Data Visualization

Create a line plot showing the sales data over months:

Months: Jan, Feb, Mar, Apr
Sales: 120, 150, 180, 200

Create a scatter plot showing the relationship between age and income:

Age: [25, 30, 35, 40, 45]
Income: [50000, 60000, 75000, 80000, 90000]

Display a histogram of exam scores:

Scores: [78, 89, 95, 82, 70, 92, 88, 75, 85, 90]

Create an interactive line plot of stock prices:

Date: [2023-01-01, 2023-01-02, 2023-01-03, 2023-01-04]
Price: [100, 105, 110, 108]

 Display a pie chart showing the distribution of fruits:

Fruits: Apple, Banana, Orange
Quantity: 30, 20, 15

Build a simple dashboard with a scatter plot and dropdown menu to select data:

Data: df with columns ‘X’, ‘Y’, ‘Category’

Create a heat map to visualize correlations between variables in a DataFrame:

DataFrame: df with numeric columns

Save a Matplotlib plot as a PNG image file:

Data: x = [1, 2, 3, 4], y = [10, 15, 7, 12]

Visualize data from a Pandas DataFrame using a Seaborn pair plot:

Data: df with numeric columns

Display a Matplotlib histogram directly from a Pandas DataFrame:

Data: df with numeric column ‘Age’