|
1
|
|
|
''' |
|
2
|
|
|
Functions for data cleaning. |
|
3
|
|
|
|
|
4
|
|
|
:author: Andreas Kanz |
|
5
|
|
|
|
|
6
|
|
|
''' |
|
7
|
|
|
|
|
8
|
|
|
# Imports |
|
9
|
|
|
import pandas as pd |
|
10
|
|
|
from sklearn.base import BaseEstimator, TransformerMixin |
|
11
|
|
|
|
|
12
|
|
|
# from .preprocess import mv_col_handler |
|
13
|
|
|
from .utils import _diff_report |
|
14
|
|
|
from .utils import _drop_duplicates |
|
15
|
|
|
from .utils import _missing_vals |
|
16
|
|
|
from .utils import _validate_input_range |
|
17
|
|
|
from .utils import _validate_input_bool |
|
18
|
|
|
|
|
19
|
|
|
|
|
20
|
|
|
def convert_datatypes(data, category=True, cat_threshold=0.05, cat_exclude=None): |
|
21
|
|
|
''' |
|
22
|
|
|
Converts columns to best possible dtypes using dtypes supporting pd.NA. Temporarily not converting integers. |
|
23
|
|
|
|
|
24
|
|
|
Parameters |
|
25
|
|
|
---------- |
|
26
|
|
|
data: 2D dataset that can be coerced into Pandas DataFrame. |
|
27
|
|
|
|
|
28
|
|
|
category: bool, default True |
|
29
|
|
|
Change dtypes of columns with dtype "object" to "category". Set threshold using cat_threshold or exclude \ |
|
30
|
|
|
columns using cat_exclude. |
|
31
|
|
|
|
|
32
|
|
|
cat_threshold: float, default 0.05 |
|
33
|
|
|
Ratio of unique values below which categories are inferred and column dtype is changed to categorical. |
|
34
|
|
|
|
|
35
|
|
|
cat_exclude: list, default None |
|
36
|
|
|
List of columns to exclude from categorical conversion. |
|
37
|
|
|
|
|
38
|
|
|
Returns |
|
39
|
|
|
------- |
|
40
|
|
|
data: Pandas DataFrame |
|
41
|
|
|
''' |
|
42
|
|
|
|
|
43
|
|
|
# Validate Inputs |
|
44
|
|
|
_validate_input_bool(category, 'Category') |
|
45
|
|
|
_validate_input_range(cat_threshold, 'cat_threshold', 0, 1) |
|
46
|
|
|
|
|
47
|
|
|
cat_exclude = [] if cat_exclude is None else cat_exclude.copy() |
|
48
|
|
|
|
|
49
|
|
|
data = pd.DataFrame(data).copy() |
|
50
|
|
|
for col in data.columns: |
|
51
|
|
|
unique_vals_ratio = data[col].nunique(dropna=False) / data.shape[0] |
|
52
|
|
|
if (category and |
|
53
|
|
|
unique_vals_ratio < cat_threshold and |
|
54
|
|
|
col not in cat_exclude and |
|
55
|
|
|
data[col].dtype == 'object'): |
|
56
|
|
|
data[col] = data[col].astype('category') |
|
57
|
|
|
data[col] = data[col].convert_dtypes(infer_objects=True, convert_string=True, |
|
58
|
|
|
convert_integer=False, convert_boolean=True) |
|
59
|
|
|
|
|
60
|
|
|
return data |
|
61
|
|
|
|
|
62
|
|
|
|
|
63
|
|
|
def drop_missing(data, drop_threshold_cols=1, drop_threshold_rows=1): |
|
64
|
|
|
''' |
|
65
|
|
|
Drops completely empty columns and rows by default and optionally provides flexibility to loosen restrictions to \ |
|
66
|
|
|
drop additional columns and rows based on the fraction of remaining NA-values. |
|
67
|
|
|
|
|
68
|
|
|
Parameters |
|
69
|
|
|
---------- |
|
70
|
|
|
data: 2D dataset that can be coerced into Pandas DataFrame. |
|
71
|
|
|
|
|
72
|
|
|
drop_threshold_cols: float, default 1 |
|
73
|
|
|
Drop columns with NA-ratio above the specified threshold. |
|
74
|
|
|
|
|
75
|
|
|
drop_threshold_rows: float, default 1 |
|
76
|
|
|
Drop rows with NA-ratio above the specified threshold. |
