Python Tutorial
Pandas Analyzing Data
Inspect shape, types, missing values, and summary statistics before you clean or model.
Preview
import pandas as pd
df = pd.DataFrame({
"name": ["Luna", "Kai", "Mia", "Jon"],
"score": [88, 92, 95, 70],
"city": ["Oslo", "Bergen", "Oslo", None],
})
print(df.head(2))
print(df.tail(2))
print(df.shape) # (4, 3)info and describe
print(df.info())
print(df.describe())
print(df["city"].value_counts(dropna=False))Correlations
print(df.corr(numeric_only=True))📘 Real-World Deep Dive
Knowing <strong>Pandas Analyzing (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 Analyzing that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
df = pd.read_csv("orders.csv")
print(df.describe())
print("by-region totals:", df.groupby("region")["amount"].sum())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 Analyzing 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.