<jupyter_start><jupyter_text>SEABORN  Distribution plots- distplot- joinlot- pairplotpractise problem in iris dataset<jupyter_code>import seaborn as sns
df = sns.load_dataset('tips')
df.head()
# one feature f1 is called univariate analysis
# 2 features f1 and f2 bivariate analysis - 2d 
# if u have more than 3 features what we will do ??? 3d, and 4d - so we use pairplot<jupyter_output><empty_output><jupyter_text>here , tips column will be dependent feature and rest all the other columnn are independent features((very important))  Correlation with HeatmapA correlation heatmap uses colored cells, typically in a monochromatic scale, to show a 2D correlation matrix (table) between two discrete dimensions or event types. It is very important in Feature selection.<jupyter_code>df.corr()
# positively correlated means when x inc than y inc , vice versa
sns.heatmap(df.corr())<jupyter_output><empty_output><jupyter_text>JoinPlot A join plot allows to study the relationship between 2 numeric variables . The central chart display their correlation. It is usually a scatterplot , a hexbin plot, a 2D histogram or a 2D density plot.  Univariate Analysis<jupyter_code>sns.jointplot(x= 'tip', y = 'total_bill', data = df, kind='hex')
sns.jointplot(x= 'tip', y = 'total_bill', data=df, kind='reg')<jupyter_output><empty_output><jupyter_text>Pair plot A “pairs plot” is also known as a scatterplot, in which one variable in the same data row is matched with another variable's value, like this: Pairs plots are just elaborations on this, showing all variables paired with all the other variables<jupyter_code>sns.pairplot(df)
sns.pairplot(df, hue='sex')<jupyter_output><empty_output><jupyter_text>Dist PlotDist plot helps us to check the distribution of the columns feature<jupyter_code>sns.distplot(df['tip'])
sns.distplot(df['tip'], kde=False, bins=10)<jupyter_output><empty_output>
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