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a-pandas-ex-plode-tool-0.44
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مشاهده بیشترتوضیحات
No more sleepless nights due to a nested dict, json, list or whatsoever
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | a-pandas-ex-plode-tool-0.44 |
| نام | a-pandas-ex-plode-tool |
| نسخه کتابخانه | 0.44 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Johannes Fischer |
| ایمیل نویسنده | <aulasparticularesdealemaosp@gmail.com> |
| آدرس صفحه اصلی | https://github.com/hansalemaos/a_pandas_ex_plode_tool |
| آدرس اینترنتی | https://pypi.org/project/a-pandas-ex-plode-tool/ |
| مجوز | MIT |
## Library to handle any nested iterable (list, tuple, dict, json, etc.) in Pandas - no matter how deeply it is nested!
## Update:
**2022/09/30:** DataFrame is now created directly from iter
**2022/09/30:** No more warning (PerformanceWarning: DataFrame is highly fragmented), when DataFrame is created from a huge nested dict (depth: 1486) Try it: https://raw.githubusercontent.com/hansalemaos/a_pandas_ex_plode_tool/main/recursion%20_hardcore_test.py
```python
pip install a-pandas-ex-plode-tool
```
```python
from a_pandas_ex_plode_tool import pd_add_explode_tools
pd_add_explode_tools()
import pandas as pd
df = pd.read_csv("https://github.com/pandas-dev/pandas/raw/main/doc/data/air_quality_long.csv")
```
**HANDLE NESTED ITERABLES**
The code above will add some methods to **pd. / pd.DataFrame / pd.Series**, you can use pandas like you did before, but you will have a couple of methods more:
- pd.Q_AnyNestedIterable_2df()
- pd.Q_CorruptJsonFile_2dict()
- pd.Q_ReadFileWithAllEncodings_2df()
- df.d_filter_dtypes()
- df.d_multiple_columns_to_one()
- df.d_df_to_nested_dict()
- df.d_add_value_to_existing_columns_with_loc()
- df.d_set_values_with_df_loc()
- df.d_drop_rows_with_df_loc()
- df.d_dfloc()
- df.d_stack()
- df.d_unstack()
- df.d_sort_columns_with_sorted()
- df.d_merge_multiple_dfs_and_series_on_one_column()
- df.d_merge_multiple_dfs_and_series_on_index()
- df.d_update_original_iter()
- df.ds_all_nans_to_pdNA()
- df.ds_explode_dicts_in_column()
- df.ds_isna()
- df.ds_normalize_lists()
- df.s_delete_duplicates_from_iters_in_cells()
- df.s_flatten_all_iters_in_cells()
- df.s_as_flattened_list()
- df.s_explode_lists_and_tuples()
**All methods added to pandas have one of these prefixes:**
- **ds_** (for DataFrames and Series)
- **s_** (only for Series)
- **d_** (only for DataFrames)
- **Q_** (added to pd.)
### pd.Q_AnyNestedIterable_2df() / df.d_filter_dtypes() / df.d_update_original_iter()
**pd.Q_AnyNestedIterable_2df()** transforms any nasty iterable into a beautiful Pandas DataFrame with a [MultiIndex](https://pandas.pydata.org/docs/user_guide/advanced.html)
**df.d_filter_dtypes()** avoids TypeError Exceptions
df.loc[df.aa_value >30,'aa_value'] = 90000000
Traceback (most recent call last):
....
TypeError: '>' not supported between instances of 'str' and 'int'
***df.loc[df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') > 30] <------- No more exception!***
**df.d_update_original_iter()** After you have updated the DataFrame, you can update the original nasty iterable and keep its ugly structure.
##### I have tested these methods a lot with examples from Stack Overflow. Until now, everything has been working like a charm. Here are about 15 examples!
```python
Nested iterable from: 'https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe'
{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},
{'review_critic': 'ABC', 'review_score': 90},
{'review_critic': '123', 'review_score': 90}],
'genres': ['Sports', 'Golf'],
'score': 85,
'title': 'Golf Simulator',
'url': 'http://example.com/golf-simulator'}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False) # create DF stacked or unstacked, it doesn't matter
aa_all_keys aa_value
critic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ
review_score (critic_reviews, 0, review_score) 90
1 review_critic (critic_reviews, 1, review_critic) ABC
review_score (critic_reviews, 1, review_score) 90
2 review_critic (critic_reviews, 2, review_critic) 123
review_score (critic_reviews, 2, review_score) 90
genres 0 NaN (genres, 0) Sports
1 NaN (genres, 1) Golf
score NaN NaN (score,) 85
title NaN NaN (title,) Golf Simulator
url NaN NaN (url,) http://example.com/golf-simulator
#Avoid exceptions with df.d_filter_dtypes()
df.loc[df.aa_value.str.contains('[Gg]',na=False),'aa_value'] = 'UPDATE1111' #df.loc to update the dataframe (VERY IMPORTANT: To update the original iterable you have to pass 'aa_value')
aa_all_keys aa_value
critic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ
review_score (critic_reviews, 0, review_score) 90
1 review_critic (critic_reviews, 1, review_critic) ABC
review_score (critic_reviews, 1, review_score) 90
2 review_critic (critic_reviews, 2, review_critic) 123
review_score (critic_reviews, 2, review_score) 90
genres 0 NaN (genres, 0) Sports
1 NaN (genres, 1) UPDATE1111
score NaN NaN (score,) 85
title NaN NaN (title,) UPDATE1111
url NaN NaN (url,) UPDATE1111
mod_iter = df.d_update_original_iter(data, verbose=True) #updating the nested iterable, the new values have to be in the column 'aa_value', if you have added new columns to the dataframe, drop them before updating the original iterable
[genres][1] Old value: Golf
[genres][1] Updated value: UPDATE1111
[title] Old value: Golf Simulator
[title] Updated value: UPDATE1111
[url] Old value: http://example.com/golf-simulator
[url] Updated value: UPDATE1111
{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},
{'review_critic': 'ABC', 'review_score': 90},
{'review_critic': '123', 'review_score': 90}],
'genres': ['Sports', 'UPDATE1111'],
'score': 85,
'title': 'UPDATE1111',
'url': 'UPDATE1111'}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe
data=
[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),
'final_value': 118},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),
'final_value': 117},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),
'final_value': 117}]
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
0 cb 0 ID (0, cb, 0, ID) 1
Name (0, cb, 0, Name) A
num (0, cb, 0, num) 50
1 ID (0, cb, 1, ID) 2
Name (0, cb, 1, Name) A
num (0, cb, 1, num) 68
final_value NaN NaN (0, final_value) 118
1 cb 0 ID (1, cb, 0, ID) 1
Name (1, cb, 0, Name) A
num (1, cb, 0, num) 50
1 ID (1, cb, 1, ID) 4
Name (1, cb, 1, Name) A
num (1, cb, 1, num) 67
final_value NaN NaN (1, final_value) 117
2 cb 0 ID (2, cb, 0, ID) 1
Name (2, cb, 0, Name) A
num (2, cb, 0, num) 50
1 ID (2, cb, 1, ID) 6
Name (2, cb, 1, Name) A
num (2, cb, 1, num) 67
final_value NaN NaN (2, final_value) 117
df.loc[df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') > 30, 'aa_value'] = 900000
aa_all_keys aa_value
0 cb 0 ID (0, cb, 0, ID) 1
Name (0, cb, 0, Name) A
num (0, cb, 0, num) 900000
1 ID (0, cb, 1, ID) 2
Name (0, cb, 1, Name) A
num (0, cb, 1, num) 900000
final_value NaN NaN (0, final_value) 900000
1 cb 0 ID (1, cb, 0, ID) 1
Name (1, cb, 0, Name) A
num (1, cb, 0, num) 900000
1 ID (1, cb, 1, ID) 4
Name (1, cb, 1, Name) A
num (1, cb, 1, num) 900000
final_value NaN NaN (1, final_value) 900000
2 cb 0 ID (2, cb, 0, ID) 1
Name (2, cb, 0, Name) A
num (2, cb, 0, num) 900000
1 ID (2, cb, 1, ID) 6
Name (2, cb, 1, Name) A
num (2, cb, 1, num) 900000
final_value NaN NaN (2, final_value) 900000
mod_iter = df.d_update_original_iter(data, verbose=True)
[0][cb][0][num] Old value: 50
[0][cb][0][num] Updated value: 900000
[0][cb][1][num] Old value: 68
[0][cb][1][num] Updated value: 900000
[0][final_value] Old value: 118
[0][final_value] Updated value: 900000
[1][cb][0][num] Old value: 50
[1][cb][0][num] Updated value: 900000
[1][cb][1][num] Old value: 67
[1][cb][1][num] Updated value: 900000
[1][final_value] Old value: 117
[1][final_value] Updated value: 900000
[2][cb][0][num] Old value: 50
[2][cb][0][num] Updated value: 900000
[2][cb][1][num] Old value: 67
[2][cb][1][num] Updated value: 900000
[2][final_value] Old value: 117
[2][final_value] Updated value: 900000
[{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},
{'ID': 2, 'Name': 'A', 'num': 900000}),
'final_value': 900000},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},
{'ID': 4, 'Name': 'A', 'num': 900000}),
'final_value': 900000},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},
{'ID': 6, 'Name': 'A', 'num': 900000}),
'final_value': 900000}]
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict
data=
[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],
'application_details': {'email': None, 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '1'},
{'application_contacts': [{'adress': 'Z', 'email': None}],
'application_details': {'email': 'testy@test_a.com', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '2'},
{'application_contacts': [{'adress': 'Y', 'email': None}],
'application_details': {'email': 'testy@test_a.com', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '3'}]
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
0 application_contacts 0 adress (0, application_contacts, 0, adress) X
email (0, application_contacts, 0, email) test@test.com
application_details email NaN (0, application_details, email) None
phone NaN (0, application_details, phone) None
employer Name NaN (0, employer, Name) Nom
email NaN (0, employer, email) None
id NaN NaN (0, id) 1
1 application_contacts 0 adress (1, application_contacts, 0, adress) Z
email (1, application_contacts, 0, email) None
application_details email NaN (1, application_details, email) testy@test_a.com
phone NaN (1, application_details, phone) None
employer Name NaN (1, employer, Name) Nom
email NaN (1, employer, email) None
id NaN NaN (1, id) 2
2 application_contacts 0 adress (2, application_contacts, 0, adress) Y
email (2, application_contacts, 0, email) None
application_details email NaN (2, application_details, email) testy@test_a.com
phone NaN (2, application_details, phone) None
employer Name NaN (2, employer, Name) Nom
email NaN (2, employer, email) None
id NaN NaN (2, id) 3
df.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'test_a\.\w+\b',na=False), 'aa_value'] = 'UPPPPPPPPPPPPPPPDATE.COM'
aa_all_keys aa_value
0 application_contacts 0 adress (0, application_contacts, 0, adress) X
email (0, application_contacts, 0, email) test@test.com
application_details email NaN (0, application_details, email) None
phone NaN (0, application_details, phone) None
employer Name NaN (0, employer, Name) Nom
email NaN (0, employer, email) None
id NaN NaN (0, id) 1
1 application_contacts 0 adress (1, application_contacts, 0, adress) Z
email (1, application_contacts, 0, email) None
application_details email NaN (1, application_details, email) UPPPPPPPPPPPPPPPDATE.COM
phone NaN (1, application_details, phone) None
employer Name NaN (1, employer, Name) Nom
email NaN (1, employer, email) None
id NaN NaN (1, id) 2
2 application_contacts 0 adress (2, application_contacts, 0, adress) Y
email (2, application_contacts, 0, email) None
application_details email NaN (2, application_details, email) UPPPPPPPPPPPPPPPDATE.COM
phone NaN (2, application_details, phone) None
employer Name NaN (2, employer, Name) Nom
email NaN (2, employer, email) None
id NaN NaN (2, id) 3
mod_iter = df.d_update_original_iter(data, verbose=True)
[1][application_details][email] Old value: testy@test_a.com
[1][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM
[2][application_details][email] Old value: testy@test_a.com
[2][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM
[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],
'application_details': {'email': None, 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '1'},
{'application_contacts': [{'adress': 'Z', 'email': None}],
'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '2'},
{'application_contacts': [{'adress': 'Y', 'email': None}],
'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '3'}]
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe
data=
{'A': [1, 2, 3],
'B': [4, 5, 6],
'departure': [{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'}]}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
A 0 NaN (A, 0) 1
1 NaN (A, 1) 2
2 NaN (A, 2) 3
B 0 NaN (B, 0) 4
1 NaN (B, 1) 5
2 NaN (B, 2) 6
departure 0 actual (departure, 0, actual) None
actual_runway (departure, 0, actual_runway) None
airport (departure, 0, airport) Findel
delay (departure, 0, delay) None
estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 0, estimated_runway) None
gate (departure, 0, gate) None
iata (departure, 0, iata) LUX
icao (departure, 0, icao) ELLX
scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 0, terminal) None
timezone (departure, 0, timezone) Europe/Luxembourg
1 actual (departure, 1, actual) None
actual_runway (departure, 1, actual_runway) None
airport (departure, 1, airport) Findel
delay (departure, 1, delay) None
estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 1, estimated_runway) None
gate (departure, 1, gate) None
iata (departure, 1, iata) LUX
icao (departure, 1, icao) ELLX
scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 1, terminal) None
timezone (departure, 1, timezone) Europe/Luxembourg
2 actual (departure, 2, actual) None
actual_runway (departure, 2, actual_runway) None
airport (departure, 2, airport) Findel
delay (departure, 2, delay) None
estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 2, estimated_runway) None
gate (departure, 2, gate) None
iata (departure, 2, iata) LUX
icao (departure, 2, icao) ELLX
scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 2, terminal) None
timezone (departure, 2, timezone) Europe/Luxembourg
df.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value')== 'ELLX', 'aa_value'] = 'ELLX-UPDATED'
aa_all_keys aa_value
A 0 NaN (A, 0) 1
1 NaN (A, 1) 2
2 NaN (A, 2) 3
B 0 NaN (B, 0) 4
1 NaN (B, 1) 5
2 NaN (B, 2) 6
departure 0 actual (departure, 0, actual) None
actual_runway (departure, 0, actual_runway) None
airport (departure, 0, airport) Findel
delay (departure, 0, delay) None
estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 0, estimated_runway) None
gate (departure, 0, gate) None
iata (departure, 0, iata) LUX
icao (departure, 0, icao) ELLX-UPDATED
scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 0, terminal) None
timezone (departure, 0, timezone) Europe/Luxembourg
1 actual (departure, 1, actual) None
actual_runway (departure, 1, actual_runway) None
airport (departure, 1, airport) Findel
delay (departure, 1, delay) None
estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 1, estimated_runway) None
gate (departure, 1, gate) None
iata (departure, 1, iata) LUX
icao (departure, 1, icao) ELLX-UPDATED
scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 1, terminal) None
timezone (departure, 1, timezone) Europe/Luxembourg
2 actual (departure, 2, actual) None
actual_runway (departure, 2, actual_runway) None
airport (departure, 2, airport) Findel
delay (departure, 2, delay) None
estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 2, estimated_runway) None
gate (departure, 2, gate) None
iata (departure, 2, iata) LUX
icao (departure, 2, icao) ELLX-UPDATED
scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 2, terminal) None
timezone (departure, 2, timezone) Europe/Luxembourg
mod_iter = df.d_update_original_iter(data, verbose=True)
[departure][0][icao] Old value: ELLX
[departure][0][icao] Updated value: ELLX-UPDATED
[departure][1][icao] Old value: ELLX
[departure][1][icao] Updated value: ELLX-UPDATED
[departure][2][icao] Old value: ELLX
[departure][2][icao] Updated value: ELLX-UPDATED
{'A': [1, 2, 3],
'B': [4, 5, 6],
'departure': [{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX-UPDATED',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX-UPDATED',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX-UPDATED',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'}]}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction
data=
{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},
'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
level1 t1 s1 col1 (level1, t1, s1, col1) 5
col2 (level1, t1, s1, col2) 4
col3 (level1, t1, s1, col3) 4
col4 (level1, t1, s1, col4) 9
s2 col1 (level1, t1, s2, col1) 1
... ...
