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Graph Plotting in Python | Set 1

  • Difficulty Level : Easy
  • Last Updated : 19 Feb, 2021

This series will introduce you to graphing in python with Matplotlib, which is arguably the most popular graphing and data visualization library for Python.


Easiest way to install matplotlib is to use pip. Type following command in terminal:

pip install matplotlib

OR, you can download it from here and install it manually.

Getting started ( Plotting a line)

# 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


The code seems self explanatory. Following steps were followed:

  • Define the x-axis and corresponding y-axis values as lists.
  • Plot them on canvas using .plot() function.
  • Give a name to x-axis and y-axis using .xlabel() and .ylabel() functions.
  • Give a title to your plot using .title() function.
  • Finally, to view your plot, we use .show() function.

Plotting two or more lines on same plot

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
# function to show the plot


  • Here, we plot two lines on same graph. We differentiate between them by giving them a name(label) which is passed as an argument of .plot() function.
  • The small rectangular box giving information about type of line and its color is called legend. We can add a legend to our plot using .legend() function.

Customization of Plots

Here, we discuss some elementary customizations applicable on almost any plot.

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
# 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


As you can see, we have done several customizations like

  • setting the line-width, line-style, line-color.
  • setting the marker, marker’s face color, marker’s size.
  • overriding the x and y axis range. If overriding is not done, pyplot module uses auto-scale feature to set the axis range and scale.


Bar Chart

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, 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

Output :


  • Here, we use function to plot a bar chart.
  • x-coordinates of left side of bars are passed along with heights of bars.
  • you can also give some name to x-axis coordinates by defining tick_labels


import matplotlib.pyplot as plt
# frequencies
ages = [2,5,70,40,30,45,50,45,43,40,44,
# 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
# frequency label
plt.ylabel('No. of people')
# plot title
plt.title('My histogram')
# function to show the plot



  • Here, we use plt.hist() function to plot a histogram.
  • frequencies are passed as the ages list.
  • Range could be set by defining a tuple containing min and max value.
  • Next step is to “bin” the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. Here we have defined bins = 10. So, there are a total of 100/10 = 10 intervals.

Scatter plot

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
# function to show the plot



  • Here, we use plt.scatter() function to plot a scatter plot.
  • Like a line, we define x and corresponding y – axis values here as well.
  • marker argument is used to set the character to use as marker. Its size can be defined using s parameter.


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
# showing the plot

Output of above program looks like this:


  • Here, we plot a pie chart by using plt.pie() method.
  • First of all, we define the labels using a list called activities.
  • Then, portion of each label can be defined using another list called slices.
  • Color for each label is defined using a list called colors.
  • shadow = True will show a shadow beneath each label in pie-chart.
  • startangle rotates the start of the pie chart by given degrees counterclockwise from the x-axis.
  • explode is used to set the fraction of radius with which we offset each wedge.
  • autopct is used to format the value of each label. Here, we have set it to show the percentage value only upto 1 decimal place.

Plotting curves of given equation

# 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)
# potting the points
plt.plot(x, y)
# function to show the plot

Output of above program looks like this:

Here, we use NumPy which is a general-purpose array-processing package in python.

  • To set the x – axis values, we use np.arange() method in which first two arguments are for range and third one for step-wise increment. The result is a numpy array.
  • To get corresponding y-axis values, we simply use predefined np.sin() method on the numpy array.
  • Finally, we plot the points by passing x and y arrays to the plt.plot() function.

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

This article is contributed by Nikhil Kumar. If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

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