Importing packages

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

from bokeh.plotting import figure, ColumnDataSource
from bokeh.io import output_notebook, output_file, show, curdoc
from bokeh.models import HoverTool, CategoricalColorMapper, Slider, Select, Button
from bokeh.layouts import column, row, gridplot, widgetbox
from bokeh.models.widgets import Tabs, Panel

Reading Datasets

housePropertyDataset = pd.read_csv('house_property_sales.csv')

Seaborn Library

There is a package seaborn library available. Using which we can generate detailed chart representation.

Here we have also used legend to display legend and positioned it top right.

sns.set()

plt.plot(housePropertyDataset['SalePrice'])

plt.legend(loc='upper right')
plt.show()

Zoom Axes

You will now create the same figure as in the previous exercise using plt.plot(), this time setting the axis extents using plt.xlim() and plt.ylim(). These commands allow you to either zoom or expand the plot or to set the axis ranges to include important values (such as the origin).

NOTE: After creating the plot, we will use plt.savefig() to export the image produced to a file.

sns.set()

# We can use the styles for the plot
# print(plt.style.available) used to print all available styles
plt.style.use('ggplot')

plt.plot(housePropertyDataset['SalePrice'])

plt.xlabel('Property ID')
plt.ylabel('Sales Price')

plt.xlim(1100,1200)
plt.ylim(100000,800000)

plt.title('Price of various property located')

plt.show()

Using axes() function

Used to define the axes where we need to plot our graphs. Axes will define the axes for each and every individual plot to be drawn.

axes comamnd accept array of four arguments which are:

axes([x_low, y_low, width, height])

x_low: is the starting point of x-axis
y_low: Is starting point of y-axis
width: Is the total size of x axis
height: Is total size of y axis

plt.axes([200, 100000, 1, 1])
plt.plot(housePropertyDataset['SalePrice'])

plt.xlabel('Property ID')
plt.ylabel('Sales Price')

plt.title('Price of various property located')

plt.show()

Using subplots() function

Using subplots like this is a better alternative to using plt.axes(). We can manually add more than one plot in the same figure.

Subplot accepts three arguments, subplot(n_rows, n_columns, n_subplot)

Subplot ordering is row wise top left corner.

plt.style.use('ggplot')

plt.subplot(1,2,1)
plt.plot(housePropertyDataset['SalePrice'], color='red')
plt.title('Property Sale Price variation')
plt.xlabel('Property ID')
plt.ylabel('Sales Price')

plt.subplot(1,2,2)
plt.scatter(housePropertyDataset['GarageArea'],housePropertyDataset['SalePrice'], color='green')
plt.title('Top 10 Female Population')
plt.xlabel('Property Sales Price')
plt.ylabel('Garbage Area')


# Improve the spacing between subplots and display them
plt.tight_layout()
plt.show()

Bivariate Plots

Let's check whether there is any significance or trend in sex_ratio and total effective literacy rate.

x = housePropertyDataset['GarageArea']
y = housePropertyDataset['SalePrice']
plt.hist2d(x,y, bins=(10,20), range=((0, 1500), (0, 700000)), cmap='viridis')

plt.colorbar()

plt.xlabel('Garbage Area')
plt.ylabel('Property Sales Price')

plt.tight_layout()
plt.show()

Plotting second order regression plots

plt.scatter(housePropertyDataset['GarageArea'],housePropertyDataset['SalePrice'], label='data', 
            color='red', marker='o')

sns.regplot(x='GarageArea', y='SalePrice', data=housePropertyDataset
            , scatter=None, color='blue', label='order 1', order=1)

sns.regplot(x='GarageArea', y='SalePrice', data=housePropertyDataset
            , scatter=None, color='green', label='order 2', order=2)
plt.legend(loc='lower right')
plt.show()

As shown above, we have created rectangular bins for 2D array. In similar way, we can also generate haxagonal bins as well.

x = housePropertyDataset['GarageArea']
y = housePropertyDataset['SalePrice']
plt.hexbin(x,y, gridsize=(15,10),extent=(0, 1500, 0, 700000), cmap='winter')

plt.colorbar()

plt.xlabel('Garbage Area')
plt.ylabel('Sales Price')
plt.show()

