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Data Science

May 2026×Notebook lesson

Notebook converted from Jupyter for blog publishing.

00-Series

Driptanil DattaSoftware Developer

Series

The first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.

A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.

Let's explore this concept through some examples:

Imports

import numpy as np
import pandas as pd

Creating a Series from Python Objects

help(pd.Series)

STDOUT

Help on class Series in module pandas.core.series:

class Series(pandas.core.base.IndexOpsMixin, pandas.core.generic.NDFrame)
 |  One-dimensional ndarray with axis labels (including time 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)

myser

RESULT

0    1776
1    1867
2    1821
dtype: int64

pd.Series(data=mydata,index=myindex)

RESULT

USA       1776
Canada    1867
Mexico    1821
dtype: int64

ran_data = np.random.randint(0,100,4)

ran_data

RESULT

array([39, 35, 37, 23])

names = ['Andrew','Bobo','Claire','David']

ages = pd.Series(ran_data,names)

ages

RESULT

Andrew    39
Bobo      35
Claire    37
David     23
dtype: int32

From a Dictionary

ages = {'Sammy':5,'Frank':10,'Spike':7}

ages

RESULT

{'Frank': 10, 'Sammy': 5, 'Spike': 7}

pd.Series(ages)

RESULT

Sammy     5
Frank    10
Spike     7
dtype: int64

Key Ideas of a Series

Named Index

# Imaginary Sales Data for 1st and 2nd Quarters for Global Company
q1 = {'Japan': 80, 'China': 450, 'India': 200, 'USA': 250}
q2 = {'Brazil': 100,'China': 500, 'India': 210,'USA': 260}

# Convert into Pandas Series
sales_Q1 = pd.Series(q1)
sales_Q2 = pd.Series(q2)

sales_Q1

RESULT

Japan     80
China    450
India    200
USA      250
dtype: int64

# Call values based on Named Index
sales_Q1['Japan']

RESULT

# Integer Based Location information also retained!
sales_Q1[0]

RESULT

Be careful with potential errors!

# Wrong Name
# sales_Q1['France']

# Accidental Extra Space
# sales_Q1['USA ']

# Capitalization Mistake
# sales_Q1['usa']

Operations

# Grab just the index keys
sales_Q1.keys()

RESULT

Index(['Japan', 'China', 'India', 'USA'], dtype='object')

# Can Perform Operations Broadcasted across entire Series
sales_Q1 * 2

RESULT

Japan    160
China    900
India    400
USA      500
dtype: int64

sales_Q2 / 100

RESULT

Brazil    1.0
China     5.0
India     2.1
USA       2.6
dtype: float64

Between Series

# Notice how Pandas informs you of mismatch with NaN
sales_Q1 + sales_Q2

RESULT

Brazil      NaN
China     950.0
India     410.0
Japan       NaN
USA       510.0

# You can fill these with any value you want
sales_Q1.add(sales_Q2,fill_value=0)

RESULT

Brazil    100.0
China     950.0
India     410.0
Japan      80.0
USA       510.0

That is all we need to know about Series, up next, DataFrames!

Numpy 01 Dataframes