Python Tutorial
Pandas Selecting Data
Use loc for labels and iloc for integer positions. Bracket notation selects columns.
Columns
import pandas as pd
df = pd.DataFrame({
"name": ["Luna", "Kai", "Mia"],
"score": [88, 92, 95],
"city": ["Oslo", "Bergen", "Oslo"],
}, index=["a", "b", "c"])
print(df["name"])
print(df[["name", "score"]])loc — labels
print(df.loc["a"])
print(df.loc["a":"b", ["name", "score"]])
print(df.loc[df["score"] > 90])iloc — positions
print(df.iloc[0])
print(df.iloc[0:2, 0:2])📘 Real-World Deep Dive
Knowing <strong>Pandas Selecting (pandas)</strong> well is what turns pandas from a curiosity into a daily tool — you'll reach for it in nearly every real project.
Real-Life Scenario
An end-to-end usage of Pandas Selecting that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
df = pd.read_csv("data.csv")
rows = df.loc[df["age"] >= 30, ["name", "age"]]
print(rows.head())Expected Output
(see source)Common mistakes
- A
DataFrameindexing pattern likedf[df.col > 5]returns a copy — use.loc[row_mask, col]for assignment to avoidSettingWithCopyWarning. - Pandas infers
objectdtype for CSVs with mixed numeric/text columns; cast withpd.to_numeric/astype("category")for big speed/memory wins. df.iterrows()is O(n) and slow; iterate withdf.itertuples()or vectorise column-wise.- Treating Pandas Selecting as a black box without reading the docs — the API has subtle defaults that bite when you scale.
🚀 Performance & Best Practices
- Enable the Arrow backend:
pd.read_csv("…", engine="pyarrow", dtype_backend="pyarrow")for faster, type-stable reads. - Use
categoricaldtype for columns with low-cardinality strings — sort/join/group-by speed up dramatically. - Switching a hot loop from row-wise Python to
df.eval("…")/df.query("…")often gives 5–50×. - When working with pandas, prefer vectorised / batched operations over Python loops.
🧪 Try It Yourself
- Reproduce the snippet on a representative slice of your own data.
- Profile the snippet with
cProfileortimeitand find the single biggest improvement. - Generalise the snippet into a small, reusable function you can drop into future projects.