Lesson 2: Creating and Inspecting Series

Course: Data Engineering | Duration: 40 minutes | Level: Intermediate

Learning Objectives

By the end of this lesson, you will be able to:

• Create a pandas Series from a list, dict, scalar, and NumPy array
• Set a custom index on a Series
• Access Series attributes: .values, .index, .dtype, .name, .size, .shape
• Perform vectorized arithmetic on a Series

Prerequisites

• Lesson 1: Introduction to Series and DataFrame

Lesson Outline

Part 1: Creating a Series — Four Methods

From a list (most common)

import pandas as pd
 
# Default integer index: 0, 1, 2, ...
scores = pd.Series([88, 92, 75, 95, 83])
print(scores)
# 0    88
# 1    92
# 2    75
# 3    95
# 4    83
# dtype: int64

From a dict (keys become the index)

# Dict keys become the index automatically
prices = pd.Series({'apple': 1.20, 'banana': 2.50, 'carrot': 0.89})
print(prices)
# apple     1.20
# banana    2.50
# carrot    0.89
# dtype: float64

From a scalar (broadcast to all index positions)

# Scalar fills all positions — must provide index explicitly
default_score = pd.Series(0, index=['Alice', 'Bob', 'Carol', 'Diana'])
print(default_score)
# Alice    0
# Bob      0
# Carol    0
# Diana    0
# dtype: int64

From a NumPy array

import numpy as np
 
# NumPy array as input — same as list creation
arr = np.array([1.5, 2.5, 3.5])
s = pd.Series(arr, name='measurements')
print(s)
# 0    1.5
# 1    2.5
# 2    3.5
# Name: measurements, dtype: float64

Part 2: The Index — Default vs Custom

Every Series has an index. If you don't provide one, pandas assigns a RangeIndex (0, 1, 2, ...). Custom indexes make your data self-documenting.

import pandas as pd
 
# Default integer index
s1 = pd.Series([10, 20, 30])
print(s1.index)  # RangeIndex(start=0, stop=3, step=1)
 
# Custom string index
s2 = pd.Series([10, 20, 30], index=['jan', 'feb', 'mar'])
print(s2.index)  # Index(['jan', 'feb', 'mar'], dtype='object')
 
# Access by label with custom index
print(s2['feb'])  # 20

Part 3: Series Attributes

After creating a Series, these attributes answer "what do I have?":

import pandas as pd
 
products = pd.Series(
    [1200.0, 25.0, 75.0, 350.0],
    index=['Laptop', 'Mouse', 'Keyboard', 'Monitor'],
    name='price_usd'
)
 
print(products.values)   # array([1200. ,   25. ,   75. ,  350. ])  — NumPy array
print(products.index)    # Index(['Laptop', 'Mouse', 'Keyboard', 'Monitor'], dtype='object')
print(products.dtype)    # float64
print(products.name)     # price_usd
print(products.size)     # 4  — total number of elements
print(products.shape)    # (4,)  — tuple, like NumPy

Part 4: Vectorized Operations

One of pandas' most powerful features: arithmetic on a Series applies to every element at once, with no loop required.

import pandas as pd
 
prices = pd.Series(
    {'apple': 1.20, 'banana': 2.50, 'carrot': 0.89},
    name='price_usd'
)
 
# Apply 10% tax to every price — no loop needed
with_tax = prices * 1.10
print(with_tax)
# apple     1.320
# banana    2.750
# carrot    0.979
# Name: price_usd, dtype: float64
 
# Arithmetic between two Series aligns by index
discount = pd.Series({'apple': 0.10, 'banana': 0.20, 'carrot': 0.05})
final_prices = prices - (prices * discount)
print(final_prices)
# apple     1.080
# banana    2.000
# carrot    0.8455
# dtype: float64

Practice

<PracticeBlock prompt="Create a Series of 5 product prices with product names as the index. Then access the price of 'keyboard' by its label name." initialCode={import pandas as pd\n\n# Create a Series with product names as the index\n# Products: laptop=1200.0, mouse=25.0, keyboard=75.0, monitor=350.0, webcam=89.0\nprices = pd.Series(\n # your values here\n # your index here\n)\n\n# Access the price of 'keyboard' by label\n} hint="Use pd.Series([1200.0, 25.0, 75.0, 350.0, 89.0], index=['laptop', 'mouse', 'keyboard', 'monitor', 'webcam']). Then access with prices['keyboard']." solution={import pandas as pd\n\n# Create a Series with product names as the index\nprices = pd.Series(\n [1200.0, 25.0, 75.0, 350.0, 89.0],\n index=['laptop', 'mouse', 'keyboard', 'monitor', 'webcam'],\n name='price_usd'\n)\n\nprint(prices)\nprint()\n# Access the price of 'keyboard' by label\nprint("Keyboard price:", prices['keyboard'])} />

<PracticeBlock prompt="Add a 10% tax to every price in your product prices Series using a single arithmetic operation. Print both the original and the tax-included prices." initialCode={import pandas as pd\n\nprices = pd.Series(\n [1200.0, 25.0, 75.0, 350.0, 89.0],\n index=['laptop', 'mouse', 'keyboard', 'monitor', 'webcam'],\n name='price_usd'\n)\n\n# Add 10% tax to every price with a single operation\nprices_with_tax = \n\nprint("Original prices:")\nprint(prices)\nprint()\nprint("Prices with 10% tax:")\nprint(prices_with_tax)\n} hint="Multiply the entire Series by 1.10 — pandas applies the multiplication to every element automatically." solution={import pandas as pd\n\nprices = pd.Series(\n [1200.0, 25.0, 75.0, 350.0, 89.0],\n index=['laptop', 'mouse', 'keyboard', 'monitor', 'webcam'],\n name='price_usd'\n)\n\n# Add 10% tax to every price with a single operation\nprices_with_tax = prices * 1.10\n\nprint("Original prices:")\nprint(prices)\nprint()\nprint("Prices with 10% tax:")\nprint(prices_with_tax)} />

Key Takeaways

• Create a Series from: a list (with optional index), a dict (keys become index), a scalar (fills all positions), or a NumPy array
• Use a custom index= parameter for human-readable label access
• Core attributes: .values (NumPy array), .index, .dtype, .name, .size, .shape
• Arithmetic on a Series is vectorized — no loops needed; operations apply element-wise
• When doing arithmetic between two Series, pandas aligns by index label, not position

Common Mistakes to Avoid

• Forgetting the index= keyword: pd.Series([1, 2, 3], ['a', 'b', 'c']) raises a TypeError — use pd.Series([1, 2, 3], index=['a', 'b', 'c'])
• Mismatched index sizes: if you provide 5 values but 4 index labels, pandas raises a ValueError
• Confusing .size and .shape: .size is an integer; .shape is a tuple (n,)

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