Pandas DataFrames

Definition: A DataFrame is a 2D table of rows and columns. Each column is a Series, and all columns share the same row index. This is the object you will use most in Pandas.

Example — build and inspect

import pandas as pd
df = pd.DataFrame({
  "name": ["Sam", "Alex", "Jo"],
  "city": ["London", "Paris", "Rome"],
  "age":  [25, 30, 22]
})
print(df)
print("Shape:", df.shape)          # (3 rows, 3 cols)
print("Columns:", df.columns.tolist())

Useful attributes

• df.shape — (rows, columns)
• df.columns — the column names
• df.dtypes — the type of each column
• df.index — the row labels

Each column is a Series

import pandas as pd
df = pd.DataFrame({"name": ["Sam", "Alex"], "age": [25, 30]})
print(type(df["age"]))   # a Series

💡 Tip: a DataFrame is just a collection of columns (Series) that line up by row — keep that picture in mind.