def __init__(self, mag_affilations, max_worker=2):
self.aff = mag_affilations
self.gc = geonamescache.GeonamesCache()
cities = self.gc.get_cities()
countries = self.gc.get_countries()
self.cities = {v['name'] for k, v in cities.items()}
self.countries = {v['name'] for k, v in countries.items()}
self._max_workers = max_worker
self.mapper = country(from_key='iso', to_key='name')
def __init__(self, data):
self.id = data.key.lower()
mapper = country(from_key='iso', to_key='name')
try:
self.title = mapper(data.key)
except AttributeError:
# if we have a country code with no name mapping, skip it to prevent 500
pass
self.count = data.doc_count
def serve_layout():
# Links to time series datasets on github:
url_confirmed = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
url_deaths = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
# Create dataframes from datasets:
df_confirmed = pd.read_csv(url_confirmed)
df_deaths = pd.read_csv(url_deaths)
# Replace null values with zeroes:
df_confirmed[df_confirmed.columns[4:]] = df_confirmed[
df_confirmed.columns[4:]].fillna(0, downcast='infer')
df_deaths[df_deaths.columns[4:]] = df_deaths[df_deaths.columns[4:]].fillna(
0, downcast='infer')
# Try today's date. If not yet updated use yesterday's date for daily reports:
try:
date = datetime.now().strftime('%m-%d-%Y')
url_daily_reports = f'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{date}.csv'
df_daily_reports = pd.read_csv(url_daily_reports,
dtype={'FIPS': object})
df_daily_reports['FIPS'] = df_daily_reports['FIPS'].str.zfill(5)
except:
date = (datetime.now() - timedelta(days=1)).strftime('%m-%d-%Y')
url_daily_reports = f'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{date}.csv'
df_daily_reports = pd.read_csv(url_daily_reports,
dtype={'FIPS': object})
df_daily_reports['FIPS'] = df_daily_reports['FIPS'].str.zfill(5)
# Subsets of confirmed cases:
df_china = df_confirmed[df_confirmed['Country/Region'] == 'China']
df_other = df_confirmed[df_confirmed['Country/Region'] != 'China']
# Add ISO3 codes to daily updating df
mapper = country(from_key='name', to_key='iso3')
country_index = {}
country_index['West Bank and Gaza'] = 'PSE'
country_index['Taiwan*'] = 'TWN'
country_index['Timor-Leste'] = 'TLS'
country_index['Holy See'] = 'VAT'
country_index['Republic of the Congo'] = 'COG'
country_index['Congo (Brazzaville)'] = 'COG'
country_index['Congo (Kinshasa)'] = 'COD'
df_confirmed['ISO3'] = df_confirmed['Country/Region'].apply(
lambda x: country_index.get(x, mapper(x)))
# Reformat for global choropleth:
df_global = df_confirmed.groupby(['ISO3',
'Country/Region']).sum().reset_index()
# Convert date columns to rows:
df_global = pd.melt(df_global,
id_vars=['ISO3', 'Country/Region', 'Lat', 'Long'],
value_vars=list(
df_global.select_dtypes(include='int64')),
var_name='Date',
value_name='Confirmed Cases')
# Setup df containing states with most cases:
df_us = df_daily_reports[df_daily_reports['Country_Region'] == 'US']
leading_states = df_us.groupby('Province_State')['Confirmed'].sum(
).sort_values(ascending=False)[0:10].index
df_us_leading_states = df_us[df_us['Province_State'].isin(
