| Name | mypytoolkit JSON |
| Version |
1.6.1
JSON |
| download |
| home_page | |
| Summary | Basic tools I feel are missing in the standard Python distribution. |
| upload_time | 2023-03-12 22:21:54 |
| maintainer | |
| docs_url | None |
| author | Prerit Das |
| requires_python | |
| license | |
| keywords |
python
tools
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/mypytoolkit)
# My Python Toolkit
This is just a miscellaneous collection of some tools I use regularly that I decided to compile into one package for easy use by mainly me. All examples will assume the package has been imported as `kit`.
Install from PyPi: `pip install mypytoolkit`.
```python
import mypytoolkit as kit
```
## Dates and Times
### Time Now
`kit.time_now()` returns a string of the current time in the format "%H-%M". Midnight is "00-00" and 8:35 a.m. is "08-35". 10 p.m. is "22-00". That's without optional parameters.
The optional parameter `int_times: bool` will allow the function to return a tuple instead of a string. If you call `kit.time_now(int_times = True)`, and if the time is 8:35 a.m., you will receive a tuple `(8, 35)` where each element is an integer. This is useful for performing relative time actions within a program that are hour/minute independent.
----
`kit.today_date()` returns a string of the current date in the format "%Y-%M-%D", where Feb 22, 2022, is "2022-02-22".
`kit.time_decimal()` returns a float of the time. For example, if it is 8:45 a.m., the function will return 8.75.
`kit.weekly_time_decimal()` returns a float of how far you're in a week. For example, midnight on Monday is `0`, noon on Wednesday is `2.5`, and 4 p.m. on Sunday is `6.66`.
`kit.time_seconds()` returns the current number of seconds past the minute. For example, if the time is 06:35:23, the function will return `'23'` as a string. Passing in the optional parameter `int_output: bool = False` as `int_output = True` will output `23` as an integer instead of a string. This is useful for timing programs.
For example, a function like `kit.time_seconds()` enables logic such as:
```python
import mypytoolkit as kit
import time
while True:
if kit.time_seconds(True) == 0:
# do some task on the minute.
time.sleep(1)
```
## Python Tools
Simple Python-specific tools to make life easier, from `print` options to functions for working with iterables.
### Threaded List Processing
the `kit.threadtools` module comes with a function, `list_process`, that makes it easy to send a list of items to an operation to be processed in threads. The return value of a thread cannot be accessed directly, and using a `queue.Queue` doesn't guarantee the resulting list comes back in the right order. `kit.threadtools.list_process` will take an `operation` function and `items` (list of items to process) and will send them to individual threads for processing, then collect all the results and return them in the _same_ order.
This is useful for API calls, file operations, and other I/O-bound tasks.
### Type Printing
`kit.tprint()` displays the contents of an object along with its type. I got fed up of constantly writing `print(obj, type(obj))` when debugging so I found myself constantly defining a `tprint()` function:
```python
def tprint(obj):
print(obj, type(obj))
return [obj, type(obj)]
```
Super simple but it makes a night and day difference when debugging in lightspeed.
### Iterable Counting
`kit.count()` will accept an iterable item and a value. It returns an integer of the exact number of occurrences of the value in the iterable. Many iterable objects have in-built class methods for counting values (`iterable_object.count()` for example). But many iterables returned by APIs, for example, don't, so it makes sense to have some global method for counting values.
This way, many objects can be passed to the method, too.
### DataFrame NaN Values
`kit.remove_nan_df_rows()` takes a `pd.DataFrame` input and returns a new DataFrame without any rows that contained a NaN value.
## Files
Tools for working with files.
### Same Document Contents
`kit.are_docs_same()` will tell you if two documents (of any type) have the _exact_ same contents. It takes two parameters.
```python
kit.are_docs_same(original_dir: str, new_dir: str)
```
It returns a boolean, `True` or `False`, depending on whether the contents of the two files are identical. There is no grey area.
### Appending Content to Files
`kit.append_by_query()` will append content to a file in a line below the first occurrence of a query.
See a demonstration:
[](https://asciinema.org/a/4lHkZOkC4kfzZMgRQs3S8wRVn)
Here is a written example. If `test.txt` has the contents:
```
this is line 1!
line 2 is here.
hello people!
line number 4.
