Exercises_solutions.ipynb

Kernel: Python 3

Regiment

Check out Regiment Exercises Video Tutorial to watch a data scientist go through the exercises

Introduction:

Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.

Step 1. Import the necessary libraries

import pandas as pd

Step 2. Create the DataFrame with the following values:

raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}

Step 3. Assign it to a variable called regiment.

Don't forget to name each column

regiment = pd.DataFrame(raw_data, columns = raw_data.keys())
regiment

Step 4. What is the mean preTestScore from the regiment Nighthawks?

regiment[regiment['regiment'] == 'Nighthawks'].groupby('regiment').mean()

Step 5. Present general statistics by company

regiment.groupby('company').describe()

Step 6. What is the mean of each company's preTestScore?

regiment.groupby('company').preTestScore.mean()

company
1st     6.666667
2nd    15.500000
Name: preTestScore, dtype: float64

Step 7. Present the mean preTestScores grouped by regiment and company

regiment.groupby(['regiment', 'company']).preTestScore.mean()

regiment    company
Dragoons    1st         3.5
            2nd        27.5
Nighthawks  1st        14.0
            2nd        16.5
Scouts      1st         2.5
            2nd         2.5
Name: preTestScore, dtype: float64

Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing

regiment.groupby(['regiment', 'company']).preTestScore.mean().unstack()

Step 9. Group the entire dataframe by regiment and company

regiment.groupby(['regiment', 'company']).mean()

Step 10. What is the number of observations in each regiment and company

regiment.groupby(['company', 'regiment']).size()

company  regiment  
1st      Dragoons      2
         Nighthawks    2
         Scouts        2
2nd      Dragoons      2
         Nighthawks    2
         Scouts        2
dtype: int64

Step 11. Iterate over a group and print the name and the whole data from the regiment

# Group the dataframe by regiment, and for each regiment,
for name, group in regiment.groupby('regiment'):
    # print the name of the regiment
    print(name)
    # print the data of that regiment
    print(group)

Dragoons
   regiment company    name  preTestScore  postTestScore
4  Dragoons     1st   Cooze             3             70
5  Dragoons     1st   Jacon             4             25
6  Dragoons     2nd  Ryaner            24             94
7  Dragoons     2nd    Sone            31             57
Nighthawks
     regiment company      name  preTestScore  postTestScore
0  Nighthawks     1st    Miller             4             25
1  Nighthawks     1st  Jacobson            24             94
2  Nighthawks     2nd       Ali            31             57
3  Nighthawks     2nd    Milner             2             62
Scouts
   regiment company   name  preTestScore  postTestScore
8    Scouts     1st  Sloan             2             62
9    Scouts     1st  Piger             3             70
10   Scouts     2nd  Riani             2             62
11   Scouts     2nd    Ali             3             70