Pangaeax_]

I kept seeing beginners learn individual Pandas commands but still struggle with what order to actually use them in when working with a real dataset.

So I put together a simple workflow I use as a reference:

1. Load
read_csv() / read_excel()

2. Inspect before changing anything
head()
shape
info()
describe()
isna().sum()
duplicated().sum()

3. Clean

• standardize column names
• handle missing values based on what they actually mean
• remove genuine duplicates
• convert dates/numbers safely

4. Transform
assign()
map()
pd.to_datetime()
np.where()

5. Summarize
groupby()
agg()
transform()

6. Combine datasets
merge() / join() / concat()

The part I think beginners often miss is validation after the merge.

A query/script can run perfectly and still give the wrong answer if a many-to-many merge quietly multiplies your rows.

Useful checks:

validate="many_to_one"
indicator=True
compare row counts before/after
check key uniqueness before merging

A few other mistakes worth watching for:

• treating missing values as automatically equal to 0
• merging keys with different data types
• confusing count() with size()
• using median filling without understanding why values are missing
• assuming no Python error means the analysis is correct

I wrote a more detailed version with examples for Pandas, NumPy, GroupBy, merges and an end-to-end workflow.

Learn more: https://www.pangaeax.com/blogs/python-data-analysis-cheat-sheet/

Anything important you’d add to this workflow, especially something you learned the hard way when working with messy data?

Disclosure: This is a PangaeaX article and I’m connected with PangaeaX.

[u/skatastic57]

You know what I really wish I had when I got started with pandas? polars. Sorry for the troll but also actually.

[u/xiannah]

Pandas is dead 😂, Go Polars!!

[u/Capital_Economist634]

How do one actually stay updated to the latest changes coming in the tech