Python Tutorial
Pandas Cleaning Wrong Format
A column should have one type. Convert dates with to_datetime and numbers with to_numeric.
to_datetime
Bad dates become NaT. Drop or fill those rows after conversion.
import pandas as pd
df = pd.DataFrame({"Date": ["2020/12/01", "20201202", "20201226", None]})
df["Date"] = pd.to_datetime(df["Date"], format="mixed")
print(df)
print(df.dropna(subset=["Date"]))to_numeric
errors='coerce' turns junk into NaN instead of raising.
import pandas as pd
df = pd.DataFrame({"n": ["1", "2", "x", "4"]})
df["n"] = pd.to_numeric(df["n"], errors="coerce")
print(df)📘 Real-World Deep Dive
Knowing <strong>Pandas Cleaning Format (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 Cleaning Format that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
df = pd.DataFrame({"name": ["ADA lovelace", "bo OLE", "carol_DEN"]})
df["name"] = df["name"].str.strip().str.title()
print(df)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 Cleaning Format 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.