Pandas Tutorial

Wrangle tabular data with Pandas — DataFrames, filtering, grouping, and joins.

1 min read 110 words Updated Mar 4, 2026

Pandas is the go-to library for tabular data in Python. This tutorial covers the operations you’ll use daily.

Loading data

import pandas as pd

df = pd.read_csv("sales.csv")
print(df.head())
print(df.shape)        # (rows, columns)

Selecting and filtering

df["price"]                       # a column (Series)
df[df["price"] > 100]             # rows where price > 100
df.loc[df["region"] == "EU", ["product", "price"]]

Grouping

agg = df.groupby("region")["price"].agg(["sum", "mean", "count"])
print(agg.sort_values("sum", ascending=False))

Handling missing data

df.dropna(subset=["price"])       # drop rows missing price
df["price"].fillna(df["price"].mean(), inplace=True)

Joining

orders = pd.read_csv("orders.csv")
users = pd.read_csv("users.csv")
merged = orders.merge(users, on="user_id", how="left")

Method	SQL analog
`merge`	JOIN
`concat`	UNION / stacking rows
`groupby`	GROUP BY

Use vectorized operations (whole-column) instead of Python loops — they’re 10–100x faster.