leading_states)].groupby('Province_State').sum().sort_values(
by=['Confirmed'], ascending=False).reset_index()
df_us_leading_states[
'Active'] = df_us_leading_states['Confirmed'] - df_us_leading_states[
'Recovered'] - df_us_leading_states['Deaths']
# Setup df containing states with most deaths:
leading_states_deaths = df_us.groupby('Province_State')['Deaths'].sum(
).sort_values(ascending=False)[0:10].index
df_us_leading_states_deaths = df_us[df_us['Province_State'].isin(
leading_states_deaths)].groupby('Province_State').sum().sort_values(
by=['Deaths'], ascending=False).reset_index()
# Setup df containing countries with most cases:
leading_countries = df_daily_reports.groupby(
'Country_Region')['Confirmed'].sum().sort_values(
ascending=False)[0:10].index
df_leading_countries = df_daily_reports[
df_daily_reports['Country_Region'].isin(leading_countries)].groupby(
'Country_Region').sum().sort_values(by=['Confirmed'],
ascending=False).reset_index()
df_leading_countries[
'Active'] = df_leading_countries['Confirmed'] - df_leading_countries[
'Recovered'] - df_leading_countries['Deaths']
# Setup df containing countries with most deaths:
leading_countries_deaths = df_daily_reports.groupby(
'Country_Region')['Deaths'].sum().sort_values(
ascending=False)[0:10].index
df_leading_countries_deaths = df_daily_reports[df_daily_reports[
'Country_Region'].isin(leading_countries_deaths)].groupby(
'Country_Region').sum().sort_values(by=['Deaths'],
ascending=False).reset_index()
# df for US choropleth:
df_us_choro = df_us.groupby('Province_State').sum().reset_index()
# Add dict for state abbreviations for US choropleth:
us_state_abbrev = {
'Alabama': 'AL',
'Alaska': 'AK',
'Arizona': 'AZ',
'Arkansas': 'AR',
'California': 'CA',
'Colorado': 'CO',
'Connecticut': 'CT',
'Delaware': 'DE',
'District of Columbia': 'DC',
'Florida': 'FL',
'Georgia': 'GA',
'Hawaii': 'HI',
'Idaho': 'ID',
'Illinois': 'IL',
'Indiana': 'IN',
'Iowa': 'IA',
'Kansas': 'KS',
'Kentucky': 'KY',
'Louisiana': 'LA',
'Maine': 'ME',
'Maryland': 'MD',
'Massachusetts': 'MA',
'Michigan': 'MI',
'Minnesota': 'MN',
'Mississippi': 'MS',
'Missouri': 'MO',
'Montana': 'MT',
'Nebraska': 'NE',
'Nevada': 'NV',
'New Hampshire': 'NH',
'New Jersey': 'NJ',
'New Mexico': 'NM',
'New York': 'NY',
'North Carolina': 'NC',
'North Dakota': 'ND',
'Northern Mariana Islands': 'MP',
'Ohio': 'OH',
'Oklahoma': 'OK',
'Oregon': 'OR',
'Palau': 'PW',
'Pennsylvania': 'PA',
'Puerto Rico': 'PR',
'Rhode Island': 'RI',
'South Carolina': 'SC',
'South Dakota': 'SD',
'Tennessee': 'TN',
'Texas': 'TX',
'Utah': 'UT',
'Vermont': 'VT',
'Virgin Islands': 'VI',
'Virginia': 'VA',
'Washington': 'WA',
'West Virginia': 'WV',
'Wisconsin': 'WI',
'Wyoming': 'WY',
}
df_us_choro['Abbrev'] = df_us_choro['Province_State'].map(
us_state_abbrev).fillna(df_us_choro['Province_State'])
df_us_choro = df_us_choro[df_us_choro['Abbrev'].apply(
lambda x: len(x) < 3)]
# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
## TIME SERIES
fig_time = go.Figure()
# Confirmed cases in mainland China
fig_time.add_trace(
go.Scatter(
x=[i[:-3] for i in list(df_other.select_dtypes(include='int64'))],
