def details_json(request, slug):
df = Expense.data_frame()
categories = df.groupby('Category')
category = Category.objects.get(slug=slug).name
yearly = categories.get_group(category).resample('A').sum()
yearly = yearly.fillna(0)
monthly = categories.get_group(category).resample('M').sum()
monthly = monthly.fillna(0)
monthly_mean = monthly.mean()['Amount']
yearly_mean = yearly.mean()['Amount']
yearly.index = yearly.index.map(lambda t: t.strftime('%Y'))
monthly.index = monthly.index.map(lambda t: t.strftime('%b %Y'))
category_data = {
'name': category,
'monthly_mean': '{0:.2f}'.format(monthly_mean),
'sum': '{0:.2f}'.format(categories.get_group(category).sum()['Amount']),
'yearly_mean': '{0:.2f}'.format(yearly_mean),
}
json_data = {
'category': category_data,
'monthly': monthly.to_dict(into=collections.OrderedDict),
'yearly': yearly.to_dict()
}
return JsonResponse(json_data)
def details_json(request, slug):
df = Expense.data_frame()
categories = df.groupby('Category')
category = Category.objects.get(slug=slug).name
yearly = categories.get_group(category).resample('A').sum()
yearly = yearly.fillna(0)
monthly = categories.get_group(category).resample('M').sum()
monthly = monthly.fillna(0)
monthly_mean = monthly.mean()['Amount']
yearly_mean = yearly.mean()['Amount']
yearly.index = yearly.index.map(lambda t: t.strftime('%Y'))
monthly.index = monthly.index.map(lambda t: t.strftime('%b %Y'))
category_data = {
'name': category,
'monthly_mean': '{0:.2f}'.format(monthly_mean),
'sum':
'{0:.2f}'.format(categories.get_group(category).sum()['Amount']),
'yearly_mean': '{0:.2f}'.format(yearly_mean),
}
json_data = {
'category': category_data,
'monthly': monthly.to_dict(into=collections.OrderedDict),
'yearly': yearly.to_dict()
}
return JsonResponse(json_data)
def cuisines(request):
days = 30
past = datetime.now() - timedelta(days=days)
expenses = Expense.objects.filter(category__name='Restaurant',
date__gte=past).order_by('-date')
last_year = Expense.objects.filter(category__name='Restaurant',
date__gte=past - timedelta(days=365),
date__lte=datetime.now() -
timedelta(days=365)).order_by('-date')
df = Expense.data_frame()
top_cuisines = df.groupby(['Cuisine'
]).count().sort_values('Category',
ascending=False).head(40)
context = {
'days':
days,
'expenses':
expenses,
'last_year':
last_year,
'top_cuisines':
top_cuisines['Category'].to_dict(into=collections.OrderedDict)
}
return render(request, 'expenses/cuisines.html', context)
def category_stats_json(request):
df = Expense.data_frame()
categories = df.groupby('Category')
data = {}
for category in Category.objects.all():
if category.name in categories.groups:
data[category.name] = '{:.2f}'.format(categories.get_group(category.name).sum()['Amount'])
return JsonResponse(data)
def category_stats_json(request):
df = Expense.data_frame()
categories = df.groupby('Category')
data = {}
for category in Category.objects.all():
if category.name in categories.groups:
data[category.name] = '{:.2f}'.format(
categories.get_group(category.name).sum()['Amount'])
return JsonResponse(data)
def cuisines(request):
days = 30
past = datetime.now() - timedelta(days=days)
expenses = Expense.objects.filter(
category__name='Restaurant',
date__gte=past).order_by('-date')
last_year = Expense.objects.filter(
category__name='Restaurant',
date__gte=past - timedelta(days=365),
date__lte=datetime.now() - timedelta(days=365)).order_by('-date')
df = Expense.data_frame()
top_cuisines = df.groupby(['Cuisine']).count().sort_values('Category', ascending=False).head(40)
context = {
'days': days,
'expenses': expenses,
'last_year': last_year,
'top_cuisines': top_cuisines['Category'].to_dict(into=collections.OrderedDict)
}
return render(
request,
'expenses/cuisines.html',
context)
def test_data_frame(self):
df = Expense.data_frame()
self.assertEqual(df.Description[0], 'Spotify')
def test_data_frame(self):
df = Expense.data_frame()
self.assertEqual(df.Description[0], 'Spotify')
