
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)

import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

import os
import time

# random state
SEED = 0
RNG = np.random.RandomState(SEED)

# Jupyter notebook settings for pandas
pd.set_option('display.max_columns', 50)
pd.set_option('display.float_format', '{:,.2g}'.format) # numbers sep by comma
pd.set_option('display.max_rows', 20) # None for all the rows
pd.set_option('display.max_colwidth', 50)

import IPython
from IPython.display import display

print([(x.__name__,x.__version__) for x in [np, pd,sns,matplotlib]])

[('numpy', '1.16.4'), ('pandas', '0.25.0'), ('seaborn', '0.9.0'), ('matplotlib', '3.1.1')]


%load_ext autoreload
%autoreload 2


%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;


import bokeh
from bokeh.io import output_file, output_notebook
from bokeh.plotting import figure, show, reset_output
from bokeh.models import ColumnDataSource
from bokeh.layouts import row, column, gridplot
from bokeh.models.widgets import Tabs, Panel
from bokeh.palettes import Spectral6
from bokeh.models import ColumnDataSource,FactorRange

# Output the visualization directly in the notebook
output_notebook()

[(x.__name__,x.__version__) for x in [bokeh]]

[('bokeh', '1.3.4')]


%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;


def show_method_attributes(method, ncols=7):
    """ Show all the attributes of a given method.
    Example:
    ========
    show_method_attributes(list)
     """
    x = [i for i in dir(method) if i[0]!='_']
    x = [i for i in x if i not in 'os np pd sys time psycopg2'.split()]

    return pd.DataFrame(np.array_split(x,ncols)).T.fillna('')


df = pd.read_csv('../data/processed/data_cleaned_encoded.csv')
print(df.shape)
df.head().T

(21613, 92)


df['bedrooms'].value_counts()

3     9824
4     6882
2     2760
5     1601
6      272
1      199
7       38
8       13
0       13
9        6
10       3
11       1
33       1
Name: bedrooms, dtype: int64


from bhishan.util_bokeh import countplot_bokeh


ofile = '../reports/bokeh_outputs/bedrooms_countplot.html'
countplot_bokeh(df, 'bedrooms',height=400,ofile=None)


countplot_bokeh(df,'view',height=300)


from bhishan.util_bokeh import histogram_bokeh

histogram_bokeh(df,'sqft_living',n_bins=20)


from bhishan.util_bokeh import scatterplot_bokeh


ofile = '../reports/bokeh_outputs/sqftLiving_vs_price.html'
scatterplot_bokeh(df,'sqft_living','price',ofile=ofile)


from bhishan.util_bokeh import stacked_countplot_bokeh

stacked_countplot_bokeh(df,'bedrooms','yr_sales','price')


df[['lat','long']].head()


from bhishan.util_bokeh import map_plot_bokeh


ofile = '../reports/bokeh_outputs/map.html'
map_plot_bokeh(df, 'lat', 'long',ofile)

	0	1	2	3	4
id	7129300520	6414100192	5631500400	2487200875	1954400510
date	2014-10-13	2014-12-09	2015-02-25	2014-12-09	2015-02-18
price	2.2e+05	5.4e+05	1.8e+05	6e+05	5.1e+05
bedrooms	3	3	2	4	3
bathrooms	1	2.2	1	3	2
sqft_living	1180	2570	770	1960	1680
sqft_lot	5650	7242	10000	5000	8080
floors	1	2	1	1	1
waterfront	0	0	0	0	0
view	0	0	0	0	0
...	...	...	...	...	...
age_after_renovation_cat_7	0	0	1	0	0
age_after_renovation_cat_8	0	0	0	0	0
age_after_renovation_cat_9	0	0	0	0	0
log1p_price	12	13	12	13	13
log1p_sqft_living	7.1	7.9	6.6	7.6	7.4
log1p_sqft_lot	8.6	8.9	9.2	8.5	9
log1p_sqft_above	7.1	7.7	6.6	7	7.4
log1p_sqft_basement	0	6	0	6.8	0
log1p_sqft_living15	7.2	7.4	7.9	7.2	7.5
log1p_sqft_lot15	8.6	8.9	9	8.5	8.9

	lat	long
0	48	-1.2e+02
1	48	-1.2e+02
2	48	-1.2e+02
3	48	-1.2e+02
4	48	-1.2e+02

Table of Contents

Data Description¶

Imports¶

Useful Scripts¶

Load the data¶

Univariate Analysis¶

Discrete variables and Categorical variables¶

Continuous variables¶

histograms¶

Bi-variate Analysis¶

Scatter plots¶

Multi-variate Analysis¶

countplots¶

Map visualization¶