DataFrame basics

A DataFrame is a 2D labeled table — like a spreadsheet, like a SQL result set. It has row labels (the index) and column labels (the columns), and each column has a single dtype.

Three ways to construct one

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In a real job you’ll almost always use option 3 (read from a file). But options 1 and 2 are perfect for tests and small examples.

Inspect a DataFrame in 10 seconds

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.head(), .shape, .dtypes, .describe() and .value_counts() are the five things you’ll run within seconds of loading any new dataset. Burn them into muscle memory.

Selecting columns

df["age"]            # one column → returns a Series
df[["age", "city"]]  # multiple columns → returns a DataFrame

A Series is a 1D labeled array — basically a single column. A DataFrame is a collection of Series sharing an index.

Selecting rows — .loc vs .iloc

This is the most common source of confusion for newcomers, and the rule is actually simple:

• .loc uses labels (the index values, column names).
• .iloc uses integer positions (0-based).

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Boolean filtering — the workhorse

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The parentheses around each comparison are required because of Python’s operator precedence. Forget them and you’ll get a confusing error.

Creating, modifying, dropping columns

df["bonus"] = df["salary"] * 0.10        # new column
df["age"] = df["age"] + 1                 # modify in place
df = df.drop(columns=["bonus"])           # drop returns a new DataFrame
df = df.rename(columns={"city": "loc"})   # rename

Quick check