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Python Academy · Lesson

Series and DataFrame Fundamentals

Create and inspect Pandas data structures from various sources.

What Is Pandas?

Pandas provides Series (1-D labelled array) and DataFrame (2-D labelled table) built on NumPy, optimised for data analysis.

import pandas as pd

s = pd.Series([10, 20, 30], index=["a","b","c"])
print(s)
# a    10
# b    20
# c    30

Creating a Series

Create a Series from a list, dict, or scalar. The index defaults to 0-based integers.

import pandas as pd

# From list:
s1 = pd.Series([1, 2, 3])

# From dict (keys become index):
s2 = pd.Series({"x": 10, "y": 20})

# Scalar (broadcast):
s3 = pd.Series(5, index=range(4))
print(s3)   # 0 5 / 1 5 / 2 5 / 3 5

All lessons in this course

  1. Series and DataFrame Fundamentals
  2. Indexing, Filtering, and Boolean Masks
  3. GroupBy, Aggregation, and Pivot Tables
  4. Merging, Joining, and Data Cleaning
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