first_project.py

 #First we will import important libraries 

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
import pandas as pd
import numpy as np
import seaborn as sns
sns.set(style="white", color_codes=True)

 #Load the dataset

iris = pd.read_csv("C:\Desktop\Iris.csv")
 # the iris dataset is now available in Pandas DataFrame

 #Let's see what is in the Iris Data

iris.head()

 #The result for this line of code can be viewed here :  https://tinyurl.com/y9lllscv

iris.tail()

 #The result for this line of code can be viewed here : https://tinyurl.com/yaupjv9j 

 #Describe kind of works as summary in R 

iris.describe()

 #The result for this line of code can be viewed here : https://tinyurl.com/y7k7jwk6

iris.dtypes

 #The result for this line of code can be viewed here : https://tinyurl.com/y83z2gbl

 # Let's see how many examples we have of each species

iris["Species"].value_counts()

 #Iris-versicolor 50
 #Iris-setosa 50 
 #Iris-virginica 50 
 #Name: Species, dtype: int64

 # We'll use this to make a scatterplot of the Iris features.

my_scatter = iris.plot(kind="scatter", x="SepalLengthCm", y="SepalWidthCm", title= "Scatter plot for sepal length vs width", +
                       fontsize=12, colormap=False, figsize=(7,7))

 #The result for this line of code can be viewed here : https://tinyurl.com/y8dz386n

 # We will make a bar plot with sepal length and sepal width

temp_bar = iris[['SepalLengthCm', 'SepalWidthCm']]

my_bar = temp_bar.plot(kind="bar", title= "Bar plot for sepal length and width", fontsize=12, figsize=(10,5), width = 2)

 #The result for this line of code can be viewed here : https://tinyurl.com/ycbz5oho

my_seaborn = sns.jointplot(x="SepalLengthCm", y="SepalWidthCm", data=iris, size=6)

 #The result for this line of code can be viewed here : https://tinyurl.com/ycug9ng5

 # We'll use seaborn's FacetGrid to color the scatterplot by species


My_Col_Seaborn = sns.FacetGrid(iris, hue="Species", size=6) \
      .map(plt.scatter, "SepalLengthCm", "SepalWidthCm") \
      .add_legend()
 #The result for this line of code can be viewed here : https://tinyurl.com/yb7lld6x
      
      

 #Add regression and kernel density fits:
My_reg = sns.jointplot("SepalLengthCm", "SepalWidthCm", data=iris, kind="reg")

 #The result for this line of code can be viewed here : https://tinyurl.com/y7hojnh4


 #We can also achieve same using following line of code
My_reg2 = sns.lmplot("SepalLengthCm", "SepalWidthCm", data=iris)
 #The result for this line of code can be viewed here : https://tinyurl.com/y8dmmk4m
 # We can look at an individual feature in Seaborn through a boxplot

 #First we will look at Petal Length 

My_box = sns.boxplot(x="Species", y="PetalLengthCm", data=iris).set_title('My Individual boxplot')
 #The result for this line of code can be viewed here : https://tinyurl.com/yarag4ko

 #Now we will look at Sepal Length

My_box2 = sns.boxplot(x="Species", y="SepalLengthCm", data=iris).set_title('My Individual boxplot 2')

 #The result for this line of code can be viewed here : https://tinyurl.com/ya4ecwbs
 # One way we can extend this plot is adding a layer of individual points on top of
 # it through Seaborn's striplot

 # We'll use jitter=True so that all the points don't fall in single vertical lines
 # above the species

 # Saving the resulting axes as ax each time causes the resulting plot to be shown
 # on top of the previous axes

ax = sns.boxplot(x="Species", y="SepalLengthCm", data=iris).set_title('Before Striplot')
 #The result for this line of code can be viewed here : https://tinyurl.com/y6vossbf
ax1 = sns.stripplot(x="Species", y="SepalLengthCm", data=iris, jitter=True, edgecolor="gray").set_title('After Striplot')
 #The result for this line of code can be viewed here : https://tinyurl.com/ybro2akh

 # A violin plot combines the benefits of the previous two plots and simplifies them
 # Denser regions of the data are fatter, and sparser thiner in a violin plot

sns.violinplot(x="Species", y="SepalLengthCm", data=iris, size=5).set_title('My Violin Plot')
 #The result for this line of code can be viewed here : https://tinyurl.com/ydeukj96

