1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
|
%global _empty_manifest_terminate_build 0
Name: python-flat-table
Version: 1.1.1
Release: 1
Summary: A broader implementation of pandas json_normalize function.
License: MIT
URL: https://github.com/metinsenturk/flat_table
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e0/77/9bdf148a4dbe87e143c722b5b7be5495b8b4ce82b431aecc9ec8e0cb4c78/flat_table-1.1.1.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
%description
## Flat-Table: Dictionary and List Normalizer
This package is a normalizer for [pandas](https://pandas.pydata.org/) dataframe objects that has dictionary or list objects within it's columns. The library will expand all of the columns that has data types in (list, dict) into individual seperate rows and columns.
PS: Flat table will use the current index of the dataframe as an identifier while expanding lists. The output will have an index column of your original dataframe. You can drop it later if you not plan to use it.
### To Install
To install, use pip.
```
pip install flat-table
```
### How to Use It
From a given pandas dataframe, the `index` of the dataframe will be used to create seperate columns and rows.
``` python
# some dataframe contains dicts and lists in it's columns
df = ...
```
``` python
import flat_table
flat_table.normalize(df)
```
This will give you all the keys in dictionaries as columns, and all the lists as seperate rows.
### Example Illustration
Lets assume that you have a dataframe of the followings shape.
id | user_info | address
-- | --------- | ------ |
1001 | { 'first_name': 'john', 'last_name': 'smith', 'phones': {'mobile': '201-..', 'home': '978-..'} }| [{ 'zip': '07014', 'city': 'clifton' }] |
1002 | NaN| [{'zip': '07014', 'address1': '1 Journal Square'}]|
1003 | { 'first_name': 'marry', 'last_name': 'kate', 'gender': 'female' } | [{ 'zip': '10001', 'city': 'new york' }, { 'zip': '10008', 'city': 'brooklyn' }]|
This table given above has some dictionaries and lists in it's columns. Normally, what you would do is to use `pd.io.json.json_normalize` function to expand dictionaries. However, in cases you have `NaN` values in your column, `pd.io.json.json_normalize` end up throwing an `AttributeError` error for `NaN` values because they are not of the same type. `flat_table` is a wraper around the `json_normalize` function where it expands it's abilities to be more robust for `NaN` values and also, it expands lists rowwise so that it will be more clear to see the information.
For the above table, the flatten table after applying `flat_table.normalize` will look like the following.
| | index | id | user_info.gender | user_info.phones.home | user_info.phones.mobile | user_info.last_name | user_info.first_name | address.address1 | address.city | address.zip |
|---:|--------:|-----:|:-------------------|:------------------------|:--------------------------|:----------------------|:-----------------------|:-------------------|:---------------|--------------:|
| 0 | 0 | 1001 | nan | 978-.. | 201-.. | smith | john | nan | clifton | 07014 |
| 1 | 1 | 1002 | nan | nan | nan | nan | nan | 1 Journal Square | nan | 07014 |
| 2 | 2 | 1003 | female | nan | nan | kate | marry | nan | new york | 10001 |
| 3 | 2 | 1003 | female | nan | nan | kate | marry | nan | brooklyn | 10008 |
### New in Version 1.1.0
The expansion for dicts and lists made optional. Now, you can choose to expand list types and dict types with normalize function.
``` python
flat_table.normalize(df, expand_dicts=False, expand_lists=True)
```
Normalized version of df will be following.
| | index | id | user_info | address.address1 | address.city | address.zip |
|---:|--------:|-----:|:-----------------------------------------------------------------------------------------------|:-------------------|:---------------|--------------:|
| 0 | 0 | 1001 | {...} | nan | clifton | 07014 |
| 1 | 1 | 1002 | nan | 1 Journal Square | nan | 07014 |
| 2 | 2 | 1003 | {...} | nan | new york | 10001 |
| 3 | 2 | 1003 | {...} | nan | brooklyn | 10008 |
### How it Works?
Basically, `flat_table` will look for each of the series in a dataframe to understand what type of data it contains.
For every series, it creates a list of information on how to expand it. It will go into all dictionaries and all lists in all levels and expand them as rows and columns. Dictionary `keys` will be used for column names, and The `index` of the giden dataframe will be used for row expansion.
If you want to see how the columns are mapped, you can use `flat_table.mapper` function to get all information about your columns in your original dataframe. For example, for the above table, the mapper function will provide the following table.
| | parent | child | type | obj |
|---:|:----------|:------------------------|:-------|:------|
| 0 | . | id | int | ... |
| 1 | . | user_info | dict | ... |
| 2 | user_info | user_info.gender | str | ... |
| 3 | user_info | user_info.phones.home | str | ... |
| 4 | user_info | user_info.phones.mobile | str | ... |
| 5 | user_info | user_info.last_name | str | ... |
| 6 | user_info | user_info.first_name | str | ... |
| 7 | . | address | list | ... |
| 8 | | address | dict | ... |
| 9 | address | address.address1 | str | ... |
| 10 | address | address.city | str | ... |
| 11 | address | address.zip | str | ... |
## Licence
Licence is use it at your own will, with whatever way you want it to use :smiley:.
