# Graph Plotting in Python | Set 1

This series will introduce you to graphing in Python with Matplotlib, which is arguably the most popular graphing and data visualization library for Python.
Installation
The easiest way to install matplotlib is to use pip. Type the following command in the terminal:Â

`pip install matplotlib`

# How to plot a graph in Python?

There are various ways to do this in Python. here we are discussing some generally used methods for plotting matplotlib in Python. those are the following.

• Plotting a Line
• Plotting Two or More Lines on the Same PlotÂ
• Customization of PlotsÂ
• Plotting Matplotlib Bar Chart
• Plotting Matplotlib Histogram
• Plotting Matplotlib Scatter plot
• Plotting Matplotlib Pie-chart
• Plotting Curves of Given Equation

### Plotting a line

In this example, the code uses Matplotlib to create a simple line plot. It defines x and y values for data points, plots them using `plt.plot()`, and labels the x and y axes with `plt.xlabel()` and `plt.ylabel()`. The plot is titled “My first graph!” using `plt.title()`. Finally, the `plt.show()` function is used to display the graph with the specified data, axis labels, and title.

## Python

 `# importing the required module``import` `matplotlib.pyplot as plt` `# x axis values``x ``=` `[``1``,``2``,``3``]``# corresponding y axis values``y ``=` `[``2``,``4``,``1``]` `# plotting the points ``plt.plot(x, y)` `# naming the x axis``plt.xlabel(``'x - axis'``)``# naming the y axis``plt.ylabel(``'y - axis'``)` `# giving a title to my graph``plt.title(``'My first graph!'``)` `# function to show the plot``plt.show()`

Output:Â

### Plotting Two or More Lines on Same PlotÂ

In this example code uses Matplotlib to create a graph with two lines. It defines two sets of x and y values for each line and plots them using `plt.plot()`. The lines are labeled as “line 1” and “line 2” with `label` parameter. Axes are labeled with `plt.xlabel()` and `plt.ylabel()`, and the graph is titled “Two lines on the same graph!” with `plt.title()`. The legend is displayed using `plt.legend()`, and the `plt.show()` function is used to visualize the graph with both lines and labels.

## Python

 `import` `matplotlib.pyplot as plt` `# line 1 points``x1 ``=` `[``1``,``2``,``3``]``y1 ``=` `[``2``,``4``,``1``]``# plotting the line 1 points ``plt.plot(x1, y1, label ``=` `"line 1"``)` `# line 2 points``x2 ``=` `[``1``,``2``,``3``]``y2 ``=` `[``4``,``1``,``3``]``# plotting the line 2 points ``plt.plot(x2, y2, label ``=` `"line 2"``)` `# naming the x axis``plt.xlabel(``'x - axis'``)``# naming the y axis``plt.ylabel(``'y - axis'``)``# giving a title to my graph``plt.title(``'Two lines on same graph!'``)` `# show a legend on the plot``plt.legend()` `# function to show the plot``plt.show()`

Output:Â

### Customization of PlotsÂ

In this example code uses Matplotlib to create a customized line plot. It defines x and y values, and the plot is styled with a green dashed line, a blue circular marker for each point, and a marker size of 12. The y-axis limits are set to 1 and 8, and the x-axis limits are set to 1 and 8 using `plt.ylim()` and `plt.xlim()`. Axes are labeled with `plt.xlabel()` and `plt.ylabel()`, and the graph is titled “Some cool customizations!” with `plt.title()`.

## Python

 `import` `matplotlib.pyplot as plt` `# x axis values``x ``=` `[``1``,``2``,``3``,``4``,``5``,``6``]``# corresponding y axis values``y ``=` `[``2``,``4``,``1``,``5``,``2``,``6``]` `# plotting the points ``plt.plot(x, y, color``=``'green'``, linestyle``=``'dashed'``, linewidth ``=` `3``,``         ``marker``=``'o'``, markerfacecolor``=``'blue'``, markersize``=``12``)` `# setting x and y axis range``plt.ylim(``1``,``8``)``plt.xlim(``1``,``8``)` `# naming the x axis``plt.xlabel(``'x - axis'``)``# naming the y axis``plt.ylabel(``'y - axis'``)` `# giving a title to my graph``plt.title(``'Some cool customizations!'``)` `# function to show the plot``plt.show()`

Output:Â

### Plotting Matplotlib Using Bar Chart

In this example code uses Matplotlib to create a bar chart. It defines x-coordinates (`left`), heights of bars (`height`), and labels for the bars (`tick_label`). The `plt.bar()` function is then used to plot the bar chart with specified parameters such as bar width, colors, and labels. Axes are labeled with `plt.xlabel()` and `plt.ylabel()`, and the chart is titled “My bar chart!” using `plt.title()`.

