Series in Python with examples | Lecture 2

 

Here’s a detailed explanation of Series in Python with examples:

What is a Series?

  • A Series is a one-dimensional labeled array in Python provided by the pandas library.
  • It can hold data of any type (integers, floats, strings, Python objects, etc.).
  • It is similar to a column in a table or a one-dimensional array but with labels (indexes).

Key Characteristics

  1. Homogeneous: All elements in a Series are of the same data type.
  2. Indexing: Each element in the Series has a unique label (default: integers starting from 0).
  3. Mutability: Series is mutable; you can change its values.

How to Create a Series?

  1. From a List

2.  import pandas as pd

3.   

4.  data = [10, 20, 30, 40, 50]

5.  series = pd.Series(data)

6.  print(series)

Output:

0    10

1    20

2    30

3    40

4    50

dtype: int64

  1. With Custom Index

8.  data = [100, 200, 300]

9.  index = ['A', 'B', 'C']

10.series = pd.Series(data, index=index)

11.print(series)

Output:

A    100

B    200

C    300

dtype: int64

  1. From a Dictionary

13.data = {'x': 1, 'y': 2, 'z': 3}

14.series = pd.Series(data)

15.print(series)

Output:

x    1

y    2

z    3

dtype: int64

  1. From a Scalar Value

17.series = pd.Series(5, index=['a', 'b', 'c'])

18.print(series)

Output:

a    5

b    5

c    5

dtype: int64

Accessing Elements in a Series

  1. By Index Position

2.  data = [10, 20, 30, 40]

3.  series = pd.Series(data)

4.  print(series[1])  # Output: 20

  1. By Index Label

6.  data = {'a': 10, 'b': 20, 'c': 30}

7.  series = pd.Series(data)

8.  print(series['b'])  # Output: 20

  1. Slicing

10.print(series[0:2])  # Output: first two elements

Operations on Series

  1. Arithmetic Operations

2.  s1 = pd.Series([1, 2, 3])

3.  s2 = pd.Series([4, 5, 6])

4.  print(s1 + s2)

Output:

0    5

1    7

2    9

dtype: int64

  1. Applying Functions

6.  series = pd.Series([1, 2, 3, 4])

7.  print(series.apply(lambda x: x**2))

Output:

0     1

1     4

2     9

3    16

dtype: int64

  1. Filtering

9.  series = pd.Series([10, 20, 30, 40])

10.print(series[series > 25])

Output:

2    30

3    40

dtype: int64

Key Methods

Method

Description

Example

head(n)

First n elements

series.head(2)

tail(n)

Last n elements

series.tail(2)

mean()

Calculate mean

series.mean()

sum()

Sum of elements

series.sum()

unique()

Unique values

series.unique()

value_counts()

Frequency of unique values

series.value_counts()

isnull()/notnull()

Check for missing values

series.isnull()

sort_values()

Sort values

series.sort_values()

Practical Example

Marks Analysis

data = {'Math': 90, 'Science': 85, 'English': 78}

marks = pd.Series(data)

 

print("Total Marks:", marks.sum())

print("Average Marks:", marks.mean())

print("Subjects with Marks > 80:")

print(marks[marks > 80])

Output:

Total Marks: 253

Average Marks: 84.33333333333333

Subjects with Marks > 80:

Math       90

Science    85

dtype: int64

This gives you a comprehensive overview of Series in Python with examples. Let me know if you'd like more clarification!

 

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