Series as labeled one-dimensional array in Pandas

Introduction

A Series is like a list with labels for each item. It helps you keep track of data with names, not just positions.

You want to store a list of numbers or words with names for each item.

You need to do math or analysis on data that has labels.

You want to select data by name instead of by position.

You want to combine data from different sources using labels.

You want to quickly summarize or describe data with labels.

Syntax

Pandas

import pandas as pd

# Create a Series with data and optional labels (index)
series = pd.Series(data, index=labels)

# data: list, array, or scalar values
# labels: list of labels for each data item (optional)

If you do not provide labels, pandas will use numbers starting from 0.

Labels can be strings, numbers, or other types, but they must be unique.

Examples

This creates a Series with values 10, 20, 30 and labels 0, 1, 2 automatically.

Pandas

import pandas as pd

# Example 1: Series with default numeric labels
series1 = pd.Series([10, 20, 30])
print(series1)

This creates a Series with values 5, 15, 25 and labels 'a', 'b', 'c'.

Pandas

import pandas as pd

# Example 2: Series with custom string labels
series2 = pd.Series([5, 15, 25], index=['a', 'b', 'c'])
print(series2)

This creates an empty Series with no data and no labels.

Pandas

import pandas as pd

# Example 3: Empty Series
series3 = pd.Series([], dtype=float)
print(series3)

This creates a Series with one value 100 and label 'only'.

Pandas

import pandas as pd

# Example 4: Series with one element and label
series4 = pd.Series([100], index=['only'])
print(series4)

Sample Program

This program creates a Series with fruit names as labels and quantities as data. It shows how to access, add, and remove items by label.

Pandas

import pandas as pd

# Create a Series with data and labels
fruits = ['apple', 'banana', 'cherry']
quantities = [10, 20, 15]

# Create Series with quantities and fruit names as labels
fruit_series = pd.Series(quantities, index=fruits)

print('Original Series:')
print(fruit_series)

# Access data by label
print('\nQuantity of bananas:')
print(fruit_series['banana'])

# Add a new fruit
fruit_series['orange'] = 12
print('\nSeries after adding orange:')
print(fruit_series)

# Remove a fruit
fruit_series = fruit_series.drop('apple')
print('\nSeries after removing apple:')
print(fruit_series)

Important Notes

Time complexity for accessing or adding items by label is usually O(1) because Series uses a hash map internally.

Space complexity depends on the number of items stored in the Series.

A common mistake is confusing position-based access (like lists) with label-based access. Use series.iloc[] for position and series.loc[] for labels.

Use Series when you want labeled data that behaves like a one-dimensional array. Use DataFrame for two-dimensional labeled data.

Summary

A Series is a one-dimensional labeled array in pandas.

It stores data with labels (index) to access items by name.

Series supports easy data selection, addition, and removal by labels.