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توضیحات

Explore, load, and get documentation for Colorado crime data.

ویژگی	مقدار
سیستم عامل	-
نام فایل	crime-0.1.5
نام	crime
نسخه کتابخانه	0.1.5
نگهدارنده	[]
ایمیل نگهدارنده	['Ryan Young <dev@ryayoung.com>']
نویسنده	-
ایمیل نویسنده	Ryan Young <dev@ryayoung.com>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/crime/
مجوز	Copyright 2022 Ryan Young Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

<h1> crime     <a href="https://pypi.org/project/crime/" alt="Version"> <img src="https://img.shields.io/pypi/v/crime.svg" /></a> </h1> #### [View Updated Documentation](https://github.com/ryayoung/crime) Source code is [here](https://github.com/ryayoung/crime/tree/main/src/crime) > Easily load online crime datasts. Explore available datasets from inside a python notebook, with descriptive cell outputs showing general info and descriptions of each dataset and documentation of each column. ## Install & Use ```text pip install crime ``` ```py import crime as cr ``` Later, run `pip install -U crime` every few days to make sure you've got the latest version. > Note: this library should work with any recent Python version, but it has **only been tested with 3.9**. --- <details><summary>How does it work?</summary> Crime pre-defines nicknames and ids for a collection of Socrata datasets [like this one](https://dev.socrata.com/foundry/data.edd.ca.gov/nt76-4rha) for you to pick from. This info isn't stored in the package itself, but rather in [this json file](https://github.com/ryayoung/crime/blob/main/colorado-crime-datasets-doc.json) on Github, which can be updated anytime without changing the code. Every time you import `crime`, a Github API request is made to retrieve this configuration, so you'll need internet. Calling `cr.sources()` without parameters will just return this info, without making any additional requests. In addition to letting you load/preview any of these datasets, `crime`'s most important feature is its ability to show a detailed description on each dataset, with full documentation on every column. When you run `cr.sources('dataset_name')`, an api request is made to Socrata to get the metadata on a particular dataset. The most useful information gets formatted & printed to your screen. [Here](https://github.com/ryayoung/crime/blob/main/all_data_info.txt) is what that output would look like if you looped through each dataset name and printed its description. **Caching:** Any dataset you load fully will get stored in memory. So next time you request it within the same Jupyter notebook session, it will be available immediately. </details> --- # Getting Started   > Use `cr.help()` for a quick intro. # Let's look at the crime data available ```py cr.sources() # returns a DataFrame ``` <img width="800" alt="image" src="https://user-images.githubusercontent.com/90723578/167237471-51ad543b-9422-4285-a35f-72562c6431bc.png"> > You'll get a DataFrame with basic info on all the sources. The index, `Name` is the nickname with which you'll refer to the dataset moving forward. To examine a source, pass the name of the dataset to `sources()`. This will make an api request to get all of its metadata.     Let's see the details on `crime_vs_incarceration` rate. All the info below is coming from Socrata's api. ```py cr.sources('crime_vs_incarceration') ``` ```text Total Crime Rate vs Incarceration Rate Chart https://dev.socrata.com/foundry/data.colorado.gov/ae3x-wvn9 Total Crime includes: Violent crimes- Murder and non-negligent manslaughter, forcible rape, robbery, and aggravated assault. Property crimes - Burglary, larceny/theft, and motor vehicle theft. National or state offense totals are based on data from all reporting agencies and estimates for unreported areas. Rates are the number of reported offenses per 100,000 population. These figures are based on end of calendar year populations. Rows: 31 Cols: 9 Period: 1982 to 2012 COLUMNS: ------- Year Field: year Type: text Null: - Count: 31 Population Field: population Type: number Null: - Count: 31 Min: 3,045,000 Max: 5,187,582 Avg: 4,019,137.06 Sum: 124,593,249 Violent Crime Total Field: violent_crime_total Type: number Null: - Count: 31 Min: 13,811 Max: 20,229 Avg: 16,445.55 Sum: 509,812 ``` (output is truncated to save space) Here's what you'll see for text/categorical columns... ```text Race Field: race Type: text Null: 30 Count: 209,241 ITEMS: White (164,446) Black (39,467) Asian/Pacific Islander (2,236) Unknown (1,889) American Indian/Alaskan Native (1,203) ``` # Now we'll load some data ```py cr.load('arrest_demographics') ``` <img width="800" alt="image" src="https://user-images.githubusercontent.com/90723578/167092481-f535265f-8cce-49d7-80e9-318c598d44b9.png"> Returns 5-row preview by default, because some datasets have several **million** rows. To get the full dataset: ```py cr.load('arrest_demographics', full=True) ``` <img width="738" alt="image" src="https://user-images.githubusercontent.com/90723578/167093267-194b382a-ff8c-4a74-ba9f-7bb8e54ac456.png"> # ### Get more info on a source Return dictionary with full metadata ```py cr.metadata('dataset_name') ``` Return dataframe with metrics on each column ```py cr.columns('dataset_name') ``` # ### Caching > Any dataset loaded fully (by passing `full=True`) will be stored in memory, regardless of whether you've assigned it to a variable. Next time you load it, you'll receive a new copy (not a reference) of the data. For example, if you run this at the top of your notebook... ```py cr.load('arrest_demographics', full=True) ``` Now, elsewhere in your notebook... EACH of these 3 lines will return the same thing: a COPY of the full dataset, returned instantly ```py # Shorthand to fetch straight from the cache. Returns empty df if none are found in cache cr.df('arrest_demographics') ``` ```py cr.load('arrest_demographics', full=True) ``` ```py cr.load('arrest_demographics') ``` # ### Add/Modify Sources > First, select a dataset on [OpenDataNetwork](https://www.opendatanetwork.com/) and hit "View API". If you're brought to an API page [like this one](https://dev.socrata.com/foundry/data.colorado.gov/4ykn-tg5h), (not all datasets have one), locate the "Dataset Identifier" on top-right side of page. Use that as `id`. For `base_url`, use the section of the url that comes after `/foundry/`. ```py cr.add_source("crime_rates", "mb89-xnkg", "data.colorado.gov") ``` > You can pass any additional values as keyword arguments. Valid ones include `rows`, `full_name`, `web_url`, `from_year`, `to_year`, `location`, `type`, and `topic`. To restore the original list of sources, use: ```py cr.reset_sources() ``` Clear sources; start with a blank slate ```py cr.clear_sources() ``` # ### No proper documentation yet. View the [source code](https://github.com/ryayoung/crime/tree/main/src/crime) if needed. #### If there's a dataset not yet listed in our pre-defined sources, you can use the [`sodapy`](https://github.com/xmunoz/sodapy) API wrapper to retrieve it manually.

نیازمندی

مقدار	نام
-	pandas
-	requests
-	sodapy

زبان مورد نیاز

مقدار	نام
>=3.9	Python

نحوه نصب

نصب پکیج whl crime-0.1.5:

pip install crime-0.1.5.whl

نصب پکیج tar.gz crime-0.1.5:

pip install crime-0.1.5.tar.gz