Solutions.ipynb

Kernel: Python 2

Housing Market

Introduction:

This time we will create our own dataset with fictional numbers to describe a house market. As we are going to create random data don't try to reason of the numbers.

Step 1. Import the necessary libraries

import pandas as pd
import numpy as np

Step 2. Create 3 differents Series, each of length 100, as follows:

1. The first a random number from 1 to 4

2. The second a random number from 1 to 3

3. The third a random number from 10,000 to 30,000

0     2
1     2
2     4
3     2
4     1
5     1
6     2
7     3
8     3
9     2
10    1
11    2
12    4
13    1
14    2
15    3
16    4
17    4
18    4
19    3
20    2
21    1
22    4
23    1
24    3
25    2
26    3
27    1
28    3
29    4
     ..
70    4
71    2
72    2
73    4
74    2
75    1
76    2
77    4
78    3
79    2
80    2
81    2
82    4
83    2
84    2
85    2
86    1
87    3
88    1
89    1
90    1
91    3
92    1
93    2
94    3
95    4
96    4
97    2
98    1
99    3
dtype: int64 0     2
1     3
2     2
3     3
4     3
5     1
6     2
7     1
8     2
9     2
10    2
11    3
12    3
13    1
14    3
15    3
16    3
17    1
18    3
19    3
20    3
21    3
22    1
23    2
24    3
25    2
26    2
27    1
28    3
29    3
     ..
70    3
71    2
72    2
73    2
74    3
75    2
76    3
77    1
78    1
79    1
80    2
81    1
82    1
83    3
84    1
85    3
86    1
87    2
88    3
89    2
90    2
91    3
92    2
93    2
94    2
95    2
96    2
97    3
98    1
99    1
dtype: int64 0     16957
1     24571
2     28303
3     14153
4     23445
5     21444
6     16179
7     22696
8     18595
9     27145
10    14406
11    15011
12    17444
13    26236
14    23808
15    21417
16    15079
17    13100
18    21470
19    17082
20    21935
21    26770
22    10059
23    11095
24    25916
25    17137
26    22023
27    21612
28    11446
29    29281
      ...  
70    23963
71    26782
72    11199
73    23600
74    26935
75    27365
76    23084
77    19052
78    19922
79    17088
80    25468
81    10924
82    10243
83    19834
84    21288
85    22410
86    22348
87    18812
88    29522
89    20838
90    28695
91    23000
92    21684
93    26316
94    10866
95    12337
96    13480
97    25158
98    25585
99    26142
dtype: int64

Step 3. Let's create a DataFrame by joinning the Series by column

Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter

Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'

<class 'pandas.core.frame.DataFrame'>

Step 6. Oops, it seems it is going only until index 99. Is it true?

300

Step 7. Reindex the DataFrame so it goes from 0 to 299