## Python

 `import` `matplotlib.pyplot as plt` `# x-coordinates of left sides of bars ``left ``=` `[``1``, ``2``, ``3``, ``4``, ``5``]` `# heights of bars``height ``=` `[``10``, ``24``, ``36``, ``40``, ``5``]` `# labels for bars``tick_label ``=` `[``'one'``, ``'two'``, ``'three'``, ``'four'``, ``'five'``]` `# plotting a bar chart``plt.bar(left, height, tick_label ``=` `tick_label,``        ``width ``=` `0.8``, color ``=` `[``'red'``, ``'green'``])` `# naming the x-axis``plt.xlabel(``'x - axis'``)``# naming the y-axis``plt.ylabel(``'y - axis'``)``# plot title``plt.title(``'My bar chart!'``)` `# function to show the plot``plt.show()`

OutputÂ :

### Plotting Matplotlib Using Histogram

In this example code uses Matplotlib to create a histogram. It defines a list of age frequencies (`ages`), sets the range of values from 0 to 100, and specifies the number of bins as 10. The `plt.hist()` function is then used to plot the histogram with the provided data and formatting, including the color, histogram type, and bar width. Axes are labeled with `plt.xlabel()` and `plt.ylabel()`, and the chart is titled “My histogram” using `plt.title()`.

## Python

 `import` `matplotlib.pyplot as plt` `# frequencies``ages ``=` `[``2``,``5``,``70``,``40``,``30``,``45``,``50``,``45``,``43``,``40``,``44``,``        ``60``,``7``,``13``,``57``,``18``,``90``,``77``,``32``,``21``,``20``,``40``]` `# setting the ranges and no. of intervals``range` `=` `(``0``, ``100``)``bins ``=` `10`  `# plotting a histogram``plt.hist(ages, bins, ``range``, color ``=` `'green'``,``        ``histtype ``=` `'bar'``, rwidth ``=` `0.8``)` `# x-axis label``plt.xlabel(``'age'``)``# frequency label``plt.ylabel(``'No. of people'``)``# plot title``plt.title(``'My histogram'``)` `# function to show the plot``plt.show()`

Output:

### Plotting Matplotlib Using Scatter PlotÂ

In this example code uses Matplotlib to create a scatter plot. It defines x and y values and plots them as scatter points with green asterisk markers (`*`) of size 30. Axes are labeled with `plt.xlabel()` and `plt.ylabel()`, and the plot is titled “My scatter plot!” using `plt.title()`. The legend is displayed with the label “stars” using `plt.legend()`, and the resulting scatter plot is shown using `plt.show()`.

## Python

 `import` `matplotlib.pyplot as plt` `# x-axis values``x ``=` `[``1``,``2``,``3``,``4``,``5``,``6``,``7``,``8``,``9``,``10``]``# y-axis values``y ``=` `[``2``,``4``,``5``,``7``,``6``,``8``,``9``,``11``,``12``,``12``]` `# plotting points as a scatter plot``plt.scatter(x, y, label``=` `"stars"``, color``=` `"green"``, ``            ``marker``=` `"*"``, s``=``30``)` `# x-axis label``plt.xlabel(``'x - axis'``)``# frequency label``plt.ylabel(``'y - axis'``)``# plot title``plt.title(``'My scatter plot!'``)``# showing legend``plt.legend()` `# function to show the plot``plt.show()`

Output:
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### Plotting Matplotlib Using Pie-chartÂ

In this example code uses Matplotlib to create a pie chart. It defines labels for different activities (`activities`), the portion covered by each label (`slices`), and colors for each label (`colors`). The `plt.pie()` function is then used to plot the pie chart with various formatting options, including start angle, shadow, explosion for a specific slice, radius, and autopct for percentage display. The legend is added with `plt.legend()`, and the resulting pie chart is displayed using `plt.show()`.

## Python

 `import` `matplotlib.pyplot as plt` `# defining labels``activities ``=` `[``'eat'``, ``'sleep'``, ``'work'``, ``'play'``]` `# portion covered by each label``slices ``=` `[``3``, ``7``, ``8``, ``6``]` `# color for each label``colors ``=` `[``'r'``, ``'y'``, ``'g'``, ``'b'``]` `# plotting the pie chart``plt.pie(slices, labels ``=` `activities, colors``=``colors, ``        ``startangle``=``90``, shadow ``=` `True``, explode ``=` `(``0``, ``0``, ``0.1``, ``0``),``        ``radius ``=` `1.2``, autopct ``=` `'%1.1f%%'``)` `# plotting legend``plt.legend()` `# showing the plot``plt.show()`

The output of above program looks like this:
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### Plotting Curves of Given EquationÂ

In this example code uses Matplotlib and NumPy to create a sine wave plot. It generates x-coordinates from 0 to 2Ï€ in increments of 0.1 using `np.arange()` and calculates the corresponding y-coordinates by taking the sine of each x-value using `np.sin()`. The points are then plotted using `plt.plot()`, resulting in a sine wave. Finally, the `plt.show()` function is used to display the sine wave plot.

## Python

 `# importing the required modules``import` `matplotlib.pyplot as plt``import` `numpy as np` `# setting the x - coordinates``x ``=` `np.arange(``0``, ``2``*``(np.pi), ``0.1``)``# setting the corresponding y - coordinates``y ``=` `np.sin(x)` `# plotting the points``plt.plot(x, y)` `# function to show the plot``plt.show()`

Output:
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So, in this part, we discussed various types of plots we can create in matplotlib. There are more plots that haven’t been covered but the most significant ones are discussed here –Â

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