Preliminaries

/* import modules */
import pandas as pd

/* Create dataframe */
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]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])
df

/* Create a groupby variable that groups preTestScores by regiment */
groupby_regiment = df['preTestScore'].groupby(df['regiment'])
groupby_regiment

<pandas.core.groupby.SeriesGroupBy object at 0x113ddb550>

“This grouped variable is now a GroupBy object. It has not actually computed anything yet except for some intermediate data about the group key df['key1']. The idea is that this object has all of the information needed to then apply some operation to each of the groups.” – Python for Data Analysis.

View a grouping

Use list() to show what a grouping looks like

list(df['preTestScore'].groupby(df['regiment']))

[('Dragoons', 4     3
  5     4
  6    24
  7    31
  Name: preTestScore, dtype: int64), ('Nighthawks', 0     4
  1    24
  2    31
  3     2
  Name: preTestScore, dtype: int64), ('Scouts', 8     2
  9     3
  10    2
  11    3
  Name: preTestScore, dtype: int64)]

Descriptive statistics by group

df['preTestScore'].groupby(df['regiment']).describe()

Mean of each regiment’s preTestScore

groupby_regiment.mean()

regiment
Dragoons      15.50
Nighthawks    15.25
Scouts         2.50
Name: preTestScore, dtype: float64

Mean preTestScores grouped by regiment and company

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

Mean preTestScores grouped by regiment and company without heirarchical indexing

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

Group the entire dataframe by regiment and company

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

Number of observations in each regiment and company

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

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

Iterate an operations over groups

/* Group the dataframe by regiment, and for each regiment, */
for name, group in df.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

Group by columns

Specifically in this case: group by the data types of the columns (i.e. axis=1) and then use list() to view what that grouping looks like

list(df.groupby(df.dtypes, axis=1))

[(dtype('int64'),     preTestScore  postTestScore
  0              4             25
  1             24             94
  2             31             57
  3              2             62
  4              3             70
  5              4             25
  6             24             94
  7             31             57
  8              2             62
  9              3             70
  10             2             62
  11             3             70),
 (dtype('O'),       regiment company      name
  0   Nighthawks     1st    Miller
  1   Nighthawks     1st  Jacobson
  2   Nighthawks     2nd       Ali
  3   Nighthawks     2nd    Milner
  4     Dragoons     1st     Cooze
  5     Dragoons     1st     Jacon
  6     Dragoons     2nd    Ryaner
  7     Dragoons     2nd      Sone
  8       Scouts     1st     Sloan
  9       Scouts     1st     Piger
  10      Scouts     2nd     Riani
  11      Scouts     2nd       Ali)]

In the dataframe “df”, group by “regiments, take the mean values of the other variables for those groups, then display them with the prefix_mean

df.groupby('regiment').mean().add_prefix('mean_')

Create a function to get the stats of a group

def get_stats(group):
    return {'min': group.min(), 'max': group.max(), 'count': group.count(), 'mean': group.mean()}

Create bins and bin up postTestScore by those pins

bins = [0, 25, 50, 75, 100]
group_names = ['Low', 'Okay', 'Good', 'Great']
df['categories'] = pd.cut(df['postTestScore'], bins, labels=group_names)

Apply the get_stats() function to each postTestScore bin

df['postTestScore'].groupby(df['categories']).apply(get_stats).unstack()