def Query():
Name = None
Country = ''
capital = ''
df = pd.read_csv(
path.join(path.dirname(__file__), 'static\\Data\\capitals.csv'))
df = df.set_index('Country')
form = QueryFormStructure(request.form)
if (request.method == 'POST'):
name = form.name.data
Country = name
if (name in df.index):
capital = df.loc[name, 'Capital']
else:
capital = name + ', no such country'
form.name.data = ''
df = pd.read_csv(
path.join(path.dirname(__file__), 'static\\Data\\users.csv'))
raw_data_table = df.to_html(classes='table table-hover')
return render_template(
'Query.html',
form=form,
name=capital,
Country=Country,
raw_data_table=raw_data_table,
title='Query by the user',
year=datetime.now().year,
message='This page will use the web forms to get user input')
def Query():
AverageWage_table = ''
fig_image = ''
df_avg = Get_NormalizedAverageWageDataset()
form = QueryFormStructure(request.form)
#set default values for time to avoid errors
#form.start_date.data = df_avg.TIME.min()
#form.end_date.data = df_avg.TIME.max()
#Set the list of countries to choose from
form.countries.choices = get_countries_choices()
if (request.method == 'POST'):
##query user parameters
countries = form.countries.data
start_date = form.start_date.data
end_date = form.end_date.data
fig = plt.figure()
for country in countries:
#Filter only the requested countries
df_avg_countries = df_avg.loc[country]
# Filter only the requested Dates
df_avg_dates = df_avg_countries.loc[lambda df:
(df['TIME'] >= start_date) &
(df['TIME'] <= end_date)]
# adds plot object for requested country in countries
plt.plot('TIME', 'Value', data=df_avg_dates, label=country)
#gives color legend
plt.legend(loc='best')
fig_image = plot_to_img(fig)
return render_template(
'query.html',
form=form,
raw_data_table=AverageWage_table,
fig_image=fig_image,
title='User Data Query',
year=datetime.now().year,
message='Please enter the parameters you choose to analyze the database'
)
def Query():
form = QueryFormStructure(request.form) #פקודה האומרת לתוכנה לקבל מהמשתמש נתונים
chart = '' #מגדיר טבלה ריקה
df = pd.read_csv(path.join(path.dirname(__file__), 'static/data/Cscore.csv')) # קורא את הקובץ עם הציון
form.Countries.choices = get_country_choices() #מגדיר לתוכנה אפשרויות למשתנים
country_list = form.Countries.data #נותן את הרשימה למשתמש
if (request.method == 'POST'):
df = df[['Country', 'Score']] #מוריד את כל שאר העמודות
#df = df.set_index('Country') #נותן אינדקס לעמודה
for country in country_list:
Countries = form.Countries.data
df2 = df[df['Country'].isin (Countries)]
df2 = df2.set_index('Country')
#df[country] = df2['Score']
#יוצר גרף
fig = plt.figure()
ax = fig.add_subplot(111)
df2.plot(ax = ax , kind = 'bar')
chart = plot_to_img(fig)
#מחזיר למשתמש גרף
return render_template(
'query.html',
form = form,
chart = chart,
title='Query by the user',
year=datetime.now().year,
message='This page will use the web forms to get user input'
)
def Query():
geolocator = Nominatim(user_agent="final data science project")
airbnbMap = 'static/pics/airbnbMap.PNG'
crimeMap = 'static/pics/crimeRatesMap.PNG'
partyMap = 'static/pics/partyMap.PNG'
Name = None
Country = ''
capital = ''
dr = pd.read_csv(
path.join(path.dirname(__file__), 'static/Data/nyc-rolling-sales.csv'))
rollingSalesAdresses = dr['ADDRESS']
def findLongLat(address):
adress = geolocator.geocode(address)
return [adress.latitude, adress.longitude]
def removeComma(string):
if string.find(',') < 0:
return string
else:
return string[0:string.find(',')]
def findRollingSales(address):
numSales = 0
for x in range(0, rollingSalesAdresses.size):
if removeComma(str(rollingSalesAdresses[x])).replace(
' ', '').lower() == address.replace(' ', '').lower():
numSales = numSales + 1
return numSales
raw_data_table = dr.to_html(classes='table table-hover',
max_rows=10,
max_cols=5)
form = QueryFormStructure(request.form)
#_________________________________________
if (request.method == 'POST'):
name = form.name.data
Country = name
if (findRollingSales(name) > 0):
capital = str(findRollingSales(name))
raw_data_table = ""
else:
capital = str(findRollingSales(name))
form.name.data = ''
ydata = findLongLat(name + " NYC")[1]
xdata = findLongLat(name + " NYC")[0]
airbnbMap = plt.imread(path.join(path.dirname(__file__), airbnbMap))
fig7 = plt.figure()
qAirbnb = fig7.add_subplot(111)
qAirbnb.imshow(airbnbMap,
extent=[
40.499790000000004, 40.913059999999994,
-74.24441999999999, -73.71299
])
qAirbnb.scatter(xdata, ydata, 10, color='red')
airbnbMap = plot_to_img(fig7)
crimeMap = plt.imread(path.join(path.dirname(__file__), crimeMap))
fig8 = plt.figure()
qCrime = fig8.add_subplot(111)
qCrime.imshow(crimeMap,
extent=[
40.499790000000004, 40.913059999999994,
-74.24441999999999, -73.71299
])
qCrime.scatter(xdata, ydata, 10, color='red')
crimeMap = plot_to_img(fig8)
partyMap = plt.imread(path.join(path.dirname(__file__), partyMap))
fig9 = plt.figure()
qParty = fig9.add_subplot(111)
qParty.imshow(partyMap,
extent=[
40.499790000000004, 40.913059999999994,
-74.24441999999999, -73.71299
])
qParty.scatter(xdata, ydata, 10, color='red')
partyMap = plot_to_img(fig9)
return render_template(
'Query.html',
qAirbnb=airbnbMap,
qCrime=crimeMap,
qParty=partyMap,
form=form,
name=capital,
Country=Country,
raw_data_table=raw_data_table,
title='Query:',
year=datetime.now().year,
message=
'Type in an address in new york to see how many rolling sales were in that address during 2016 and the long and lat coordinates'
)