y=list(df_china.select_dtypes(include='int64').sum()),
name='China',
line_color='#7f7f7f'))
# Confirmed cases for the rest of the world
fig_time.add_trace(
go.Scatter(
x=[i[:-3] for i in list(df_other.select_dtypes(include='int64'))],
y=list(df_other.select_dtypes(include='int64').sum()),
name='Rest of World',
line_color='#ff7f0e'))
# Worldwide deaths
fig_time.add_trace(
go.Scatter(
x=[i[:-3] for i in list(df_other.select_dtypes(include='int64'))],
y=list(df_deaths.select_dtypes(include='int64').sum()),
name='Worldwide Deaths',
line_color='#d62728'))
for trace in fig_time.data:
trace.hovertemplate = '%{x}<br>%{y}'
fig_time.update_yaxes(hoverformat=',f')
fig_time.update_layout(
title_text='Coronavirus over Time',
legend={
'x': 0.02,
'y': 0.55
},
legend_bgcolor='rgba(0,0,0,0.1)',
height=350,
margin={
'r': 10,
't': 50,
'l': 10,
'b': 70
},
annotations=[
dict(xshift=10,
yshift=-10,
x=0,
y=1.0,
showarrow=False,
text='Total Cases: ' +
f'{sum(df_daily_reports["Confirmed"]):,}' +
'<br>Total Deaths: ' + f'{sum(df_daily_reports["Deaths"]):,}',
xref='paper',
yref='paper',
font=dict(size=16, color='#ffffff'),
align='left',
bordercolor='rgba(0,0,0,0.1)',
borderwidth=2,
borderpad=4,
bgcolor='#ff7f0e')
])
## GLOBAL CHOROPLETH
fig_global = px.choropleth(
df_global,
locations='ISO3',
color='Confirmed Cases',
hover_name='Country/Region',
hover_data=['Date'],
projection='natural earth',
animation_frame='Date',
range_color=(0, df_global['Confirmed Cases'].max()),
color_continuous_scale=[
[0, 'rgb(250, 250, 250)'], #0
[1 / 10000, 'rgb(250, 175, 100)'], #10
[1 / 1000, 'rgb(250, 125, 0)'], #100
[1 / 100, 'rgb(200, 100, 0)'], #1000
[1 / 10, 'rgb(250, 50, 50)'], #10000
[1, 'rgb(100, 0, 0)'], #100000
])
# Must loop though traces AND frames to format hovertemplate
for trace in fig_global.data:
trace.hovertemplate = '<b>%{hovertext}</b> (%{customdata[0]})<br>%{z:,f}'
for frame in fig_global.frames:
frame.data[
0].hovertemplate = '<b>%{hovertext}</b> (%{customdata[0]})<br>%{z:,f}'
# Animation speed and slider/button locations
fig_global.layout.updatemenus[0].buttons[0].args[1]['frame'][
'duration'] = 50
fig_global.layout.updatemenus[0].pad = {'l': 10, 't': 0}
fig_global.layout.sliders[0].pad = {'b': 10, 't': -20, 'l': 10}
fig_global.layout.sliders[0].currentvalue = {'prefix': 'Date = '}
fig_global.layout.coloraxis.colorbar.title.text = 'Confirmed<br>Cases'
fig_global.update_layout(
title='Global Time Series',
margin={
'r': 0,
't': 50,
'l': 0,
'b': 10
},
)
## US CHOROPLETH
fig_us = px.choropleth(
df_daily_reports,
geojson=counties,
locations='FIPS',
scope='usa',
color='Confirmed',
hover_name='Admin2',
hover_data=['Province_State'],
range_color=(0, df_daily_reports[df_daily_reports['Country_Region'] ==
'US']['Confirmed'].max()),
color_continuous_scale=[
[0, 'rgb(250, 250, 250)'], #0
[1 / 10000, 'rgb(250, 175, 100)'], #10
[1 / 1000, 'rgb(250, 125, 0)'], #100
[1 / 100, 'rgb(200, 100, 0)'], #1000
[1 / 10, 'rgb(250, 50, 50)'], #10000
[1, 'rgb(100, 0, 0)'], #100000
])
for trace in fig_us.data:
trace.hovertemplate = '<b>%{hovertext}</b> (%{customdata[0]})<br>%{z:,f}'
