Pandas: Intro to Series

This notebook covers the Pandas Series object, which is a one-dimensional labeled array.

Series is pandas’ one-dimensional labeled array. This notebook covers creating, accessing, and operating on Series.

You will learn how to: - Create a Pandas Series - Access elements and slices - Perform arithmetic operations - View Series properties and methods - Sort and describe Series data

Importing Libraries

Import pandas and visualization libraries.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Creating a Series

Create a Series from a list with custom index labels.

my_series = pd.Series([10,20,30,40,50], index=['A', 'B', 'C', 'D', 'E'])
my_series

A    10
B    20
C    30
D    40
E    50
dtype: int64

type(my_series)

pandas.core.series.Series

Accessing Elements

Access elements by label or slice ranges.

my_series['C':'E']

C    30
D    40
E    50
dtype: int64

Arithmetic Operations

Perform element-wise operations on Series.

my_series + 15

A    25
B    35
C    45
D    55
E    65
dtype: int64

my_series * 25

A     250
B     500
C     750
D    1000
E    1250
dtype: int64

Series Properties

Check data type, size, and shape of the Series.

my_series.dtype

dtype('int64')

my_series.size

5

my_series.shape

(5,)

Series Methods

Use methods like head(), tail(), describe(), and sort_values() for data exploration.

my_series.head(3)

A    10
B    20
C    30
dtype: int64

my_series.tail(2)

D    40
E    50
dtype: int64

my_series.describe()

count     5.000000
mean     30.000000
std      15.811388
min      10.000000
25%      20.000000
50%      30.000000
75%      40.000000
max      50.000000
dtype: float64

my_series.sort_values(ascending=False)

E    50
D    40
C    30
B    20
A    10
dtype: int64

Best Practices

• Use meaningful index labels for clarity.
• Series operations are vectorized for performance.
• Check for NaN values with isna().

Summary

This notebook introduced pandas Series: creation, access, operations, properties, and methods. Series are building blocks for DataFrames!