python basic operation variable, operator, comparison numpy mini sample code inclide in thgis ppt

1. DATA ANALYTICS FOR PYTHON
DAY-2

2. Operators
 Arithmetic
Comparison
 Logical
 Assignment

3. Arithmetic Operators
Common operators:
addition ‘+’
Subtraction ‘-’
Multiplication ‘*’
Division ‘/’
Modulo ‘%’
Exponentiation ‘**’

4. List Operations
Indexing: Access elements
by position (starts at 0).
Example:
◦ fruits = ["apple",
"banana", "cherry"]
◦ print(fruits[1])
◦ fruits.append("date")
◦ print(fruits)
Example:
fruits = ["apple", "banana", "cherry"]
print(fruits[1]) # banana
fruits.append("date") # add at end
print(fruits)
fruits.insert(0, "orange") # add at first position
print(fruits)

5. List Methods
Common methods insert(), remove(), sort(), len()
Example:
◦ numbers = [3, 1, 4, 1]
◦ numbers.sort()
◦ print(numbers) # Output: [1, 1, 3, 4]
◦ print(len(numbers)) # Output: 4

6. NumPy – Definition
NumPy (Numerical Python) is a Python library used for numerical and
mathematical operations.
It provides fast, memory-efficient array processing.

7. Why NumPy is Important
• Faster than Python lists
• Handles large datasets efficiently
• Core library for Data Analytics & ML

8. Installing NumPy & Import
Command to install NumPy:
◦ "pip install numpy"
Import NumPy using alias:
import numpy as np

9. What is NumPy Array?
A NumPy array is a collection of elements of the same data type.
It is stored in contiguous memory locations.
Example:
import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)

10. 1D Array – Code Example
import numpy as np
a = np.array([10, 20, 30, 40])
print(a)

11. 2D Array – Code Example
a = np.array([[1,2,3],[4,5,6]])
print(a)

12. Statistical Functions
np.mean(a)
np.sum(a)
np.min(a)
np.max(a)

13. Reshaping Array
a = np.arange(6)
a.reshape(2,3)

14. Pandas – Definition
Pandas is a Python library used for data manipulation and analysis.
It works with structured data like tables.
adds data structures and tools designed to work with table-like data
(similar to Series and Data Frames in R)
provides tools for data manipulation: reshaping, merging, sorting,
slicing, aggregation etc.
allows handling missing data

15. Why Pandas?
• Easy data cleaning
• Fast analysis
• Works with Excel, CSV, SQL

16. Installing Pandas
Command:
pip install pandas

17. Importing Pandas
import pandas as pd

18. Pandas Data Structures
Series – One dimensional
DataFrame – Two dimensional (rows & columns)

19. Series – Code Example
import pandas as pd
s = pd.Series([10,20,30])
print(s)

20. DataFrame – Code Example
df = pd.DataFrame({
'Name':['A','B'],
'Age':[20,25]
})

21. Reading CSV File
df = pd.read_csv('data.csv')

22. Viewing Data
df.head()
df.tail()

23. Data Information
df.info()
df.describe()

24. Selecting Columns
df['Age']
df[['Name','Age']]

25. Filtering Rows
df[df['Age'] > 22]

26. Handling Missing Values
df.isnull()
df.dropna()
df.fillna(0)

27. GroupBy Operation
df.groupby('Department')['Salary'].sum()

28. Pandas Use Cases
• Data Cleaning
• Business Reports
• Data Analytics