fig_us.layout.coloraxis.colorbar.title.text = 'Confirmed<br>Cases'
fig_us.update_traces(marker_line_width=0.1)
fig_us.update_layout(
title=f'US Counties ({date})',
margin={
'r': 0,
't': 50,
'l': 0,
'b': 30
},
)
## MOST AFFECTED
trace_glob_c = go.Bar(x=df_leading_countries['Country_Region'],
y=df_leading_countries['Confirmed'],
marker={'color': 'rgb(250, 175, 100)'},
visible=True)
trace_glob_d = go.Bar(x=df_leading_countries_deaths['Country_Region'],
y=df_leading_countries_deaths['Deaths'],
marker={'color': 'rgb(250, 50, 50)'},
visible=False)
trace_us_c = go.Bar(x=df_us_leading_states['Province_State'],
y=df_us_leading_states['Confirmed'],
marker={'color': 'rgb(250, 175, 100)'},
visible=True)
trace_us_d = go.Bar(x=df_us_leading_states_deaths['Province_State'],
y=df_us_leading_states_deaths['Deaths'],
marker={'color': 'rgb(250, 50, 50)'},
visible=False)
fig_most_affected = make_subplots(rows=1, cols=2)
fig_most_affected.append_trace(trace_glob_c, 1, 1)
fig_most_affected.append_trace(trace_glob_d, 1, 1)
fig_most_affected.append_trace(trace_us_c, 1, 2)
fig_most_affected.append_trace(trace_us_d, 1, 2)
for trace in fig_most_affected.data:
trace.name = ''
trace.hovertemplate = '%{x}<br>%{y}'
fig_most_affected.update_yaxes(hoverformat=',f')
fig_most_affected.update_layout(
title=f'Leading Countries and US States ({date})',
showlegend=False,
height=350,
margin={
'r': 10,
't': 50,
'l': 40,
'b': 10
},
updatemenus=[
dict(pad={
'r': 10,
't': 10
},
x=1.0,
y=1.0,
active=0,
buttons=list([
dict(label='Confirmed',
method='update',
args=[{
'visible': [True, False, True, False]
}]),
dict(label='Deaths',
method='update',
args=[{
'visible': [False, True, False, True]
}]),
]))
])
# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
# ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
return html.Div(children=[
html.Div(
[
html.H3('Coronavirus Dashboard'),
html.Div([
html.P(f'Updated: {date}', style={'font-style': 'italic'}),
],
style={'display': 'inline-block'}),
html.Div(
[
dcc.Markdown(
'''Source: [Johns Hopkins University CSSE](https://github.com/CSSEGISandData/COVID-19)''',
style={'font-style': 'italic'})
],
style={
'display': 'inline-block',
'float': 'right',
'color': '#ff7f0e'
}),
],
style={
'color': 'white',
'paddingLeft': '10px',
'background':
'linear-gradient(to right, #ff7f0e 25%, 50%, white)'
}),
html.Div(children=[
html.Div([dcc.Graph(figure=fig_time, )],
style={'margin': '0'},
className='five columns'),
html.Div([dcc.Graph(figure=fig_most_affected, )],
style={'margin': '0'},
className='seven columns'),
],
className='twelve columns'),
html.Div(children=[
html.Div([dcc.Graph(figure=fig_us, )],
style={'margin': '0'},
className='six columns'),
html.Div([dcc.Graph(figure=fig_global, )],
style={'margin': '0'},
className='six columns')
],
className='twelve columns'),
html.Div([
html.Div([
dcc.Markdown(
'''If you find this dashboard helpful, please share it and consider donating to a charity on the frontlines
of COVID-19, such as [Doctors Without Borders](https://donate.doctorswithoutborders.org/onetime.cfm). \nCreated
and maintained by [John Larson](https://www.linkedin.com/in/johnlarson2016/).'''
