Matplotlib  Simple Plot
In this chapter, we will learn how to create a simple plot with Matplotlib.
We shall now display a simple line plot of angle in radians vs. its sine value in Matplotlib. To begin with, the Pyplot module from Matplotlib package is imported, with an alias plt as a matter of convention.
import matplotlib.pyplot as pltNext we need an array of numbers to plot. Various array functions are defined in the NumPy library which is imported with the np alias.
import numpy as npWe now obtain the ndarray object of angles between 0 and 2π using the arange() function from the NumPy library.
x = np.arange(0, math.pi*2, 0.05)The ndarray object serves as values on x axis of the graph. The corresponding sine values of angles in x to be displayed on y axis are obtained by the following statement −
y = np.sin(x)The values from two arrays are plotted using the plot() function.
plt.plot(x,y)You can set the plot title, and labels for x and y axes.
You can set the plot title, and labels for x and y axes. plt.xlabel("angle") plt.ylabel("sine") plt.title('sine wave')The Plot viewer window is invoked by the show() function −
plt.show()The complete program is as follows −
from matplotlib import pyplot as plt import numpy as np import math #needed for definition of pi x = np.arange(0, math.pi*2, 0.05) y = np.sin(x) plt.plot(x,y) plt.xlabel("angle") plt.ylabel("sine") plt.title('sine wave') plt.show()When the above line of code is executed, the following graph is displayed −
Now, use the Jupyter notebook with Matplotlib.
Launch the Jupyter notebook from Anaconda navigator or command line as described earlier. In the input cell, enter import statements for Pyplot and NumPy −
from matplotlib import pyplot as plt import numpy as npTo display plot outputs inside the notebook itself (and not in the separate viewer), enter the following magic statement −
%matplotlib inlineObtain x as the ndarray object containing angles in radians between 0 to 2π, and y as sine value of each angle −
import math x = np.arange(0, math.pi*2, 0.05) y = np.sin(x)Set labels for x and y axes as well as the plot title −
plt.xlabel("angle") plt.ylabel("sine") plt.title('sine wave')Finally execute the plot() function to generate the sine wave display in the notebook (no need to run the show() function) −
plt.plot(x,y)After the execution of the final line of code, the following output is displayed −
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
Easiest way to install matplotlib is to use pip. Type following command in terminal:
OR, you can download it from here and install it manually.
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Getting started ( Plotting a line)
Python

Output:
The code seems self explanatory. Following steps were followed:
 Define the xaxis and corresponding yaxis values as lists.
 Plot them on canvas using .plot() function.
 Give a name to xaxis and yaxis 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
Python

Output:
 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.
Python

Output:
As you can see, we have done several customizations like
 setting the linewidth, linestyle, linecolor.
 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 autoscale feature to set the axis range and scale.
Bar Chart
Python

Output :
 Here, we use plt.bar() function to plot a bar chart.
 xcoordinates of left side of bars are passed along with heights of bars.
 you can also give some name to xaxis coordinates by defining tick_labels
Histogram
Python

Output:
 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
Python

Output:
 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.
Piechart
Python

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 piechart.
 startangle rotates the start of the pie chart by given degrees counterclockwise from the xaxis.
 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
Python

Output of above program looks like this:
Here, we use NumPy which is a generalpurpose arrayprocessing 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 stepwise increment. The result is a numpy array.
 To get corresponding yaxis 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 –
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Matplotlib Plotting
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Plotting x and y points
The function is used to draw points (markers) in a diagram.
By default, the function draws a line from point to point.
The function takes parameters for specifying points in the diagram.
Parameter 1 is an array containing the points on the xaxis.
Parameter 2 is an array containing the points on the yaxis.
If we need to plot a line from (1, 3) to (8, 10), we have to pass two arrays [1, 8] and [3, 10] to the plot function.
Example
Draw a line in a diagram from position (1, 3) to position (8, 10):
import matplotlib.pyplot as plt
import numpy as np
xpoints = np.array([1, 8])
ypoints = np.array([3, 10])
plt.plot(xpoints, ypoints)
plt.show()
Result:
Try it Yourself »The xaxis is the horizontal axis.
The yaxis is the vertical axis.
Plotting Without Line
To plot only the markers, you can use shortcut string notation parameter 'o', which means 'rings'.
Example
Draw two points in the diagram, one at position (1, 3) and one in position (8, 10):
import matplotlib.pyplot as plt
import numpy as np
xpoints = np.array([1, 8])
ypoints = np.array([3, 10])
plt.plot(xpoints, ypoints, 'o')
plt.show()
Result:
Try it Yourself »You will learn more about markers in the next chapter.
Multiple Points
You can plot as many points as you like, just make sure you have the same number of points in both axis.
Example
Draw a line in a diagram from position (1, 3) to (2, 8) then to (6, 1) and finally to position (8, 10):
import matplotlib.pyplot as plt
import numpy as np
xpoints = np.array([1, 2, 6, 8])
ypoints = np.array([3, 8, 1, 10])
plt.plot(xpoints, ypoints)
plt.show()
Result:
Try it Yourself »Default XPoints
If we do not specify the points in the xaxis, they will get the default values 0, 1, 2, 3, (etc. depending on the length of the ypoints.
So, if we take the same example as above, and leave out the xpoints, the diagram will look like this:
Example
Plotting without xpoints:
import matplotlib.pyplot as plt
import numpy as np
ypoints = np.array([3, 8, 1, 10, 5, 7])
plt.plot(ypoints)
plt.show()
Result:
Try it Yourself »The xpoints in the example above is [0, 1, 2, 3, 4, 5].
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In python plotting simple
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SIMPLE PLOT IN MATPLOTLIB  LINE PLOT IN MATPLOTLIB  MATPLOTLIB LIBRARY  PYTHON PROGRAMMING.
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