## Author
Build by [@metinsenturk](https://github.com/metinsenturk/)
%package -n python3-flat-table
Summary: A broader implementation of pandas json_normalize function.
Provides: python-flat-table
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-flat-table
## Flat-Table: Dictionary and List Normalizer
This package is a normalizer for [pandas](https://pandas.pydata.org/) dataframe objects that has dictionary or list objects within it's columns. The library will expand all of the columns that has data types in (list, dict) into individual seperate rows and columns.
PS: Flat table will use the current index of the dataframe as an identifier while expanding lists. The output will have an index column of your original dataframe. You can drop it later if you not plan to use it.
### To Install
To install, use pip.
```
pip install flat-table
```
### How to Use It
From a given pandas dataframe, the `index` of the dataframe will be used to create seperate columns and rows.
``` python
# some dataframe contains dicts and lists in it's columns
df = ...
```
``` python
import flat_table
flat_table.normalize(df)
```
This will give you all the keys in dictionaries as columns, and all the lists as seperate rows.
### Example Illustration
Lets assume that you have a dataframe of the followings shape.
id | user_info | address
-- | --------- | ------ |
1001 | { 'first_name': 'john', 'last_name': 'smith', 'phones': {'mobile': '201-..', 'home': '978-..'} }| [{ 'zip': '07014', 'city': 'clifton' }] |
1002 | NaN| [{'zip': '07014', 'address1': '1 Journal Square'}]|
1003 | { 'first_name': 'marry', 'last_name': 'kate', 'gender': 'female' } | [{ 'zip': '10001', 'city': 'new york' }, { 'zip': '10008', 'city': 'brooklyn' }]|
This table given above has some dictionaries and lists in it's columns. Normally, what you would do is to use `pd.io.json.json_normalize` function to expand dictionaries. However, in cases you have `NaN` values in your column, `pd.io.json.json_normalize` end up throwing an `AttributeError` error for `NaN` values because they are not of the same type. `flat_table` is a wraper around the `json_normalize` function where it expands it's abilities to be more robust for `NaN` values and also, it expands lists rowwise so that it will be more clear to see the information.
For the above table, the flatten table after applying `flat_table.normalize` will look like the following.
| | index | id | user_info.gender | user_info.phones.home | user_info.phones.mobile | user_info.last_name | user_info.first_name | address.address1 | address.city | address.zip |
|---:|--------:|-----:|:-------------------|:------------------------|:--------------------------|:----------------------|:-----------------------|:-------------------|:---------------|--------------:|
| 0 | 0 | 1001 | nan | 978-.. | 201-.. | smith | john | nan | clifton | 07014 |
| 1 | 1 | 1002 | nan | nan | nan | nan | nan | 1 Journal Square | nan | 07014 |
| 2 | 2 | 1003 | female | nan | nan | kate | marry | nan | new york | 10001 |
| 3 | 2 | 1003 | female | nan | nan | kate | marry | nan | brooklyn | 10008 |
### New in Version 1.1.0
The expansion for dicts and lists made optional. Now, you can choose to expand list types and dict types with normalize function.
``` python
flat_table.normalize(df, expand_dicts=False, expand_lists=True)
```
Normalized version of df will be following.
| | index | id | user_info | address.address1 | address.city | address.zip |
|---:|--------:|-----:|:-----------------------------------------------------------------------------------------------|:-------------------|:---------------|--------------:|
| 0 | 0 | 1001 | {...} | nan | clifton | 07014 |
| 1 | 1 | 1002 | nan | 1 Journal Square | nan | 07014 |
| 2 | 2 | 1003 | {...} | nan | new york | 10001 |
| 3 | 2 | 1003 | {...} | nan | brooklyn | 10008 |
### How it Works?
Basically, `flat_table` will look for each of the series in a dataframe to understand what type of data it contains.
For every series, it creates a list of information on how to expand it. It will go into all dictionaries and all lists in all levels and expand them as rows and columns. Dictionary `keys` will be used for column names, and The `index` of the giden dataframe will be used for row expansion.
If you want to see how the columns are mapped, you can use `flat_table.mapper` function to get all information about your columns in your original dataframe. For example, for the above table, the mapper function will provide the following table.
| | parent | child | type | obj |
|---:|:----------|:------------------------|:-------|:------|
| 0 | . | id | int | ... |
| 1 | . | user_info | dict | ... |
| 2 | user_info | user_info.gender | str | ... |
| 3 | user_info | user_info.phones.home | str | ... |
| 4 | user_info | user_info.phones.mobile | str | ... |
| 5 | user_info | user_info.last_name | str | ... |
| 6 | user_info | user_info.first_name | str | ... |
| 7 | . | address | list | ... |
| 8 | | address | dict | ... |
| 9 | address | address.address1 | str | ... |
| 10 | address | address.city | str | ... |
| 11 | address | address.zip | str | ... |
## Licence
Licence is use it at your own will, with whatever way you want it to use :smiley:.
