Description
Imagine you are a data scientist and you're currently working with the data for local hospitals. You have several files with information about patients from different districts. Sometimes, the data is split into many datasets or may contain empty or invalid values. The first step is to preprocess the data before the analysis: merge the files into one, delete empty or incorrect rows, fill the missing values, and so on.
In this stage, you will deal with datasets that contain information about patients from three hospitals: a general, a prenatal, and a sports one. You need to upload the data from the hidden test directory of the project for further processing.
Objectives
In this stage, your program should:
- After all the libraries imports write the following line of code:
It sets the number of columns, whichpd.set_option('display.max_columns', 8)pandaslets to display in the terminal. Unfortunately, this number differs occasionally and causes problems in the tests, so we need to fix the number. - Read 3 CSV files containing the datasets
- Print the first 20 rows of each DataFrame. Use the following order:
general,prenatal,sports
If you have corrupted CSV files, please download them and unzip in your working directory. You need to copy all three file paths to use them when loading datasets in your program.
Example
The input is 3 CSV files, test/general.csv, test/prenatal.csv, and test/sports.csv. As mentioned above, the test directory is hidden. Don't worry that you can't see it, just use the paths from the previous sentence. If it doesn't work try to download data as described in Objectives.
The output is the following:
(This data is given for reference only, the actual values might be different)
Unnamed: 0 hospital gender age ... mri xray children months
8 0 Brighton girl NaN ... NaN no NaN 2.0
12 1 Brighton female 16.0 ... NaN no 2.0 2.0
13 2 Brighton girl 64.0 ... 185.0 yes 0.0 1.0
17 3 Brighton NaN 33.0 ... NaN NaN 2.0 2.0
19 4 Brighton NaN NaN ... 168.0 NaN 1.0 1.0
27 5 Brighton female 30.0 ... 85.0 NaN 1.0 NaN
29 6 Brighton female 51.0 ... NaN no 0.0 NaN
36 7 Brighton NaN 29.0 ... NaN NaN 1.0 1.0
41 8 Brighton female NaN ... 55.0 yes NaN NaN
45 9 Brighton man 24.0 ... 53.0 NaN 3.0 NaN
50 10 Brighton female 37.0 ... 155.0 no NaN 2.0
51 11 Brighton girl NaN ... NaN yes NaN 2.0
52 12 Brighton girl 40.0 ... 59.0 no 1.0 2.0
55 13 Brighton NaN 56.0 ... NaN no 2.0 2.0
56 14 Brighton man 41.0 ... 52.0 yes NaN NaN
61 15 Brighton NaN 45.0 ... 144.0 NaN 1.0 1.0
64 16 Brighton female 59.0 ... 71.0 NaN 3.0 1.0
74 17 Brighton NaN 54.0 ... 85.0 no 1.0 1.0
75 18 Brighton NaN 48.0 ... 42.0 no NaN NaN
77 19 Brighton NaN 26.0 ... 200.0 NaN 3.0 NaN
[20 rows x 15 columns]
Unnamed: 0 hospital gender age ... mri xray children months
0 0 Cambridge boy 52.0 ... 151.0 yes NaN 2.0
2 1 Cambridge female 60.0 ... 141.0 yes 0.0 2.0
4 2 Cambridge NaN NaN ... 135.0 yes 2.0 1.0
5 3 Cambridge female NaN ... 97.0 yes NaN 1.0
11 4 Cambridge NaN NaN ... 69.0 no 0.0 1.0
16 5 Cambridge NaN 33.0 ... NaN no 1.0 1.0
21 6 Cambridge girl 21.0 ... 49.0 yes 2.0 2.0
26 7 Cambridge boy NaN ... 137.0 NaN 3.0 1.0
32 8 Cambridge NaN 48.0 ... 341.0 yes 1.0 NaN
35 9 Cambridge girl NaN ... 102.0 NaN 1.0 2.0
37 10 Cambridge boy 55.0 ... 276.0 yes NaN 1.0
39 11 Cambridge NaN 46.0 ... NaN no 3.0 2.0
49 12 Cambridge man 51.0 ... 142.0 yes 3.0 2.0
58 13 Cambridge man 29.0 ... NaN no NaN NaN
62 14 Cambridge boy 64.0 ... NaN no NaN 1.0
66 15 Cambridge boy NaN ... 150.0 yes 2.0 1.0
68 16 Cambridge female NaN ... 160.0 NaN 1.0 1.0
70 17 Cambridge NaN NaN ... 48.0 NaN 2.0 1.0
72 18 Cambridge man 56.0 ... NaN no 2.0 2.0
83 19 Cambridge female 30.0 ... NaN yes 2.0 1.0
[20 rows x 15 columns]
Unnamed: 0 hospital gender age ... mri xray children months
1 0 Oxford boy 52.0 ... 75.0 yes 2.0 1.0
7 1 Oxford NaN NaN ... 63.0 no 0.0 NaN
9 2 Oxford man 18.0 ... 310.0 yes 0.0 2.0
10 3 Oxford boy 56.0 ... NaN no NaN NaN
14 4 Oxford girl NaN ... NaN no 1.0 NaN
23 5 Oxford boy 61.0 ... 245.0 yes 0.0 NaN
25 6 Oxford NaN 15.0 ... NaN yes 1.0 NaN
33 7 Oxford man 43.0 ... 87.0 NaN 0.0 1.0
38 8 Oxford girl NaN ... 252.0 no 1.0 1.0
43 9 Oxford man 26.0 ... 92.0 no NaN 2.0
44 10 Oxford NaN 54.0 ... 259.0 yes NaN 1.0
48 11 Oxford NaN NaN ... 75.0 yes 1.0 1.0
54 12 Oxford NaN 20.0 ... 182.0 yes 2.0 NaN
57 13 Oxford female NaN ... 37.0 yes 2.0 NaN
60 14 Oxford female NaN ... NaN yes 1.0 2.0
65 15 Oxford NaN 49.0 ... 163.0 NaN 2.0 2.0
67 16 Oxford NaN NaN ... 97.0 no 3.0 1.0
69 17 Oxford female NaN ... 178.0 no 1.0 1.0
71 18 Oxford female 66.0 ... 270.0 yes 3.0 2.0
73 19 Oxford female NaN ... NaN NaN NaN NaN
[20 rows x 15 columns]