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Practice

Creating and Inspecting Series and DataFrames

Now that you’ve been introduced to Pandas, it’s time to start creating real data structures.

In this lesson, you’ll learn how to build Series and DataFrames from Python lists and dictionaries.

These objects allow you to store, label, and organize data efficiently — and Pandas makes the process simple and intuitive.

Series​

A Series is a one-dimensional labeled array — similar to a single column in a spreadsheet.

Below is an example of creating a Series from a list.

Build a Series and a DataFrame
import pandas as pd

# Create a Series of daily step counts
steps = pd.Series([8000, 9200, 10200], name="Steps")

print("Steps Series:", steps)

Series are useful for storing and manipulating single columns of data.


DataFrame​

A DataFrame is a two-dimensional labeled table with rows and columns — much like a spreadsheet.

Below is an example of creating a DataFrame from a dictionary.

Build a Series and a DataFrame
import pandas as pd

# Create a Series of daily step counts
steps = pd.Series([8000, 9200, 10200], name="Steps")

# Create a DataFrame of sales records
sales = pd.DataFrame({
    "Product": ["Book", "Pen", "Notebook"],
    "Price": [12.99, 1.50, 4.75]
})

print("Steps Series:", steps)
print("Sales DataFrame:", sales)

DataFrames are useful for storing and manipulating multiple columns of data.

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