76 commands · 5 cheat sheets · Python

Pandas Data Analysis master quick reference

Browse 76 commands from 5 focused cheat sheets on page 1 of 1. Each example opens its matching detailed section.

Pandas Data Analysis · 15 commands

Pandas Data Cleaning and Missing Data Cheat Sheet

Use	Syntax	Examples
Count missing values	`missing = df.isna().sum()`	View examples
Select complete rows	`complete = df.loc[df[['id', 'email']].notna().all(axis='columns')]`	View examples
Drop incomplete records	`clean = df.dropna(subset=['customer_id', 'ordered_at'])`	View examples
Fill column defaults	`clean = df.fillna({'country': 'unknown', 'quantity': 0})`	View examples
Forward-fill briefly	`df['status'] = df['status'].ffill(limit=1)`	View examples
Interpolate numeric gaps	`df['reading'] = df['reading'].interpolate(limit_area='inside')`	View examples
Replace sentinels	`df['status'] = df['status'].replace({'N/A': pd.NA, '': pd.NA})`	View examples
Normalize text	`df['email'] = df['email'].str.strip().str.lower()`	View examples
Normalize categories	`df['status'] = df['status'].replace({'in progress': 'active', 'open': 'active'})`	View examples
Parse numeric values	`df['amount'] = pd.to_numeric(df['amount'], errors='coerce')`	View examples
Parse exact dates	`df['ordered_at'] = pd.to_datetime(df['ordered_at'], format='%Y-%m-%d', errors='coerce')`	View examples
Use nullable dtypes	`df = df.convert_dtypes()`	View examples
Find values out of range	`invalid = df.loc[df['quantity'].notna() & ~df['quantity'].between(1, 100)]`	View examples
Find duplicate keys	`duplicates = df.loc[df.duplicated('order_id', keep=False)]`	View examples
Keep the latest record	`latest = df.sort_values('updated_at').drop_duplicates('order_id', keep='last')`	View examples

Pandas Data Analysis · 16 commands

Pandas DataFrames and Indexing Cheat Sheet

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Use	Syntax	Examples
Create a DataFrame	`df = pd.DataFrame({'name': ['Ada', 'Lin'], 'score': [91, 84]})`	View examples
Read CSV data	`df = pd.read_csv('scores.csv', usecols=['name', 'score'])`	View examples
Preview rows	`preview = df.head(3)`	View examples
Inspect column types	`types = df.dtypes`	View examples
Select one column	`scores = df['score']`	View examples
Select by labels	`result = df.loc[df.index[:2], ['name', 'score']]`	View examples
Select by position	`result = df.iloc[:2, [0, 2]]`	View examples
Filter with a mask	`high_scores = df.loc[df['score'].ge(90)]`	View examples
Match allowed values	`selected = df.loc[df['team'].isin({'red', 'blue'})]`	View examples
Query rows	`selected = df.query('score >= @minimum and active')`	View examples
Update matching rows	`df.loc[df['score'].ge(90), 'level'] = 'advanced'`	View examples
Create a derived column	`result = df.assign(percent=lambda frame: frame['score'] / frame['possible'] * 100)`	View examples
Rename columns	`result = df.rename(columns={'score': 'points'})`	View examples
Sort rows	`result = df.sort_values(['team', 'score'], ascending=[True, False])`	View examples
Set an index	`indexed = df.set_index('student_id', verify_integrity=True)`	View examples
Restore a column index	`flat = indexed.reset_index()`	View examples

