Python Tutorial
Pandas Series
A Series is a one-dimensional labeled array. The labels are the index.
Create a Series
import pandas as pd
a = [1, 7, 2]
s = pd.Series(a)
print(s)
print(s[0]) # 1Custom Index
s = pd.Series([1, 7, 2], index=["x", "y", "z"])
print(s["y"]) # 7From a Dictionary
calories = {"day1": 420, "day2": 380, "day3": 390}
s = pd.Series(calories)
print(s)
print(pd.Series(calories, index=["day1", "day2"]))📘 Real-World Deep Dive
Knowing <strong>Pandas Series (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 Series that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
s = pd.Series([10, 20, 30], index=["a", "b", "c"], name="score")
print(s)
print(s.describe())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 Series 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.