importmatplotlib.pyplotaspltx=[1,2,3,4]y=[1,4,9,16]plt.plot(x,y)plt.show()

Output

Explanation:

• plt.plot(x, y):creates a line plot by connectingxandydata points.
• plt.show(): displays final plot window.

2. Pie Charts

Pie charts show the share of each category in a whole. Thepie() function takes a list of values and optional labels to represent each slice of the pie.

Example:

This code creates a pie chart usingplt.pie()function to visualize the percentage share of different programming languages.

importmatplotlib.pyplotaspltlabels=['Python','Java','C++','JavaScript']sizes=[40,30,20,10]plt.pie(sizes,labels=labels,autopct='%1.1f%%')plt.show()

Output

Explanation: plt.pie(sizes, labels=labels, autopct='%1.1f%%')creates a pie chart withslice sizes from sizes, labelsfor each sliceandautopctshows percentage values with one decimal place.

3. Bar Charts

Bar charts are ideal for comparing quantities across categories. The bar() function takes two lists one forcategorylabels (x-axis) and one for their correspondingvalues(y-axis).

Example:

This Example creates a bar chart usingbar() function to display values for different categories.

importmatplotlib.pyplotaspltcategories=['A','B','C','D']values=[3,7,2,5]plt.bar(categories,values)plt.show()

Output

Explanation:plt.bar(categories, values) draws a vertical bar chart using given categories and values.

4. Scatter Plots

Scatter plots are used for displaying individual data points and showing relationship between two variables. The scatter() function takes two lists like plot(), but it only plots the individual points without connecting them.

Example:

Here, scatter plot is plotted usingscatter() function to visualize individual data points.

importmatplotlib.pyplotaspltx=[1,2,3,4,5]y=[5,7,9,11,13]plt.scatter(x,y)plt.show()

Output

Explanation:plt.scatter(x, y) plots individual data points as dots at specified(x, y) coordinates.

5. Histograms

Histograms are used to visualize distribution of a dataset. Thehist() function takes a list or array of numerical data and divides it into bins.

Example:

A histogram is plotted usinghist() function to visualize the distribution of 1000 random data points.

importmatplotlib.pyplotaspltimportnumpyasnpdata=np.random.randn(1000)plt.hist(data,bins=30)plt.show()

Output

Explanation:

• np.random.randn(1000): generates1000random numbers from a standard normal distribution.
• plt.hist(data, bins=30): creates a histogram of the data with 30 bins (intervals).