Python Tutorial
Pandas Removing Duplicates
duplicated() marks extra copies of a row. drop_duplicates() removes them.
Find and Drop
keep='first' is the default. subset limits which columns define a duplicate.
import pandas as pd
df = pd.DataFrame({"name": ["Luna", "Kai", "Luna"], "score": [88, 90, 88]})
print(df.duplicated())
print(df.drop_duplicates())
print(df.drop_duplicates(subset=["name"], keep="last"))📘 Real-World Deep Dive
Knowing <strong>Pandas Cleaning Duplicates (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 Duplicates that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import pandas as pd
df = pd.DataFrame({"id": [1, 2, 2, 3], "val": [10, 20, 20, 30]})
print("duplicated:", df.duplicated().sum())
df = df.drop_duplicates()
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 Duplicates 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.