 # A final seaborn plot useful for looking at univariate relations is the kdeplot,
 # which creates and visualizes a kernel density estimate of the underlying feature
sns.FacetGrid(iris, hue="Species", size=7) \
   .map(sns.kdeplot, "SepalLengthCm") \
   .add_legend()

 #The result for this line of code can be viewed here : https://tinyurl.com/yawohnpc

 # Another useful seaborn plot is the pairplot, which shows the bivariate relation
 # between each pair of features
 # 
 # From the pairplot, we'll see that the Iris-setosa species is separataed from the other
 # two across all feature combinations

sns.pairplot(iris.drop("Id", axis=1), hue="Species", size=4)

 #The result for this line of code can be viewed here : https://tinyurl.com/y98cg8ag

 # Now that we've covered seaborn, let's go back to some of the ones we can make with Pandas
 # We can quickly make a boxplot with Pandas on each feature split out by species
iris.drop("Id", axis=1).boxplot(by="Species", figsize=(14, 8))
 #The result for this line of code can be viewed here : https://tinyurl.com/yaxbza6o

 # One cool more sophisticated technique pandas has available is called Andrews Curves
 # Andrews Curves involve using attributes of samples as coefficients for Fourier series
 # and then plotting these
from pandas.tools.plotting import andrews_curves
andrews_curves(iris.drop("Id", axis=1), "Species")
 #The result for this line of code can be viewed here : https://tinyurl.com/y7mxq3c9

 # Another multivariate visualization technique pandas has is parallel_coordinates
 # Parallel coordinates plots each feature on a separate column & then draws lines
 # connecting the features for each data sample
from pandas.tools.plotting import parallel_coordinates
parallel_coordinates(iris.drop("Id", axis=1), "Species")
 #The result for this line of code can be viewed here : https://tinyurl.com/yc3z67p8

 # A final multivariate visualization technique pandas has is radviz
 # Which puts each feature as a point on a 2D plane, and then simulates
 # having each sample attached to those points through a spring weighted
 # by the relative value for that feature

from pandas.tools.plotting import radviz
radviz(iris.drop("Id", axis=1), "Species")

 #The result for this line of code can be viewed here : https://tinyurl.com/y968dt3b

 #Violin plots are great for visualizing distributions. However, we may want to simply display #each point.
 #That's where the swarm plot comes in. This visualization will show each point, while "stacking" those with 
 #similar values:


type_of_colors=['#78C850',  # Grass 
              '#F08030',  # Fire
              ]

sns.swarmplot(x='SepalWidthCm', y='PetalWidthCm', data=iris, 
              palette=type_of_colors).set_title('My Swarm Plot for Sepal & Petal Width in cm')

 #The result for this line of code can be viewed here : https://tinyurl.com/y9ols9f5



 #We will try to overlay two plots using seaborn
 #Overlaying swarm and violin plots


 # Set figure size with matplotlib
plt.figure(figsize=(10,6))
 
 # Create plot

sns.violinplot(x='SepalWidthCm',
               y='PetalWidthCm', 
               data=iris, 
               inner=None, # Remove the bars inside the violins
               palette=type_of_colors)

 
sns.swarmplot(x='SepalWidthCm', 
              y='PetalWidthCm', 
              data=iris, 
              color='k', # Make points black
              alpha=0.7) # and slightly transparent
 
 # Set title with matplotlib
plt.title('Overlay plot of Violin and Swarm')

 #The result for this line of code can be viewed here : https://tinyurl.com/yb7qfyro


 #We will try he Bokeh package for visualization 
 #First we will try to create a line chart with Bokeh 
 #Import library
from bokeh.charts import *

 # Output to Line.HTML
output_file("lines.html", title="line plot example with Bokeh")


 # create a new line chat with a title and axis labels
p = Bar(iris, title="Line Chart Example", xlabel='SepalLengthCm', ylabel='PetalLengthCm', width=500, height=600)

 # show the results

show(p)

 #The result for this line of code can be viewed here : https://tinyurl.com/yahw48au

 #Wrapping up:
 #We used rich libraries for visualizations like matplotlib,  mpl_toolkits, sklearn, pandas, numpy, seaborn, Bokeh, etc. 
 #We performed basic operations on dataset to have a good look at it. We created Scatterplots, bar charts, joint plot,  
 #box plot, strip plot, pair plot, Andrew curves, swarm plots, violin plots, etc. We also used Bokeh package. 
 #Hope this helps you in creating good visualization using Python.