Seaborn Library in python for Data Visualization Tutorial

Seaborn for Data Visualization:

Want learn Seaborn Library in python for Data Visualization Tutorial?
Hello everyone, I'm already post the blog of Data Visualization for Matplotlib so it's time to see second part of the post Data Visualization with Seaborn liabrary.  Let's start toward the seaborn.
There the different plot are used in Saborn, we have to see  some plot in this blog:

Distribution Plots

Let's discuss some plots that allow us to visualize the distribution of a data set. These plots are:
  1. distplot
  2. jointplot
  3. pairplot
  4. rugplot
  5. kdeplot

Categorical Data Plots

Seaborn Library in python for Data Visualization Tutorial

Now let's discuss using seaborn to plot categorical data! There are a few main plot type for this:
  1. factorplot
  2. boxplot
  3. violinplot
  4. stripplot
  5. swarmplot
  6. barplot
  7. countplot
Let's go through examples of each!

Matrix Plot

Matrixs plot allow you to plot data as color-encoded matrices and can also be used to indicate cluster within the data (later in the machine learning section we will learn how to formally cluster data).
The following lines of code will help you import the dataset −
# Seaborn for plotting and styling
import seaborn as sb
df = sb.load_dataset('tips')
print df.head()
The above lines of code will generate the following output −
   totalbill  tip   sexs    smoker day  time   size
0    16.99    1.01   Female  Yes    Sun  Dinner  2
1    10.34    1.66   Male    No    Sun  Dinner  3
2    21.01    3.50   Male    No    Sun  Dinner  3
3    23.68    3.31   Male    No    Sun  Dinner  2
4    24.59    3.61   Female  Yes   Sun  Dinner  4
import seaborn as sb
print sb.get_dataset_names()
The above lines of code will return the list of datasets available as the following output
[u'anscombe', u'attention', u'brain_networks', u'car_crashes', u'dots', 
u'exercise', u'flights', u'fmri', u'gammas', u'iris', u'planets', u'tips', 
(Note: Drive link in .ipynb format so it's only open in python notebook and if you like the blog then please like and share)
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