Hey guys, So here comes the fourth blog of the Handwritten notes series which we started. We will be talking about **Seaborn** in this blog. I have uploaded my handwritten notes below and tried to explain them in the shortest and best way possible.

The first blog of this series was **NumPy Handwritten Notes** and the second was **Pandas Handwritten Notes**. If you haven’t seen those yet, go check them out.

### Let’s go through the Seaborn notes…

**Distribution Plot**is used to show the distribution of a univariate set of observations. This means this plot is used to show the distribution of a column. It can also be thought of as a Histogram.**Jointplot**is simply a plot between two numerical columns.

**kind**is a very important parameter in jointplot which by default is set as**‘scatter’**. We can also set it as**‘hex’, ‘reg’, ‘kde’**.**Pairplot**produces jointplots in scatter mode for all the possible numerical column combinations present in our data.- We can also provide a
*‘hue’*argument which is a categorical column that in turn will color our plot in two or three different colors depending on the number of categories present in that column.

- I have shown the example of usage of hue in the first plot.
- A special argument
*‘palette’*can be used to change the color scheme of the plot. **Rugplot**is a type of plot which looks similar to a*barcode*. It just puts a**|**where the data point is.- Now let’s talk about some
**Categorical Plots**. - First one is obviously the
**Barplot**. We have to provide (x, y, data) as the arguments. Here on y axis we have taken total_bill which is a numerical column. The default estimator for bar plots is**mean**. We can also change that.

**Barplot**is playing a sort of**GroupBy**mechanism here as it grouped all the columns by*‘sex’*column and then took its mean to show on the y axis.**Countplot**as the name says is just used to depict the counts of certain categories in a column.**Boxplot**is also a very useful plot that seaborn provides. It is basically used to check for outliers and Interquartile ranges.

**Violin Plot**is a plot that is shaped like a violin as shown above.**Factor Plot**is the most general plot of all these forms where we just have to specify the kind(bar, violin, strip, etc.).

**Heatmap**is a very useful plot when dealing with raw data while doing preprocessing. Seaborn provides an easy way to plot Heatmaps using**sns.heatmap()**. If we want our values written on the plot, we have to pass ‘*annot=True*‘ as a parameter to the function. We can also change the colormap using cmap parameter.**Clustermap**is also a heatmap but the difference is that it clusters similar data together in the heatmap.**Pairgrid**is a very special feature of seaborn class that allows us to plot different plots on diagonals, upper triangle, and lower triangle.

**sns.lmplot()**is simply the linear regression plot.- We can also specify
*col = ‘sex’*to make n number of columns (male and female) having their respective plots. Similarly, we can do this for the row also. - In the example above we have split cols on sex and rows on time.

**aspect**is the ratio between the height and width of the plot.

Do let me know if there’s any query regarding Seaborn by contacting me on email or LinkedIn.

*So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time ?…*

*READ MY PREVIOUS BLOG: MATPLOTLIB – LAST-MINUTE NOTES – HANDWRITTEN NOTES*

**Check out my other machine learning projects, deep learning projects, computer vision projects, NLP projects, Flask projects at machinelearningprojects.net**.