Typing & Docstrings: Python

1_typing_docstrings_documentation.py

# function to iterate over specified variables and view their value counts

# add typing to help understand our function if reused elsewhere
from typing import List
def value_count_rep(columns: List, df: pd.DataFrame) -> None:
    '''
    Parameters: List of columns to iterate over
    Returns:    No return value. Prints the value counts of each column(feature) to stdout
    '''
    for column in columns:
        output = f'{column} - {df[column].dtype}\n'\
                 '--------------------------------------\n'\
                 'Value|Count\n'\
                 '--------------------------------------\n'\
                 f'{df[column].value_counts()}]\n'\
                 '======================================\n'
        print(output)
        
# Now we can see the description of the function with Shift+Tab in Jupyter Notebook
        
numerical_features = ['Age', 'Experience', 'Income', 'ZIPCode', 'Family', 'CCAvg', 'Mortgage']
value_count_rep(numerical_features,data)

2_function_description_jupyter.png

3_output.txt

Age - int64
--------------------------------------
Value|Count
--------------------------------------
35    151
43    149
52    145
58    143
54    143
50    138
41    136
30    136
56    135
34    134
39    133
57    132
59    132
51    129
60    127
45    127
46    127
42    126
31    125
40    125
55    125
29    123
62    123
61    122
44    121
33    120
32    120
48    118
49    115
38    115
47    113
53    112
63    108
36    107
37    106
28    103
27     91
65     80
64     78
26     78
25     53
24     28
66     24
23     12
67     12
Name: Age, dtype: int64]
======================================

Experience - int64
--------------------------------------
Value|Count
--------------------------------------
 32    154
 20    148
 9     147
 5     146
 23    144
 35    143
 25    142
 28    138
 18    137
 19    135
 26    134
 24    131
 3     129
 14    127
 16    127
 30    126
 34    125
 27    125
 17    125
 22    124
 29    124
 7     121
 15    119
 8     119
 6     119
 10    118
 33    117
 13    117
 11    116
 37    116
 36    114
 4     113
 21    113
 31    104
 12    102
 38     88
 2      85
 39     85
 1      74
 0      66
 40     57
 41     43
-1      33
-2      15
 42      8
-3       4
 43      3
Name: Experience, dtype: int64]
======================================

Income - int64
--------------------------------------
Value|Count
--------------------------------------
44     85
38     84
81     83
41     82
39     81
       ..
202     2
189     2
203     2
218     1
224     1
Name: Income, Length: 162, dtype: int64]
======================================

ZIPCode - int64
--------------------------------------
Value|Count
--------------------------------------
94720    169
94305    127
95616    116
90095     71
93106     57
        ... 
94087      1
96145      1
90068      1
92694      1
94404      1
Name: ZIPCode, Length: 467, dtype: int64]
======================================

Family - int64
--------------------------------------
Value|Count
--------------------------------------
1    1472
2    1296
4    1222
3    1010
Name: Family, dtype: int64]
======================================

CCAvg - float64
--------------------------------------
Value|Count
--------------------------------------
0.30    241
1.00    231
0.20    204
2.00    188
0.80    187
       ... 
5.33      1
8.20      1
3.67      1
9.30      1
8.90      1
Name: CCAvg, Length: 108, dtype: int64]
======================================

Mortgage - int64
--------------------------------------
Value|Count
--------------------------------------
0      3462
98       17
89       16
91       16
83       16
       ... 
206       1
210       1
258       1
278       1
635       1
Name: Mortgage, Length: 347, dtype: int64]
======================================

4_typing_docstrings_alias.py

# function to iterate over specified variables and view their value counts

# add typing to help understand our function if reused elsewhere
from typing import List
import numpy as np

# our alias - Vector is a List of integers
Vector = List[np.int64]

def value_count_rep(columns: Vector, df: pd.DataFrame) -> None:
    '''
    Parameters: List of columns to iterate over
    Returns:    No return value. Prints the value counts of each column(feature) to stdout
    '''
    for column in columns:
        output = f'{column} - {df[column].dtype}\n'\
                 '--------------------------------------\n'\
                 'Value|Count\n'\
                 '--------------------------------------\n'\
                 f'{df[column].value_counts()}]\n'\
                 '======================================\n'
        print(output)
        
# Now we can see the description of the function with Shift+Tab in Jupyter Notebook
        
numerical_features = ['Age', 'Experience', 'Income', 'ZIPCode', 'Family', 'CCAvg', 'Mortgage']
value_count_rep(numerical_features,data)