Python for Machine Learning | Visualize Iris Data with Seaborn and Matplotlib | Visualisation - P51

Link for Github Repository - https://github.com/technologycult/PythonForMachineLearning/tree/master/Part51 ''' Topics to be Covered - 1. Seaborn lmplot for plotting linear regression. 2. Generate a residual plot. 3. Generate a Scatter plot. 4. Plot a linear regression between the variables of iris dataset by specifing the hue. 5. Plot a linear regression between the variables of iris dataset grouped by row-wise. 6. Make a striplot of SL, SW and PL, PW grouped by Species. 7. Generate a swarmplot. 8. Generate a Violin plot 9. Generate a joint plot 10. Pairwise joint Distribution 11. Pairwise joint Distribution grouped by Species 12. Heatmap 13. Boxplot 14. kdeplot 15. Andrews Curve 16. Radviz. ''' import pandas as pd import matplotlib.pyplot as plt import seaborn as sns iris = pd.read_csv('iris.csv') # 1. Seaborn lmplot for plotting linear regression. sns.lmplot(x='SepalLength',y='SepalWidth',data=iris) sns.lmplot(x='PetalLength',y='PetalWidth',data=iris) # 2. Generate a residual plot. sns.residplot(x = iris['SepalLength'], y =iris['SepalWidth'], color='red') sns.residplot(x = iris['PetalLength'], y =iris['PetalWidth'], color='red') # 3. Generate a Scatter plot. plt.scatter(iris['SepalLength'], iris['SepalWidth'], label='iris', color='red',marker = 'o') #plt.scatter(iris['SepalLength'], iris['SepalWidth'], label='iris', color='red',marker = 'o') sns.regplot(x = iris['SepalLength'], y =iris['SepalWidth'], data = 'iris', color='red', label = 'Order 1', order=1) sns.regplot(x = iris['SepalLength'], y =iris['SepalWidth'], data = 'iris', color='blue', label = 'Order 2', order=2) sns.regplot(x = iris['SepalLength'], y =iris['SepalWidth'], data = 'iris', color='yellow', label = 'Order 3', order=3) # 4. Plot a linear regression between the variables of iris dataset by specifing the hue. sns.lmplot(x='SepalLength',y='SepalWidth',data=iris,hue='Species', palette='Set1') # 5. Plot a linear regression between the variables of iris dataset grouped by row-wise. sns.lmplot(x='SepalLength',y='SepalWidth',data=iris,row='Species') # 6. Make a striplot of SL, SW and PL, PW grouped by Species. plt.subplot(2,2,1) sns.stripplot(x='Species',y='SepalLength',data=iris) plt.subplot(2,2,2) sns.stripplot(x='Species',y='SepalWidth',data=iris, jitter = True, size=4) #7. Generate a swarmplot. sns.swarmplot(x='Species', y = 'SepalWidth', data=iris) sns.swarmplot(x='Species', y = 'SepalLength', data=iris) #8. Generate a Violin plot sns.violinplot(x='Species', y = 'SepalWidth', data=iris) #9. Generate a joint plot sns.jointplot(x='SepalLength', y = 'SepalWidth', data=iris) #10. Pairwise joint Distribution sns.jointplot(x='SepalLength', y = 'SepalWidth', data=iris, kind='resid') #11. Pairwise joint Distribution grouped by Species sns.pairplot(iris) # Pairwise joint Distribution grouped by Species sns.pairplot(iris,hue='Species',kind='reg') # 12. Heatmap sns.heatmap(iris.corr(),linewidth=0.3,vmax=1.0,square=True, linecolor='black',annot=True) # 13 Boxplot sns.boxplot(x='Species', y = 'SepalLength', data=iris) # 14. Kdeplot sns.FacetGrid(iris,hue='Species',size=4) \ .map(sns.kdeplot,'SepalLength') \ .add_legend() # Andrews Curve from pandas.tools.plotting import andrews_curves andrews_curves(iris.drop("Id", axis=1), 'Species') # radviz from pandas.tools.plotting import radviz radviz(iris.drop("Id", axis=1), 'Species')