Lesson 2 · pandas fundamentals

DataFrames and basic analysis

Creating a DataFrame

A DataFrame is a table: rows and named columns.

import pandas as pd

data = {
    "Month": ["January", "February", "March", "April"],
    "Sales": [15000, 18000, 22000, 19000],
    "Region": ["North", "South", "East", "West"],
}

df = pd.DataFrame(data)
print(df)

      Month  Sales Region
0   January  15000  North
1  February  18000  South
2     March  22000   East
3     April  19000   West

The sales figures are example data.

Basic analysis

print(f"Average sales: ${df['Sales'].mean():,.0f}")
print(f"Total sales: ${df['Sales'].sum():,.0f}")
print(f"Highest month: {df.loc[df['Sales'].idxmax(), 'Month']}")

Average sales: $18,500
Total sales: $74,000
Highest month: March

Key ideas

• pd.DataFrame(...) builds a table from a dictionary of columns.
• Column methods such as .mean(), .sum() and .idxmax() summarise the data.
• df.loc[row, column] reads a single value.