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https://github.com/m1kc/notalib
Collection of small Python utility functions and classes
https://github.com/m1kc/notalib
django django-rest-framework pandas python python3 python3-library utility utility-library
Last synced: about 2 months ago
JSON representation
Collection of small Python utility functions and classes
- Host: GitHub
- URL: https://github.com/m1kc/notalib
- Owner: m1kc
- License: mit
- Created: 2019-01-30T13:36:12.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-24T09:15:18.000Z (8 months ago)
- Last Synced: 2024-04-24T14:51:40.106Z (8 months ago)
- Topics: django, django-rest-framework, pandas, python, python3, python3-library, utility, utility-library
- Language: Python
- Homepage: https://pypi.org/project/notalib/
- Size: 194 KB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# notalib [](https://github.com/m1kc/notalib/actions/workflows/django.yml) [](https://coveralls.io/github/m1kc/notalib?branch=master)
Collection of small Python utility functions and classes. Each one was created because I needed it and it didn't exist or I didn't like the existing implementations. 100% of code is used in real-world projects.
(And one day, the documentation is going to be actually good. In the meanwhile, don't hesitate to ask if something's not clear.)
## Install
```sh
pip install notalib
```
Or with [poetry](https://python-poetry.org/):
```sh
poetry add notalib
```
## Compatibility promise
Version numbers follow the semver rules.
* Minor releases are backwards-compatible. For example, an upgrade from 1.2 to 1.4 should be safe.
* Major releases are not backwards-compatible. For example, an upgrade from 1.4 to 2.0 is unsafe — read the release notes to see what's changed.
## Maintenance & bugfixes
While I try to fix bugs, add new features, and review any PRs when I have time, there're no promises and no set timeframes, even if a bug is critical. That's a project I do in my free time, free of charge.
If that's not enough for you or you have an urgent request, there are paid maintenance options (bugfixing, features, expedite PR review, 24h security responses). Contact me for prices: [email protected]
Also feel free to just send me money:
* MasterCard: 5559 4925 7484 0297
* PayPal: [paypal.me/thisism1kc](https://paypal.me/thisism1kc)
Donations are always appreciated, even if you send 10$.
## Constants included
* `notalib.utf.BOM`: contains string `b'\xEF\xBB\xBF'` (UTF-8 little endian byte order mark).
## Utils included
#### notalib.array.as_chunks :fire:
Iterates over your array in chunks of at most N elements.
```python
from notalib.array import as_chunks
arr = [1,2,3,4,5]
for chunk in as_chunks(arr, 2):
print(chunk)
# [1,2]
# [3,4]
# [5]
```
#### notalib.array.ensure_iterable :fire:
Keeps iterable things like lists intact, turns single values into single-element lists. Useful for functions that can accept both.
```python
ensure_iterable([1,2,3]) # --> [1,2,3]
ensure_iterable((1,2,3)) # --> (1,2,3)
ensure_iterable(1) # --> [1]
ensure_iterable('smth') # --> ['smth']
def my_function(one_or_multiple_args):
for arg in ensure_iterable(one_or_multiple_args):
...
my_function(['log', 'smog'])
my_function('dog')
```
#### notalib.array.batched
Batch data from the iterable into tuples of length n.
```python
from notalib.array import batched
def generate_numbers():
for i in range(10):
yield i
batches = list(batched(generate_numbers(), 5)) # --> [(0, 1, 2, 3, 4), (5, 6, 7, 8, 9)]
batches = list(batched("Hello", 2)) # --> [('H', 'e'), ('l', 'l'), ('o',)]
```
#### notalib.combinator.Combinator :fire:
#### notalib.date.parse_month
#### notalib.date.parse_date
#### notalib.date.normalize_date :fire:
Re-formats a date, parsing it as any of the `input_formats` and outputting it as `output_format`.
This function uses Arrow date formats. See [Arrow docs](https://arrow.readthedocs.io/en/latest/guide.html#supported-tokens) for details.