level2 t3 s3 col4 (level2, t3, s3, col4) 12
s4 col1 (level2, t3, s4, col1) 13
col2 (level2, t3, s4, col2) 14
col3 (level2, t3, s4, col3) 15
col4 (level2, t3, s4, col4) 16
[96 rows x 2 columns]
df.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') > 5) & (df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') < 10), 'aa_value'] = 1000000
aa_all_keys aa_value
level1 t1 s1 col1 (level1, t1, s1, col1) 5
col2 (level1, t1, s1, col2) 4
col3 (level1, t1, s1, col3) 4
col4 (level1, t1, s1, col4) 1000000
s2 col1 (level1, t1, s2, col1) 1
... ...
level2 t3 s3 col4 (level2, t3, s3, col4) 12
s4 col1 (level2, t3, s4, col1) 13
col2 (level2, t3, s4, col2) 14
col3 (level2, t3, s4, col3) 15
col4 (level2, t3, s4, col4) 16
[96 rows x 2 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[level1][t1][s1][col4] Old value: 9
[level1][t1][s1][col4] Updated value: 1000000
[level1][t1][s2][col4] Old value: 8
[level1][t1][s2][col4] Updated value: 1000000
[level1][t1][s3][col2] Old value: 8
[level1][t1][s3][col2] Updated value: 1000000
[level1][t1][s3][col4] Old value: 9
[level1][t1][s3][col4] Updated value: 1000000
[level1][t1][s4][col4] Old value: 9
[level1][t1][s4][col4] Updated value: 1000000
[level1][t2][s1][col4] Old value: 9
[level1][t2][s1][col4] Updated value: 1000000
[level1][t2][s2][col4] Old value: 8
[level1][t2][s2][col4] Updated value: 1000000
[level1][t2][s3][col2] Old value: 8
[level1][t2][s3][col2] Updated value: 1000000
[level1][t2][s3][col4] Old value: 9
[level1][t2][s3][col4] Updated value: 1000000
[level1][t2][s4][col4] Old value: 9
[level1][t2][s4][col4] Updated value: 1000000
[level1][t3][s2][col2] Old value: 6
[level1][t3][s2][col2] Updated value: 1000000
[level1][t3][s2][col3] Old value: 7
[level1][t3][s2][col3] Updated value: 1000000
[level1][t3][s2][col4] Old value: 8
[level1][t3][s2][col4] Updated value: 1000000
[level1][t3][s3][col1] Old value: 9
[level1][t3][s3][col1] Updated value: 1000000
[level2][t1][s1][col3] Old value: 9
[level2][t1][s1][col3] Updated value: 1000000
[level2][t1][s1][col4] Old value: 9
[level2][t1][s1][col4] Updated value: 1000000
[level2][t1][s3][col2] Old value: 8
[level2][t1][s3][col2] Updated value: 1000000
[level2][t2][s1][col4] Old value: 9
[level2][t2][s1][col4] Updated value: 1000000
[level2][t2][s2][col4] Old value: 8
[level2][t2][s2][col4] Updated value: 1000000
[level2][t2][s3][col2] Old value: 8
[level2][t2][s3][col2] Updated value: 1000000
[level2][t2][s3][col4] Old value: 9
[level2][t2][s3][col4] Updated value: 1000000
[level2][t2][s4][col4] Old value: 9
[level2][t2][s4][col4] Updated value: 1000000
[level2][t3][s2][col2] Old value: 6
[level2][t3][s2][col2] Updated value: 1000000
[level2][t3][s2][col3] Old value: 7
[level2][t3][s2][col3] Updated value: 1000000
[level2][t3][s2][col4] Old value: 8
[level2][t3][s2][col4] Updated value: 1000000
[level2][t3][s3][col1] Old value: 9
[level2][t3][s3][col1] Updated value: 1000000
{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},
's3': {'col1': 11,
'col2': 1000000,
'col3': 2,
'col4': 1000000},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},
's3': {'col1': 11,
'col2': 1000000,
'col3': 2,
'col4': 1000000},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5,
'col2': 1000000,
'col3': 1000000,
'col4': 1000000},
's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},
'level2': {'t1': {'s1': {'col1': 5,
'col2': 4,
'col3': 1000000,
'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': 1000000, 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},
's3': {'col1': 11,
'col2': 1000000,
'col3': 2,
'col4': 1000000},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5,
'col2': 1000000,
'col3': 1000000,
'col4': 1000000},
's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary
data=
{'_links': {'next': None, 'prev': None},
'limit': 250,
'offset': 0,
'runs': [{'assignedto_id': None,
'blocked_count': 0,
'completed_on': None,
'config': None,
'config_ids': [],
'created_by': 1,
'created_on': 1651790693,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 1,
'id': 13,
'include_all': False,
'is_completed': False,
'milestone_id': None,
'name': '2022-05-05-testrun',
'passed_count': 2,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 0,
'updated_on': 1651790693,
'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},
{'assignedto_id': None,
'blocked_count': 0,
'completed_on': 1650989972,
'config': None,
'config_ids': [],
'created_by': 5,
'created_on': 1650966329,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 0,
'id': 9,
'include_all': False,
'is_completed': True,
'milestone_id': None,
'name': 'This is a new test run',
'passed_count': 0,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 3,
'updated_on': 1650966329,
'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],
'size': 2}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
_links next NaN (_links, next) None
prev NaN (_links, prev) None
limit NaN NaN (limit,) 250
offset NaN NaN (offset,) 0
runs 0 assignedto_id (runs, 0, assignedto_id) None
... ...
1 suite_id (runs, 1, suite_id) 1
untested_count (runs, 1, untested_count) 3
updated_on (runs, 1, updated_on) 1650966329
url (runs, 1, url) https://xxxxxxxxxx.testrail.io/index.php?/runs...
size NaN NaN (size,) 2
[63 rows x 2 columns]
df.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') == False ), 'aa_value'] = True
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'https?://.*',na=False) ), 'aa_value'] = 'WWW.PYTHON.ORG'
aa_all_keys aa_value
_links next NaN (_links, next) None
prev NaN (_links, prev) None
limit NaN NaN (limit,) 250
offset NaN NaN (offset,) 0
runs 0 assignedto_id (runs, 0, assignedto_id) None
... ...
1 suite_id (runs, 1, suite_id) 1
untested_count (runs, 1, untested_count) 3
updated_on (runs, 1, updated_on) 1650966329
url (runs, 1, url) WWW.PYTHON.ORG
size NaN NaN (size,) 2
[63 rows x 2 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[runs][0][include_all] Old value: False
[runs][0][include_all] Updated value: True
[runs][0][is_completed] Old value: False
[runs][0][is_completed] Updated value: True
[runs][0][url] Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13
[runs][0][url] Updated value: WWW.PYTHON.ORG
[runs][1][include_all] Old value: False
[runs][1][include_all] Updated value: True
[runs][1][url] Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9
[runs][1][url] Updated value: WWW.PYTHON.ORG
{'_links': {'next': None, 'prev': None},
'limit': 250,
'offset': 0,
'runs': [{'assignedto_id': None,
'blocked_count': 0,
'completed_on': None,
'config': None,
'config_ids': [],
'created_by': 1,
'created_on': 1651790693,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 1,
'id': 13,
'include_all': True,
'is_completed': True,
'milestone_id': None,
'name': '2022-05-05-testrun',
'passed_count': 2,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 0,
'updated_on': 1651790693,
'url': 'WWW.PYTHON.ORG'},
{'assignedto_id': None,
'blocked_count': 0,
'completed_on': 1650989972,
'config': None,
'config_ids': [],
'created_by': 5,
'created_on': 1650966329,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 0,
'id': 9,
'include_all': True,
'is_completed': True,
'milestone_id': None,
'name': 'This is a new test run',
'passed_count': 0,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 3,
'updated_on': 1650966329,
'url': 'WWW.PYTHON.ORG'}],
'size': 2}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/73708706/how-to-get-values-from-list-of-nested-dictionaries/73839430#73839430
data=
{'results': [{'end_time': '2021-01-21',
'key': 'q1',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['1']},
{'end_time': '2021-01-21',
'key': 'q2',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['False']},
{'end_time': '2021-01-21',
'key': 'q3',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['3']},
{'end_time': '2021-01-21',
'key': 'q4',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['3']}]}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
results 0 end_time NaN (results, 0, end_time) 2021-01-21
key NaN (results, 0, key) q1
result_type NaN (results, 0, result_type) multipleChoice
start_time NaN (results, 0, start_time) 2021-01-21
value 0 (results, 0, value, 0) 1
1 end_time NaN (results, 1, end_time) 2021-01-21
key NaN (results, 1, key) q2
result_type NaN (results, 1, result_type) multipleChoice
start_time NaN (results, 1, start_time) 2021-01-21
value 0 (results, 1, value, 0) False
2 end_time NaN (results, 2, end_time) 2021-01-21
key NaN (results, 2, key) q3
result_type NaN (results, 2, result_type) multipleChoice
start_time NaN (results, 2, start_time) 2021-01-21
value 0 (results, 2, value, 0) 3
3 end_time NaN (results, 3, end_time) 2021-01-21
key NaN (results, 3, key) q4
result_type NaN (results, 3, result_type) multipleChoice
start_time NaN (results, 3, start_time) 2021-01-21
value 0 (results, 3, value, 0) 3
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^2021.*',na=False) ), 'aa_value'] = 10000000000
aa_all_keys aa_value
results 0 end_time NaN (results, 0, end_time) 10000000000
key NaN (results, 0, key) q1
result_type NaN (results, 0, result_type) multipleChoice
start_time NaN (results, 0, start_time) 10000000000
value 0 (results, 0, value, 0) 1
1 end_time NaN (results, 1, end_time) 10000000000
key NaN (results, 1, key) q2
result_type NaN (results, 1, result_type) multipleChoice
start_time NaN (results, 1, start_time) 10000000000
value 0 (results, 1, value, 0) False
2 end_time NaN (results, 2, end_time) 10000000000
key NaN (results, 2, key) q3
result_type NaN (results, 2, result_type) multipleChoice
start_time NaN (results, 2, start_time) 10000000000
value 0 (results, 2, value, 0) 3
3 end_time NaN (results, 3, end_time) 10000000000
key NaN (results, 3, key) q4
result_type NaN (results, 3, result_type) multipleChoice
start_time NaN (results, 3, start_time) 10000000000
value 0 (results, 3, value, 0) 3
mod_iter = df.d_update_original_iter(data, verbose=True)
[results][0][end_time] Old value: 2021-01-21
[results][0][end_time] Updated value: 10000000000
[results][0][start_time] Old value: 2021-01-21
[results][0][start_time] Updated value: 10000000000
[results][1][end_time] Old value: 2021-01-21
[results][1][end_time] Updated value: 10000000000
[results][1][start_time] Old value: 2021-01-21
[results][1][start_time] Updated value: 10000000000
[results][2][end_time] Old value: 2021-01-21
[results][2][end_time] Updated value: 10000000000
[results][2][start_time] Old value: 2021-01-21
[results][2][start_time] Updated value: 10000000000
[results][3][end_time] Old value: 2021-01-21
[results][3][end_time] Updated value: 10000000000
[results][3][start_time] Old value: 2021-01-21
[results][3][start_time] Updated value: 10000000000
{'results': [{'end_time': 10000000000,
'key': 'q1',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['1']},
{'end_time': 10000000000,
'key': 'q2',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['False']},
{'end_time': 10000000000,
'key': 'q3',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['3']},
{'end_time': 10000000000,
'key': 'q4',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['3']}]}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/66461902/flattening-nested-dictionary-into-dataframe-python
data=
{1: {2: {'IDs': {'BookID': ['543533254353', '4324232342'],
'SalesID': ['543267765345', '4353543'],
'StoreID': ['111111', '1121111']},
'Name': 'boring Tales of Dragon Slayers'},
'IDs': {'BookID': ['543533254353'],
'SalesID': ['543267765345'],
'StoreID': ['123445452543']},
'Name': 'Thrilling Tales of Dragon Slayers'}}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
1 IDs BookID 0 NaN (1, IDs, BookID, 0) 543533254353
SalesID 0 NaN (1, IDs, SalesID, 0) 543267765345
StoreID 0 NaN (1, IDs, StoreID, 0) 123445452543
Name NaN NaN NaN (1, Name) Thrilling Tales of Dragon Slayers
2 IDs BookID 0 (1, 2, IDs, BookID, 0) 543533254353
1 (1, 2, IDs, BookID, 1) 4324232342
SalesID 0 (1, 2, IDs, SalesID, 0) 543267765345
1 (1, 2, IDs, SalesID, 1) 4353543
StoreID 0 (1, 2, IDs, StoreID, 0) 111111
1 (1, 2, IDs, StoreID, 1) 1121111
Name NaN NaN (1, 2, Name) boring Tales of Dragon Slayers
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^\d+$',na=False) ), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^\d+$',na=False) ), 'aa_value'].astype(float)
aa_all_keys aa_value
1 IDs BookID 0 NaN (1, IDs, BookID, 0) 543533254353.0
SalesID 0 NaN (1, IDs, SalesID, 0) 543267765345.0
StoreID 0 NaN (1, IDs, StoreID, 0) 123445452543.0
Name NaN NaN NaN (1, Name) Thrilling Tales of Dragon Slayers
2 IDs BookID 0 (1, 2, IDs, BookID, 0) 543533254353.0
1 (1, 2, IDs, BookID, 1) 4324232342.0
SalesID 0 (1, 2, IDs, SalesID, 0) 543267765345.0
1 (1, 2, IDs, SalesID, 1) 4353543.0
StoreID 0 (1, 2, IDs, StoreID, 0) 111111.0
1 (1, 2, IDs, StoreID, 1) 1121111.0
Name NaN NaN (1, 2, Name) boring Tales of Dragon Slayers
mod_iter = df.d_update_original_iter(data, verbose=True)
[1][2][IDs][BookID][0] Old value: 543533254353
[1][2][IDs][BookID][0] Updated value: 543533254353.0
[1][2][IDs][BookID][1] Old value: 4324232342
[1][2][IDs][BookID][1] Updated value: 4324232342.0
[1][2][IDs][SalesID][0] Old value: 543267765345
[1][2][IDs][SalesID][0] Updated value: 543267765345.0
[1][2][IDs][SalesID][1] Old value: 4353543
[1][2][IDs][SalesID][1] Updated value: 4353543.0
[1][2][IDs][StoreID][0] Old value: 111111
[1][2][IDs][StoreID][0] Updated value: 111111.0
[1][2][IDs][StoreID][1] Old value: 1121111
[1][2][IDs][StoreID][1] Updated value: 1121111.0
[1][IDs][BookID][0] Old value: 543533254353
[1][IDs][BookID][0] Updated value: 543533254353.0
[1][IDs][SalesID][0] Old value: 543267765345
[1][IDs][SalesID][0] Updated value: 543267765345.0
[1][IDs][StoreID][0] Old value: 123445452543
[1][IDs][StoreID][0] Updated value: 123445452543.0
{1: {2: {'IDs': {'BookID': [543533254353.0, 4324232342.0],
'SalesID': [543267765345.0, 4353543.0],
'StoreID': [111111.0, 1121111.0]},
'Name': 'boring Tales of Dragon Slayers'},
'IDs': {'BookID': [543533254353.0],
'SalesID': [543267765345.0],
'StoreID': [123445452543.0]},
'Name': 'Thrilling Tales of Dragon Slayers'}}
Nested iterable from: 'https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe'
{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},
{'review_critic': 'ABC', 'review_score': 90},
{'review_critic': '123', 'review_score': 90}],
'genres': ['Sports', 'Golf'],
'score': 85,
'title': 'Golf Simulator',