),
],
style={
'paddingLeft': '10px',
'paddingTop': '20px'
}),
],
className='twelve columns')
])
from geonamescache.mappers import country
import csv
mapper = country(from_key='name', to_key='continentcode')
with open("meteor-fell-before.csv",
"rb") as csvdata, open("meteor-fell.csv", "wb") as out:
input = csv.reader(csvdata, delimiter=',')
out = csv.writer(out, delimiter=',')
header = input.next()
header.append('Continent')
out.writerow(header)
index = header.index("Country")
for row in input:
row.append(mapper(row[index]))
out.writerow(row)
Introduction to Web Science
Assignment 5
Question 3
Team : golf
Script used to extract data from the article-per-line file and process it
to finaly write it in a csv file
"""
import pandas as pd
from geonamescache import GeonamesCache
from geonamescache.mappers import country
gc = GeonamesCache() # we use the GeonamesCache to get the name of countries
# creating a mapper between the iso3 code and the country name
mapper = country(from_key='name', to_key='iso3')
countries = list(gc.get_dataset_by_key(
gc.get_countries(),
'name',
).keys())
# for the US we are going to use the states
states = list(gc.get_us_states_by_names())
#print(countries)
# any of these key words could indicate that we are reading about a star
key_words = ['movie', 'film', 'TV', 'television', 'actor', 'actress']
articles = []
dataset = {}
with open('article-per-line.txt', 'r', encoding="utf8") as f:
articles = f.read().splitlines()
def test_country_name_iso3_mapper(self):
mapper = country(from_key='name', to_key='iso3')
self.assertEquals(mapper('Burma'), 'MMR')
self.assertEquals(mapper('South Korea'), 'KOR')
self.assertEquals(mapper('The Netherlands'), 'NLD')
self.assertEquals(mapper('USA'), 'USA')
def convert_iso3_to_iso2(cls, iso3):
mapper = country(from_key='iso3', to_key='iso')
return mapper(iso3)
def parse():
#combining locations into an array
with open('compiled_data_ourworld.json') as f:
our_world_data = json.load(f)
countries_dict = our_world_data['location']
tests_dict = our_world_data['tests']
date_dict = our_world_data['dt']
#zeros in on values in dictionary
countries_list = list(countries_dict.values())
tests_list = list(tests_dict.values())
date_list = list(date_dict.values())
#convert tests list into integers: N/A is converted to -1
i=0
while(i < len(tests_list)):
tests_list[i] = tests_list[i].replace(',','')
if(tests_list[i].isdigit()):
tests_list[i] = int(tests_list[i])
else:
tests_list[i] = -1
i+=1
#create ISO_3_list
ISO_3_list = coco.convert(names = countries_list, to = 'ISO3')
#create ISO_2_list
ISO_2_list = coco.convert(names = ISO_3_list, to = "ISO2")
for z in range(len(ISO_2_list)):
ISO_2_list[z] = ISO_2_list[z].lower()
#mapper converts iso name to population
mapper = country(from_key='iso3', to_key='population')
population_list = []
#remove local data points or countries with no aggregate figures (-1)
index = 0
length = len(countries_list)
while(index < length):
if ("–" in countries_list[index]):
countries_list.remove(countries_list[index])
ISO_3_list.remove(ISO_3_list[index])
ISO_2_list.remove(ISO_2_list[index])
tests_list.remove(tests_list[index])
date_list.remove(date_list[index])
length = length - 1
continue
index += 1
index = 0
length = len(tests_list)
while(index < length):
if (tests_list[index] == -1):
countries_list.remove(countries_list[index])
ISO_3_list.remove(ISO_3_list[index])
ISO_2_list.remove(ISO_2_list[index])
tests_list.remove(tests_list[index])
date_list.remove(date_list[index])
length = length - 1
continue
index += 1
#create population list
j=0
while(j < len(ISO_3_list)):
population_list.append(mapper(ISO_3_list[j]))
j+=1
#fix duplicates - add tests in same country together
#
# l=0
# m=1
# length = len(ISO_3_list)
# while (m < length):
# if(ISO_3_list[l] == ISO_3_list[m]):
# total = tests_list[l] + tests_list[m]
# tests_list[l] = total
# tests_list[m] = total
# length -= 1
# l += 1
# m += 1
#tpc - tests per million people = (tests/population) * million
tpc_list = []
#create tests per million list
k=0
while(k < len(tests_list)):
