Data Wrangling with Pandas Cheat Sheet
Core pandas workflows for reading, cleaning, filtering, handling missing values, and merging datasets during everyday data preparation tasks.
2 PagesBeginnerMar 8, 2026
Reading & Inspecting Data
Load a dataset and get an overview before cleaning it.
python
import pandas as pddf = pd.read_csv("data.csv")df.head()df.info()df.describe()df.dtypesdf.shape# Rename and drop columnsdf = df.rename(columns={"old_name": "new_name"})df = df.drop(columns=["unused_col"])# Filter rowsdf_filtered = df[df["amount"] > 100]df_filtered = df.query("amount > 100 and region == 'US'")
Handling Missing & Duplicate Data
Detect, fill, or drop nulls and remove duplicate rows.
python
df.isna().sum() # count nulls per columndf.dropna(subset=["customer_id"]) # drop rows missing a key fielddf["amount"] = df["amount"].fillna(0)df["category"] = df["category"].fillna("Unknown")df["amount"] = df["amount"].fillna(df["amount"].median())df = df.drop_duplicates(subset=["order_id"], keep="first")# Type conversiondf["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
Merging & Transforming
Combine tables and derive new columns.
python
merged = pd.merge(orders, customers, on="customer_id", how="left")combined = pd.concat([df_2023, df_2024], axis=0, ignore_index=True)# apply / map for row-wise transformationsdf["amount_category"] = df["amount"].apply(lambda x: "high" if x > 1000 else "low")df["region_code"] = df["region"].map({"US": 1, "EU": 2, "APAC": 3})
Key Concepts
Ideas that come up in almost every wrangling task.
- df.info()- Shows column dtypes, non-null counts, and memory usage at a glance
- NaN vs None- Pandas represents missing numeric data as NaN (float); use isna()/notna() to test, not ==
- how='left'/'inner'/'outer'- Controls which rows survive a merge based on matches in the join key
- apply vs vectorized ops- Vectorized operations (df['a'] + df['b']) are much faster than .apply() with a Python function
- loc vs iloc- .loc selects by label, .iloc selects by integer position
Pro Tip
Avoid looping over rows with iterrows() for transformations - it's orders of magnitude slower than a vectorized operation or .apply() on a Series for anything beyond a few thousand rows.
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