Series

Index and Data Lists:

We can create a Series from Python lists (also from NumPy arrays)

myindex = ['USA','Canada','Mexico']
mydata = [1776,1867,1821]
myser = pd.Series(data=mydata) # it can give default numeric index
myser1 = pd.Series(data=mydata,index=myindex) # Access values by label instead of just position
myser1[0] 
myser1['USA'] 

From NumPy Arrays

ran_data = np.random.randint(0,100,4)
names = ['Andrew','Bobo','Claire','David']
ages = pd.Series(ran_data, names)

From Dictionaries

ages = {'Sammy':5, 'Frank':10, 'Spike':7}
age_series = pd.Series(ages)

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Key Features

Named Index

sales_Q1 = pd.Series({'Japan': 80, 'China': 450, 'India': 200, 'USA': 250})

Accessing Values

# By label
sales_Q1['Japan']  # Returns 80
 
# By position
sales_Q1[0]  # Returns 80

Important Attributes

sales_Q1.keys()  # Returns index labels
sales_Q1.values  # Returns array of values

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Operations

Scalar Operations

sales_Q1 * 2  # Multiplies all values by 2
sales_Q2 / 100  # Divides all values by 100

Series-to-Series Operations

# Automatic alignment by index
sales_Q1 + sales_Q2  # NaN for mismatched indices
 
# Handling missing values
sales_Q1.add(sales_Q2, fill_value=0)

Once we did the Operations, the datatype will transform into float.

🚨 Caution

• Case-sensitive: sales_Q1['usa'] ≠ sales_Q1['USA']
• Exact matches required: sales_Q1['USA '] (with space) will error
• Non-existent keys throw KeyError