def one_hot_numeric(ds, attr, header):
"""Transforms the given attribute of the given DataFrame object into one hot encoding.
If you plan to use this, don't use split attribute.
Returns a DataFrame object."""
vals = d.values_of(ds, attr)
new_cols = vals
for new in new_cols:
ds[header + str(new)] = p.Series(np.zeros(len(ds)), ds.index)
for i in ds.index.tolist():
if d.content_of(ds, attr, i) == new:
ds.set_value(i, header + str(new), 1)
return ds
def frequencystatplot(df, show=True, save=False):
regions = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
compstat = []
for i in regions:
compstat.append(len(values_of(df[df["Region"] == i], "StoreID")))
sb.barplot(x=regions, y=compstat)
fig = plt.gcf()
fig.set_size_inches(18, 9)
if show:
plt.show()
if save:
fig.savefig("frequencystatplot.png")
def add_min_per_shop(df, data_from=None):
if data_from is None:
data_from = df
ids = d.values_of(data_from, 'StoreID')
stds = dict()
for id in ids:
try:
_ = stds[str(id)]
except KeyError:
stds[str(id)] = min_per_shop(data_from, id)
df['min_shop'] = p.Series(np.zeros(len(df)), df.index)
for i in df.index.tolist():
df.set_value(i, 'min_shop', stds[str(d.content_of(df, 'StoreID', i))])
return df
def add_avg_cust_per_shop(df, data_from=None):
if data_from is None:
data_from = df
ids = d.values_of(data_from, 'StoreID')
means = dict()
for id in ids:
try:
_ = means[str(id)]
except KeyError:
means[str(id)] = average_cust_per_shop(data_from, id)
df['meancustshop'] = p.Series(np.zeros(len(df)), df.index)
for i in df.index.tolist():
df.set_value(i, 'meancustshop', means[str(d.content_of(df, 'StoreID', i))])
return df
def one_hot(ds, attr, header, split=False):
"""Transforms the given attribute of the given DataFrame object into one hot encoding.
If you plan to use this, don't use split attribute.
Returns a DataFrame object."""
vals = d.values_of(ds, attr)
if split:
new_cols = []
for v in vals:
split = v.split("-")
for s in split:
if not new_cols.__contains__(s):
new_cols.append(s)
else:
new_cols = vals
for new in new_cols:
ds[header + new] = p.Series(np.zeros(len(ds)), ds.index)
for i in ds.index.tolist():
if d.content_of(ds, attr, i).find(new) != -1:
ds.set_value(i, header + new, 1)
return ds
print("merged dataframe:", new_df.shape)
new_df_corr = new_df.corr(method="pearson")
numeric_clustermap = sb.clustermap(new_df_corr,
square="True",
cmap="Blues",
annot=True)
pl.figure(figsize=(20, 20))
pl.show()
#ax = sb.jointplot(x="NearestCompetitor", y="NumberOfSales", data=data_numeric, marker='+')
#pl.show()
#ax = sb.jointplot(x="NearestCompetitor", y="NumberOfCustomers", data=data_numeric, marker='+')
#pl.show()
df = datasetfun.read_dataset()
num_hyperm = [] # the index identifies the region
for regionId in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
ds = df[df["StoreType"] == "Hyper Market"]
ds = ds[ds["Region"] == regionId]
num_hyperm.append(len(datasetfun.values_of(ds, "StoreID")))
print(num_hyperm)
sb.barplot(x=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], y=num_hyperm)
pl.show()
sb.pointplot()
def monthlyplot(df,
bm=3,
by=2016,
em=2,
ey=2018,
regions=None,
storetype=False,
perstore=False,