```
Args:
s: The source date in one of the input_formats to be converted to target format.
input_formats: Source date representation formats.
output_format: The format in which the date will be output.
allow_empty: if true, `None` input will produce `None` output, otherwise a ValueError will be thrown.
Example:
>>> normalize_date('12.07.2023', ('D.M.YYYY', 'DD.MM.YYYY'), 'YYYY-MM-DD', False)
'2023-07-12'
Returns:
Converted date string from any of the input formats to the specified output format.
```
#### notalib.date.get_week_number
_Removed in 2.0.0. Use `get_week` instead. If you want the "old" week numbering, use get_week with `WeekNumbering.MATCH_YEAR` and add 1 to week number._
#### notalib.date.get_week
Returns named tuple with week number for the given date. Accepts Python dates and Arrow timestamps.
Optional argument `mode` tells what to do if the week started in previous year:
* WeekNumbering.NORMAL (default): consider it the last week of the previous year
* WeekNumbering.MATCH_YEAR: consider it 0-th week of current year
```python
from notalib.date import get_week, WeekNumbering
from datetime import date
date1, date2 = date(2022, 12, 31), date(2023, 1, 1)
get_week(date1, WeekNumbering.NORMAL)
# Week(week=52, year=2022)
get_week(date1, WeekNumbering.MATCH_YEAR)
# Week(week=52, year=2022)
get_week(date2, WeekNumbering.NORMAL)
# Week(week=52, year=2022)
get_week(date2, WeekNumbering.MATCH_YEAR)
# Week(week=0, year=2023)
```
#### notalib.dict.deep_merge
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Merges two dicts.
Modifies input. Use `copy.deepcopy` to create a deep copy of the original dictionary if you need it.
Accepts three arguments:
* original dict (also target)
* second dict
* overwrite=True if you want to overwrite matching paths, overwrite=False to raise an exception on path conflicts (default)
#### notalib.dict.find_field
#### notalib.dict.find_value
#### notalib.dict.normalize_dict :fire:
#### notalib.dict.filter_dict
Filters a dictionary, removing any keys except for the ones you choose.
```python
from notalib.dict import filter_dict
src = {
'Some': "BODY",
'once': "told me",
'the world': "is gonna roll me",
}
filter_dict(src, ["Some", "once"])
# {'Some': 'BODY', 'once': 'told me'}
filter_dict(src, [])
# {}
```
#### notalib.format.format_long_list
#### notalib.timedelta.convert_timedelta
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Converts standard timedelta object to specified formats.
Allowed formats: 's', 'ms'.
```python
from notalib.timedelta import convert_timedelta
from datetime import timedelta
td = timedelta(seconds=1, milliseconds=23)
convert_timedelta(td, 's')
# 1.023
convert_timedelta(td, 'ms')
# 1023
```
#### notalib.hypertext.strip_tags :fire:
#### notalib.hypertext.TablePrinter :fire:
Prints an HTML table, row by row, from the given data, using attrs or dictionary keys as columns.
Two ways to use it:
* Call header() / entry() / footer() manually
```python
from notalib.hypertext import TablePrinter
t = TablePrinter(['a', 'b'])
t.header()
# 'ab'
t.entry({'a': 1, 'b': 2})
# '12\n'
t.entry({'a': 11, 'b': 22})
# '1122\n'
t.footer()
# ''
```
* Pass an iterable to iterator_over()
```python
from notalib.hypertext import TablePrinter
t = TablePrinter(['a', 'b'])
list(t.iterator_over([ {'a': 11, 'b': 22} ]))
# ['ab',
# '1122\n',
# '']
```
#### notalib.polosa.polosa :fire: :fire: :fire: :fire: :fire:
```
18023/2000000 294.8/sec Processing transaction ID#84378473 (2020-01-04)
```
The CLI progress indicator you've always dreamt of: shows current and total if available, measures current speed, can show your comments for each element, makes sure not to slow down your terminal with frequent updates. [See this short demo](https://asciinema.org/a/UI1aOqjQC1KXx303kaVGrxjQp).
