Plotting in Python | Set 1

Arrays | Counters | NumPy | Python functions

Michael Zippo 18.07.2021

Install

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

 pip install matplotlib

OR you can download it from here and install manually.

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

       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-method/">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

       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 ()

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

       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

   
 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 ()

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

       # 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 ()

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 —

Plotting in Python | Set 2
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