This series will introduce you to graphing in python with Matplotlib, which is arguably the foremost popular graphing and data visualization library for Python.
Installation
Easiest way to put in matplotlib is to use pip. Type following command in terminal:
ip install matplotlib
OR, you’ll download it from here and install it manually.
Getting started ( Plotting a line)
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# importing the specified 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 point out the plot
plt.show()
Output:
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 look at your plot, we use .show() function.
Plotting two or more lines on same plot
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import matplotlib.pyplot as plt
# line 1 points
x1 = [1,2,3]
y1 = [2,4,1]
# plotting the road 1 points
plt.plot(x1, y1, label = “line 1”)
# line 2 points
x2 = [1,2,3]
y2 = [4,1,3]
# plotting the road 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 point out the plot
plt.show()
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 sort of line and its color is named legend. we will add a legend to our plot using .legend() function.
Customization of Plots
Here, we discuss some elementary customizations applicable on almost any plot.
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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 point out the plot
plt.show()
Output:
As you’ll see, we’ve 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 isn’t done, pyplot module uses auto-scale feature to line the axis range and scale.
Bar Chart
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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
# function to show the plot
plt.show()
Here, we use plt.bar() 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
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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()
Here, we use plt.hist() function to plot a histogram.
frequencies are passed because the ages list.
Range might be set by defining a tuple containing min and max value.
Next step is to “bin” the range of values—that is, divide the whole range of values into a series of intervals—and then count what percentage values fall under each interval. Here we’ve defined bins = 10. So, there are a complete of 100/10 = 10 intervals.
Scatter plot
ere, we use plt.scatter() function to plot a scatter plot.
Like a line, we define x and corresponding y – axis values here also .
marker argument is employed to line the character to use as marker. Its size are often defined using s parameter.
Pie-chart
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import matplotlib.pyplot as plt
# defining labels
activities = [‘eat’, ‘sleep’, ‘work’, ‘play’]
# portion covered by each label
slices = [3, 7, 8, 6]
# color for every label
colors = [‘r’, ‘y’, ‘g’, ‘b’]
# plotting the 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()
Output of above program seems like this:
Here, we plot a chart by using plt.pie() method.
First of all, we define the labels employing a list called activities.
Then, portion of every label are often defined using another list called slices.
Color for every label is defined employing a list called colors.
shadow = True will show a shadow beneath each label in pie-chart.
startangle rotates the beginning of the chart by given degrees counterclockwise from the x-axis.
explode is employed to line the fraction of radius with which we offset each wedge.
autopct is employed to format the worth of every label. Here, we’ve set it to point out the share value only upto 1 decimal place.
Plotting curves of given equation
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# 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
plt.show()
Here, we use NumPy which may be a general-purpose array-processing package in python.
To set the x – axis values, we use np.arange() method during which first two arguments are for range and third one for step-wise increment. The result’s 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, during this part, we discussed various sorts of plots we will create in matplotlib. There are more plots which haven’t been covered but the foremost significant ones are discussed here –