'url': 'http://example.com/golf-simulator'}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False) # create DF stacked or unstacked, it doesn't matter
aa_all_keys aa_value
critic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ
review_score (critic_reviews, 0, review_score) 90
1 review_critic (critic_reviews, 1, review_critic) ABC
review_score (critic_reviews, 1, review_score) 90
2 review_critic (critic_reviews, 2, review_critic) 123
review_score (critic_reviews, 2, review_score) 90
genres 0 NaN (genres, 0) Sports
1 NaN (genres, 1) Golf
score NaN NaN (score,) 85
title NaN NaN (title,) Golf Simulator
url NaN NaN (url,) http://example.com/golf-simulator
df.loc[df.aa_value.str.contains('[Gg]',na=False),'aa_value'] = 'UPDATE1111' #df.loc to update the dataframe (VERY IMPORTANT: To update the original iterable you have to pass 'aa_value')
aa_all_keys aa_value
critic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ
review_score (critic_reviews, 0, review_score) 90
1 review_critic (critic_reviews, 1, review_critic) ABC
review_score (critic_reviews, 1, review_score) 90
2 review_critic (critic_reviews, 2, review_critic) 123
review_score (critic_reviews, 2, review_score) 90
genres 0 NaN (genres, 0) Sports
1 NaN (genres, 1) UPDATE1111
score NaN NaN (score,) 85
title NaN NaN (title,) UPDATE1111
url NaN NaN (url,) UPDATE1111
mod_iter = df.d_update_original_iter(data, verbose=True) #updating the nested iterable, the new values have to be in the column 'aa_value', if you have added new columns to the dataframe, drop them before updating the original iterable
[genres][1] Old value: Golf
[genres][1] Updated value: UPDATE1111
[title] Old value: Golf Simulator
[title] Updated value: UPDATE1111
[url] Old value: http://example.com/golf-simulator
[url] Updated value: UPDATE1111
{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},
{'review_critic': 'ABC', 'review_score': 90},
{'review_critic': '123', 'review_score': 90}],
'genres': ['Sports', 'UPDATE1111'],
'score': 85,
'title': 'UPDATE1111',
'url': 'UPDATE1111'}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/72990265/convert-nested-list-in-dictionary-to-dataframe/72990346
data=
{'a': 'test',
'b': 1657,
'c': 'asset',
'd': [['2089', '0.0'], ['2088', '0.0']],
'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],
'f': [['2088', '0.0', 'x', 'foo'],
['2088', '0.0', 'bar', 'i'],
['2088', '0.00', 'z', '0.2']],
'x': ['test1', 'test2']}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
a NaN NaN (a,) test
b NaN NaN (b,) 1657
c NaN NaN (c,) asset
d 0 0 (d, 0, 0) 2089
1 (d, 0, 1) 0.0
1 0 (d, 1, 0) 2088
1 (d, 1, 1) 0.0
e 0 0 (e, 0, 0) 2088
1 (e, 0, 1) 0.0
1 0 (e, 1, 0) 2088
1 (e, 1, 1) 0.0
2 0 (e, 2, 0) 2088
1 (e, 2, 1) 0.00
f 0 0 (f, 0, 0) 2088
1 (f, 0, 1) 0.0
2 (f, 0, 2) x
3 (f, 0, 3) foo
1 0 (f, 1, 0) 2088
1 (f, 1, 1) 0.0
2 (f, 1, 2) bar
3 (f, 1, 3) i
2 0 (f, 2, 0) 2088
1 (f, 2, 1) 0.00
2 (f, 2, 2) z
3 (f, 2, 3) 0.2
x 0 NaN (x, 0) test1
1 NaN (x, 1) test2
df.loc[df.aa_value == 1657,'aa_value'] = 1657*30
aa_all_keys aa_value
a NaN NaN (a,) test
b NaN NaN (b,) 49710
c NaN NaN (c,) asset
d 0 0 (d, 0, 0) 2089
1 (d, 0, 1) 0.0
1 0 (d, 1, 0) 2088
1 (d, 1, 1) 0.0
e 0 0 (e, 0, 0) 2088
1 (e, 0, 1) 0.0
1 0 (e, 1, 0) 2088
1 (e, 1, 1) 0.0
2 0 (e, 2, 0) 2088
1 (e, 2, 1) 0.00
f 0 0 (f, 0, 0) 2088
1 (f, 0, 1) 0.0
2 (f, 0, 2) x
3 (f, 0, 3) foo
1 0 (f, 1, 0) 2088
1 (f, 1, 1) 0.0
2 (f, 1, 2) bar
3 (f, 1, 3) i
2 0 (f, 2, 0) 2088
1 (f, 2, 1) 0.00
2 (f, 2, 2) z
3 (f, 2, 3) 0.2
x 0 NaN (x, 0) test1
1 NaN (x, 1) test2
mod_iter = df.d_update_original_iter(data, verbose=True)
[b] Old value: 1657
[b] Updated value: 49710
{'a': 'test',
'b': 49710,
'c': 'asset',
'd': [['2089', '0.0'], ['2088', '0.0']],
'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],
'f': [['2088', '0.0', 'x', 'foo'],
['2088', '0.0', 'bar', 'i'],
['2088', '0.00', 'z', '0.2']],
'x': ['test1', 'test2']}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe
data=
[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),
'final_value': 118},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),
'final_value': 117},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),
'final_value': 117}]
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
0 cb 0 ID (0, cb, 0, ID) 1
Name (0, cb, 0, Name) A
num (0, cb, 0, num) 50
1 ID (0, cb, 1, ID) 2
Name (0, cb, 1, Name) A
num (0, cb, 1, num) 68
final_value NaN NaN (0, final_value) 118
1 cb 0 ID (1, cb, 0, ID) 1
Name (1, cb, 0, Name) A
num (1, cb, 0, num) 50
1 ID (1, cb, 1, ID) 4
Name (1, cb, 1, Name) A
num (1, cb, 1, num) 67
final_value NaN NaN (1, final_value) 117
2 cb 0 ID (2, cb, 0, ID) 1
Name (2, cb, 0, Name) A
num (2, cb, 0, num) 50
1 ID (2, cb, 1, ID) 6
Name (2, cb, 1, Name) A
num (2, cb, 1, num) 67
final_value NaN NaN (2, final_value) 117
df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') > 30, 'aa_value'] = 900000
aa_all_keys aa_value
0 cb 0 ID (0, cb, 0, ID) 1
Name (0, cb, 0, Name) A
num (0, cb, 0, num) 900000
1 ID (0, cb, 1, ID) 2
Name (0, cb, 1, Name) A
num (0, cb, 1, num) 900000
final_value NaN NaN (0, final_value) 900000
1 cb 0 ID (1, cb, 0, ID) 1
Name (1, cb, 0, Name) A
num (1, cb, 0, num) 900000
1 ID (1, cb, 1, ID) 4
Name (1, cb, 1, Name) A
num (1, cb, 1, num) 900000
final_value NaN NaN (1, final_value) 900000
2 cb 0 ID (2, cb, 0, ID) 1
Name (2, cb, 0, Name) A
num (2, cb, 0, num) 900000
1 ID (2, cb, 1, ID) 6
Name (2, cb, 1, Name) A
num (2, cb, 1, num) 900000
final_value NaN NaN (2, final_value) 900000
mod_iter = df.d_update_original_iter(data, verbose=True)
[0][cb][0][num] Old value: 50
[0][cb][0][num] Updated value: 900000
[0][cb][1][num] Old value: 68
[0][cb][1][num] Updated value: 900000
[0][final_value] Old value: 118
[0][final_value] Updated value: 900000
[1][cb][0][num] Old value: 50
[1][cb][0][num] Updated value: 900000
[1][cb][1][num] Old value: 67
[1][cb][1][num] Updated value: 900000
[1][final_value] Old value: 117
[1][final_value] Updated value: 900000
[2][cb][0][num] Old value: 50
[2][cb][0][num] Updated value: 900000
[2][cb][1][num] Old value: 67
[2][cb][1][num] Updated value: 900000
[2][final_value] Old value: 117
[2][final_value] Updated value: 900000
[{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},
{'ID': 2, 'Name': 'A', 'num': 900000}),
'final_value': 900000},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},
{'ID': 4, 'Name': 'A', 'num': 900000}),
'final_value': 900000},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},
{'ID': 6, 'Name': 'A', 'num': 900000}),
'final_value': 900000}]
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict
data=
[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],
'application_details': {'email': None, 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '1'},
{'application_contacts': [{'adress': 'Z', 'email': None}],
'application_details': {'email': 'testy@test_a.com', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '2'},
{'application_contacts': [{'adress': 'Y', 'email': None}],
'application_details': {'email': 'testy@test_a.com', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '3'}]
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
0 application_contacts 0 adress (0, application_contacts, 0, adress) X
email (0, application_contacts, 0, email) test@test.com
application_details email NaN (0, application_details, email) None
phone NaN (0, application_details, phone) None
employer Name NaN (0, employer, Name) Nom
email NaN (0, employer, email) None
id NaN NaN (0, id) 1
1 application_contacts 0 adress (1, application_contacts, 0, adress) Z
email (1, application_contacts, 0, email) None
application_details email NaN (1, application_details, email) testy@test_a.com
phone NaN (1, application_details, phone) None
employer Name NaN (1, employer, Name) Nom
email NaN (1, employer, email) None
id NaN NaN (1, id) 2
2 application_contacts 0 adress (2, application_contacts, 0, adress) Y
email (2, application_contacts, 0, email) None
application_details email NaN (2, application_details, email) testy@test_a.com
phone NaN (2, application_details, phone) None
employer Name NaN (2, employer, Name) Nom
email NaN (2, employer, email) None
id NaN NaN (2, id) 3
df.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'test_a\.\w+\b',na=False), 'aa_value'] = 'UPPPPPPPPPPPPPPPDATE.COM'
aa_all_keys aa_value
0 application_contacts 0 adress (0, application_contacts, 0, adress) X
email (0, application_contacts, 0, email) test@test.com
application_details email NaN (0, application_details, email) None
phone NaN (0, application_details, phone) None
employer Name NaN (0, employer, Name) Nom
email NaN (0, employer, email) None
id NaN NaN (0, id) 1
1 application_contacts 0 adress (1, application_contacts, 0, adress) Z
email (1, application_contacts, 0, email) None
application_details email NaN (1, application_details, email) UPPPPPPPPPPPPPPPDATE.COM
phone NaN (1, application_details, phone) None
employer Name NaN (1, employer, Name) Nom
email NaN (1, employer, email) None
id NaN NaN (1, id) 2
2 application_contacts 0 adress (2, application_contacts, 0, adress) Y
email (2, application_contacts, 0, email) None
application_details email NaN (2, application_details, email) UPPPPPPPPPPPPPPPDATE.COM
phone NaN (2, application_details, phone) None
employer Name NaN (2, employer, Name) Nom
email NaN (2, employer, email) None
id NaN NaN (2, id) 3
mod_iter = df.d_update_original_iter(data, verbose=True)
[1][application_details][email] Old value: testy@test_a.com
[1][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM
[2][application_details][email] Old value: testy@test_a.com
[2][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM
[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],
'application_details': {'email': None, 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '1'},
{'application_contacts': [{'adress': 'Z', 'email': None}],
'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '2'},
{'application_contacts': [{'adress': 'Y', 'email': None}],
'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '3'}]
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe
data=
{'A': [1, 2, 3],
'B': [4, 5, 6],
'departure': [{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'}]}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
A 0 NaN (A, 0) 1
1 NaN (A, 1) 2
2 NaN (A, 2) 3
B 0 NaN (B, 0) 4
1 NaN (B, 1) 5
2 NaN (B, 2) 6
departure 0 actual (departure, 0, actual) None
actual_runway (departure, 0, actual_runway) None
airport (departure, 0, airport) Findel
delay (departure, 0, delay) None
estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 0, estimated_runway) None
gate (departure, 0, gate) None
iata (departure, 0, iata) LUX
icao (departure, 0, icao) ELLX
scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 0, terminal) None
timezone (departure, 0, timezone) Europe/Luxembourg
1 actual (departure, 1, actual) None
actual_runway (departure, 1, actual_runway) None
airport (departure, 1, airport) Findel
delay (departure, 1, delay) None
estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 1, estimated_runway) None
gate (departure, 1, gate) None
iata (departure, 1, iata) LUX
icao (departure, 1, icao) ELLX
scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 1, terminal) None
timezone (departure, 1, timezone) Europe/Luxembourg
2 actual (departure, 2, actual) None
actual_runway (departure, 2, actual_runway) None
airport (departure, 2, airport) Findel
delay (departure, 2, delay) None
estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 2, estimated_runway) None
gate (departure, 2, gate) None
iata (departure, 2, iata) LUX
icao (departure, 2, icao) ELLX
scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 2, terminal) None
timezone (departure, 2, timezone) Europe/Luxembourg
df.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value')== 'ELLX', 'aa_value'] = 'ELLX-UPDATED'
aa_all_keys aa_value
A 0 NaN (A, 0) 1
1 NaN (A, 1) 2
2 NaN (A, 2) 3
B 0 NaN (B, 0) 4
1 NaN (B, 1) 5
2 NaN (B, 2) 6
departure 0 actual (departure, 0, actual) None
actual_runway (departure, 0, actual_runway) None
airport (departure, 0, airport) Findel
delay (departure, 0, delay) None
estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 0, estimated_runway) None
gate (departure, 0, gate) None
iata (departure, 0, iata) LUX
icao (departure, 0, icao) ELLX-UPDATED
scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 0, terminal) None
timezone (departure, 0, timezone) Europe/Luxembourg
1 actual (departure, 1, actual) None
actual_runway (departure, 1, actual_runway) None
airport (departure, 1, airport) Findel
delay (departure, 1, delay) None
estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 1, estimated_runway) None
gate (departure, 1, gate) None
iata (departure, 1, iata) LUX
icao (departure, 1, icao) ELLX-UPDATED
scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 1, terminal) None
timezone (departure, 1, timezone) Europe/Luxembourg
2 actual (departure, 2, actual) None
actual_runway (departure, 2, actual_runway) None
airport (departure, 2, airport) Findel
delay (departure, 2, delay) None
estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00
estimated_runway (departure, 2, estimated_runway) None
gate (departure, 2, gate) None
iata (departure, 2, iata) LUX
icao (departure, 2, icao) ELLX-UPDATED
scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00
terminal (departure, 2, terminal) None
timezone (departure, 2, timezone) Europe/Luxembourg
mod_iter = df.d_update_original_iter(data, verbose=True)
[departure][0][icao] Old value: ELLX
[departure][0][icao] Updated value: ELLX-UPDATED
[departure][1][icao] Old value: ELLX
[departure][1][icao] Updated value: ELLX-UPDATED
[departure][2][icao] Old value: ELLX
[departure][2][icao] Updated value: ELLX-UPDATED
{'A': [1, 2, 3],
'B': [4, 5, 6],
'departure': [{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX-UPDATED',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX-UPDATED',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX-UPDATED',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'}]}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction
data=
{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},
'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
level1 t1 s1 col1 (level1, t1, s1, col1) 5
col2 (level1, t1, s1, col2) 4
col3 (level1, t1, s1, col3) 4
col4 (level1, t1, s1, col4) 9
s2 col1 (level1, t1, s2, col1) 1
... ...