## Author
Build by [@metinsenturk](https://github.com/metinsenturk/)
%package help
Summary: Development documents and examples for flat-table
Provides: python3-flat-table-doc
%description help
## Flat-Table: Dictionary and List Normalizer
This package is a normalizer for [pandas](https://pandas.pydata.org/) dataframe objects that has dictionary or list objects within it's columns. The library will expand all of the columns that has data types in (list, dict) into individual seperate rows and columns.
PS: Flat table will use the current index of the dataframe as an identifier while expanding lists. The output will have an index column of your original dataframe. You can drop it later if you not plan to use it.
### To Install
To install, use pip.
```
pip install flat-table
```
### How to Use It
From a given pandas dataframe, the `index` of the dataframe will be used to create seperate columns and rows.
``` python
# some dataframe contains dicts and lists in it's columns
df = ...
```
``` python
import flat_table
flat_table.normalize(df)
```
This will give you all the keys in dictionaries as columns, and all the lists as seperate rows.
### Example Illustration
Lets assume that you have a dataframe of the followings shape.
id | user_info | address
-- | --------- | ------ |
1001 | { 'first_name': 'john', 'last_name': 'smith', 'phones': {'mobile': '201-..', 'home': '978-..'} }| [{ 'zip': '07014', 'city': 'clifton' }] |
1002 | NaN| [{'zip': '07014', 'address1': '1 Journal Square'}]|
1003 | { 'first_name': 'marry', 'last_name': 'kate', 'gender': 'female' } | [{ 'zip': '10001', 'city': 'new york' }, { 'zip': '10008', 'city': 'brooklyn' }]|
This table given above has some dictionaries and lists in it's columns. Normally, what you would do is to use `pd.io.json.json_normalize` function to expand dictionaries. However, in cases you have `NaN` values in your column, `pd.io.json.json_normalize` end up throwing an `AttributeError` error for `NaN` values because they are not of the same type. `flat_table` is a wraper around the `json_normalize` function where it expands it's abilities to be more robust for `NaN` values and also, it expands lists rowwise so that it will be more clear to see the information.
For the above table, the flatten table after applying `flat_table.normalize` will look like the following.
| | index | id | user_info.gender | user_info.phones.home | user_info.phones.mobile | user_info.last_name | user_info.first_name | address.address1 | address.city | address.zip |
|---:|--------:|-----:|:-------------------|:------------------------|:--------------------------|:----------------------|:-----------------------|:-------------------|:---------------|--------------:|
| 0 | 0 | 1001 | nan | 978-.. | 201-.. | smith | john | nan | clifton | 07014 |
| 1 | 1 | 1002 | nan | nan | nan | nan | nan | 1 Journal Square | nan | 07014 |
| 2 | 2 | 1003 | female | nan | nan | kate | marry | nan | new york | 10001 |
| 3 | 2 | 1003 | female | nan | nan | kate | marry | nan | brooklyn | 10008 |
### New in Version 1.1.0
The expansion for dicts and lists made optional. Now, you can choose to expand list types and dict types with normalize function.
``` python
flat_table.normalize(df, expand_dicts=False, expand_lists=True)
```
Normalized version of df will be following.
| | index | id | user_info | address.address1 | address.city | address.zip |
|---:|--------:|-----:|:-----------------------------------------------------------------------------------------------|:-------------------|:---------------|--------------:|
| 0 | 0 | 1001 | {...} | nan | clifton | 07014 |
| 1 | 1 | 1002 | nan | 1 Journal Square | nan | 07014 |
| 2 | 2 | 1003 | {...} | nan | new york | 10001 |
| 3 | 2 | 1003 | {...} | nan | brooklyn | 10008 |
### How it Works?
Basically, `flat_table` will look for each of the series in a dataframe to understand what type of data it contains.
For every series, it creates a list of information on how to expand it. It will go into all dictionaries and all lists in all levels and expand them as rows and columns. Dictionary `keys` will be used for column names, and The `index` of the giden dataframe will be used for row expansion.
If you want to see how the columns are mapped, you can use `flat_table.mapper` function to get all information about your columns in your original dataframe. For example, for the above table, the mapper function will provide the following table.
| | parent | child | type | obj |
|---:|:----------|:------------------------|:-------|:------|
| 0 | . | id | int | ... |
| 1 | . | user_info | dict | ... |
| 2 | user_info | user_info.gender | str | ... |
| 3 | user_info | user_info.phones.home | str | ... |
| 4 | user_info | user_info.phones.mobile | str | ... |
| 5 | user_info | user_info.last_name | str | ... |
| 6 | user_info | user_info.first_name | str | ... |
| 7 | . | address | list | ... |
| 8 | | address | dict | ... |
| 9 | address | address.address1 | str | ... |
| 10 | address | address.city | str | ... |
| 11 | address | address.zip | str | ... |
## Licence
Licence is use it at your own will, with whatever way you want it to use :smiley:.
## Author
Build by [@metinsenturk](https://github.com/metinsenturk/)
%prep
%autosetup -n flat-table-1.1.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-flat-table -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
- Package Spec generated
|
