A tutorial to data visualization in python with Matplotlib, Seaborn, and Plotly

• Two basic python packages are required for visualization:

  1. Matplotlib – a Python based plotting library offers matplotlib with a complete 2D support with limited 3D graphic support. It is useful in producing publication quality figures in interactive environment across platforms.
  2. Seaborn – Based on Matplotlib, Seaborn provides various features such as built-in themes, color palettes, functions and tools to visualize univariate, bivariate, linear regression, data matrices, time series, etc in order to build more complex visualizations.

• The sample dataset used in this tutorial 

• Import dataset:

• import matplotlib.pyplot as plt
  import pandas as pd
  import numpy as np
  import seaborn as sns 
  df=pd.read_excel("pathtodataset", "sample")

• Create histogram:

• fig=plt.figure() #Plots in matplotlib reside within a figure object, use plt.figure to create new figure
  #Create one or more subplots using add_subplot, because you can't create blank figure
  ax = fig.add_subplot(1,1,1)
  #Variable
  ax.hist(df['Age'],bins = 7) # Here you can play with number of bins
  Labels and Tit
  plt.title('Age distribution')
  plt.xlabel('Age')
  plt.ylabel('#Employee')
  plt.show()

• Create boxplot:

• fig=plt.figure()
  ax = fig.add_subplot(1,1,1)
  #Variable
  ax.boxplot(df['Age'])
  plt.show()

• Create violin plot

• sns.violinplot(df['Age'], df['Gender']) #Variable Plot
  sns.despine()

• Create bar chart

• var = df.groupby('Gender').Sales.sum() #grouped sum of sales at Gender level
  fig = plt.figure()
  ax1 = fig.add_subplot(1,1,1)
  ax1.set_xlabel('Gender')
  ax1.set_ylabel('Sum of Sales')
  ax1.set_title("Gender wise Sum of Sales")
  var.plot(kind='bar')

• Create line chart

• var = df.groupby('BMI').Sales.sum()
  fig = plt.figure()
  ax1 = fig.add_subplot(1,1,1)
  ax1.set_xlabel('BMI')
  ax1.set_ylabel('Sum of Sales')
  ax1.set_title("BMI wise Sum of Sales")
  var.plot(kind='line')

• Create Stacked Column Chart

• var = df.groupby(['BMI','Gender']).Sales.sum()
  var.unstack().plot(kind='bar',stacked=True,  color=['red','blue'], grid=False)

• Create Scatter Plot

• fig = plt.figure()
  ax = fig.add_subplot(1,1,1)
  ax.scatter(df['Age'],df['Sales']) #You can also add more variables here to represent color and size.
  plt.show()

• Create Bubble Plot

• fig = plt.figure()
  ax = fig.add_subplot(1,1,1)
  ax.scatter(df['Age'],df['Sales'], s=df['Income']) # Added third variable income as size of the bubble
  plt.show()

• Create Pie chart

• var=df.groupby(['Gender']).sum().stack()
  temp=var.unstack()
  type(temp)
  x_list = temp['Sales']
  label_list = temp.index
  plt.axis("equal") #The pie chart is oval by default. To make it a circle use pyplot.axis("equal")
  #To show the percentage of each pie slice, pass an output format to the autopctparameter plt.pie(x_list,labels=label_list,autopct="%1.1f%%") plt.title("Pastafarianism expenses") 
  plt.show()

• Create Heat Map

• #Generate a random number, you can refer your data values also
  data = np.random.rand(4,2)
  rows = list('1234') #rows categories
  columns = list('MF') #column categories
  fig,ax=plt.subplots()
  #Advance color controls
  ax.pcolor(data,cmap=plt.cm.Reds,edgecolors='k')
  ax.set_xticks(np.arange(0,2)+0.5)
  ax.set_yticks(np.arange(0,4)+0.5)
  # Here we position the tick labels for x and y axis
  ax.xaxis.tick_bottom()
  ax.yaxis.tick_left()
  #Values against each labels
  ax.set_xticklabels(columns,minor=False,fontsize=20)
  ax.set_yticklabels(rows,minor=False,fontsize=20)
  plt.show()