|
77
|
|
|
|
|
78
|
|
|
Returns |
|
79
|
|
|
------- |
|
80
|
|
|
data_cleaned: Pandas DataFrame |
|
81
|
|
|
|
|
82
|
|
|
Notes |
|
83
|
|
|
----- |
|
84
|
|
|
Columns are dropped first. Rows are dropped based on the remaining data. |
|
85
|
|
|
''' |
|
86
|
|
|
|
|
87
|
|
|
# Validate Inputs |
|
88
|
|
|
_validate_input_range(drop_threshold_cols, 'drop_threshold_cols', 0, 1) |
|
89
|
|
|
_validate_input_range(drop_threshold_rows, 'drop_threshold_rows', 0, 1) |
|
90
|
|
|
|
|
91
|
|
|
data = pd.DataFrame(data).copy() |
|
92
|
|
|
data = data.dropna(axis=0, how='all').dropna(axis=1, how='all') |
|
93
|
|
|
data = data.drop(columns=data.loc[:, _missing_vals(data)['mv_cols_ratio'] > drop_threshold_cols].columns) |
|
94
|
|
|
data_cleaned = data.drop(index=data.loc[_missing_vals(data)['mv_rows_ratio'] > drop_threshold_rows, :].index) |
|
95
|
|
|
|
|
96
|
|
|
return data_cleaned |
|
97
|
|
|
|
|
98
|
|
|
|
|
99
|
|
|
def data_cleaning(data, drop_threshold_cols=0.9, drop_threshold_rows=0.9, drop_duplicates=True, |
|
100
|
|
|
convert_dtypes=True, category=True, cat_threshold=0.03, cat_exclude=None, show='changes'): |
|
101
|
|
|
''' |
|
102
|
|
|
Perform initial data cleaning tasks on a dataset, such as dropping single valued and empty rows, empty \ |
|
103
|
|
|
columns as well as optimizing the datatypes. |
|
104
|
|
|
|
|
105
|
|
|
Parameters |
|
106
|
|
|
---------- |
|
107
|
|
|
data: 2D dataset that can be coerced into Pandas DataFrame. |
|
108
|
|
|
|
|
109
|
|
|
drop_threshold_cols: float, default 0.9 |
|
110
|
|
|
Drop columns with NA-ratio above the specified threshold. |
|
111
|
|
|
|
|
112
|
|
|
drop_threshold_rows: float, default 0.9 |
|
113
|
|
|
Drop rows with NA-ratio above the specified threshold. |
|
114
|
|
|
|
|
115
|
|
|
drop_duplicates: bool, default True |
|
116
|
|
|
Drop duplicate rows, keeping the first occurence. This step comes after the dropping of missing values. |
|
117
|
|
|
|
|
118
|
|
|
convert_dtypes: bool, default True |
|
119
|
|
|
Convert dtypes using pd.convert_dtypes(). |
|
120
|
|
|
|
|
121
|
|
|
category: bool, default True |
|
122
|
|
|
Change dtypes of columns to "category". Set threshold using cat_threshold. Requires convert_dtypes=True |
|
123
|
|
|
|
|
124
|
|
|
cat_threshold: float, default 0.03 |
|
125
|
|
|
Ratio of unique values below which categories are inferred and column dtype is changed to categorical. |
|
126
|
|
|
|
|
127
|
|
|
cat_exclude: list, default None |
|
128
|
|
|
List of columns to exclude from categorical conversion. |
|
129
|
|
|
|
|
130
|
|
|
show: {'all', 'changes', None} default 'all' |
|
131
|
|
|
Specify verbosity of the output. |
|
132
|
|
|
* 'all': Print information about the data before and after cleaning as well as information about changes. |
|
133
|
|
|
* 'changes': Print out differences in the data before and after cleaning. |
|
134
|
|
|
* None: No information about the data and the data cleaning is printed. |
|
135
|
|
|
|
|
136
|
|
|
Returns |
|
137
|
|
|
------- |
|
138
|
|
|
data_cleaned: Pandas DataFrame |
|
139
|
|
|
|
|
140
|
|
|
See Also |
|
141
|
|
|
-------- |
|
142
|
|
|
convert_datatypes: Convert columns to best possible dtypes. |
