Printable Quick Reference

Data Analysis Cheat Sheets

High-density, printable reference guides for Python (Pandas) and SQL data manipulation. 1-click copy syntax snippets.

1. Loading & Inspecting Data

pd.read_csv("file.csv")

Load CSV into DataFrame

pd.read_excel("file.xlsx")

Load Excel workbook

df.head(n)

View first n rows (default 5)

df.shape

Tuple of (rows, columns)

df.info() / df.dtypes

Column memory & data types

df.describe()

Summary statistics for numeric columns

df[df["col"] > 100]

Filter rows by boolean condition

df[(cond1) & (cond2)]

Logical AND filter across columns

df[df["cat"].isin(["A", "B"])]

Filter rows in list of values

df.loc[rows, cols]

Label-based selection

df.iloc[0:10, 0:3]

Integer index position slicing

df.groupby("col")["metric"].sum()

Group by category & sum

df.groupby("col").agg({"m1": "sum", "m2": "mean"})

Multi-metric custom aggregations

df.sort_values(by="col", ascending=False)

Sort DataFrame descending

df.nlargest(5, "col")

Top 5 highest values

df.isna().sum()

Count missing NaN values per column

df.fillna(value)

Impute missing values

df.dropna()

Drop rows with any missing fields

df.drop_duplicates()

Remove duplicate rows

df["col"] = df["col"].str.strip()

Trim whitespace strings