level2 t3 s3 col4 (level2, t3, s3, col4) 12
s4 col1 (level2, t3, s4, col1) 13
col2 (level2, t3, s4, col2) 14
col3 (level2, t3, s4, col3) 15
col4 (level2, t3, s4, col4) 16
[96 rows x 2 columns]
df.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') > 5) & (df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') < 10), 'aa_value'] = 1000000
aa_all_keys aa_value
level1 t1 s1 col1 (level1, t1, s1, col1) 5
col2 (level1, t1, s1, col2) 4
col3 (level1, t1, s1, col3) 4
col4 (level1, t1, s1, col4) 1000000
s2 col1 (level1, t1, s2, col1) 1
... ...
level2 t3 s3 col4 (level2, t3, s3, col4) 12
s4 col1 (level2, t3, s4, col1) 13
col2 (level2, t3, s4, col2) 14
col3 (level2, t3, s4, col3) 15
col4 (level2, t3, s4, col4) 16
[96 rows x 2 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[level1][t1][s1][col4] Old value: 9
[level1][t1][s1][col4] Updated value: 1000000
[level1][t1][s2][col4] Old value: 8
[level1][t1][s2][col4] Updated value: 1000000
[level1][t1][s3][col2] Old value: 8
[level1][t1][s3][col2] Updated value: 1000000
[level1][t1][s3][col4] Old value: 9
[level1][t1][s3][col4] Updated value: 1000000
[level1][t1][s4][col4] Old value: 9
[level1][t1][s4][col4] Updated value: 1000000
[level1][t2][s1][col4] Old value: 9
[level1][t2][s1][col4] Updated value: 1000000
[level1][t2][s2][col4] Old value: 8
[level1][t2][s2][col4] Updated value: 1000000
[level1][t2][s3][col2] Old value: 8
[level1][t2][s3][col2] Updated value: 1000000
[level1][t2][s3][col4] Old value: 9
[level1][t2][s3][col4] Updated value: 1000000
[level1][t2][s4][col4] Old value: 9
[level1][t2][s4][col4] Updated value: 1000000
[level1][t3][s2][col2] Old value: 6
[level1][t3][s2][col2] Updated value: 1000000
[level1][t3][s2][col3] Old value: 7
[level1][t3][s2][col3] Updated value: 1000000
[level1][t3][s2][col4] Old value: 8
[level1][t3][s2][col4] Updated value: 1000000
[level1][t3][s3][col1] Old value: 9
[level1][t3][s3][col1] Updated value: 1000000
[level2][t1][s1][col3] Old value: 9
[level2][t1][s1][col3] Updated value: 1000000
[level2][t1][s1][col4] Old value: 9
[level2][t1][s1][col4] Updated value: 1000000
[level2][t1][s3][col2] Old value: 8
[level2][t1][s3][col2] Updated value: 1000000
[level2][t2][s1][col4] Old value: 9
[level2][t2][s1][col4] Updated value: 1000000
[level2][t2][s2][col4] Old value: 8
[level2][t2][s2][col4] Updated value: 1000000
[level2][t2][s3][col2] Old value: 8
[level2][t2][s3][col2] Updated value: 1000000
[level2][t2][s3][col4] Old value: 9
[level2][t2][s3][col4] Updated value: 1000000
[level2][t2][s4][col4] Old value: 9
[level2][t2][s4][col4] Updated value: 1000000
[level2][t3][s2][col2] Old value: 6
[level2][t3][s2][col2] Updated value: 1000000
[level2][t3][s2][col3] Old value: 7
[level2][t3][s2][col3] Updated value: 1000000
[level2][t3][s2][col4] Old value: 8
[level2][t3][s2][col4] Updated value: 1000000
[level2][t3][s3][col1] Old value: 9
[level2][t3][s3][col1] Updated value: 1000000
{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},
's3': {'col1': 11,
'col2': 1000000,
'col3': 2,
'col4': 1000000},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},
's3': {'col1': 11,
'col2': 1000000,
'col3': 2,
'col4': 1000000},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5,
'col2': 1000000,
'col3': 1000000,
'col4': 1000000},
's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},
'level2': {'t1': {'s1': {'col1': 5,
'col2': 4,
'col3': 1000000,
'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': 1000000, 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},
's3': {'col1': 11,
'col2': 1000000,
'col3': 2,
'col4': 1000000},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5,
'col2': 1000000,
'col3': 1000000,
'col4': 1000000},
's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary
data=
{'_links': {'next': None, 'prev': None},
'limit': 250,
'offset': 0,
'runs': [{'assignedto_id': None,
'blocked_count': 0,
'completed_on': None,
'config': None,
'config_ids': [],
'created_by': 1,
'created_on': 1651790693,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 1,
'id': 13,
'include_all': False,
'is_completed': False,
'milestone_id': None,
'name': '2022-05-05-testrun',
'passed_count': 2,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 0,
'updated_on': 1651790693,
'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},
{'assignedto_id': None,
'blocked_count': 0,
'completed_on': 1650989972,
'config': None,
'config_ids': [],
'created_by': 5,
'created_on': 1650966329,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 0,
'id': 9,
'include_all': False,
'is_completed': True,
'milestone_id': None,
'name': 'This is a new test run',
'passed_count': 0,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 3,
'updated_on': 1650966329,
'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],
'size': 2}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
_links next NaN (_links, next) None
prev NaN (_links, prev) None
limit NaN NaN (limit,) 250
offset NaN NaN (offset,) 0
runs 0 assignedto_id (runs, 0, assignedto_id) None
... ...
1 suite_id (runs, 1, suite_id) 1
untested_count (runs, 1, untested_count) 3
updated_on (runs, 1, updated_on) 1650966329
url (runs, 1, url) https://xxxxxxxxxx.testrail.io/index.php?/runs...
size NaN NaN (size,) 2
[63 rows x 2 columns]
df.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') ==False ), 'aa_value'] = True
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'https?://.*',na=False) ), 'aa_value'] = 'WWW.PYTHON.ORG'
aa_all_keys aa_value
_links next NaN (_links, next) None
prev NaN (_links, prev) None
limit NaN NaN (limit,) 250
offset NaN NaN (offset,) 0
runs 0 assignedto_id (runs, 0, assignedto_id) None
... ...
1 suite_id (runs, 1, suite_id) 1
untested_count (runs, 1, untested_count) 3
updated_on (runs, 1, updated_on) 1650966329
url (runs, 1, url) WWW.PYTHON.ORG
size NaN NaN (size,) 2
[63 rows x 2 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[runs][0][include_all] Old value: False
[runs][0][include_all] Updated value: True
[runs][0][is_completed] Old value: False
[runs][0][is_completed] Updated value: True
[runs][0][url] Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13
[runs][0][url] Updated value: WWW.PYTHON.ORG
[runs][1][include_all] Old value: False
[runs][1][include_all] Updated value: True
[runs][1][url] Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9
[runs][1][url] Updated value: WWW.PYTHON.ORG
{'_links': {'next': None, 'prev': None},
'limit': 250,
'offset': 0,
'runs': [{'assignedto_id': None,
'blocked_count': 0,
'completed_on': None,
'config': None,
'config_ids': [],
'created_by': 1,
'created_on': 1651790693,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 1,
'id': 13,
'include_all': True,
'is_completed': True,
'milestone_id': None,
'name': '2022-05-05-testrun',
'passed_count': 2,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 0,
'updated_on': 1651790693,
'url': 'WWW.PYTHON.ORG'},
{'assignedto_id': None,
'blocked_count': 0,
'completed_on': 1650989972,
'config': None,
'config_ids': [],
'created_by': 5,
'created_on': 1650966329,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 0,
'id': 9,
'include_all': True,
'is_completed': True,
'milestone_id': None,
'name': 'This is a new test run',
'passed_count': 0,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 3,
'updated_on': 1650966329,
'url': 'WWW.PYTHON.ORG'}],
'size': 2}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/73708706/how-to-get-values-from-list-of-nested-dictionaries/73839430#73839430
data=
{'results': [{'end_time': '2021-01-21',
'key': 'q1',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['1']},
{'end_time': '2021-01-21',
'key': 'q2',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['False']},
{'end_time': '2021-01-21',
'key': 'q3',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['3']},
{'end_time': '2021-01-21',
'key': 'q4',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['3']}]}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
results 0 end_time NaN (results, 0, end_time) 2021-01-21
key NaN (results, 0, key) q1
result_type NaN (results, 0, result_type) multipleChoice
start_time NaN (results, 0, start_time) 2021-01-21
value 0 (results, 0, value, 0) 1
1 end_time NaN (results, 1, end_time) 2021-01-21
key NaN (results, 1, key) q2
result_type NaN (results, 1, result_type) multipleChoice
start_time NaN (results, 1, start_time) 2021-01-21
value 0 (results, 1, value, 0) False
2 end_time NaN (results, 2, end_time) 2021-01-21
key NaN (results, 2, key) q3
result_type NaN (results, 2, result_type) multipleChoice
start_time NaN (results, 2, start_time) 2021-01-21
value 0 (results, 2, value, 0) 3
3 end_time NaN (results, 3, end_time) 2021-01-21
key NaN (results, 3, key) q4
result_type NaN (results, 3, result_type) multipleChoice
start_time NaN (results, 3, start_time) 2021-01-21
value 0 (results, 3, value, 0) 3
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^2021.*',na=False) ), 'aa_value'] = 10000000000
aa_all_keys aa_value
results 0 end_time NaN (results, 0, end_time) 10000000000
key NaN (results, 0, key) q1
result_type NaN (results, 0, result_type) multipleChoice
start_time NaN (results, 0, start_time) 10000000000
value 0 (results, 0, value, 0) 1
1 end_time NaN (results, 1, end_time) 10000000000
key NaN (results, 1, key) q2
result_type NaN (results, 1, result_type) multipleChoice
start_time NaN (results, 1, start_time) 10000000000
value 0 (results, 1, value, 0) False
2 end_time NaN (results, 2, end_time) 10000000000
key NaN (results, 2, key) q3
result_type NaN (results, 2, result_type) multipleChoice
start_time NaN (results, 2, start_time) 10000000000
value 0 (results, 2, value, 0) 3
3 end_time NaN (results, 3, end_time) 10000000000
key NaN (results, 3, key) q4
result_type NaN (results, 3, result_type) multipleChoice
start_time NaN (results, 3, start_time) 10000000000
value 0 (results, 3, value, 0) 3
mod_iter = df.d_update_original_iter(data, verbose=True)
[results][0][end_time] Old value: 2021-01-21
[results][0][end_time] Updated value: 10000000000
[results][0][start_time] Old value: 2021-01-21
[results][0][start_time] Updated value: 10000000000
[results][1][end_time] Old value: 2021-01-21
[results][1][end_time] Updated value: 10000000000
[results][1][start_time] Old value: 2021-01-21
[results][1][start_time] Updated value: 10000000000
[results][2][end_time] Old value: 2021-01-21
[results][2][end_time] Updated value: 10000000000
[results][2][start_time] Old value: 2021-01-21
[results][2][start_time] Updated value: 10000000000
[results][3][end_time] Old value: 2021-01-21
[results][3][end_time] Updated value: 10000000000
[results][3][start_time] Old value: 2021-01-21
[results][3][start_time] Updated value: 10000000000
{'results': [{'end_time': 10000000000,
'key': 'q1',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['1']},
{'end_time': 10000000000,
'key': 'q2',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['False']},
{'end_time': 10000000000,
'key': 'q3',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['3']},
{'end_time': 10000000000,
'key': 'q4',
'result_type': 'multipleChoice',
'start_time': 10000000000,
'value': ['3']}]}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/66461902/flattening-nested-dictionary-into-dataframe-python
data=
{1: {2: {'IDs': {'BookID': ['543533254353', '4324232342'],
'SalesID': ['543267765345', '4353543'],
'StoreID': ['111111', '1121111']},
'Name': 'boring Tales of Dragon Slayers'},
'IDs': {'BookID': ['543533254353'],
'SalesID': ['543267765345'],
'StoreID': ['123445452543']},
'Name': 'Thrilling Tales of Dragon Slayers'}}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
1 IDs BookID 0 NaN (1, IDs, BookID, 0) 543533254353
SalesID 0 NaN (1, IDs, SalesID, 0) 543267765345
StoreID 0 NaN (1, IDs, StoreID, 0) 123445452543
Name NaN NaN NaN (1, Name) Thrilling Tales of Dragon Slayers
2 IDs BookID 0 (1, 2, IDs, BookID, 0) 543533254353
1 (1, 2, IDs, BookID, 1) 4324232342
SalesID 0 (1, 2, IDs, SalesID, 0) 543267765345
1 (1, 2, IDs, SalesID, 1) 4353543
StoreID 0 (1, 2, IDs, StoreID, 0) 111111
1 (1, 2, IDs, StoreID, 1) 1121111
Name NaN NaN (1, 2, Name) boring Tales of Dragon Slayers
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^\d+$',na=False) ), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^\d+$',na=False) ), 'aa_value'].astype(float)
aa_all_keys aa_value
1 IDs BookID 0 NaN (1, IDs, BookID, 0) 543533254353.0
SalesID 0 NaN (1, IDs, SalesID, 0) 543267765345.0
StoreID 0 NaN (1, IDs, StoreID, 0) 123445452543.0
Name NaN NaN NaN (1, Name) Thrilling Tales of Dragon Slayers
2 IDs BookID 0 (1, 2, IDs, BookID, 0) 543533254353.0
1 (1, 2, IDs, BookID, 1) 4324232342.0
SalesID 0 (1, 2, IDs, SalesID, 0) 543267765345.0
1 (1, 2, IDs, SalesID, 1) 4353543.0
StoreID 0 (1, 2, IDs, StoreID, 0) 111111.0
1 (1, 2, IDs, StoreID, 1) 1121111.0
Name NaN NaN (1, 2, Name) boring Tales of Dragon Slayers
mod_iter = df.d_update_original_iter(data, verbose=True)
[1][2][IDs][BookID][0] Old value: 543533254353
[1][2][IDs][BookID][0] Updated value: 543533254353.0
[1][2][IDs][BookID][1] Old value: 4324232342
[1][2][IDs][BookID][1] Updated value: 4324232342.0
[1][2][IDs][SalesID][0] Old value: 543267765345
[1][2][IDs][SalesID][0] Updated value: 543267765345.0
[1][2][IDs][SalesID][1] Old value: 4353543
[1][2][IDs][SalesID][1] Updated value: 4353543.0
[1][2][IDs][StoreID][0] Old value: 111111
[1][2][IDs][StoreID][0] Updated value: 111111.0
[1][2][IDs][StoreID][1] Old value: 1121111
[1][2][IDs][StoreID][1] Updated value: 1121111.0
[1][IDs][BookID][0] Old value: 543533254353
[1][IDs][BookID][0] Updated value: 543533254353.0
[1][IDs][SalesID][0] Old value: 543267765345
[1][IDs][SalesID][0] Updated value: 543267765345.0
[1][IDs][StoreID][0] Old value: 123445452543
[1][IDs][StoreID][0] Updated value: 123445452543.0
{1: {2: {'IDs': {'BookID': [543533254353.0, 4324232342.0],
'SalesID': [543267765345.0, 4353543.0],
'StoreID': [111111.0, 1121111.0]},
'Name': 'boring Tales of Dragon Slayers'},
'IDs': {'BookID': [543533254353.0],
'SalesID': [543267765345.0],
'StoreID': [123445452543.0]},
'Name': 'Thrilling Tales of Dragon Slayers'}}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/72017771/key-error-when-accessing-a-nested-dictionary
