drkane

# from https://xlsxwriter.readthedocs.io/example_pandas_table.html

df.index.name = "org_id"
criteria = df["Include"]=="y"
sheets = {
    "Included - active": (criteria & df["active"]),
    "Included - inactive": (criteria & ~df["active"]),
    "Excluded": ~criteria,
}
column_widths = df.apply(lambda x: x.astype(str).str.len().max(), axis=0).apply(lambda x: min(x, 50)).to_dict()

drkane

"reg_number","name","constitution","geographical_spread","address","postcode","phone","active","date_registered","date_removed","web","email","company_number","objects","source","last_updated","income","spending","fye","dual_registered"
SC004401,University Of Glasgow Court,Statutory corporation (Royal Charter etc),UK and overseas,"Directorate, Finance Office, Gilbert Scott Building, University Avenue, Glasgow",G12 8QQ,,true,1992-01-01,,www.gla.ac.uk,,,"The advancement of education. The advancement of health. The advancement of, arts, heritage, culture, or science.",Office of Scottish Charity Regulator,2021-01-25 18:48:11,690648000,758804000,2019-07-31,false
SC015263,University Of Strathclyde,Statutory corporation (Royal Charter etc),UK and overseas,"Finance, McCance Building, 16 Richmond Street, Glasgow",G1 1XQ,,true,1965-04-02,,www.strath.ac.uk,,,"The advancement of education. The advancement of arts, heritage, culture, or science.",Office of Scottish Charity Regulator,2021-01-25 18:48:20,354611000,404702000

drkane

{
    "id_and_name": "[\"Exeter City Council\", \"GB-LAE-EXE\"]",
    "locationLookup": [
        {
            "ewcd": "K04000001",
            "ewnm": "England and Wales",
            "gbcd": "K03000001",
            "gbnm": "Great Britain",
            "ukcd": "K02000001",
            "uknm": "United Kingdom",

drkane

drkane

# Bins used for numeric fields
AMOUNT_BINS = [0, 500, 1000, 2000, 5000, 10000, 100000, 1000000, float("inf")]
AMOUNT_BIN_LABELS = ["Under £500", "£500 - £1k", "£1k - £2k", "£2k - £5k", "£5k - £10k",
                    "£10k - £100k", "£100k - £1m", "Over £1m"]
INCOME_BINS = [-1, 10000, 100000, 250000, 500000, 1000000, 10000000, float("inf")]
INCOME_BIN_LABELS = ["Under £10k", "£10k - £100k", "£100k - £250k",
                     "£250k - £500k", "£500k - £1m", "£1m - £10m", "Over £10m"]
AGE_BINS = pd.to_timedelta(
    [x * 365 for x in [-1, 1, 2, 5, 10, 25, 200]], unit="D")
AGE_BIN_LABELS = ["Under 1 year", "1-2 years", "2-5 years",

drkane

LSOA11CD,LSOA11NM,MSOA11CD,MSOA11NM,TTWA11CD,TTWA11NM,LAD11CD,LAD11NM,LAD11NMW,LAD20CD,LAD20NM,LAD20NMW,CTY20CD,CTY20NM,CTRYCD,CTRYNM
95DD04W1,Ballee,95DD04W1,Ballee,N12000001,Ballymena,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,N92000002,Northern Ireland
95DD05W1,Ballykeel,95DD05W1,Ballykeel,N12000001,Ballymena,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,N92000002,Northern Ireland
95DD06W1,Ballyloughan,95DD06W1,Ballyloughan,N12000001,Ballymena,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,N92000002,Northern Ireland
95DD07S1,Broughshane 1,95DD07S1,Broughshane 1,N12000001,Ballymena,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,N92000002,Northern Ireland
95DD07S2,Broughshane 2,95DD07S2,Broughshane 2,N12000001,Ballymena,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,,N09000008,Mid and East Antrim,N92000002,Northe

drkane

import io
from zipfile import ZipFile, ZIP_DEFLATED
import random

import bcp
import click
import tqdm


@click.command()

drkane

# use with https://pypi.org/project/titlecase/
# eg: titlecase.titlecase(x, title_exceptions)

def title_exceptions(word, **kwargs):

    word_test = word.strip("(){}<>.")

    # lowercase words
    if word_test.lower() in ['a', 'an', 'of', 'the', 'is', 'or']:
        return word.lower()

drkane

{
    "extra_context": {
        "amountAwardedFixed_facet_size": 3,
        "awardYear_facet_size": 3
    },
    "sort": {
        "_score": {
            "order": "desc"
        }
    },

drkane