Python

Pandas Cheat Sheet

The operations that cover most day-to-day dataframe work

A working reference for selection, cleaning, grouping and reshaping — the four things almost every pandas session is made of.

Beginner 2 min read 15 Entries Version 1.0 Sabir Updated 1
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Selecting

Expression Returns
df["col"] One column as a Series
df[["a", "b"]] Several columns as a DataFrame
df.loc[rows, cols] By LABEL — the end of a slice is included
df.iloc[0:5, 0:3] By POSITION — the end is excluded
df[df["age"] > 30] Boolean mask
df.query("age > 30 and city == 'Oslo'") The same, readably
df.loc[df["a"] > 1, "b"] = 0 The assignment form that is always safe

Cleaning and grouping

Expression Does
df.isna().sum() Count missing values per column
df.dropna(subset=["a"]) Drop rows missing a specific column
df.fillna({"a": 0}) Fill per column, not globally
df.drop_duplicates(subset=["id"], keep="last") De-duplicate on a key
df.groupby("k").agg(n=("x", "size"), avg=("x", "mean")) Named aggregations — readable output columns
df.merge(other, on="id", how="left", indicator=True) Join; _merge shows what matched
df.pivot_table(index="a", columns="b", values="v", aggfunc="sum") Reshape wide

SettingWithCopyWarning

The warning means pandas cannot tell whether you are writing to the original frame or to a temporary copy — so your edit may silently vanish. It is caused by chained indexing: df[df.a > 1]["b"] = 0. Write it as one .loc call instead: df.loc[df.a > 1, "b"] = 0. If you genuinely wanted a separate frame, make that explicit with .copy().

Code examples

Group, aggregate and rank in one pass

Named aggregations give readable output columns; rank() then orders inside each group.

Python groupby_rank.py Download 
import pandas as pd

summary = (
    df.groupby('category')
      .agg(
          n=('price', 'size'),
          avg_price=('price', 'mean'),
          top_price=('price', 'max'),
      )
      .reset_index()
)

# Rank WITHIN each category, best first
df['rank'] = (
    df.groupby('category')['price']
      .rank(method='dense', ascending=False)
      .astype(int)
)

# Safe conditional assignment — one .loc, never chained indexing
df.loc[df['price'] > 100, 'tier'] = 'premium'

Frequently asked questions

What causes SettingWithCopyWarning?
Chained indexing — df[mask]["col"] = value — where pandas cannot tell whether you are writing to the original frame or a temporary copy. Write it as one .loc call instead.
Should I use loc or iloc?
loc for labels, iloc for positions. The trap is that loc INCLUDES the end of a slice while iloc excludes it, exactly like the rest of Python.

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