data=
[{'blocks': [{'block_id': 'BJNTn',
'text': {'text': 'You have a new message.',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'WPn/l',
'text': {'text': '*Heard By*\nFriend',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': '5yp',
'text': {'text': '*Which Direction? *\nNorth',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'fKEpF',
'text': {'text': '*Which Destination*\nNew York',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'qjAH',
'text': {'text': '*New Customer:*\\Yes',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'yt4',
'elements': [{'action_id': '+bc',
'text': {'bar': 'View results',
'emoji': True,
'type': 'plain_text'},
'type': 'button',
'url': 'www.example.com/results'}],
'type': 'actions'},
{'block_id': 'IBr',
'text': {'text': ' ', 'type': 'mrkdwn', 'verbatim': False},
'type': 'section'}],
'bot_id': 'BPD4K3SJW',
'subtype': 'bot_message',
'text': "This content can't be displayed.",
'timestamp': '1650905606.755969',
'type': 'message',
'username': 'admin'},
{'blocks': [{'block_id': 'Smd',
'text': {'text': 'You have a new message.',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': '6YaLt',
'text': {'text': '*Heard By*\nOnline Search',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'w3o',
'text': {'text': '*Which Direction: *\nNorth',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'PTQ',
'text': {'text': '*Which Destination? *\nMiami',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'JCfSP',
'text': {'text': '*New Customer? *\nNo',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'yt4',
'elements': [{'action_id': '+bc',
'text': {'bar': 'View results',
'emoji': True,
'type': 'plain_text'},
'type': 'button',
'url': 'www.example.com/results'}],
'type': 'actions'},
{'block_id': 'RJOA',
'text': {'text': ' ', 'type': 'mrkdwn', 'verbatim': False},
'type': 'section'}],
'bot_id': 'BPD4K3SJW',
'subtype': 'bot_message',
'text': "This content can't be displayed.",
'timestamp': '1650899428.077709',
'type': 'message',
'username': 'admin'}]
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
0 blocks 0.0 block_id NaN NaN NaN (0, blocks, 0, block_id) BJNTn
text text NaN NaN (0, blocks, 0, text, text) You have a new message.
type NaN NaN (0, blocks, 0, text, type) mrkdwn
verbatim NaN NaN (0, blocks, 0, text, verbatim) False
type NaN NaN NaN (0, blocks, 0, type) section
... ...
1 subtype NaN NaN NaN NaN NaN (1, subtype) bot_message
text NaN NaN NaN NaN NaN (1, text) This content can't be displayed.
timestamp NaN NaN NaN NaN NaN (1, timestamp) 1650899428.077709
type NaN NaN NaN NaN NaN (1, type) message
username NaN NaN NaN NaN NaN (1, username) admin
[88 rows x 2 columns]
df.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') == False), 'aa_value'] = 'NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'
aa_all_keys aa_value
0 blocks 0.0 block_id NaN NaN NaN (0, blocks, 0, block_id) BJNTn
text text NaN NaN (0, blocks, 0, text, text) You have a new message.
type NaN NaN (0, blocks, 0, text, type) mrkdwn
verbatim NaN NaN (0, blocks, 0, text, verbatim) NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE B...
type NaN NaN NaN (0, blocks, 0, type) section
... ...
1 subtype NaN NaN NaN NaN NaN (1, subtype) bot_message
text NaN NaN NaN NaN NaN (1, text) This content can't be displayed.
timestamp NaN NaN NaN NaN NaN (1, timestamp) 1650899428.077709
type NaN NaN NaN NaN NaN (1, type) message
username NaN NaN NaN NaN NaN (1, username) admin
[88 rows x 2 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[0][blocks][0][text][verbatim] Old value: False
[0][blocks][0][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[0][blocks][1][text][verbatim] Old value: False
[0][blocks][1][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[0][blocks][2][text][verbatim] Old value: False
[0][blocks][2][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[0][blocks][3][text][verbatim] Old value: False
[0][blocks][3][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[0][blocks][4][text][verbatim] Old value: False
[0][blocks][4][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[0][blocks][6][text][verbatim] Old value: False
[0][blocks][6][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[1][blocks][0][text][verbatim] Old value: False
[1][blocks][0][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[1][blocks][1][text][verbatim] Old value: False
[1][blocks][1][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[1][blocks][2][text][verbatim] Old value: False
[1][blocks][2][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[1][blocks][3][text][verbatim] Old value: False
[1][blocks][3][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[1][blocks][4][text][verbatim] Old value: False
[1][blocks][4][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[1][blocks][6][text][verbatim] Old value: False
[1][blocks][6][text][verbatim] Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL
[{'blocks': [{'block_id': 'BJNTn',
'text': {'text': 'You have a new message.',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'WPn/l',
'text': {'text': '*Heard By*\nFriend',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': '5yp',
'text': {'text': '*Which Direction? *\nNorth',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'fKEpF',
'text': {'text': '*Which Destination*\nNew York',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'qjAH',
'text': {'text': '*New Customer:*\\Yes',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'yt4',
'elements': [{'action_id': '+bc',
'text': {'bar': 'View results',
'emoji': True,
'type': 'plain_text'},
'type': 'button',
'url': 'www.example.com/results'}],
'type': 'actions'},
{'block_id': 'IBr',
'text': {'text': ' ',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'}],
'bot_id': 'BPD4K3SJW',
'subtype': 'bot_message',
'text': "This content can't be displayed.",
'timestamp': '1650905606.755969',
'type': 'message',
'username': 'admin'},
{'blocks': [{'block_id': 'Smd',
'text': {'text': 'You have a new message.',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': '6YaLt',
'text': {'text': '*Heard By*\nOnline Search',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'w3o',
'text': {'text': '*Which Direction: *\nNorth',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'PTQ',
'text': {'text': '*Which Destination? *\nMiami',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'JCfSP',
'text': {'text': '*New Customer? *\nNo',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'},
{'block_id': 'yt4',
'elements': [{'action_id': '+bc',
'text': {'bar': 'View results',
'emoji': True,
'type': 'plain_text'},
'type': 'button',
'url': 'www.example.com/results'}],
'type': 'actions'},
{'block_id': 'RJOA',
'text': {'text': ' ',
'type': 'mrkdwn',
'verbatim': 'NOOOOOOOOOOOOOOOOOOO '
'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},
'type': 'section'}],
'bot_id': 'BPD4K3SJW',
'subtype': 'bot_message',
'text': "This content can't be displayed.",
'timestamp': '1650899428.077709',
'type': 'message',
'username': 'admin'}]
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/73643077/how-to-transform-a-list-of-nested-dictionaries-into-a-data-frame-pd-json-normal
data=
[{'apple': {'price': 4, 'units': 3}},
{'banana': {'price': 2, 'units': 20}},
{'orange': {'price': 5, 'units': 15}}]
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
0 apple price (0, apple, price) 4
units (0, apple, units) 3
1 banana price (1, banana, price) 2
units (1, banana, units) 20
2 orange price (2, orange, price) 5
units (2, orange, units) 15
df.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') >3) & (df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_1').str.contains("banana")), 'aa_value'] = 50000
level_0 level_1 level_2 aa_all_keys aa_value
0 0 apple price (0, apple, price) 4
1 0 apple units (0, apple, units) 3
2 1 banana price (1, banana, price) 2
3 1 banana units (1, banana, units) 50000
4 2 orange price (2, orange, price) 5
5 2 orange units (2, orange, units) 15
mod_iter = df.d_update_original_iter(data, verbose=True)
[1][banana][units] Old value: 20
[1][banana][units] Updated value: 50000
[{'apple': {'price': 4, 'units': 3}},
{'banana': {'price': 2, 'units': 50000}},
{'orange': {'price': 5, 'units': 15}}]
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/58110440/opening-nested-dict-in-a-single-column-to-multiple-columns-in-pandas
data=
{'simple25b': {'hands': {'0': {'currency': 'rm',
'handId': 'xyz',
'time': '2019-09-23 11:00:01'},
'1': {'currency': 'rm',
'handId': 'abc',
'time': '2019-09-23 11:01:18'}}},
'simple5af': {'hands': {'0': {'currency': 'rm',
'handId': 'akg',
'time': '2019-09-23 10:53:22'},
'1': {'currency': 'rm',
'handId': 'mzc',
'time': '2019-09-23 10:54:15'},
'2': {'currency': 'rm',
'handId': 'swk',
'time': '2019-09-23 10:56:03'},
'3': {'currency': 'rm',
'handId': 'pQc',
'time': '2019-09-23 10:57:15'},
'4': {'currency': 'rm',
'handId': 'ywh',
'time': '2019-09-23 10:58:53'}}}}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
simple25b hands 0 currency (simple25b, hands, 0, currency) rm
handId (simple25b, hands, 0, handId) xyz
time (simple25b, hands, 0, time) 2019-09-23 11:00:01
1 currency (simple25b, hands, 1, currency) rm
handId (simple25b, hands, 1, handId) abc
time (simple25b, hands, 1, time) 2019-09-23 11:01:18
simple5af hands 0 currency (simple5af, hands, 0, currency) rm
handId (simple5af, hands, 0, handId) akg
time (simple5af, hands, 0, time) 2019-09-23 10:53:22
1 currency (simple5af, hands, 1, currency) rm
handId (simple5af, hands, 1, handId) mzc
time (simple5af, hands, 1, time) 2019-09-23 10:54:15
2 currency (simple5af, hands, 2, currency) rm
handId (simple5af, hands, 2, handId) swk
time (simple5af, hands, 2, time) 2019-09-23 10:56:03
3 currency (simple5af, hands, 3, currency) rm
handId (simple5af, hands, 3, handId) pQc
time (simple5af, hands, 3, time) 2019-09-23 10:57:15
4 currency (simple5af, hands, 4, currency) rm
handId (simple5af, hands, 4, handId) ywh
time (simple5af, hands, 4, time) 2019-09-23 10:58:53
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_3').str.contains("time")), 'aa_value'] = pd.to_datetime(df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_3').str.contains("time")), 'aa_value'])
level_0 level_1 ... aa_all_keys aa_value
0 simple25b hands ... (simple25b, hands, 0, currency) rm
1 simple25b hands ... (simple25b, hands, 0, handId) xyz
2 simple25b hands ... (simple25b, hands, 0, time) 2019-09-23 11:00:01
3 simple25b hands ... (simple25b, hands, 1, currency) rm
4 simple25b hands ... (simple25b, hands, 1, handId) abc
5 simple25b hands ... (simple25b, hands, 1, time) 2019-09-23 11:01:18
6 simple5af hands ... (simple5af, hands, 0, currency) rm
7 simple5af hands ... (simple5af, hands, 0, handId) akg
8 simple5af hands ... (simple5af, hands, 0, time) 2019-09-23 10:53:22
9 simple5af hands ... (simple5af, hands, 1, currency) rm
10 simple5af hands ... (simple5af, hands, 1, handId) mzc
11 simple5af hands ... (simple5af, hands, 1, time) 2019-09-23 10:54:15
12 simple5af hands ... (simple5af, hands, 2, currency) rm
13 simple5af hands ... (simple5af, hands, 2, handId) swk
14 simple5af hands ... (simple5af, hands, 2, time) 2019-09-23 10:56:03
15 simple5af hands ... (simple5af, hands, 3, currency) rm
16 simple5af hands ... (simple5af, hands, 3, handId) pQc
17 simple5af hands ... (simple5af, hands, 3, time) 2019-09-23 10:57:15
18 simple5af hands ... (simple5af, hands, 4, currency) rm
19 simple5af hands ... (simple5af, hands, 4, handId) ywh
20 simple5af hands ... (simple5af, hands, 4, time) 2019-09-23 10:58:53
[21 rows x 6 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
{'simple25b': {'hands': {'0': {'currency': 'rm',
'handId': 'xyz',
'time': Timestamp('2019-09-23 11:00:01')},
'1': {'currency': 'rm',
'handId': 'abc',
'time': Timestamp('2019-09-23 11:01:18')}}},
'simple5af': {'hands': {'0': {'currency': 'rm',
'handId': 'akg',
'time': Timestamp('2019-09-23 10:53:22')},
'1': {'currency': 'rm',
'handId': 'mzc',
'time': Timestamp('2019-09-23 10:54:15')},
'2': {'currency': 'rm',
'handId': 'swk',
'time': Timestamp('2019-09-23 10:56:03')},
'3': {'currency': 'rm',
'handId': 'pQc',
'time': Timestamp('2019-09-23 10:57:15')},
'4': {'currency': 'rm',
'handId': 'ywh',
'time': Timestamp('2019-09-23 10:58:53')}}}}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/62059970/how-can-i-convert-nested-dictionary-to-pd-dataframe-faster
data=
{'file': 'name',
'main': [{'answer': [{'comment': 'It is defined as',
'user': 'John',
'value': [{'my_value': 5, 'value_2': 10},
{'my_value': 24, 'value_2': 30}]},
{'comment': 'as John said above it simply means',
'user': 'Sam',
'value': [{'my_value': 9, 'value_2': 10},
{'my_value': 54, 'value_2': 19}]}],
'closed': 'no',
'question': 'what is ?',
'question_no': 'Q.1'}]}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
file NaN NaN NaN NaN NaN NaN (file,) name
main 0 answer 0 comment NaN NaN (main, 0, answer, 0, comment) It is defined as
user NaN NaN (main, 0, answer, 0, user) John
value 0 my_value (main, 0, answer, 0, value, 0, my_value) 5
value_2 (main, 0, answer, 0, value, 0, value_2) 10
1 my_value (main, 0, answer, 0, value, 1, my_value) 24
value_2 (main, 0, answer, 0, value, 1, value_2) 30
1 comment NaN NaN (main, 0, answer, 1, comment) as John said above it simply means
user NaN NaN (main, 0, answer, 1, user) Sam
value 0 my_value (main, 0, answer, 1, value, 0, my_value) 9
value_2 (main, 0, answer, 1, value, 0, value_2) 10
1 my_value (main, 0, answer, 1, value, 1, my_value) 54
value_2 (main, 0, answer, 1, value, 1, value_2) 19
closed NaN NaN NaN NaN (main, 0, closed) no
question NaN NaN NaN NaN (main, 0, question) what is ?
question_no NaN NaN NaN NaN (main, 0, question_no) Q.1
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value']*1000
level_0 ... aa_value
0 file ... name
1 main ... It is defined as
2 main ... John
3 main ... 5
4 main ... 10000
5 main ... 24
6 main ... 30000
7 main ... as John said above it simply means
8 main ... Sam
9 main ... 9
10 main ... 10000
11 main ... 54
12 main ... 19000
13 main ... no
14 main ... what is ?