tpc_list.append(round(tests_list[k]/population_list[k] * 1000000))
k+=1
# #fix duplicates - remove diplicate entries
# n=0
# o=1
# length = len(ISO_3_list)
# while (o < length):
# if(ISO_3_list[n] == ISO_3_list[o]):
# del ISO_3_list[n]
# del population_list[n]
# del tests_list[n]
# del tpc_list[n]
# length -= 1
# n += 1
# o += 1
#create lattitude and longitude list
location_lat=[]
location_long=[]
with open('countrycode-latlong.json') as g:
lat_long_data = json.load(g)
for iso2_code in ISO_2_list:
location_lat.append(lat_long_data[iso2_code]["lat"])
location_long.append(lat_long_data[iso2_code]["long"])
#create dataframe and export
compiled_data = pd.DataFrame(
{
"location": countries_list,
"location_code": ISO_3_list,
"location_lat": location_lat,
"location_long": location_long,
"location_pop": population_list,
"tests": tests_list,
"tests_per_million": tpc_list,
"date": date_list
})
print(compiled_data)
compiled_data.to_json('./our_world_parsed.json', orient='records')
storage.child('/')
storage.child('our_world_parsed.json').put('./our_world_parsed.json')
def tzname(self, dt):
return self.name
INFLUX_HOST = 'localhost'
INFLUX_DB = 'covid19'
INFLUX_MEASUREMENT = 'covid19_JHU'
INFLUX_DBPORT = 8086
INFLUX_USER = ''
INFUX_PASS = ''
INFLUX_DROPMEASUREMENT = True
client = InfluxDBClient(INFLUX_HOST, INFLUX_DBPORT, INFLUX_USER, INFUX_PASS,
INFLUX_DB)
GMT = Zone(0, False, 'GMT')
mapperPop = country(from_key='name', to_key='population')
mapperISO3 = country(from_key='name', to_key='iso3')
#Direct Links to the 3 CSV Files maintained by JHU CCSE
inputfiles = {
"confirmed":
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
"deaths":
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
"recovered":
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
}
measurements = []
measurements_hash = {}
#Iterate through each Source File and build hash table
for i in sorted(inputfiles.keys()):
field = i
def test_country_name_iso3_mapper(self):
mapper = country(from_key='name', to_key='iso3')
self.assertEquals(mapper('Burma'), 'MMR')
self.assertEquals(mapper('South Korea'), 'KOR')
self.assertEquals(mapper('The Netherlands'), 'NLD')
self.assertEquals(mapper('USA'), 'USA')
def main():
fy.setup_fiscal_calendar(start_month=9)
now = fy.FiscalDateTime.now()
start_date = now.prev_quarter.start.strftime('%Y-%m-%d')
end_date = now.prev_quarter.end.strftime('%Y-%m-%d')
print(start_date)
print(end_date)
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Retrieve Google Analytics data.")
parser.add_argument("-d", "--debug",
help="Enable debugging messages", action="store_true")
parser.add_argument("output_file", metavar="OUTPUT_FILE",
nargs="?",
default="sessions.csv",
help="Output CSV file")
parser.add_argument("-s", "--start-date",
default=start_date,
help="Start date")
parser.add_argument("-e", "--end-date",
default=end_date,
help="End date")
args = parser.parse_args()
if args.start_date != start_date:
args.start_date = parse_date(args.start_date)
print(args.start_date)
if args.end_date != end_date:
args.end_date = parse_date(args.end_date)
print(args.end_date)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
# pd.set_option('display.float_format', '{:,.0f}'.format)
gc = GeonamesCache()
global countries
countries = gc.get_countries()
countries['ZZ'] = {'iso3': 'ZZZ'}
mapper = country(from_key='iso', to_key='iso3')
scope = ['https://www.googleapis.com/auth/analytics.readonly']
# Authenticate and construct service.
service = get_service('analytics', 'v3', scope, 'client_secrets.json')
profile_ids = get_profile_ids(service)
pprint.pprint(profile_ids)
total = pd.DataFrame()
for profile_id in profile_ids:
results = get_results(service, profile_id,
args.start_date, args.end_date)
df = create_dataframe(results)
with pd.option_context('display.max_rows', None,
'display.max_columns', None):
print(df)
total = total.add(df, fill_value=0)
total.index = [conv_iso_2_to_3(i) for i in total.index]
total.index.name = 'iso3'
total.columns = [re.sub(r'^ga:', '', col) for col in total.columns]
set_int(total)
total.to_csv(args.output_file)