Panda Introduction

Table of Contents: Pandas Series

1. Introduction to Series
   └── 1.1 What is a Series?
   └── 1.2 Key Features of Series

2. Creating a Series
   ├── 2.1 From Python List
   ├── 2.2 With Custom Index
   ├── 2.3 From Dictionary
   ├── 2.4 From Scalar Value

3. Accessing Elements
   ├── 3.1 By Position
   ├── 3.2 By Label
   ├── 3.3 Slicing (By Position and Label)

4. Operations on Series
   ├── 4.1 Vectorized Operations
   ├── 4.2 Conditional Filtering
   ├── 4.3 Mathematical Functions

5. Attributes of a Series
   ├── 5.1 `.index`
   ├── 5.2 `.values`
   ├── 5.3 `.dtype`

6. Handling Missing Data
   ├── 6.1 Checking for Missing (`isnull()`)
   ├── 6.2 Filling Missing (`fillna()`)

7. Series vs Dictionary
   ├── 7.1 Key Lookup
   ├── 7.2 Safe Retrieval using `.get()`

8. Use Cases of Series
   ├── 8.1 Time Series Data
   ├── 8.2 Sensor Readings
   ├── 8.3 Mapping Tables
   ├── 8.4 Intermediate Processing
   ├── 8.5 Enhanced NumPy Arrays
   ├── 8.6 Data Preprocessing

9. Advanced Examples
   ├── 9.1 Series with Datetime Index
   ├── 9.2 Applying Custom Functions
   ├── 9.3 Sorting Series
   ├── 9.4 Combining Series

10. Summary
   └── 10.1 Key Takeaways of Pandas Series

A Series in Pandas is a one-dimensional labeled array that can hold any data type: integers, strings, floats, Python objects, etc. It’s similar to a column in an Excel sheet or SQL table.

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🔹 Core Features of Series

• Homogeneous data
• Axis labels (index)
• Size immutable
• Value mutable
• Built on NumPy arrays

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✅ 1. Creating a Series

A. From a Python List

import pandas as pd

data = [10, 20, 30, 40]
s = pd.Series(data)
print(s)

Output:

0    10
1    20
2    30
3    40
dtype: int64

Default index is 0,1,2,…

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B. With Custom Index

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(s)

a    10
b    20
c    30
dtype: int64

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C. From Dictionary

data = {'a': 100, 'b': 200, 'c': 300}
s = pd.Series(data)
print(s)

a    100
b    200
c    300
dtype: int64

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D. From Scalar Value

s = pd.Series(5, index=['a', 'b', 'c'])
print(s)

a    5
b    5
c    5
dtype: int64

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✅ 2. Accessing Elements in Series

A. By Position (like lists)

print(s[0])   # Access first element

B. By Label (like dicts)

print(s['a'])  # Access by index label

C. Slicing

print(s[0:2])      # By position
print(s['a':'b'])  # By label

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✅ 3. Operations on Series

A. Vectorized Operations

s = pd.Series([10, 20, 30])
print(s * 2)

0    20
1    40
2    60
dtype: int64

B. Conditional Filtering

print(s[s > 15])

1    20
2    30
dtype: int64

C. Mathematical Functions

print(s.mean())
print(s.sum())
print(s.max())

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✅ 4. Attributes of Series

s = pd.Series([1, 2, 3], index=['x', 'y', 'z'])
print(s.index)     # Index(['x', 'y', 'z'])
print(s.values)    # array([1, 2, 3])
print(s.dtype)     # int64

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✅ 5. Series with Missing Data

s = pd.Series([1, None, 3])
print(s)

print(s.isnull())   # Check missing
print(s.fillna(0))  # Fill missing

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✅ 6. Series vs Dictionary

data = {'a': 1, 'b': 2}
s = pd.Series(data)

print('a' in s)   # True
print(s.get('c', 'Not Found'))  # Not Found

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✅ 7. Use Cases of Series

Use Case	Description
1. Time Series Data	Stock prices, temperature over time
2. Sensor Readings	IoT devices readings over time
3. Mapping Tables	Map IDs to values (like country code to names)
4. Intermediate Processing	When working on a single column of a DataFrame
5. Indexing/Numpy Enhancement	More functionality than NumPy 1D array
6. Data Preprocessing	Filling missing values, transformations

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✅ 8. Advanced Series Examples

A. Creating a Series with DateTime Index

import pandas as pd

dates = pd.date_range('2023-01-01', periods=5)
values = [1, 2, 3, 4, 5]
s = pd.Series(values, index=dates)
print(s)

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B. Applying Custom Function

s = pd.Series([1, 2, 3, 4])
print(s.apply(lambda x: x ** 2))  # Square each element

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C. Sorting

s = pd.Series([10, 5, 8], index=['a', 'b', 'c'])

print(s.sort_values())  # Sort by value
print(s.sort_index())   # Sort by index

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D. Combining Series

s1 = pd.Series([1, 2], index=['a', 'b'])
s2 = pd.Series([3, 4], index=['b', 'c'])

print(s1 + s2)

a    NaN
b    5.0
c    NaN
dtype: float64

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🧠 Summary

Feature	Series Description
One-dimensional	Single column of data
Supports indexing	Label-based access
NumPy-compatible	Works with vectorized operations
Missing values	Supports NaN
Ideal for time data	Supports DatetimeIndex

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If you’d like a visual diagram of Series vs DataFrame, or want this in a Notion or PDF format, let me know!