15 main ... Q.1
[16 rows x 9 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[main][0][answer][0][value][0][value_2] Old value: 10
[main][0][answer][0][value][0][value_2] Updated value: 10000
[main][0][answer][0][value][1][value_2] Old value: 30
[main][0][answer][0][value][1][value_2] Updated value: 30000
[main][0][answer][1][value][0][value_2] Old value: 10
[main][0][answer][1][value][0][value_2] Updated value: 10000
[main][0][answer][1][value][1][value_2] Old value: 19
[main][0][answer][1][value][1][value_2] Updated value: 19000
{'file': 'name',
'main': [{'answer': [{'comment': 'It is defined as',
'user': 'John',
'value': [{'my_value': 5, 'value_2': 10000},
{'my_value': 24, 'value_2': 30000}]},
{'comment': 'as John said above it simply means',
'user': 'Sam',
'value': [{'my_value': 9, 'value_2': 10000},
{'my_value': 54, 'value_2': 19000}]}],
'closed': 'no',
'question': 'what is ?',
'question_no': 'Q.1'}]}
```
```python
#Nested iterable from:
https://stackoverflow.com/questions/39634369/4-dimensional-nested-dictionary-to-pandas-data-frame
data=
{'orders': [{'created_at': '2016-09-20T22:04:49+02:00',
'email': 'test@aol.com',
'id': 4314127108,
'line_items': [{'destination_location': {'address1': 'Teststreet '
'12',
'address2': '',
'city': 'Berlin',
'country_code': 'DE',
'id': 2383331012,
'name': 'Test Test',
'zip': '10117'},
'gift_card': False,
'name': 'Blueberry Cup'},
{'destination_location': {'address1': 'Teststreet '
'12',
'address2': '',
'city': 'Berlin',
'country_code': 'DE',
'id': 2383331012,
'name': 'Test Test',
'zip': '10117'},
'gift_card': False,
'name': 'Strawberry Cup'}]}]}
df = pd.Q_AnyNestedIterable_2df(data,unstack=False)
aa_all_keys aa_value
orders 0 created_at NaN NaN NaN (orders, 0, created_at) 2016-09-20T22:04:49+02:00
email NaN NaN NaN (orders, 0, email) test@aol.com
id NaN NaN NaN (orders, 0, id) 4314127108
line_items 0 destination_location address1 (orders, 0, line_items, 0, destination_locatio... Teststreet 12
address2 (orders, 0, line_items, 0, destination_locatio...
city (orders, 0, line_items, 0, destination_locatio... Berlin
country_code (orders, 0, line_items, 0, destination_locatio... DE
id (orders, 0, line_items, 0, destination_locatio... 2383331012
name (orders, 0, line_items, 0, destination_locatio... Test Test
zip (orders, 0, line_items, 0, destination_locatio... 10117
gift_card NaN (orders, 0, line_items, 0, gift_card) False
name NaN (orders, 0, line_items, 0, name) Blueberry Cup
1 destination_location address1 (orders, 0, line_items, 1, destination_locatio... Teststreet 12
address2 (orders, 0, line_items, 1, destination_locatio...
city (orders, 0, line_items, 1, destination_locatio... Berlin
country_code (orders, 0, line_items, 1, destination_locatio... DE
id (orders, 0, line_items, 1, destination_locatio... 2383331012
name (orders, 0, line_items, 1, destination_locatio... Test Test
zip (orders, 0, line_items, 1, destination_locatio... 10117
gift_card NaN (orders, 0, line_items, 1, gift_card) False
name NaN (orders, 0, line_items, 1, name) Strawberry Cup
df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value']*1000
level_0 ... aa_value
0 orders ... 2016-09-20T22:04:49+02:00
1 orders ... test@aol.com
2 orders ... 4314127108
3 orders ... Teststreet 12
4 orders ...
5 orders ... FRANKFURT IST VIEL BESSER ALS BERLIN
6 orders ... DE
7 orders ... 2383331012
8 orders ... Test Test
9 orders ... 10117
10 orders ... False
11 orders ... Blueberry Cup
12 orders ... Teststreet 12
13 orders ...
14 orders ... FRANKFURT IST VIEL BESSER ALS BERLIN
15 orders ... DE
16 orders ... 2383331012
17 orders ... Test Test
18 orders ... 10117
19 orders ... False
20 orders ... Strawberry Cup
[21 rows x 8 columns]
mod_iter = df.d_update_original_iter(data, verbose=True)
[orders][0][line_items][0][destination_location][city] Old value: Berlin
[orders][0][line_items][0][destination_location][city] Updated value: FRANKFURT IST VIEL BESSER ALS BERLIN
[orders][0][line_items][1][destination_location][city] Old value: Berlin
[orders][0][line_items][1][destination_location][city] Updated value: FRANKFURT IST VIEL BESSER ALS BERLIN
{'orders': [{'created_at': '2016-09-20T22:04:49+02:00',
'email': 'test@aol.com',
'id': 4314127108,
'line_items': [{'destination_location': {'address1': 'Teststreet '
'12',
'address2': '',
'city': 'FRANKFURT IST '
'VIEL BESSER ALS '
'BERLIN',
'country_code': 'DE',
'id': 2383331012,
'name': 'Test Test',
'zip': '10117'},
'gift_card': False,
'name': 'Blueberry Cup'},
{'destination_location': {'address1': 'Teststreet '
'12',
'address2': '',
'city': 'FRANKFURT IST '
'VIEL BESSER ALS '
'BERLIN',
'country_code': 'DE',
'id': 2383331012,
'name': 'Test Test',
'zip': '10117'},
'gift_card': False,
'name': 'Strawberry Cup'}]}]}
```
### **df.s_delete_duplicates_from_iters_in_cells**
```python
delete_duplicates_in_column_full_of_iters(df: pandas.core.series.Series) -> pandas.core.series.Series
df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
getdi = lambda x: [ (randrange(1, 4), randrange(1, 4)) for v in range(20)] #create some random tuples
df["dicttest"] = df.lkey.apply(lambda x: getdi(x))
print(df)
df["dicttest"]=df.dicttest.s_delete_duplicates_from_iters_in_cells()
print(df)
lkey value dicttest
0 foo 1 [(2, 1), (3, 3), (3, 3), (2, 1), (1, 2), (1, 2...
1 bar 2 [(3, 2), (1, 1), (1, 1), (1, 2), (3, 2), (1, 2...
2 baz 3 [(1, 2), (3, 1), (2, 1), (2, 1), (1, 1), (2, 3...
3 foo 5 [(2, 3), (2, 3), (3, 3), (2, 2), (1, 2), (1, 2...
lkey value dicttest
0 foo 1 [(2, 1), (3, 3), (1, 2), (1, 3), (3, 2), (2, 3...
1 bar 2 [(3, 2), (1, 1), (1, 2), (2, 1), (3, 1), (3, 3...
2 baz 3 [(1, 2), (3, 1), (2, 1), (1, 1), (2, 3), (3, 3...
3 foo 5 [(2, 3), (3, 3), (2, 2), (1, 2), (1, 1), (1, 3...
Parameters:
df : pd.Series
Column with duplicates that are difficult to handle
Returns:
pd.Series
```
### df.ds_explode_dicts_in_column()
```python
explode_dicts_in_column(df: pandas.core.frame.DataFrame, column_to_explode: str, drop_exploded_column: bool = True) -> pandas.core.frame.DataFrame
df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
getdi = lambda x: { v: {v * randrange(1, 10): v * randrange(1, 10)} for v in range((randrange(1, 10)))} #create random nested dicts
df["dicttest"] = df.lkey.apply(getdi)
print(df)
print(df.ds_explode_dicts_in_column('dicttest'))
lkey value dicttest
0 foo 1 {0: {0: 0}, 1: {1: 7}, 2: {2: 8}, 3: {3: 18}, ...
1 bar 2 {0: {0: 0}, 1: {9: 4}, 2: {10: 6}, 3: {3: 21},...
2 baz 3 {0: {0: 0}, 1: {9: 7}, 2: {2: 10}, 3: {21: 27}...
3 foo 5 {0: {0: 0}}
lkey value level_0 level_1 aa_all_keys aa_value
0 foo 1 0 0 (0, 0) 0
1 foo 1 1 1 (1, 1) 7
2 foo 1 2 2 (2, 2) 8
3 foo 1 3 3 (3, 3) 18
4 foo 1 4 32 (4, 32) 16
5 foo 1 5 35 (5, 35) 15
6 bar 2 0 0 (0, 0) 0
7 bar 2 1 9 (1, 9) 4
8 bar 2 2 10 (2, 10) 6
9 bar 2 3 3 (3, 3) 21
10 bar 2 4 24 (4, 24) 36
11 baz 3 0 0 (0, 0) 0
12 baz 3 1 9 (1, 9) 7
13 baz 3 2 2 (2, 2) 10
14 baz 3 3 21 (3, 21) 27
15 baz 3 4 28 (4, 28) 20
16 baz 3 5 15 (5, 15) 30
17 baz 3 6 6 (6, 6) 6
18 baz 3 7 21 (7, 21) 7
19 baz 3 8 24 (8, 24) 48
20 foo 5 0 0 (0, 0) 0
Parameters:
df:pd.DataFrame
pd.DataFrame
column_to_explode:str
column with dict in cells
drop_exploded_column:bool
Drop column after exploding (default = True )
Returns:
pd.DataFrame
```
### df.d_df_to_nested_dict()
```python
Parameters
----------
df: pd.DataFrame
DataFrame to convert
groupby: str
column whose values will be the top level keys
subkeys: list
columns wholse values will be the nested keys
df = pd.read_csv( "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
Nested dict from DataFrame:
df[:5].d_df_to_nested_dict(groupby='Survived', subkeys=['PassengerId', 'Age', 'Pclass', 'Name', 'Sex'])
{0: {'PassengerId': {0: 1, 4: 5},
'Age': {0: 22.0, 4: 35.0},
'Pclass': {0: 3, 4: 3},
'Name': {0: 'Braund, Mr. Owen Harris', 4: 'Allen, Mr. William Henry'},
'Sex': {0: 'male', 4: 'male'}},
1: {'PassengerId': {1: 2, 2: 3, 3: 4},
'Age': {1: 38.0, 2: 26.0, 3: 35.0},
'Pclass': {1: 1, 2: 3, 3: 1},
'Name': {1: 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',
2: 'Heikkinen, Miss. Laina',
3: 'Futrelle, Mrs. Jacques Heath (Lily May Peel)'},
'Sex': {1: 'female', 2: 'female', 3: 'female'}}}
df[:5].d_df_to_nested_dict(groupby='Sex', subkeys=['PassengerId', 'Name'])
Out[39]:
{'male': {'PassengerId': {0: 1, 4: 5},
'Name': {0: 'Braund, Mr. Owen Harris', 4: 'Allen, Mr. William Henry'}},
'female': {'PassengerId': {1: 2, 2: 3, 3: 4},
'Name': {1: 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',
2: 'Heikkinen, Miss. Laina',
3: 'Futrelle, Mrs. Jacques Heath (Lily May Peel)'}}}
```
### df.s_explode_lists_and_tuples()
```python
explode_lists_and_tuples_in_column(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], column: Optional[str] = None, concat_with_df: bool = False) -> pandas.core.frame.DataFrame
df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
getdi = lambda x: [ (randrange(1, 4), randrange(1, 4)) for v in range(randrange(1,5))] #create some random tuples
df["dicttest"] = df.lkey.apply(lambda x: getdi(x))
print(df)
df1=df.s_explode_lists_and_tuples(column='dicttest', concat_with_df=True)
print(df1)
df2=df.s_explode_lists_and_tuples(column='dicttest', concat_with_df=False)
print(df2)
df3=df.dicttest.s_explode_lists_and_tuples(column=None)
print(df3)
lkey value dicttest
0 foo 1 [(3, 3)]
1 bar 2 [(2, 3), (2, 1), (2, 2)]
2 baz 3 [(2, 3), (2, 3)]
3 foo 5 [(1, 2)]
lkey value dicttest dicttest_0 dicttest_1 dicttest_2
0 foo 1 [(3, 3)] (3, 3) <NA> <NA>
1 bar 2 [(2, 3), (2, 1), (2, 2)] (2, 3) (2, 1) (2, 2)
2 baz 3 [(2, 3), (2, 3)] (2, 3) (2, 3) <NA>
3 foo 5 [(1, 2)] (1, 2) <NA> <NA>
dicttest_0 dicttest_1 dicttest_2
0 (3, 3) <NA> <NA>
1 (2, 3) (2, 1) (2, 2)
2 (2, 3) (2, 3) <NA>
3 (1, 2) <NA> <NA>
dicttest_0 dicttest_1 dicttest_2
0 (3, 3) <NA> <NA>
1 (2, 3) (2, 1) (2, 2)
2 (2, 3) (2, 3) <NA>
3 (1, 2) <NA> <NA>
Parameters:
df: Union[pd.Series, pd.DataFrame]
pd.Series, pd.DataFrame with lists/tuples in cells
column: Union[str, None]
None can only be used if a pd.Series is passed. If a DataFrame is passed, a column needs to be passed too.
concat_with_df: bool
if True -> returns df + exploded Series as DataFrame
if False -> returns exploded Series as DataFrame
Returns:
pd.DataFrame
Missing values are filled with pd.NA
```
### df.s_flatten_all_iters_in_cells()
```python
flatten_all_iters_in_cells(df: pandas.core.series.Series) -> pandas.core.series.Series
df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
getdi = lambda x: [ (randrange(1, 4), randrange(1, 4)) for v in range(20)] #create some random tuples
df["dicttest"] = df.lkey.apply(lambda x: getdi(x))
print(df)
df["dicttest"]=df.dicttest.s_flatten_all_iters_in_cells()
print(df)
lkey value dicttest
0 foo 1 [(2, 2), (3, 3), (3, 2), (1, 3), (1, 2), (2, 2...
