Q1. You have months = [''Jan'',''Feb'',''Mar''] and sales = [200, 250, 300]. Create a line plot using matplotlib. Label axes and add title.
import matplotlib.pyplot as plt
plt.plot(months, sales)
plt.xlabel(''Month'')
plt.ylabel(''Sales'')
plt.title(''Monthly Sales'')
plt.show()Q2. Display a bar chart of product sales: products = [''A'',''B'',''C'']; values = [30, 45, 25]. Use plt.bar and customize colors.
plt.bar(products, values, color=[''red'',''green'',''blue''])
plt.title(''Product Sales'')
plt.show()plt.barh.Q3. Overlay two line plots on same axes: actual vs predicted values over 10 epochs. Add legend.
epochs = range(1,11)
actual = [0.9,0.85,0.8,0.78,0.75,0.73,0.7,0.68,0.65,0.63]
pred = [0.92,0.88,0.82,0.79,0.76,0.74,0.71,0.69,0.66,0.64]
plt.plot(epochs, actual, label=''Actual'')
plt.plot(epochs, pred, label=''Predicted'')
plt.legend()
plt.show()Q4. Create a grouped bar chart comparing two groups across categories. Use numpy to set bar positions.
import numpy as np
categories = [''A'',''B'',''C'']
group1 = [15,20,25]
group2 = [10,18,22]
x = np.arange(len(categories))
width = 0.35
plt.bar(x - width/2, group1, width, label=''Group1'')
plt.bar(x + width/2, group2, width, label=''Group2'')
plt.xticks(x, categories)
plt.legend()
plt.show()Q5. Save a plot to a file ''plot.png'' with high dpi.
plt.savefig(''plot.png'', dpi=300, bbox_inches=''tight'')plt.show(). bbox_inches=''tight'' removes extra whitespace. Use format=''pdf'' for vector graphics.