Visualization Seaborn.ipynb

Kernel: Python 3

Seaborn

• is a library for making statistical graphics in Python. It is built on top of matplotlib and closely integrated with pandas data structures.

Here is some of the functionality that seaborn offers:

• A dataset-oriented API for examining relationships between multiple variables

• Specialized support for using categorical variables to show observations or aggregate statistics

• Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data

• Automatic estimation and plotting of linear regression models for different kinds dependent variables

• Convenient views onto the overall structure of complex datasets

• High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations

• Concise control over matplotlib figure styling with several built-in themes

• Tools for choosing color palettes that faithfully reveal patterns in your data

Sale data Survey

• Data Prepare

• Insights for Sales: Person type, Product Category

• Visualization

import pandas as pd
import seaborn as sns
import numpy as np
import warnings                                                                 # Ignore warning related to pandas_profiling
warnings.filterwarnings('ignore') 
sns.set()

viz_data=pd.read_csv(r"https://raw.githubusercontent.com/suyashi29/python-su/master/Sale%20day.csv")

viz_data.shape

(537577, 12)

viz_data.head(2)

viz_data.describe()

viz_data.isnull().sum()

User_ID                            0
Product_ID                         0
Gender                             0
Age                                0
Occupation                         0
City_Category                      0
Stay_In_Current_City_Years         0
Marital_Status                   286
Product_Category_1                 0
Product_Category_2            166986
Product_Category_3            373297
Purchase                           0
dtype: int64

sns.countplot('Gender',data=viz_data)

<matplotlib.axes._subplots.AxesSubplot at 0x199091ed9c8>

sns.boxplot(x="Gender", y="Purchase", data=viz_data,hue="Marital_Status")

<matplotlib.axes._subplots.AxesSubplot at 0x199093af208>

sns.violinplot(x='Marital_Status',y='Purchase',hue='Gender',data=viz_data,split=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1990954f908>