1 bar 2 [(1, 1), (3, 1), (1, 3), (3, 2), (3, 1), (2, 2...
2 baz 3 [(3, 1), (1, 1), (3, 3), (1, 3), (3, 2), (3, 3...
3 foo 5 [(3, 3), (3, 3), (3, 2), (2, 3), (3, 3), (2, 3...
lkey value dicttest
0 foo 1 [2, 2, 3, 3, 3, 2, 1, 3, 1, 2, 2, 2, 1, 3, 1, ...
1 bar 2 [1, 1, 3, 1, 1, 3, 3, 2, 3, 1, 2, 2, 3, 1, 3, ...
2 baz 3 [3, 1, 1, 1, 3, 3, 1, 3, 3, 2, 3, 3, 1, 3, 1, ...
3 foo 5 [3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, ...
Parameters:
df : pd.Series
Column with duplicates that are difficult to handle
Returns:
pd.Series
```
### df.d_multiple_columns_to_one()
```python
make_several_columns_fit_in_one(df: pandas.core.frame.DataFrame, columns: list) -> list
df = pd.read_csv(
"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
)[:20]
print(df)
df['Ticket_Fare_Embarked'] = df.d_multiple_columns_to_one(columns=['Ticket','Fare', 'Embarked'])
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
5 6 0 3 Moran, Mr. James male ... 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male ... 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male ... 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female ... 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female ... 0 237736 30.0708 NaN C
10 11 1 3 Sandstrom, Miss. Marguerite Rut female ... 1 PP 9549 16.7000 G6 S
11 12 1 1 Bonnell, Miss. Elizabeth female ... 0 113783 26.5500 C103 S
12 13 0 3 Saundercock, Mr. William Henry male ... 0 A/5. 2151 8.0500 NaN S
13 14 0 3 Andersson, Mr. Anders Johan male ... 5 347082 31.2750 NaN S
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female ... 0 350406 7.8542 NaN S
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female ... 0 248706 16.0000 NaN S
16 17 0 3 Rice, Master. Eugene male ... 1 382652 29.1250 NaN Q
17 18 1 2 Williams, Mr. Charles Eugene male ... 0 244373 13.0000 NaN S
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female ... 0 345763 18.0000 NaN S
19 20 1 3 Masselmani, Mrs. Fatima female ... 0 2649 7.2250 NaN C
[20 rows x 12 columns]
df
Out[30]:
PassengerId Survived Pclass Name Sex ... Ticket Fare Cabin Embarked Ticket_Fare_Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... A/5 21171 7.2500 NaN S [A/5 21171, 7.25, S]
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... PC 17599 71.2833 C85 C [PC 17599, 71.2833, C]
2 3 1 3 Heikkinen, Miss. Laina female ... STON/O2. 3101282 7.9250 NaN S [STON/O2. 3101282, 7.925, S]
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 113803 53.1000 C123 S [113803, 53.1, S]
4 5 0 3 Allen, Mr. William Henry male ... 373450 8.0500 NaN S [373450, 8.05, S]
5 6 0 3 Moran, Mr. James male ... 330877 8.4583 NaN Q [330877, 8.4583, Q]
6 7 0 1 McCarthy, Mr. Timothy J male ... 17463 51.8625 E46 S [17463, 51.8625, S]
7 8 0 3 Palsson, Master. Gosta Leonard male ... 349909 21.0750 NaN S [349909, 21.075, S]
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female ... 347742 11.1333 NaN S [347742, 11.1333, S]
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female ... 237736 30.0708 NaN C [237736, 30.0708, C]
10 11 1 3 Sandstrom, Miss. Marguerite Rut female ... PP 9549 16.7000 G6 S [PP 9549, 16.7, S]
11 12 1 1 Bonnell, Miss. Elizabeth female ... 113783 26.5500 C103 S [113783, 26.55, S]
12 13 0 3 Saundercock, Mr. William Henry male ... A/5. 2151 8.0500 NaN S [A/5. 2151, 8.05, S]
13 14 0 3 Andersson, Mr. Anders Johan male ... 347082 31.2750 NaN S [347082, 31.275, S]
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female ... 350406 7.8542 NaN S [350406, 7.8542, S]
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female ... 248706 16.0000 NaN S [248706, 16.0, S]
16 17 0 3 Rice, Master. Eugene male ... 382652 29.1250 NaN Q [382652, 29.125, Q]
17 18 1 2 Williams, Mr. Charles Eugene male ... 244373 13.0000 NaN S [244373, 13.0, S]
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female ... 345763 18.0000 NaN S [345763, 18.0, S]
19 20 1 3 Masselmani, Mrs. Fatima female ... 2649 7.2250 NaN C [2649, 7.225, C]
[20 rows x 13 columns]
Parameters:
df: pd.DataFrame
DataFrame
columns: list
columns to squeeze
Returns:
list
```
### df.ds_normalize_lists()
```python
normalize_lists_in_column_end_user(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], column: Optional[str] = None) -> pandas.core.series.Series
df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
getdi = lambda x: [ (randrange(1, 4), randrange(1, 4)) for v in range(randrange(1,5))] #create some random tuples
df["dicttest"] = df.lkey.apply(lambda x: getdi(x))
print(df)
df1=df.ds_normalize_lists(column='dicttest')
print(df1)
df2=df.dicttest.ds_normalize_lists(column='dicttest')
print(df2)
lkey value dicttest
0 foo 1 [(3, 2)]
1 bar 2 [(3, 1)]
2 baz 3 [(3, 2), (3, 3)]
3 foo 5 [(2, 3), (2, 1)]
0 [(3, 2), <NA>]
1 [(3, 1), <NA>]
2 [(3, 2), (3, 3)]
3 [(2, 3), (2, 1)]
Name: dicttest, dtype: object
0 [(3, 2), <NA>]
1 [(3, 1), <NA>]
2 [(3, 2), (3, 3)]
3 [(2, 3), (2, 1)]
Name: dicttest, dtype: object
Parameters:
df: Union[pd.Series, pd.DataFrame]
pd.Series, pd.DataFrame with lists/tuples in cells
column: Union[str, None]
None can only be used if a pd.Series is passed. If a DataFrame is passed, a column needs to be passed too.
Returns:
pd.DataFrame
Missing values are filled with pd.NA
```
### df.d_merge_multiple_dfs_and_series_on_index() / df.d_merge_multiple_dfs_and_series_on_one_column()
```python
qq_ds_merge_multiple_dfs_and_series_on_index(df: pandas.core.frame.DataFrame, list_with_ds: list[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame]], how='inner', on=None, sort=False, suffixes=('_x', '_y'), indicator=False, validate=None) -> pandas.core.frame.DataFrame
df1 = pd.DataFrame({'lkeyaaaaaaaaaaaaaaaaaa': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'lkeybbbbbbbbbb': ['foo', 'bar', 'baz', 'foo'],
'value': [5, 6, 7, 8]})
df3 = pd.DataFrame({'lkeyccccccccccccccc': ['foo', 'bar', 'baz', 'foo'],
'value': [15, 16, 17, 18]})
df4 = pd.DataFrame({'lkeyddddddddddddd': ['foo', 'bar', 'baz', 'foo'],
'value': [115, 116, 117, 118]})
df5 = pd.DataFrame({'lkeyeeeee': ['foo', 'bar', 'baz', 'foo'],
'value': [1115, 1116, 1117, 1118]})
df1.d_merge_multiple_dfs_and_series_on_index(list_with_ds=[df2,df3,df4,df5], how="outer")
Out[85]:
lkeyaaaaaaaaaaaaaaaaaa value_x_000 lkeybbbbbbbbbb value_y_000 lkeyccccccccccccccc value_x_002 lkeyddddddddddddd value_y_002 lkeyeeeee value
0 foo 1 foo 5 foo 15 foo 115 foo 1115
1 bar 2 bar 6 bar 16 bar 116 bar 1116
2 baz 3 baz 7 baz 17 baz 117 baz 1117
3 foo 5 foo 8 foo 18 foo 118 foo 1118
Parameters:
df : pd.DataFrame
DataFrame
list_with_ds: list[Union[pd.Series, pd.DataFrame]]
A list of DataFrames and Series you want to merge
how: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = "inner" )
on: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = None )
sort: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = False )
suffixes: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = ("_x", "_y"))
indicator: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = False )
validate: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = None )
Returns:
pd.DataFrame
```
```python
qq_ds_merge_multiple_dfs_and_series_on_column(df: pandas.core.frame.DataFrame, list_with_ds: list[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame]], column: str, how='inner', sort=False, suffixes=('_x', '_y'), indicator=False, validate=None) -> pandas.core.frame.DataFrame
df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [5, 6, 7, 8]})
df3 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [15, 16, 17, 18]})
df4 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [115, 116, 117, 118]})
df5 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1115, 1116, 1117, 1118]})
df1.d_merge_multiple_dfs_and_series_on_one_column(list_with_ds=[df2,df3,df4,df5],column='lkey', how="outer",
sort=False,
suffixes=("_x", "_y"),
indicator=False,
validate=None,)
lkey value_x_000 value_y_000 value_x_002 value_y_002 value
0 foo 1 5 15 115 1115
1 foo 1 5 15 115 1118
2 foo 1 5 15 118 1115
3 foo 1 5 15 118 1118
4 foo 1 5 18 115 1115
5 foo 1 5 18 115 1118
6 foo 1 5 18 118 1115
7 foo 1 5 18 118 1118
8 foo 1 8 15 115 1115
9 foo 1 8 15 115 1118
10 foo 1 8 15 118 1115
11 foo 1 8 15 118 1118
12 foo 1 8 18 115 1115
13 foo 1 8 18 115 1118
14 foo 1 8 18 118 1115
15 foo 1 8 18 118 1118
16 foo 5 5 15 115 1115
17 foo 5 5 15 115 1118
18 foo 5 5 15 118 1115
19 foo 5 5 15 118 1118
20 foo 5 5 18 115 1115
21 foo 5 5 18 115 1118
22 foo 5 5 18 118 1115
23 foo 5 5 18 118 1118
24 foo 5 8 15 115 1115
25 foo 5 8 15 115 1118
26 foo 5 8 15 118 1115
27 foo 5 8 15 118 1118
28 foo 5 8 18 115 1115
29 foo 5 8 18 115 1118
30 foo 5 8 18 118 1115
31 foo 5 8 18 118 1118
32 bar 2 6 16 116 1116
33 baz 3 7 17 117 1117
Parameters:
df:pd.DataFrame:
DataFrame
list_with_ds:list[Union[pd.Series, pd.DataFrame]]
A list of DataFrames and Series you want to merge
column:str
Column to merge on - has to be present in every df
how: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = "inner" )
sort: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = False )
suffixes: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = ("_x", "_y"))
indicator: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = False )
validate: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
(default = None )
Returns:
pd.DataFrame
```
### df.dicttest.s_as_flattened_list()
```python
series_as_flattened_list(df) -> list
df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
getdi = lambda x: [ (randrange(1, 4), randrange(1, 4)) for v in range(20)] #create some random tuples
df["dicttest"] = df.lkey.apply(lambda x: getdi(x))
print(df)
lkey value dicttest
0 foo 1 [(3, 2), (3, 3), (3, 1), (1, 2), (2, 1), (3, 2...
1 bar 2 [(1, 3), (3, 3), (1, 2), (3, 3), (2, 3), (1, 3...
2 baz 3 [(1, 1), (1, 1), (3, 3), (1, 2), (1, 1), (2, 2...
3 foo 5 [(2, 1), (2, 1), (1, 3), (1, 3), (3, 2), (2, 1...
list_=df.dicttest.s_as_flattened_list()
print(list_[:20])
[3, 2, 3, 3, 3, 1, 1, 2, 2, 1, 3, 2, 2, 1, 1, 2, 3, 3, 2, 2]
Parameters:
df: pd.Series
Series to flatten (removes all keys in dicts, only keeps the values)
Returns:
list
```
### df1.d_stack() / df1.d_unstack()
```python
unstacked_df_back_to_multiindex(dataframe: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
Don't use df.stack()!!!!
nested = {
"Moli": {
"Buy": 75,
"Sell": 53,
"Quantity": 300,
"TF": True},
"Anna": {
"Buy": 55,
"Sell": 83,
"Quantity": 154,
"TF": False},
"Bob": {
"Buy": 25,
"Sell": 33,
"Quantity": 100,
"TF": False},
"Annie": {
"Buy": 74,
"Sell": 83,
"Quantity": 96,
"TF": True}
}
df1=pd.Q_AnyNestedIterable_2df(nested, unstack=True)
print(df1)
df1.d_stack()
level_0 level_1 aa_all_keys aa_value
0 Anna Buy (Anna, Buy) 55
1 Anna Quantity (Anna, Quantity) 154
2 Anna Sell (Anna, Sell) 83
3 Anna TF (Anna, TF) False
4 Annie Buy (Annie, Buy) 74
5 Annie Quantity (Annie, Quantity) 96
6 Annie Sell (Annie, Sell) 83
7 Annie TF (Annie, TF) True
8 Bob Buy (Bob, Buy) 25
9 Bob Quantity (Bob, Quantity) 100
10 Bob Sell (Bob, Sell) 33
11 Bob TF (Bob, TF) False
12 Moli Buy (Moli, Buy) 75
13 Moli Quantity (Moli, Quantity) 300
14 Moli Sell (Moli, Sell) 53
15 Moli TF (Moli, TF) True
Out[64]:
aa_all_keys aa_value
level_0 level_1
Anna Buy (Anna, Buy) 55
Quantity (Anna, Quantity) 154
Sell (Anna, Sell) 83
TF (Anna, TF) False
Annie Buy (Annie, Buy) 74
Quantity (Annie, Quantity) 96
Sell (Annie, Sell) 83
TF (Annie, TF) True
Bob Buy (Bob, Buy) 25
Quantity (Bob, Quantity) 100
Sell (Bob, Sell) 33
TF (Bob, TF) False
Moli Buy (Moli, Buy) 75
Quantity (Moli, Quantity) 300
Sell (Moli, Sell) 53
TF (Moli, TF) True
Parameters:
dataframe:pd.DataFrame
pd.DataFrame
Returns:
pd.DataFrame
```
### pd.Q_ReadFileWithAllEncodings_2df()
There are plenty of good libraries out there that help you with finding the right encoding for your file,
but sometimes they don't work like expected, and you have to choose the best encoding manually. This method
opens any file in all encodings available in your env and returns all results in a DataFrame.