```
And we run:
```python
import mypytoolkit as kit
kit.append_by_query(
query = 'hello',
content = 'Just added this.',
file_path = 'test.txt',
insert_above = False # unnecessary, it is by default
)
```
`test.txt` is modified in place and is now:
```
this is line 1!
line 2 is here.
hello people!
Just added this.
line number 4.
```
| Parameter | Necessity | Behavior |
| --- | --- | --- |
| `query` | Required | The first occurrence of this string is where the toolkit will look to insert `content` (above or below depending on `insert_above`). |
| `content` | Required | The content inserted above or below the first occurrence of `query` in the document. |
| `file_path` | Required | A string of the path to the file the toolkit will search through and write in. |
| `insert_above` | Optional | Boolean. By default, `insert_above = False`, and the toolkit will insert `content` the line below the first occurrence of `query`. If `True`, the toolkit will insert `content` in a line above instead. |
## Math
Added a `LinearEquation` class that takes attributes of slope and intercept on instantiation. It has a `plot()` method which plots the linear graph. For example:
```python
import mypytoolkit as kit
equation = kit.LinearEquation(slope = 4, intercept = 10)
equation.plot(interval = 1000)
```
This will output a `matplotlib` plot of the linear equation from 0 to 1000.
## Finance
The finance module must be called directly. It's contents are not imported to the main namespace like the other modules. To call `finance` functions you have to use `kit.finance.function()`.
### Sharpe Ratio
Given a list of returns (each item of numeric format, not percentage), and a risk-free rate, `kit.finance.sharpe_ratio()` will return the Sharpe Ratio of the investment.
| Parameter | Format | Necessity | Behavior |
| --- | --- | --- | --- |
| `returns` | `list` | Required | Is used to compute the standard deviation and mean, necessary to result a Sharpe Ratio. |
| `risk_free` | `float` | Required | Is used to compare statistics on the return with the best risk-free investment at the time. Necessary as Sharpe Ratio is a risk-factored metric. |
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"description": "\n\n\n\n[](https://badge.fury.io/py/mypytoolkit)\n\n\n\n# My Python Toolkit\n\nThis is just a miscellaneous collection of some tools I use regularly that I decided to compile into one package for easy use by mainly me. All examples will assume the package has been imported as `kit`. \n\nInstall from PyPi: `pip install mypytoolkit`. \n\n```python\nimport mypytoolkit as kit\n```\n\n## Dates and Times\n\n### Time Now\n`kit.time_now()` returns a string of the current time in the format \"%H-%M\". Midnight is \"00-00\" and 8:35 a.m. is \"08-35\". 10 p.m. is \"22-00\". That's without optional parameters.\n\nThe optional parameter `int_times: bool` will allow the function to return a tuple instead of a string. If you call `kit.time_now(int_times = True)`, and if the time is 8:35 a.m., you will receive a tuple `(8, 35)` where each element is an integer. This is useful for performing relative time actions within a program that are hour/minute independent. \n\n----\n`kit.today_date()` returns a string of the current date in the format \"%Y-%M-%D\", where Feb 22, 2022, is \"2022-02-22\". \n\n`kit.time_decimal()` returns a float of the time. For example, if it is 8:45 a.m., the function will return 8.75. \n\n`kit.weekly_time_decimal()` returns a float of how far you're in a week. For example, midnight on Monday is `0`, noon on Wednesday is `2.5`, and 4 p.m. on Sunday is `6.66`. \n\n`kit.time_seconds()` returns the current number of seconds past the minute. For example, if the time is 06:35:23, the function will return `'23'` as a string. Passing in the optional parameter `int_output: bool = False` as `int_output = True` will output `23` as an integer instead of a string. This is useful for timing programs.\n\nFor example, a function like `kit.time_seconds()` enables logic such as:\n\n```python\nimport mypytoolkit as kit\nimport time\n\nwhile True:\n if kit.time_seconds(True) == 0:\n # do some task on the minute.\n \n time.sleep(1)\n```\n\n## Python Tools\n\nSimple Python-specific tools to make life easier, from `print` options to functions for working with iterables.