# Looking at output of the graph, can we say males are more educated from female?

# Plot a linear regression between 'GarageArea' and 'SalePrice'
sns.lmplot(x='GarageArea', y='SalePrice', data=housePropertyDataset, 
           col='Street') # We can also use 'hue' parameter instead of col parameter to plt on the same graph

# Display the plot
plt.show()

sns.residplot(x='GarageArea', y='SalePrice', data=housePropertyDataset, color='blue')

# Display the plot
plt.show()

Mesh grid

u = list(range(1, 10))
v = list(range(11, 20))
X,Y = np.meshgrid(u,v)
Z  = 3*np.sqrt(X**2 + Y**2)

plt.subplot(2,1,1)
plt.contour(X, Y, Z)

plt.subplot(2,1,2)
plt.contour(X, Y, Z, 20) # 20 contour


plt.show()

u = list(range(1, 10))
v = list(range(11, 20))
X,Y = np.meshgrid(u,v)
Z  = 3*np.sqrt(X**2 + Y**2)

plt.subplot(2,1,1)
plt.contourf(X, Y, Z)

plt.subplot(2,1,2)
plt.contourf(X, Y, Z, 20, cmap='winter') # 20 contour will be mapped


plt.show()

Strip plot

plt.subplot(1,2,1)
sns.stripplot(x='Street', y='SalePrice', data=housePropertyDataset, jitter=True, size=3)
plt.xticks(rotation=90)

plt.subplot(1,2,2)
sns.stripplot(x='Neighborhood', y='SalePrice', data=housePropertyDataset, jitter=True, size=3)
plt.xticks(rotation=90)

plt.subplots_adjust(right=3)
plt.show()

Swarn Plot

Swarm plots are generally easier to understand than strip plots because they spread out the points to avoid overlap.

plt.subplot(1,2,1)
sns.swarmplot(x='Street', y='SalePrice', data=housePropertyDataset, hue='SaleCondition')
plt.xticks(rotation=90)

plt.subplot(2,2,2)
sns.stripplot(x='Neighborhood', y='SalePrice', data=housePropertyDataset, hue='SaleCondition')
plt.xticks(rotation=90)

plt.subplots_adjust(right=3)
plt.show()

Violin Plot

violin plots are a nice way of visualizing the relationship between a continuous variable and a categorical variable

plt.subplot(2,1,1)
sns.violinplot(x='SaleType', y='SalePrice', data=housePropertyDataset)

plt.subplot(2,1,2)
sns.violinplot(x='SaleType', y='SalePrice', data=housePropertyDataset, color='lightgray', inner=None)

sns.stripplot(x='SaleType', y='SalePrice', data=housePropertyDataset, jitter=True, size=1.5)

plt.show()

Joint Plot

fig = plt.figure(figsize = (16,89))
sns.jointplot(x='GarageArea', y='SalePrice', data=housePropertyDataset)
plt.xticks(rotation=90)
plt.show()

<matplotlib.figure.Figure at 0x1a211dc0f0>

Pair Plot

It uses only integer columns only. Remaining columns are ignored.

data = housePropertyDataset[['SaleCondition', 'SalePrice','OverallQual',
                             'TotalBsmtSF','GrLivArea','GarageArea','FullBath','YearBuilt','YearRemodAdd']]
sns.pairplot(data, hue='SaleCondition')
plt.show()

Heat maps

numeric_features = housePropertyDataset.select_dtypes(include=[np.number])
sns.heatmap(numeric_features.corr())
plt.title('Correlation heatmap')
plt.show()

Box Plot

sns.boxplot(x='Street', y='SalePrice', data=housePropertyDataset)
plt.title('Sale price by Street')
plt.show()

Univariate Plots

def ecdf(data):
    # Number of data points: n
    n = len(data)
    # x-data for the ECDF: x
    x = np.sort(data)
    # y-data for the ECDF: y
    y = np.arange(1, n+1) / n
    return x, y

SalePrice = housePropertyDataset['SalePrice']
x_vers, y_vers = ecdf(SalePrice)

plt.plot(x_vers, y_vers, marker = '.', linestyle = 'none')

plt.ylabel('ECDF')

plt.show()