import numpy as np 
import pandas as pd 
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from wordcloud import WordCloud, STOPWORDS , ImageColorGenerator
import re
from PIL import Image
import pandas_profiling as pp
"""
### Import Data sets 
"""
intake_df=pd.read_csv('../input/animal-shelter-analytics/Austin_Animal_Center_Intakes.csv')
outcome_df=pd.read_csv('../input/animal-shelter-analytics/Austin_Animal_Center_Outcomes.csv')
stray_df=pd.read_csv('../input/animal-shelter-analytics/Austin_Animal_Center_Stray_Map.csv')

"""
### Using Pandas Profiling to Explore intake dataset
"""
pp.ProfileReport(intake_df)
"""
### Map Visualization With Datawrapper

Datawrapper is an open source tool helping everyone to create simple, correct and embeddable charts in minutes.

* visit and try it from here:

https://app.datawrapper.de/
"""
"""
![Xn-Gm-G-map-for-animal-shelter-intakes.png](https://i.postimg.cc/zBYJS0tQ/Xn-Gm-G-map-for-animal-shelter-intakes.png)
"""
"""
### Sweetviz Library Setup
"""
!pip install sweetviz

# importing sweetviz
import sweetviz as sv
#analyzing the dataset

"""
### EDA with Sweetviz to Outcomes Dataset
"""
advert_report = sv.analyze(outcome_df)

advert_report.show_notebook()