```python
pd.Q_ReadFileWithAllEncodings_2df(r"C:\Users\Gamer\Documents\Downloads\corruptjson1.json")
codec strict_encoded \
0 ascii ['ascii' codec can't decode byte 0xef in posit...
1 base64_codec [Incorrect padding]
2 big5 [({\r\n"doc_id": "some_number",\r\n"url": "www...
3 big5hkscs [({\r\n"doc_id": "some_number",\r\n"url": "www...
4 bz2_codec [Invalid data stream]
.. ... ...
115 utf_7 ['utf7' codec can't decode byte 0xef in positi...
116 utf_8 [({\r\n"doc_id": "some_number",\r\n"url": "www...
117 utf_8_sig [({\r\n"doc_id": "some_number",\r\n"url": "www...
118 uu_codec [Missing "begin" line in input data]
119 zlib_codec [Error -3 while decompressing data: incorrect ...
strict_bad ignore_encoded \
0 True [({\r\n"doc_id": "some_number",\r\n"url": "www...
1 True []
Parameters:
filepath (str): file path
Returns:
pd.DataFrame
```
### pd.Q_CorruptJsonFile_2dict()
```python
read_corrupt_json(filepath: str) -> dict
Usage: pdQ_CorruptJsonFile_2dictf(r'C:\corruptjson1.json')
If you need to read a corrupted JSON file, you can try this method.
It will first try to read the file using ujson.
Second step: The file will be read using all encoders found in your env. Each result will be passed to ast.literal_eval, json.loads and ujson.loads
Third step: Keys and values are extracted using regex
All positive results are returned as a dict, you have to check which one fits best to your needs
finaldict = {
"ujson_file_reading_result": ujson_file_reading_result,
"literal_eval_after_newline_removed": literal_eval_after_newline_removed,
"json_after_head_tail_removed": json_after_head_tail_removed,
"ujson_after_head_tail_removed": ujson_after_head_tail_removed,
"regex_get_single_item_keys": allgoodresultsdict,
}
If the keys are not double-quoted, it won't work.
It works well with spaces and not correctly escaped characters
Example from https://stackoverflow.com/questions/59927549/how-to-fix-a-possibly-corrupted-json-file-problems-with-a-curly-bracket-charact
{
"doc_id": "some_number",
"url": "www.seedurl1.com",
"scrape_date": "2019-10-22 16:17:22",
"publish_date": "unknown",
"author": "unknown",
"urls_out": [
"https://www.something.com",
"https://www.sometingelse.com/smth"
],
"text": "lots of text here"
}
{
"doc_id": "some_other_number",
"url": "www.seedurl2.com/smth",
"scrape_date": "2019-10-22 17:44:40",
"publish_date": "unknown",
"author": "unknown",
"urls_out": [
"www.anotherurl.com/smth",
"http://urlx.com/smth.htm"
],
"text": "lots more text over here."
}
Result:
{'ujson_file_reading_result': None,
'literal_eval_after_newline_removed': Empty DataFrame
Columns: [level_0, level_1, level_2, aa_all_keys, aa_value, ast_results]
Index: [],
'json_after_head_tail_removed': level_0 ... json_loads
862 punycode ... {'doc_id': 'some_number', 'url': 'www.seedurl1...
865 punycode ... {'doc_id': 'some_number', 'url': 'www.seedurl1...
[2 rows x 8 columns],
'ujson_after_head_tail_removed': level_0 ... ujson_loads
862 punycode ... {'doc_id': 'some_number', 'url': 'www.seedurl1...
865 punycode ... {'doc_id': 'some_number', 'url': 'www.seedurl1...
[2 rows x 9 columns],
'regex_get_single_item_keys': [{'aa_value': {0: 'some_number',
1: 'www.seedurl1.com',
2: '2019-10-22 16:17:22',
3: 'unknown',
4: 'unknown',
5: ['https://www.something.com', 'https://www.sometingelse.com/smth'],
6: 'lots of text here'},
'aa_key': {0: 'doc_id',
1: 'url',
2: 'scrape_date',
3: 'publish_date',
4: 'author',
5: 'urls_out',
6: 'text'}},
{'aa_value': {7: 'some_other_number',
8: 'www.seedurl2.com/smth',
9: '2019-10-22 17:44:40',
10: 'unknown',
........
Parameters:
filepath (str): file path
Returns:
dict
```
### df.d_sort_columns_with_sorted()
```python
qq_d_sort_columns_alphabetically(df: pandas.core.frame.DataFrame, reverse: bool = False) -> pandas.core.frame.DataFrame
Sorts columns alphabetically with sorted()! Not with natsort()!
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
print(df)
df.d_sort_columns_with_sorted()
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q
[891 rows x 12 columns]
Out[66]:
Age Cabin Embarked Fare Name ... Pclass Sex SibSp Survived Ticket
0 22.0 NaN S 7.2500 Braund, Mr. Owen Harris ... 3 male 1 0 A/5 21171
1 38.0 C85 C 71.2833 Cumings, Mrs. John Bradley (Florence Briggs Th... ... 1 female 1 1 PC 17599
2 26.0 NaN S 7.9250 Heikkinen, Miss. Laina ... 3 female 0 1 STON/O2. 3101282
3 35.0 C123 S 53.1000 Futrelle, Mrs. Jacques Heath (Lily May Peel) ... 1 female 1 1 113803
4 35.0 NaN S 8.0500 Allen, Mr. William Henry ... 3 male 0 0 373450
.. ... ... ... ... ... ... ... ... ... ... ...
886 27.0 NaN S 13.0000 Montvila, Rev. Juozas ... 2 male 0 0 211536
887 19.0 B42 S 30.0000 Graham, Miss. Margaret Edith ... 1 female 0 1 112053
888 NaN NaN S 23.4500 Johnston, Miss. Catherine Helen "Carrie" ... 3 female 1 0 W./C. 6607
889 26.0 C148 C 30.0000 Behr, Mr. Karl Howell ... 1 male 0 1 111369
890 32.0 NaN Q 7.7500 Dooley, Mr. Patrick ... 3 male 0 0 370376
[891 rows x 12 columns]
Parameters:
df : pd.DataFrame
reverse: bool
Z-A instead of A-Z (default = False)
Returns:
pd.DataFrame
```
### df.ds_isna()
```python
is_nan_true_false_check(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], include_na_strings: bool = True, include_empty_iters: bool = False, include_0_len_string: bool = False) -> Union[pandas.core.series.Series, pandas.core.frame.DataFrame]
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df.ds_isna()
Out[107]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 False False False False False ... False False False True False
1 False False False False False ... False False False False False
2 False False False False False ... False False False True False
3 False False False False False ... False False False False False
4 False False False False False ... False False False True False
.. ... ... ... ... ... ... ... ... ... ... ...
886 False False False False False ... False False False True False
887 False False False False False ... False False False False False
888 False False False False False ... False False False True False
889 False False False False False ... False False False False False
890 False False False False False ... False False False True False
[891 rows x 12 columns]
df.Cabin.ds_isna()
Out[108]:
0 True
1 False
2 True
3 False
4 True
...
886 True
887 False
888 True
889 False
890 True
Name: Cabin, Length: 891, dtype: bool
Parameters:
df: Union[pd.Series, pd.DataFrame]
pd.Series, pd.DataFrame
include_na_strings: bool
When True -> treated as nan:
[
"<NA>",
"<NAN>",
"<nan>",
"np.nan",
"NoneType",
"None",
"-1.#IND",
"1.#QNAN",
"1.#IND",
"-1.#QNAN",
"#N/A N/A",
"#N/A",
"N/A",
"n/a",
"NA",
"#NA",
"NULL",
"null",
"NaN",
"-NaN",
"nan",
"-nan",
]
(default =True)
include_empty_iters: bool
When True -> [], {} are treated as nan (default = False )
include_0_len_string: bool
When True -> '' is treated as nan (default = False )
Returns:
dict
Returns:
Union[pd.Series, pd.DataFrame]
```
### df.d_add_value_to_existing_columns_with_loc()
```python
df_loc_add(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], add_to_colum: Any, column: str, throw_towel_early: bool = False, as_last_chance_convert_to_string: bool = False) -> pandas.core.frame.DataFrame
df = pd.read_csv(
"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
)
print(df[:6])
df[:6].d_add_value_to_existing_columns_with_loc(condition=(df.Pclass == 3), add_to_colum=100000, column="Fare")
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
5 6 0 3 Moran, Mr. James male ... 0 330877 8.4583 NaN Q
[6 rows x 12 columns]
Out[37]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 100007.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 100007.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 100008.0500 NaN S
5 6 0 3 Moran, Mr. James male ... 0 330877 100008.4583 NaN Q
[6 rows x 12 columns]
Parameters:
df: pd.DataFrame
DataFrame to update
condition: Union[pd.Series, pd.DataFrame]
Pass a condition with df.loc: df.loc[df['shield'] > 6]
add_to_colum:: Any
Value that you want to add to old values
column: str
Column which should be updated
throw_towel_early: bool
If False: If there is an exception, will be iterating line by line changing each value.
If it fails, it will keep the old value. (default = False)
as_last_chance_convert_to_string: bool
If you want to change the value at any cost, you can change both values to strings and add them up, which will result in:
1+1 = "11"
"Big" + "Brother" = "BigBrother"
Returns:
pd.DataFrame
```
### df.d_drop_rows_with_df_loc()
```python
df_loc_drop(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame]) -> pandas.core.frame.DataFrame
df.d_drop_rows_with_df_loc(df.level_1.str.contains("aa_k")) is the same as df.loc[~df.level_1.str.contains('aa_k')].copy()
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df
Out[54]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q
[891 rows x 12 columns]
df.d_drop_rows_with_df_loc(df.Sex.str.contains(r"male$", regex=True, na=False))
Out[55]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female ... 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female ... 0 237736 30.0708 NaN C
.. ... ... ... ... ... ... ... ... ... ... ...
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female ... 1 230433 26.0000 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female ... 0 7552 10.5167 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female ... 5 382652 29.1250 NaN Q
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
[314 rows x 12 columns]
Parameters:
df: pd.DataFrame
DataFrame
condition: Union[pd.Series, pd.DataFrame]
Condition with df.loc: df.loc[df['shield'] > 6]
Returns:
pd.DataFrame
```
### df.d_set_values_with_df_loc
```python
df_loc_set(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], new_data: Any, column: str) -> pandas.core.frame.DataFrame
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df
Out[51]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q
[891 rows x 12 columns]
df.d_set_values_with_df_loc(condition = df.Sex.str.contains(r"male$", regex=True, na=False),column = 'Fare',new_data = 100000)
Out[52]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 100000.0000 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 100000.0000 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 100000.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 100000.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 100000.0000 NaN Q
[891 rows x 12 columns]
Parameters:
df: pd.Dataframe
DataFrame
condition: Union[pd.Series, pd.DataFrame]
Pass a condition with df.loc: df.loc[df['shield'] > 6]
new_data: Any
New values for update
column: str
Column which should be updated
Returns:
pd.DataFrame
```
### df.d_dfloc()
```python
df_loc(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], column: Optional[str] = None) -> Union[pandas.core.series.Series, pandas.core.frame.DataFrame]
df.d_dfloc(df.aa_value.str.contains("author")) is the same as df.loc[df.aa_value.str.contains('author')].copy()
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
print(df)
print(df.d_dfloc(df.Sex.str.contains(r"male$", regex=True, na=False)))
df.d_dfloc(df.Sex.str.contains(r"male$", regex=True, na=False),column='Name')
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q
[891 rows x 12 columns]
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
5 6 0 3 Moran, Mr. James male ... 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male ... 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male ... 1 349909 21.0750 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
883 884 0 2 Banfield, Mr. Frederick James male ... 0 C.A./SOTON 34068 10.5000 NaN S
884 885 0 3 Sutehall, Mr. Henry Jr male ... 0 SOTON/OQ 392076 7.0500 NaN S
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q
[577 rows x 12 columns]
Out[60]:
0 Braund, Mr. Owen Harris
4 Allen, Mr. William Henry
5 Moran, Mr. James
6 McCarthy, Mr. Timothy J
7 Palsson, Master. Gosta Leonard
...
883 Banfield, Mr. Frederick James
884 Sutehall, Mr. Henry Jr
886 Montvila, Rev. Juozas
889 Behr, Mr. Karl Howell
890 Dooley, Mr. Patrick
Name: Name, Length: 577, dtype: object
Parameters:
df: pd.DataFrame
DataFrame
condition: Union[pd.Series, pd.DataFrame]
Pass a condition with df.loc: df.loc[df['shield'] > 6]
column: Union[None, str]
if a string is passed, the method will return pd.Series
None will return the whole DataFrame (default = None )
Returns:
Union[pd.Series, pd.DataFrame]
```
### df.df.ds_all_nans_to_pdNA()
```python
all_nans_in_df_to_pdNA(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], include_na_strings: bool = True, include_empty_iters: bool = False, include_0_len_string: bool = False) -> Union[pandas.core.series.Series, pandas.core.frame.DataFrame]
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df
Out[86]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q
[891 rows x 12 columns]
df.ds_all_nans_to_pdNA()
Out[87]:
PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 <NA> S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 <NA> S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 <NA> S
.. ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 <NA> S
887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 <NA> S
889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 <NA> Q
[891 rows x 12 columns]
df.Cabin.ds_all_nans_to_pdNA()
Out[88]:
0 <NA>
1 C85
2 <NA>
3 C123
4 <NA>
...
886 <NA>
887 B42
888 <NA>
889 C148
890 <NA>
Name: Cabin, Length: 891, dtype: object
Parameters:
df: Union[pd.Series, pd.DataFrame]
pd.Series, pd.DataFrame
include_na_strings: bool
When True -> treated as nan:
[
"<NA>",
"<NAN>",
"<nan>",
"np.nan",
"NoneType",
"None",
"-1.#IND",
"1.#QNAN",
"1.#IND",
"-1.#QNAN",
"#N/A N/A",
"#N/A",
"N/A",
"n/a",
"NA",
"#NA",
"NULL",
"null",
"NaN",
"-NaN",
"nan",
"-nan",
]
(default =True)
include_empty_iters: bool
When True -> [], {} are treated as nan (default = False )
include_0_len_string: bool
When True -> '' is treated as nan (default = False )
Returns:
dict
Returns:
Union[pd.Series, pd.DataFrame]
```
نیازمندی
| مقدار | نام |
|---|---|
| - | a-pandas-ex-df-to-string |
| - | BruteCodecChecker |
| - | flatten-any-dict-iterable-or-whatsoever |
| - | flatten-everything |
| - | numpy |
| - | pandas |
| - | regex |
| - | ujson |
نحوه نصب
نصب پکیج whl a-pandas-ex-plode-tool-0.44:
pip install a-pandas-ex-plode-tool-0.44.whl
نصب پکیج tar.gz a-pandas-ex-plode-tool-0.44:
pip install a-pandas-ex-plode-tool-0.44.tar.gz