Plotting and visualization with python using matplotlib

 

Course content:

• 
  A brief matplotlib API primer:
  

1. 
   Colors, Markers, and Line styles.
   


2. Figures and Subplots.


3. 
   Ticks, labels, legends, and saving plots to file.
   

• 
  Different plots using matplotlib, pandas, and seaborn:
  

1. Line & bar plots.


2. 
   Histograms & density plots.
   


3. Scatter or point plots.


4. 
   Facet grids and categorical data.
   

Overview:

• 
  Making informative visualizations (sometimes called plots) is one of
  the most important tasks in data analysis. It may be a part of the
  exploratory process—for example, to help identify outliers or needed
  data transformations, or as a way of generating ideas for models.
  


• 
  Python has many add-on libraries for making static or dynamic
  visualizations, we will mainly be focused on matplotlib and libraries
  that build on top of it. matplotlib is a desktop plotting package
  designed for creating (mostly two dimensional) publication-quality
  plots.
  


• 
  The project was started by John Hunter in 2002 to enable MATLAB-like
  plotting interface in Python.
  


• 
  Over time, matplotlib has spawned a number of add-on toolkits for data
  visualization that uses matplotlib for their underlying plotting. One of
  these is seaborn, which we will explore later in this
  presentation.
  

A brief matplotlib API Primer:

• 
  With matplotlib, we use the following import convention.
  


• 
  After running it in jupyter, we can try creating a simple plot.If
  everything is set up right, a line plot like below appear.
  

  
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data=np.array([2,6,4,8,3,4])
plt.plot(data)

Figures and subplots:

• 
  Plots in matplotlib reside within a Figure object. You can create a
  new figure with pit.figure(). 
  


• 
  plt.figure() has a number of options; notably, figsize will guarantee
  the figure has a certain size and aspect ratio if saved to disk. One
  can’t make a plot with a blank figure.One have to create one or more
  subplots using add_subplot().
  

ax1 = fig.add_subplot(2, 2, 1)

• 
  This means that the figure should be 2 x2 (so up to four plots in
  total), and we’re selecting the first of four subplots (numbered from
  1).
  


• 
  The objects returned by fig.add_subplot are Axessubplot objects, on
  which one can directly plot on the other empty subplots by calling
  each one's instance method.
  

fig=plt.figure()
ax1=fig.add_subplot(2,2,1)
ax2=fig.add_subplot(2,2,2)
ax3=fig.add_subplot(2,2,3)


x=np.arange(0,3*np.pi,0.1)
fig=plt.figure()
ax1=fig.add_subplot(2,2,1)
ax2=fig.add_subplot(2,2,2)
ax3=fig.add_subplot(2,2,3)
ax1.plot(np.random.randn(10))
ax2.plot(np.sin(x))
ax3.plot(np.cos(x))


fig,axes=plt.subplots(2,2)
axes

 Colors,Markers,and line styles:

• 
  Matplotlib’s main plot function accepts arrays of x and y coordinates
  and optionallyastring abbreviation indicating color and line style.
  


• 
  There are a number of color abbreviations provided for commonly used
  colors, but one can use any color on the spectrum by specifying its hex
  code (e.g.,'#CECECE’).
  


• 
  One can see the full set of line styles by looking at the command
  help(pit.plot). 
  


• 
  Line plots can additionally have markers to highlight the actual data
  points.
  

  help(plt.plot)
Output=
Help on function plot in module matplotlib.pyplot:

plot(*args, scalex=True, scaley=True, data=None, **kwargs)
    Plot y versus x as lines and/or markers.
    
    Call signatures::
    
        plot([x], y, [fmt], *, data=None, **kwargs)
        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
    
    The coordinates of the points or line nodes are given by *x*, *y*.
    
 
data=np.random.randn(30)
data
Output=array([-0.55620921,  1.15354551,  1.88428619, -0.25361657,  0.11720929,
        0.99380999, -1.61673998,  0.15259621,  1.07661434,  0.57335323,
        1.51775676,  2.16336357,  0.29821075,  0.80887232, -0.23170136,
       -0.15509185, -2.13596732, -0.25341181, -0.81853445, -1.75597032,
       -2.66753714, -1.10821393,  1.40548605,  0.06021546, -0.4025957 ,
       -0.18417526,  3.20519396, -1.15411711,  0.58264083, -0.070976  ])

plt.plot(data,linestyle='--',color='g')

plt.plot(data,color='y',linestyle='dashed',marker='o')
 

Ticks, Labels and Legends:

• 
  The pyplot interface, designed for interactive use, consists of methods
  like xlim, xticks, and xticklabels. These control the plot range, tick
  locations, and tick labels respectively.
  


• 
  To change the x-axis ticks, it’s easiest to use set_xticks and
  set_xticklabels. The former instructs matplotlib where to place the
  ticks along the data range; by default these locations will also be the
  labels. The rotation option sets the x tick labels at a any degree
  rotation.  
  


• 
  Lastly, set_xlabel gives a name to the x-axis 1 and set_title the
  subplot title.
  


• 
  Legends are another critical element for identifying plot elements. One
  pass the label argument when adding each piece of the plot.
  

fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(data)



fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(data)
ax.set_xticks([0,10,20,30])
ax.set_xticklabels(['zero','ten','twenty','thirty'],rotation=30,fontsize='large')
ax.set_title("My plot")
ax.set_xlabel("Stages")



fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(np.sin(x),'b--',label="Sine wave")
ax.plot(np.cos(x),'c.',label="Cosine wave")
ax.legend()

Saving plots to file:

• 
  One can save the active figure to file using plt.savefig. This method
  is equivalent the figure object’s savefig instance method. 
  


• 
  For example, to save an PNG version of a figure, we need only type:
  plt.savefig(‘image.png')
  


• 
  The file type is inferred from the file extension. So if one used .pdf
  instead, one would get a PDF. 
  


• 
  There are a couple of important options that are used frequently : dpi,
  which controls the dots-per-inch resolution, and bbox_inches, which can
  trim the whitespace around the actual figure.
  

plt.plot(data,color='k',linestyle='dashed',marker='o')
plt.savefig("image11.png")

Different plots using matplotlib, pandas, and seaborn:

• 
  Bar plot

• 
  Histogram
  

• 
  Scatter plot 
  

• 
  Stack plot 
  

• 
  Pie plot 
  

Plotting using Seaborn
import seaborn as sns
sns.set()
data=np.random.randn(30)
sns.distplot(data,kde=0)

I hope that you have understood this topic, I have tried my best, This is a vast topic but I have covered it in short....

Best Regards from,

msbtenote:)

THANK YOU!!!