|
143
|
|
|
drop_missing : Flexibly drop columns and rows. |
|
144
|
|
|
_memory_usage: Gives the total memory usage in kilobytes. |
|
145
|
|
|
_missing_vals: Metrics about missing values in the dataset. |
|
146
|
|
|
|
|
147
|
|
|
Notes |
|
148
|
|
|
----- |
|
149
|
|
|
The category dtype is not grouped in the summary, unless it contains exactly the same categories. |
|
150
|
|
|
''' |
|
151
|
|
|
|
|
152
|
|
|
# Validate Inputs |
|
153
|
|
|
_validate_input_range(drop_threshold_cols, 'drop_threshold_cols', 0, 1) |
|
154
|
|
|
_validate_input_range(drop_threshold_rows, 'drop_threshold_rows', 0, 1) |
|
155
|
|
|
_validate_input_bool(drop_duplicates, 'drop_duplicates') |
|
156
|
|
|
_validate_input_bool(convert_dtypes, 'convert_datatypes') |
|
157
|
|
|
_validate_input_bool(category, 'category') |
|
158
|
|
|
_validate_input_range(cat_threshold, 'cat_threshold', 0, 1) |
|
159
|
|
|
|
|
160
|
|
|
data = pd.DataFrame(data).copy() |
|
161
|
|
|
data_cleaned = drop_missing(data, drop_threshold_cols, drop_threshold_rows) |
|
162
|
|
|
|
|
163
|
|
|
single_val_cols = data_cleaned.columns[data_cleaned.nunique(dropna=False) == 1].tolist() |
|
164
|
|
|
data_cleaned = data_cleaned.drop(columns=single_val_cols) |
|
165
|
|
|
|
|
166
|
|
|
if drop_duplicates: |
|
167
|
|
|
data_cleaned, dupl_rows = _drop_duplicates(data_cleaned) |
|
168
|
|
|
if convert_dtypes: |
|
169
|
|
|
data_cleaned = convert_datatypes(data_cleaned, category=category, cat_threshold=cat_threshold, |
|
170
|
|
|
cat_exclude=cat_exclude) |
|
171
|
|
|
|
|
172
|
|
|
_diff_report(data, data_cleaned, dupl_rows=dupl_rows, single_val_cols=single_val_cols, show=show) |
|
173
|
|
|
|
|
174
|
|
|
return data_cleaned |
|
175
|
|
|
|
|
176
|
|
|
|
|
177
|
|
|
class DataCleaner(BaseEstimator, TransformerMixin): |
|
178
|
|
|
'''Docstring of a class? methods also have docstrings or commments?''' |
|
179
|
|
|
'''possible component of a cleaning pipeline --> e.g. followed by MCH''' |
|
180
|
|
|
|
|
181
|
|
|
def __init__(self, drop_threshold_cols=0.9, drop_threshold_rows=0.9, drop_duplicates=True, convert_dtypes=True, |
|
182
|
|
|
category=True, cat_threshold=0.03, cat_exclude=None, show='changes'): |
|
183
|
|
|
self.drop_threshold_cols = drop_threshold_cols |
|
184
|
|
|
self.drop_threshold_rows = drop_threshold_rows |
|
185
|
|
|
self.drop_duplicates = drop_duplicates |
|
186
|
|
|
self.convert_dtypes = convert_dtypes |
|
187
|
|
|
self.category = category |
|
188
|
|
|
self.cat_threshold = cat_threshold |
|
189
|
|
|
self.cat_exclude = cat_exclude |
|
190
|
|
|
self.show = show |
|
191
|
|
|
|
|
192
|
|
|
def fit(self, data, target=None): |
|
193
|
|
|
return self |
|
194
|
|
|
|
|
195
|
|
|
def transform(self, data, target=None): |
|
196
|
|
|
data_cleaned = data_cleaning(data, drop_threshold_cols=self.drop_threshold_cols, |
|
197
|
|
|
drop_threshold_rows=self.drop_threshold_rows, drop_duplicates=self.drop_duplicates, |
|
198
|
|
|
convert_dtypes=self.convert_dtypes, category=self.category, cat_threshold=self. |
|
199
|
|
|
cat_threshold, cat_exclude=self.cat_exclude, show=self.show) |
|
200
|
|
|
return data_cleaned |
|
201
|
|
|
|
akanz1 /
klib