_Cheat sheet_
```python
## Basic usage
with polosa() as p:
p.tick()
# 467344 201.2/sec
## Specify total number of elements:
with polosa(total=1337) as p:
# 26/1337 1.2/sec
## Print something useful about every element:
p.tick(caption=my_order.time_created)
# 1723910/2000000 319231.2/sec 2020-01-01 15:37:00
```
#### notalib.range.Range
#### notalib.time.Timing :fire:
Measures time spent on executing your code. Killer feature: it can be used as a reusable context.
```python
timing = Timing()
...
with timing:
do_something()
# That's it, do something with the measurement
log(f'Operation took {timing.result} sec')
```
If you just want to print measurements into console, there's a shorthand:
```python
timing = Timing(auto_print=True)
...
with timing:
do_something()
```
#### notalib.trendsetter.Trendsetter :fire:
#### notalib.git.get_current_commit
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Returns short description and hash of last commit of current branch.
If function is called outside git repo or there are no commits in the history, `None` will be returned.
```python
from notalib.git import get_current_commit
commit = get_current_commit()
# Commit(hash='db0e5c1de83f233abef823fd92490727f4ee9d50', short_description='Add timedelta module with convert_timedelta function')
```
#### notalib.git.get_last_tag
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Returns last tag label and hash. If function is called outside git repo or there are no tags in the history, `None` will be returned.
```python
from notalib.git import get_last_tag
tag = get_last_tag()
# Tag(hash='c4b6e06f57ab4773e2881d286804d4e3141b5195', label='v1.4.0')
```
#### notalib.file_iterator.file_iterator
Iterates over byte buffer and yields chunks of specified size.
```python
with open("", mode="rb") as file:
for chunk in file_iterator(file):
...
```
## Tools for Pandas
#### notalib.pandas.pandasplus.row_to_dict
#### notalib.pandas.pandasplus.replace_null_objects
Replaces all types of null values in a DataFrame with the given value.
```python
df = pd.DataFrame({'A': [pd.NA, pd.NaT, 'SomeVal', None]})
new_df = replace_null_objects(df, "Hello, notalib!")
new_df
# A
# 0 Hello, notalib!
# 1 Hello, notalib!
# 2 SomeVal
# 3 Hello, notalib!
```
## Tools for Django
#### notalib.django.auth.StaticBackend
#### notalib.django.auth.SettingsBackend
#### notalib.django.xauth
Endpoints for easier authentication in APIs. Requires Django REST framework.
Provides endpoints:
* `GET /xauth/check` — returns code 200 if client is authenticated (or global permissions are set to AllowAny), 403 if not
* `POST /xauth/auth-post` — authenticates a client; accepts two POST parameters `username` and `password`; returns code 200 on success and 403 on failure
* `POST /xauth/logout` — de-authenticates a client
How to use:
1. Make sure Django REST framework is installed.
2. Add `'notalib.django_xauth'` to INSTALLED_APPS.
3. Run `manage.py migrate django_xauth` (doesn't actually change your DB).
4. Add something like this to your urls.py: `path('xauth/', include('notalib.django_xauth.urls')),`
#### notalib.django.colorlog.ColorFormatter
#### notalib.django.filterset :fire:
#### notalib.django.formplus.MonthField
#### notalib.django.formplus.ChoiceWithDefault
#### notalib.django.formplus.IntegerArrayField
#### notalib.django.formplus.StringArrayField
#### notalib.django.formplus.MonthArrayField
#### notalib.django.http.HttpResponseSeeOther
Spec-compliant HTTP 303 See Other redirect (Django only provides deprecated 301 and 302).
#### notalib.django.http.HttpResponseTemporaryRedirect
Spec-compliant HTTP 307 Temporary Redirect (Django only provides deprecated 301 and 302).
#### notalib.django.request_time_middleware.RequestTimeLoggingMiddleware
#### notalib.django.stream.stream_json
Stream all elements of iterable object as JSON array using the StreamingHttpResponse class. Unlike DRF's Response class, it can handle arrays of any size.
