Skip to main content
Practice

Creating and Inspecting Series and DataFrames

Now that you've been introduced to Pandas, it's time to start building with it.

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

These objects let you store, label, and organize data efficiently, and Pandas makes it straightforward.

Series​

A Series is a one-dimensional array with labels. It's like 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 table with rows and columns. It's 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.

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.