  # Plotting in Python | Set 1

Arrays | Counters | NumPy | Python Methods and Functions

Install

The easiest way to install matplotlib — this is to use pip. Enter the following command in terminal:

` pip install matplotlib `

Getting started (laying the line)

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

Output:

The code seems self-evident. The following steps were taken:

• Define the X-axis and corresponding Y-axis values ​​as lists.
• Place them on the canvas using the .plot () function .
• Name the X and Y axes using the .xlabel () and .ylabel () functions.
• Name your graphic, using the .title () function .
• Finally, we use .show () function .

Draw two or more lines on the same site

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

Output:

• Here we are plotting two lines on one graph. We distinguish them by giving them a name ( label ), which is passed as an argument to the .plot () function.
• The small rectangle with information about the line type and its color is called a legend. We can add a legend to our graphics using the .legend () function .

C ustomization from parcels

Here we discuss some basic settings that apply to 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 ` `] `   ` # dots ` ` plt.plot (x, y, color ` ` = ` ` `green` ` `, linestyle ` ` = ` `` dashed` ` `, linewidth = 3 , `` marker = `o` , markerfacecolor = ` blue ` , markersize = 12 )    # set the x and y axis range plt.ylim ( 1 , 8 ) plt.xlim ( 1 , 8 )   # x-axis title plt.xlabel ( `x - axis` ) # Y-axis name plt .ylabel ( `y - axis` )    # giving a title to my graphic plt.title ( `Some cool customizations!` )   # plot show function plt.show () `

Output:

As you can see, we have made several settings such as

• setting line width, line style, line color.
• mar setting ker, marker face color, marker size.
• override the X and Y axis range. If not overridden, the pyplot module uses autoscale to set the range and scale of the axis.

Histogram

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

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Output:

• Here we use the plt.bar () function to plot the bar graph.
• The X-coordinates of the left side of the bars are passed along with the height of the bars.
• You can also name the X-axis coordinates by defining tick_labels

Histogram

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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 ranges and no. ranges range = ( 0 , 100 ) bins = 10     # building a histogram plt.hist (ages, bins, range , color = `green` ,   histtype = `bar` , rwidth = 0.8 )   X-axis label plt.xlabel ( `age` ) # frequency tag plt.ylabel ( `No. of people` ) # story name plt.title ( `My histogram` )   # plot show function plt.show ( ) `

Output:

• Here we use the plt.hist () function to plot the histogram.
• frequencies are transmitted as a list of ages .
• The range can be set by specifying a tuple containing the minimum and maximum values.
• The next step is “ selection "range of values, that is, section Dividing the entire range of values ​​into a number of bins, and then counting the number of values ​​that fall within each bin. Here we have defined bins = 10. So there are 100/10 = 10 bins.

Dot plot

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` Output: Here we use the plt.scatter () function to plot the scatter plot. Similar to the line, we also define the x and the corresponding y-axis values. The marker argument is used to set the character to use as a marker. Its size can be determined using the s parameter. Pie Chart 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 tag plt.ylabel ( ` y - axis` ) # plot title plt.title ( `My scatter plot!` ) # shows the legend plt.legend ()   # plot show function plt.show () import matplotlib.pyplot as plt    #define tags activities = [ ` eat` , `sleep` , ` work` , `play` ]    # the part covered by each label slices = [ 3 , 7 , 8 , 6 ]   # color for each tag colors = [ `r` , ` y ` , ` g` , `b` ]    # building a 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 %%` )   # sketching the legend plt.legend ()    # show plot plt.show () `

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The output of the above program looks like this:

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

Plot the curves of this equation

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

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The output of the above program looks like this:

Here we use NumPy, which is a generic package for handling arrays in Python.

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

So, in this part, we discussed the different types of plots that we can create in matplotlib. There are a few more plots that were not covered, but the most significant ones are discussed here —