In [6]:

    

from __future__ import division, print_function, unicode_literals

import os
import numpy as np

np.random.seed(42)

%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt

plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12

# Function to save the figures. 
PROJECT_ROOT_DIR = "."
PROGRAM = "california_state_data"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", PROGRAM)

def save_fig (fig_id, tight_layout=True, fig_extension='png',resolution=300):
    path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
    print("Saving figure", fig_id)
    if tight_layout:
        plt.tight_layout()
    plt.savefig(path,format=fig_extension, dpi=resolution)


    

In [7]:

    

import os
import tarfile
from six.moves import urllib

DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
HOUSING_PATH = os.path.join("datasets", "housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"


def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):
    if not os.path.isdir(housing_path):
        os.makedirs(housing_path)
    tgz_path = os.path.join(housing_path,"housing.tgz")
    urllib.request.urlretrieve(housing_url,tgz_path)
    housing_tgz = tarfile.open(tgz_path)
    housing_tgz.extractall(path=housing_path)
    housing_tgz.close()


    

In [8]:

    

fetch_housing_data()


    

In [9]:

    

import pandas as pd

def load_housing_data(housing_path=HOUSING_PATH):
    csv_path = os.path.join(housing_path, "housing.csv")
    return pd.read_csv(csv_path)


    

In [11]:

    

housing = load_housing_data()
housing.head()


    

    
Out[11]:







  

    

      

      
longitude
      
latitude
      
housing_median_age
      
total_rooms
      
total_bedrooms
      
population
      
households
      
median_income
      
median_house_value
      
ocean_proximity
    
  
  

    

      
0
      
-122.23
      
37.88
      
41.0
      
880.0
      
129.0
      
322.0
      
126.0
      
8.3252
      
452600.0
      
NEAR BAY
    
    

      
1
      
-122.22
      
37.86
      
21.0
      
7099.0
      
1106.0
      
2401.0
      
1138.0
      
8.3014
      
358500.0
      
NEAR BAY
    
    

      
2
      
-122.24
      
37.85
      
52.0
      
1467.0
      
190.0
      
496.0
      
177.0
      
7.2574
      
352100.0
      
NEAR BAY
    
    

      
3
      
-122.25
      
37.85
      
52.0
      
1274.0
      
235.0
      
558.0
      
219.0
      
5.6431
      
341300.0
      
NEAR BAY
    
    

      
4
      
-122.25
      
37.85
      
52.0
      
1627.0
      
280.0
      
565.0
      
259.0
      
3.8462
      
342200.0
      
NEAR BAY
    
  

In [12]:

    

housing.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
longitude             20640 non-null float64
latitude              20640 non-null float64
housing_median_age    20640 non-null float64
total_rooms           20640 non-null float64
total_bedrooms        20433 non-null float64
population            20640 non-null float64
households            20640 non-null float64
median_income         20640 non-null float64
median_house_value    20640 non-null float64
ocean_proximity       20640 non-null object
dtypes: float64(9), object(1)
memory usage: 1.6+ MB

In [13]:

    

housing["ocean_proximity"].value_counts()


    

    
Out[13]:





<1H OCEAN     9136
INLAND        6551
NEAR OCEAN    2658
NEAR BAY      2290
ISLAND           5
Name: ocean_proximity, dtype: int64

In [14]:

    

housing.describe()


    

    
Out[14]:







  

    

      

      
longitude
      
latitude
      
housing_median_age
      
total_rooms
      
total_bedrooms
      
population
      
households
      
median_income
      
median_house_value
    
  
  

    

      
count
      
20640.000000
      
20640.000000
      
20640.000000
      
20640.000000
      
20433.000000
      
20640.000000
      
20640.000000
      
20640.000000
      
20640.000000
    
    

      
mean
      
-119.569704
      
35.631861
      
28.639486
      
2635.763081
      
537.870553
      
1425.476744
      
499.539680
      
3.870671
      
206855.816909
    
    

      
std
      
2.003532
      
2.135952
      
12.585558
      
2181.615252
      
421.385070
      
1132.462122
      
382.329753
      
1.899822
      
115395.615874
    
    

      
min
      
-124.350000
      
32.540000
      
1.000000
      
2.000000
      
1.000000
      
3.000000
      
1.000000
      
0.499900
      
14999.000000
    
    

      
25%
      
-121.800000
      
33.930000
      
18.000000
      
1447.750000
      
296.000000
      
787.000000
      
280.000000
      
2.563400
      
119600.000000
    
    

      
50%
      
-118.490000
      
34.260000
      
29.000000
      
2127.000000
      
435.000000
      
1166.000000
      
409.000000
      
3.534800
      
179700.000000
    
    

      
75%
      
-118.010000
      
37.710000
      
37.000000
      
3148.000000
      
647.000000
      
1725.000000
      
605.000000
      
4.743250
      
264725.000000
    
    

      
max
      
-114.310000
      
41.950000
      
52.000000
      
39320.000000
      
6445.000000
      
35682.000000
      
6082.000000
      
15.000100
      
500001.000000
    
  

In [17]:

    

%matplotlib inline 
import matplotlib.pyplot as plt
housing.hist(bins=50, figsize=(20,15))
save_fig("attribute_histogram_plots")
plt.show()


    

    




Saving figure attribute_histogram_plots






    

In [ ]: