Summary
This chapter introduces how to use the pandas library to create and manipulate DataFrame objects, including viewing data with head and describe, selecting columns by label, and filtering rows based on conditions.

General domain of usage
Business data analysis

When you need to make sense of business data, the **pandas** library in Python is one of your most valuable tools. Pandas makes it easy to organize, explore, and analyze **tabular data**—like sales records, customer lists, or inventory tables—using a familiar spreadsheet-like structure. With pandas, you can quickly spot trends, summarize key numbers, and prepare your data for deeper analysis, all using straightforward Python code.

import pandas as pd

# Create a DataFrame with sales data
data = {
    "product": ["Widget", "Gadget", "Doohickey", "Widget", "Gadget"],
    "units_sold": [10, 15, 7, 12, 9],
    "revenue": [200, 450, 140, 240, 270]
}

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

A **pandas DataFrame** is a two-dimensional table with labeled columns (like `"product"`, `"units_sold"`, and `"revenue"`) and rows, similar to a spreadsheet. Each column can hold different types of data, such as numbers or strings. You can use `head()` to view the first few rows of your data, or `describe()` to get quick summary statistics for each numeric column. Selecting a column is as simple as using its label in square brackets, for example `df["revenue"]` to see all revenue values.

# Filter for products with revenue greater than 200
high_revenue = df[df["revenue"] > 200]
print(high_revenue)

How can you select a specific column from a DataFrame?

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Exploring Data with Pandas

1. What is a pandas DataFrame?

2. How can you select a specific column from a DataFrame?

3. Why is pandas useful for startup founders?