fsds_100719 package

Subpackages

• fsds_100719.ds package
  • Submodules
  • fsds_100719.ds.flatiron_stats module
  • fsds_100719.ds.regression_project module
  • fsds_100719.ds.tsa module
• fsds_100719.ft package
  • Submodules
  • fsds_100719.ft.hakkeray module
  • fsds_100719.ft.jirving module
• fsds_100719.jmi package
  • Submodules
  • fsds_100719.jmi.jmi module
  • fsds_100719.jmi.jmi_WIP module
• fsds_100719.pt package

Submodules

fsds_100719.datasets module

A collection of convenient csv urls and sklearn datasets as dataframes.

fsds_100719.datasets.load_AB_multiple_choice(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_autompg(verbose=True, read_csv_kwds={})

fsds_100719.datasets.load_boston(verbose=False)

fsds_100719.datasets.load_data(*args, **kwargs)

fsds_100719.datasets.load_height_by_country(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_height_weight(verbose=False, read_csv_kwds={})

Loads height vs weight dataset

fsds_100719.datasets.load_heroes_info(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_heroes_powers(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_iowa_prisoners(verbose=False, vers='raw', read_csv_kwds={})

fsds_100719.datasets.load_iris(verbose=False)

fsds_100719.datasets.load_mod1_proj(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_population(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_titanic(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_ts_american_sex_frequency(read_csv_kwds={})

fsds_100719.datasets.load_ts_baltimore_crime_counts(read_csv_kwds={})

fsds_100719.datasets.load_ts_baltimore_crime_full(read_csv_kwds={})

fsds_100719.datasets.load_ts_exch_rates(verbose=False, read_csv_kwds={})

fsds_100719.datasets.load_ts_google_trends(read_csv_kwds={})

fsds_100719.datasets.load_ts_mintemp(verbose=False, read_csv_kwds={})

Loads min_temp.csv from

fsds_100719.datasets.load_ts_nyse_monthly(verbose=False, read_csv_kwds={})

Loads NYSE_.csv from

fsds_100719.datasets.load_ts_std_cases(read_csv_kwds={})

fsds_100719.datasets.load_ts_winning_400m(read_csv_kwds={})

fsds_100719.datasets.read_csv_from_url(url, verbose=False, read_csv_kwds={})

Loading function to load all .csv datasets. Args:

url (str): csv raw link verbose (bool): Controls display of loading message and .head() read_csv_kwds (dict): dict of commands to feed to pd.read_csv()

Returns:

df (DataFrame): the dataset(

fsds_100719.imports module

Convience module. ‘from bs_ds.imports import *’ will pre-load pd,np,plt,mpl,sns

fsds_100719.imports.clear()

Helper function to clear notebook display

fsds_100719.imports.global_imports(modulename, shortname=None, asfunction=False)

from stackoverflow: https://stackoverflow.com/questions/11990556/how-to-make-global-imports-from-a-function, https://stackoverflow.com/a/46878490

fsds_100719.imports.import_packages(import_list_of_tuples=None, display_table=True)

Uses the exec function to load in a list of tuples with: [(‘module’,’md’,’example generic tuple item’)] formatting. >> Default imports_list: [(‘pandas’, ‘pd’, ‘High performance data structures and tools’), (‘numpy’, ‘np’, ‘scientific computing with Python’), (‘matplotlib’, ‘mpl’, “Matplotlib’s base OOP module with formatting artists”), (‘matplotlib.pyplot’, ‘plt’, “Matplotlib’s matlab-like plotting module”), (‘seaborn’, ‘sns’, “High-level data visualization library based on matplotlib”), (‘IPython.display’,’dp’,’Display modules with helpful display and clearing commands.’) (‘fsds_10072019’,’fs’,’Custom data science bootcamp student package’)]

fsds_100719.learn_scrape module

fsds_100719.learn_scrape.cohort_driver_to_csv(driver, output_file='cohort_output.csv', debug=False, load=False, load_kws=None)

Exports the table content inside of the driver.page_source to csv file.

Args:

driver (WebDriver): cohort instruct page’s driver output_file (str): name of csv file to save.

TO DO: Add link extraction

fsds_100719.learn_scrape.github_login(driver, login_data=None)

Logs into GitHub Account (for instruction.learn) url = ‘https://instruction.learn.co/staff/students’

fsds_100719.learn_scrape.help()

fsds_100719.learn_scrape.instruct_menu_to_cohort_roster(driver, cohort='pt')

fsds_100719.learn_scrape.load_login_data(login_data_file='/Users/jamesirving/.secret/learn_login.json', verbose=True)

Loads in json file from path

fsds_100719.learn_scrape.start_driver(url='https://instruction.learn.co/staff/students')

fsds_100719.quick_refs module

A collection of functions containing convenient links to documentation and other resources.

class fsds_100719.quick_refs.Library

Bases: object

class fsds_100719.quick_refs.Link(name=None, url=None)

Bases: object

click()

class fsds_100719.quick_refs.LinkLibray(topic)

Bases: object

fsds_100719.quick_refs.cohort_links(md=False, ret=False)

Displays quick reference url links and info.

fsds_100719.quick_refs.evaluation_metrics(markdown=True)

fsds_100719.quick_refs.html_colors()

fsds_100719.quick_refs.matplotlib_links()

Display links to matplotlib documentation and references.

fsds_100719.quick_refs.prob_combinations()

fsds_100719.quick_refs.prob_permutations()

fsds_100719.quick_refs.statistical_power(return_url=False)

Displays link to interactive statistical power calculator. Returns url if return_url = True (Default is False)

fsds_100719.quick_refs.string_formatting()

fsds_100719.quick_refs.ts_date_str_formatting()

fsds_100719.quick_refs.ts_datetime_object_properties()

fsds_100719.quick_refs.ts_pandas_freq_aliases(ipython=True, return_str=False)

fsds_100719.quick_refs.ts_pandas_freq_aliases_anchored(ipython=True, return_str=False)