Lab Assignment-1

Assignment-1- week 1 July

Learning Objective: Learn numpy, pandas ,matplotlib and seaborn

1. Learn creation and manipulation of NumPy arrays.

Create a 1-D array containing numbers 1 to 20.

Create a 4×5 matrix.

Find

• Shape
• Size
• Number of dimensions
• Data type

Reshape the matrix into 2×10.

Flatten the matrix.

Reverse the array.Find the maximum, minimum and average value.

2.NumPy Arithmetic and Indexing

Given

A =
[[10 20 30]
 [40 50 60]
 [70 80 90]]

B =
[[1 2 3]
 [4 5 6]
 [7 8 9]]

Perform

1. Addition
2. Subtraction
3. Multiplication
4. Division
5. Square every element
6. Extract second row
7. Extract first and third columns
8. Replace all values greater than 50 with 0.

3. Random Numbers and Statistics using NumPy

Problem

Generate marks of 100 students randomly between 35 and 100.

Find

• Mean
• Median
• Standard deviation
• Variance
• Highest mark
• Lowest mark
• Students scoring above 90
• Students scoring below average

Display the topper and bottom scorer.

4.Linear Algebra using NumPy

Problem

Create two matrices

A (3×3)

B (3×3)

Perform

1. Matrix addition
2. Matrix multiplication
3. Transpose
4. Determinant
5. Inverse
6. Eigenvalues
7. Eigenvectors

solve the following system of equations

2x1+3x2 +5x3= 10
3x1-2x2+x3=3
x1+5x2+7x3=8

5.Pandas Series and DataFrame

Problem

Create a DataFrame for 15 students.

Columns

RollNo
Name
Marks

Perform

1. Display first 5 rows.
2. Display last 5 rows.
3. Find average marks.
4. Add Total.
5. Add Percentage.
6. Add Grade.
7. Display students scoring above 85%.
8. Sort according to percentage.

6.Data Cleaning using Pandas

Problem

Create a CSV file containing

Name
Age
Department
Salary
Experience

Introduce

• Missing values
• Duplicate rows
• Incorrect values

Write a program to

1. Read the CSV.
2. Display information.
3. Detect null values.
4. Fill missing salary using average.
5. Remove duplicates.
6. Rename columns.
7. Save cleaned data into another CSV.

7.Data Analysis using Pandas

Problem

Consider sales data of an electronics shop.

Columns

Date
Product
Category
Price
Quantity
City

Find

1. Total sales.
2. Highest selling product.
3. City with highest sales.
4. Average quantity sold.
5. Total sales category-wise.
6. Top five transactions.
7. Monthly sales.

8.Matplotlib Visualization

Problem

Using student marks dataset,

Draw

1. Line chart
2. Bar chart
3. Pie chart
4. Histogram
5. Scatter plot

9.Plot the following functions

Write seperate functions for computing these functions

1.ReLU

2.Sigmoid

3.tanh

4.log

draw all these in a single plot. Use subplot.

10.Statistical Visualization using Seaborn

Problem

Using the famous Iris dataset,

Create

1. Scatter plot
2. Pair plot
3. Box plot
4. Violin plot
5. Histogram
6. Heatmap of correlation matrix

Interpret

• Which feature separates species best?
• Which feature has highest correlation?