Pandas Data Analysis · 15 commands

Pandas GroupBy, Aggregation, and Reshaping Cheat Sheet

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Use	Syntax	Examples
Sum by group	`totals = df.groupby('region')['revenue'].sum()`	View examples
Keep group columns	`totals = df.groupby('region', as_index=False)['revenue'].sum()`	View examples
Group by several keys	`totals = df.groupby(['region', 'product'], as_index=False)['revenue'].sum()`	View examples
Name aggregate columns	`summary = df.groupby('region').agg(total=('revenue', 'sum'), average=('revenue', 'mean'))`	View examples
Count group rows	`counts = df.groupby('region').size()`	View examples
Count present values	`counts = df.groupby('region')['revenue'].count()`	View examples
Broadcast group totals	`df['region_total'] = df.groupby('region')['revenue'].transform('sum')`	View examples
Calculate group share	`df['share'] = df['revenue'] / df.groupby('region')['revenue'].transform('sum')`	View examples
Calculate grouped running totals	`df['running'] = df.groupby('account')['amount'].cumsum()`	View examples
Keep qualifying groups	`result = df.groupby('region').filter(lambda group: len(group) >= 2)`	View examples
Pivot unique coordinates	`wide = df.pivot(index='date', columns='metric', values='value')`	View examples
Aggregate a pivot table	`wide = df.pivot_table(index='region', columns='product', values='revenue', aggfunc='sum', fill_value=0)`	View examples
Unpivot wide columns	`long = df.melt(id_vars='id', var_name='metric', value_name='value')`	View examples
Explode list values	`long = df.explode('tags', ignore_index=True)`	View examples
Build a cross-tabulation	`counts = pd.crosstab(df['region'], df['status'], margins=True)`	View examples

Pandas Data Analysis · 14 commands

Pandas Merging, Joining, and Concatenation Cheat Sheet

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Use	Syntax	Examples
Keep matching keys	`result = orders.merge(customers, on='customer_id', how='inner')`	View examples
Preserve left rows	`result = orders.merge(customers, on='customer_id', how='left')`	View examples
Join different key names	`result = orders.merge(customers, left_on='customer_id', right_on='id', how='left')`	View examples
Validate key cardinality	`result = orders.merge(customers, on='customer_id', validate='many_to_one')`	View examples
Track row provenance	`audit = left.merge(right, on='id', how='outer', indicator=True)`	View examples
Label overlapping columns	`result = current.merge(previous, on='id', suffixes=('_current', '_previous'))`	View examples
Stack row batches	`combined = pd.concat([january, february], ignore_index=True)`	View examples
Require identical columns	`combined = pd.concat(frames, join='inner', ignore_index=True)`	View examples
Align columns by index	`combined = pd.concat([features, targets], axis='columns')`	View examples
Preserve batch identity	`combined = pd.concat({'jan': january, 'feb': february}, names=['month'])`	View examples
Join on indexes	`result = customers.join(accounts, how='left', validate='one_to_one')`	View examples
Fill from a fallback	`combined = primary.combine_first(fallback)`	View examples
Match the nearest prior key	`result = pd.merge_asof(events, rates, on='time', direction='backward')`	View examples
Show changed values	`changes = before.compare(after, result_names=('before', 'after'))`	View examples

Pandas Data Analysis · 16 commands

Pandas Time Series, Resampling, and Rolling Cheat Sheet

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Use	Syntax	Examples
Parse exact timestamps	`df['time'] = pd.to_datetime(df['time'], format='%Y-%m-%d %H:%M', errors='raise')`	View examples
Normalize to UTC	`df['time'] = pd.to_datetime(df['time'], utc=True, errors='coerce')`	View examples
Create a regular index	`index = pd.date_range('2026-08-01', periods=24, freq='h', tz='UTC')`	View examples
Set a sorted time index	`series = df.set_index('time').sort_index()`	View examples
Extract time components	`df['weekday'] = df['time'].dt.day_name()`	View examples
Localize wall times	`localized = naive.dt.tz_localize('America/Sao_Paulo', ambiguous='raise', nonexistent='raise')`	View examples
Convert time zones	`local = utc.dt.tz_convert('America/Sao_Paulo')`	View examples
Aggregate daily totals	`daily = series.resample('D').sum(min_count=1)`	View examples
Control interval edges	`daily = series.resample('D', closed='right', label='right').sum()`	View examples
Expose missing intervals	`hourly = series.asfreq('h')`	View examples
Interpolate by time	`hourly = series.resample('h').asfreq().interpolate(method='time', limit_area='inside')`	View examples
Calculate a rolling mean	`result = series.rolling('3h', min_periods=2).mean()`	View examples
Calculate an expanding total	`result = series.expanding(min_periods=1).sum()`	View examples
Calculate an EWM mean	`result = series.ewm(span=3, adjust=False).mean()`	View examples
Create a one-period lag	`df['previous'] = df['value'].shift(1)`	View examples
Calculate fractional change	`df['change'] = df['value'].pct_change(fill_method=None)`	View examples

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