Basic Data Analysis and Visualization Using Python

by Yanal Kashou

This is a free dataset of exoplanets from the RDatasets.

The data was obtained from the URLs below of the .csv data file and the .html documentation file, respectively:

It contains 30 observations on the following 3 variables.

Price Asking price for the car (in $1,000's)
Age Age of the car (in years)
Mileage Previous miles driven (in 1,000's)

Load the Dataset



In [4]:

    
import pandas as pd
porsche = pd.read_csv("PorschePrice.csv")

Explore the Dataset



In [5]:

    
porsche.shape









    Out[5]:





(30, 4)



In [6]:

    
porsche.head(5)



In [7]:

    
porsche = porsche.rename(columns = {'Unnamed: 0':'Number'})



In [8]:

    
porsche.head(5)



In [9]:

    
porsche.describe()

Plotting Using Seaborn and PyPlot



In [10]:

    
import seaborn as sns
import matplotlib.pyplot as plt

Pairplot



In [11]:

    
sns.pairplot(porsche[["Price", "Age", "Mileage"]])
plt.show()

Radial Visualization



In [12]:

    
from pandas.tools.plotting import radviz

plt.figure()
radviz(porsche, 'Age')
plt.show()

Vertical Barchart



In [13]:

    
plt.figure();
porsche.plot(kind = 'bar', stacked = True);
plt.show()









    





<matplotlib.figure.Figure at 0x16699b38cc0>

Horizontal Barchart



In [14]:

    
porsche.plot(kind='barh', stacked=True);
plt.show()

Histogram



In [15]:

    
plt.figure();
porsche['Mileage'].diff().hist(bins = 7)
plt.show()

Andrews Curves



In [30]:

    
from pandas.tools.plotting import andrews_curves
plt.figure()
andrews_curves(porsche, 'Age', colormap = 'autumn')
plt.show()

	Unnamed: 0	Price	Age	Mileage
0	1	69.4	3	21.5
1	2	56.9	3	43.0
2	3	49.9	2	19.9
3	4	47.4	4	36.0
4	5	42.9	4	44.0

	Number	Price	Age	Mileage
0	1	69.4	3	21.5
1	2	56.9	3	43.0
2	3	49.9	2	19.9
3	4	47.4	4	36.0
4	5	42.9	4	44.0

	Number	Price	Age	Mileage
count	30.000000	30.000000	30.000000	30.000000
mean	15.500000	50.536667	6.200000	34.872333
std	8.803408	15.542211	5.868678	23.504416
min	1.000000	16.000000	0.000000	0.670000
25%	8.250000	40.650000	3.000000	19.300000
50%	15.500000	51.900000	4.000000	33.150000
75%	22.750000	58.650000	7.750000	48.375000
max	30.000000	83.000000	22.000000	89.600000