```python
class SomeViewSet(...):
...
def list(self, request, *args, **kwargs):
...
return stream_json(data)
```
#### notalib.django.bytes_stream.get_stream_bytes_response
Stream bytes IO part by the RANGE header value or all buffer content.
```python
class SomeView(...):
def get(self, request, *args, **kwargs):
with open("", mode="rb") as file:
...
return get_stream_bytes_response(file, request, content_type="")
```
## Django/Clickhouse
_Deprecated since 2.2.0._
Click to expand
Required packages: `clickhouse-sqlalchemy`
Required two django.settings variables:
* **CLICKHOUSE_URL** — database URL
* **CLICKHOUSE_PROFILE** — dump all queries and their timing to console, true/false
#### notalib.django.clickhouse.base.get_connection
#### notalib.django.clickhouse.base.get_database_name
#### notalib.django.clickhouse.base.Query
A wrapper for SQLAlchemy's `select` with some useful postprocessing options.
* `execute` — no postprocessing
* `execute_val` — returns single value
* `execute_list` — returns single column as list
* `execute_kv` — returns dict, first column becomes keys, second column becomes values
* `execute_na` — returns number of affected rows
Usage example:
```python
q = Query(
select([ SomeTable.c.notalib ])
)
q.execute_list()
# ["Example", "OOOOO", "my", "defence", ...]
```
#### notalib.django.clickhouse.mutations.get_mutations_in_progress_count
Returns the number of mutations in progress for the specified table.
```python
get_mutations_in_progress_count("SOME_DATABASE", "SOME_TABLE_IN_DATABASE")
# 5
```
#### notalib.django.clickhouse.wait.wait_result :fire:
Waits until all mutations for the given table are complete.
```python
# blah blah blah, ALTER TABLE ... UPDATE ...
wait_result("SOME_DATABASE", "SOME_TABLE_IN_DATABASE")
# UPDATE complete, continue
```
## Tools for Tablib
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Required packages: [tablib](https://pypi.org/project/tablib/)
#### notalib.tablib.shortcuts.load_dataset
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Allows to simplify the loading of datasets, even custom ones.
```python
from notalib.tablib.shortcuts import load_dataset
with open("report.xlsx", mode='rb') as file:
ds = load_dataset(file)
### Or you can use custom Dataset class and format
from tablib import Dataset
class MyDataset(Dataset):
pass
with open("report.csv", mode='rb') as file:
ds = load_dataset(file, 'csv', MyDataset)
```
#### notalib.tablib.dataset.ExtendedDataset
_:warning: Experimental API. Subject to change. Don't use in production. You've been warned._
Extended tablib.Dataset class, which adds useful data processing methods.
#### notalib.tablib.dataset.ExtendedDataset.drop_duplicates
Removes all duplicate rows from the `ExtendedDataset` object while maintaining the original order.
#### notalib.tablib.dataset.ExtendedDataset.drop_empty
Removes rows with empty data in specified columns.
#### notalib.tablib.dataset.ExtendedDataset.drop_empty_rows
Removes rows in which all values are empty.
#### notalib.tablib.dataset.ExtendedDataset.apply_to_column
Applies the function to the values in the specified column.
#### notalib.tablib.dataset.ExtendedDataset.replace_empty_objects
Replaces empty values with a new one.
#### notalib.tablib.dataset.ExtendedDataset.set_used_columns
Returns a new dataset based on the specified header labels.
#### notalib.tablib.dataset.ExtendedDataset.get_headers_map
Returns a list of Boolean objects based on a given set of header labels.
#### notalib.tablib.dataset.ExtendedDataset.get_header_index
Calculates the index of the header by its label.
#### notalib.tablib.dataset.ExtendedDataset.groupby
Sets tags to rows and returns list of groups for filtering.
#### notalib.tablib.dataset.ExtendedDataset.rename_headers
Renames header labels.