# See Seaborn

Today we shall explore the world of python libraries such as Seaborn and Matplotlib which serves as a tool to visualise various statistical models.Preparation, plotting and presenting are regarded as the preliminary steps in data analysis and we should have a strong hold on some basic functions of these libraries.

# 1.Matrix Plot

Matrix plot allow us to plot data as color encoded matrices. Most common among them is heatmap.

*plt* is alias of matplotlib.pyplot and* plt.figure* with mentioned dimensions of figure initiate figure. *sns .heatmap *takes dayhour dataframe (all numeric entries) and with cmap variable initialised with particular color scheme plots heatmap for mentioned dataframe.

In following heatmap we have *annot *as additional parameter which when **True** write data value in each cell.

# 2.Swarm Plot

This plot falls under the category of categorical data plots.The swarmplot is similar to *stripplot()*, but the points are adjusted (only along the categorical axis) so that they don’t overlap. This gives a better representation of the distribution of values, although it does not scale as well to large numbers of observations (both in terms of the ability to show all the points and in terms of the computation needed to arrange them).

# 3. Regression Plot

Regression plots are very useful in machine learning projects. Basically these plots helps in creating regression line between two parameters.*lmplot() *function is used to create linear model plot between variable x=’total_bill’ and y=’tip’,hue=’sex’ is used to categorise plot on the basis of gender or sex.

# 4.Pair Plot

This function is used to plot bi variable multi pairwise distribution.Here it took *iris dataframe* and* hue* variable which on basis of species ,plot distribution separately on each subplot.

This is all for this post , till then keep exploring some amazing functions and libraries of Python.