Seaborn Tutorial.py

#!/usr/bin/env python
# coding: utf-8

# ### Seaborn Tutorial

# #### Distrbution Plots  (for continuous features-numerical)
#   #Distplot
#   #Joinplot
#   #Pairplot

# #### Practise problems on IRIS Dataset

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import seaborn as sns


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df=sns.load_dataset("tips")


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df


# ### Corelation Using Heatmap

# #### only integer types are selected A corelation heatmap uses coloued cells,Typically in monochromatic scale,to show a 2D 
# #### matrixbetween two discrete dimensions or event types.It is very important in feature selection

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df.corr()  #only selects integer dimensions


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sns.heatmap(df.corr())


# ### JoinPlot

# #### it allows to study the relationship between 2 numeric variables.The central chart display their corelation.
# #### It is usually a scatter plot, a hexbin plot,2D Histogram or a 2D Density plot

# ### Biverate Analysis

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sns.jointplot(x='tip',y='total_bill',data=df,kind='hex')


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sns.jointplot(x='tip',y='total_bill',data=df,kind='reg')

# draws probability density function pdf curv around histogram curve and regression line inside box


# ### Pairplot(Scatterplot)

# #### One variable in the same data row is matched with another variables values,
# #### like this: pair plots are just elborations on this,showing all variables paired with all the other variables.

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sns.pairplot(df)


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sns.pairplot(df,hue='sex')


# ### Distplot

# #### It helps us to check the districution of the column feature

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sns.distplot(df['tip'])


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# the line is called KDE - Kernel density estimation


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sns.distplot(df['tip'],kde=False,bins=10)


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