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.
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:
- distplot
- jointplot
- pairplot
- rugplot
- kdeplot
Categorical Data Plots
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fig 01) Seaborn Library in python for Data Visualization Tutorial |
- factorplot
- boxplot
- violinplot
- stripplot
- swarmplot
- barplot
- 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).
SOME BASIC EXAMPLE
1. EXAMPLE:
1. EXAMPLE:
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
2. EXAMPLE:
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',
u'titanic']
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