def listing_count(request,
quadrant="all",
community="all",
p_type="all",
active=1):
"""
Count of rental listings for selection
"""
try:
community = community.replace("_", " ")
if active == 1:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "price", "quadrant",
"community").filter(position="active")))
else:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "price", "quadrant", "community")))
# Format quadrant names
df = vf.quadrant_format(df)
# Slice
df = vf.query_slice(df, p_type, quadrant, community)
val = df.shape[0]
return JsonResponse(
{
"quadrant": quadrant,
"community": community,
"p_type": p_type,
"count": val
},
safe=False,
)
except:
return JsonResponse(
{
"quadrant": quadrant,
"community": community,
"p_type": p_type,
"count": 0
},
safe=False,
)
def time_series(request,
quadrant="all",
community="all",
p_type="all",
active=1):
"""
Time series data on selection
"""
community = community.replace("_", " ")
if active == 1:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "retrieval_date", "price", "quadrant",
"community").filter(position="active")))
else:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "retrieval_date", "price", "quadrant",
"community")))
# Remove properties that have 0 dollars rent
df = df[df.price != 0]
df.set_index("retrieval_date", inplace=True)
df = df.sort_index()
# Format quadrant names
df = vf.quadrant_format(df)
# Slice
df = vf.query_slice(df, p_type, quadrant, community)
df.drop(columns=["_type", "quadrant"], inplace=True)
agg = df.groupby(df.index).mean()
agg = agg.round()
flat = agg.to_dict()
return JsonResponse(flat, safe=False)
def scatter_data(request,
quadrant="all",
community="all",
p_type="all",
active=1):
""" JSON API for scatter plot """
community = community.replace("_", " ")
if active == 1:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
'community', 'quadrant', 'price', 'sq_feet',
'_type').filter(position="active")))
else:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
'community', 'quadrant', 'price', 'sq_feet', '_type')))
# Format quadrant names
df = vf.quadrant_format(df)
# Slice
df = vf.query_slice(df, p_type, quadrant, community)
# We only need price/sq feet
df.drop(columns=["_type", "quadrant", "community"], inplace=True)
# Remove properties that have 0 dollars rent
df = df.loc[(df.price > 100) & (df.price < 10000)]
# Remove properties that have 0 dollars rent
df = df.loc[(df.sq_feet > 100) & (df.sq_feet < 10000)]
df.sq_feet = df.sq_feet.astype(int)
if df.shape[0] > 2500:
df = df.sample(n=2500)
flat = df.to_dict('split')
return JsonResponse(flat["data"], safe=False)
def map_data(request, quadrant="all", community="all", p_type="all", active=1):
""" JSON API for Leaflet data """
community = community.replace("_", " ")
if active == 1:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"latitude", "longitude", "quadrant", "community", "price",
"_type", "address", "sq_feet").filter(position="active")))
else:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"latitude", "longitude", "quadrant", "community", "price",
"_type", "address", "sq_feet")))
# Format quadrant names
df = vf.quadrant_format(df)
# Slice
df = vf.query_slice(df, p_type, quadrant, community)
# We only need price/sq feet
df.drop(columns=["_type", "quadrant", "community"], inplace=True)
# Remove properties that have 0 dollars rent
df = df.loc[(df.price > 100) & (df.price < 10000)]
# Remove properties that have 0 dollars rent
df = df.loc[(df.sq_feet > 100) & (df.sq_feet < 10000)]
df.sq_feet = df.sq_feet.astype(int)
df.dropna(inplace=True)
flat = df.to_dict('records')
return JsonResponse(flat, safe=False)
def price_metrics(request,
fun,
quadrant="all",
community="all",
p_type="all",
active=1):
"""
Price stats on selection
"""
try:
community = community.replace("_", " ")
if active == 1:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "price", "quadrant",
"community").filter(position="active")))
else:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "price", "quadrant", "community")))
# Format quadrant names
df = vf.quadrant_format(df)
# Slice
df = vf.query_slice(df, p_type, quadrant, community)
# Remove properties that have 0 dollars rent
df = df[df.price != 0]
if fun == "avg":
val = df["price"].mean()
elif fun == "min":
val = df["price"].min()
elif fun == "max":
val = df["price"].max()
else:
val = df["price"].mean()
val = round(val)
return JsonResponse(
{
"fun": fun,
"quadrant": quadrant,
"community": community,
"p_type": p_type,
"val": val,
},
safe=False,
)
except:
return JsonResponse(
{
"fun": fun,
"quadrant": quadrant,
"community": community,
"p_type": p_type,
"val": 0,
},
safe=False,
)
def market_share(request,
quadrant="all",
community="all",
p_type="all",
active=1):
"""
Market cap of selection
"""
try:
community = community.replace("_", " ")
if active == 1:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "price", "quadrant",
"community").filter(position="active")))
else:
df = pd.DataFrame(
list(
RentalData.objects.using("rental_data").values(
"_type", "price", "quadrant", "community")))
# Format quadrant names
df = vf.quadrant_format(df)
# Cache before slice
df_total = df.copy()
df = vf.query_slice(df, p_type, quadrant, community)
val = df.shape[0] / df_total.shape[0]
return JsonResponse(
{
"quadrant": quadrant,
"p_type": p_type,
"community": community,
"val": df.shape[0],
"total": df_total.shape[0],
"ms": val,
},
safe=False,
)
except:
return JsonResponse(
{
"quadrant": quadrant,
"p_type": p_type,
"community": community,
"val": 0,
"total": 0,
"ms": 0,
},
safe=False,
)