\n\n### Threaded List Processing\n\nthe `kit.threadtools` module comes with a function, `list_process`, that makes it easy to send a list of items to an operation to be processed in threads. The return value of a thread cannot be accessed directly, and using a `queue.Queue` doesn't guarantee the resulting list comes back in the right order. `kit.threadtools.list_process` will take an `operation` function and `items` (list of items to process) and will send them to individual threads for processing, then collect all the results and return them in the _same_ order. \n\nThis is useful for API calls, file operations, and other I/O-bound tasks. \n\n### Type Printing\n\n`kit.tprint()` displays the contents of an object along with its type. I got fed up of constantly writing `print(obj, type(obj))` when debugging so I found myself constantly defining a `tprint()` function:\n\n```python\ndef tprint(obj):\n print(obj, type(obj))\n return [obj, type(obj)]\n```\n\nSuper simple but it makes a night and day difference when debugging in lightspeed.\n\n### Iterable Counting\n\n`kit.count()` will accept an iterable item and a value. It returns an integer of the exact number of occurrences of the value in the iterable. Many iterable objects have in-built class methods for counting values (`iterable_object.count()` for example). But many iterables returned by APIs, for example, don't, so it makes sense to have some global method for counting values. \n\nThis way, many objects can be passed to the method, too.\n\n### DataFrame NaN Values\n\n`kit.remove_nan_df_rows()` takes a `pd.DataFrame` input and returns a new DataFrame without any rows that contained a NaN value.\n\n## Files\n\nTools for working with files.\n\n### Same Document Contents\n\n`kit.are_docs_same()` will tell you if two documents (of any type) have the _exact_ same contents. It takes two parameters. \n\n```python\nkit.are_docs_same(original_dir: str, new_dir: str)\n```\n\nIt returns a boolean, `True` or `False`, depending on whether the contents of the two files are identical. There is no grey area.\n\n### Appending Content to Files\n\n`kit.append_by_query()` will append content to a file in a line below the first occurrence of a query. \n\nSee a demonstration:\n\n[](https://asciinema.org/a/4lHkZOkC4kfzZMgRQs3S8wRVn)\n\nHere is a written example. If `test.txt` has the contents:\n\n```\nthis is line 1!\nline 2 is here.\nhello people!\nline number 4.\n```\n\nAnd we run:\n\n```python\nimport mypytoolkit as kit\n\nkit.append_by_query(\n query = 'hello', \n content = 'Just added this.', \n file_path = 'test.txt',\n insert_above = False # unnecessary, it is by default\n)\n```\n\n`test.txt` is modified in place and is now:\n\n```\nthis is line 1!\nline 2 is here.\nhello people!\nJust added this.\nline number 4.\n```\n\n| Parameter | Necessity | Behavior |\n| --- | --- | --- |\n| `query` | Required | The first occurrence of this string is where the toolkit will look to insert `content` (above or below depending on `insert_above`). |\n| `content` | Required | The content inserted above or below the first occurrence of `query` in the document. |\n| `file_path` | Required | A string of the path to the file the toolkit will search through and write in. | \n| `insert_above` | Optional | Boolean. By default, `insert_above = False`, and the toolkit will insert `content` the line below the first occurrence of `query`. If `True`, the toolkit will insert `content` in a line above instead. |\n\n## Math\n\nAdded a `LinearEquation` class that takes attributes of slope and intercept on instantiation. It has a `plot()` method which plots the linear graph. For example:\n\n```python\nimport mypytoolkit as kit\n\nequation = kit.LinearEquation(slope = 4, intercept = 10)\nequation.plot(interval = 1000)\n```\n\nThis will output a `matplotlib` plot of the linear equation from 0 to 1000.\n\n## Finance\n\nThe finance module must be called directly. It's contents are not imported to the main namespace like the other modules. To call `finance` functions you have to use `kit.finance.function()`. \n\n### Sharpe Ratio\n\nGiven a list of returns (each item of numeric format, not percentage), and a risk-free rate, `kit.finance.sharpe_ratio()` will return the Sharpe Ratio of the investment. \n\n| Parameter | Format | Necessity | Behavior | \n| --- | --- | --- | --- |\n| `returns` | `list` | Required | Is used to compute the standard deviation and mean, necessary to result a Sharpe Ratio. |\n| `risk_free` | `float` | Required | Is used to compare statistics on the return with the best risk-free investment at the time. Necessary as Sharpe Ratio is a risk-factored metric. |\n",
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