target="NumberOfSales",
show=True,
save=False):
if em == 12:
em = 1
ey += 1
else:
em += 1
base_bm = bm
base_by = by
base_em = em
base_ey = ey
if regions is None and not storetype and not perstore:
month_x = []
sales_y = []
while bm != em or by != ey:
dfframe = get_frame_in_range(df, bm, by, bm, by)
month_x.append(str(bm) + "-" + str(by))
sales_y.append(dfframe[target].sum())
bm += 1
if bm == 13:
bm = 1
by += 1
sb.barplot(x=month_x,
y=sales_y).set_title("Monthly " + target + " (All shops)")
elif storetype:
title = "Monthly " + target + " per StoreType"
palette = sb.color_palette("hls", 4)
month_x = []
sales_y_1 = []
sales_y_2 = []
sales_y_3 = []
sales_y_4 = []
while bm != em or by != ey:
dfframe = get_frame_in_range(df, bm, by, bm, by)
month_x.append(str(bm) + "-" + str(by))
sales_y_1.append(
dfframe[dfframe["StoreType_Hyper Market"] == 1][target].mean())
sales_y_2.append(
dfframe[dfframe["StoreType_Super Market"] == 1][target].mean())
sales_y_3.append(dfframe[dfframe["StoreType_Standard Market"] == 1]
[target].mean())
sales_y_4.append(dfframe[dfframe["StoreType_Shopping Center"] == 1]
[target].mean())
bm += 1
if bm == 13:
bm = 1
by += 1
pdf1 = pd.DataFrame(data={
"Month": month_x,
target: sales_y_1,
"Type": "Hyper Market"
})
pdf2 = pd.DataFrame(data={
"Month": month_x,
target: sales_y_2,
"Type": "Super Market"
})
pdf3 = pd.DataFrame(data={
"Month": month_x,
target: sales_y_3,
"Type": "Standard Market"
})
pdf4 = pd.DataFrame(data={
"Month": month_x,
target: sales_y_4,
"Type": "Shopping Center"
})
pdf = pd.concat([pdf1, pdf2, pdf3, pdf4])
sb.pointplot(x="Month", y=target, data=pdf,
hue="Type").set_title(title)
elif perstore:
pdf = []
palette = sb.color_palette("hls", 750)
for i in values_of(df, "StoreID"):
month_x = []
sales_y = []
while bm != em or by != ey:
dfframe = get_frame_in_range(df[df["StoreID"] == i], bm, by,
bm, by)
month_x.append(str(bm) + "-" + str(by))
sales_y.append(dfframe[target].mean())
bm += 1
if bm == 13:
bm = 1
by += 1
pdf.append(
pd.DataFrame(data={
"Month": month_x,
target: sales_y,
"StoreID": i
}))
bm = base_bm
by = base_by
em = base_em
ey = base_ey
pdf = pd.concat(pdf)
sb.pointplot(
x="Month", y=target, data=pdf,
hue="StoreID").set_title("Monthly NumberOfCustomers per Store")
else:
pdf = []
palette = sb.color_palette("hls", 11)
for i in regions:
month_x = []
sales_y = []
while bm != em or by != ey:
dfframe = get_frame_in_range(df[df["Region"] == i], bm, by, bm,
by)
month_x.append(str(bm) + "-" + str(by))
sales_y.append(dfframe[target].mean())
bm += 1
if bm == 13:
bm = 1
by += 1
pdf.append(
pd.DataFrame(data={
"Month": month_x,
target: sales_y,
"Region": i
}))
bm = base_bm
by = base_by
em = base_em
ey = base_ey
pdf = pd.concat(pdf)
sb.pointplot(
x="Month", y=target, data=pdf,
hue="Region").set_title("Monthly NumberOfCustomers per Region")
plt.legend()
fig = plt.gcf()
fig.set_size_inches(18, 9)
if show:
plt.show()
if save:
fig.savefig("monthly_plot.png")
def frequencypershop(df,
storeID,
target="NumberOfSales",
daily=False,
shoptype=False,
cloudcover=False,
events=False,
region=False,
show=True,
save=False):
title = target + " Distribution"
if storeID > 0:
df = df[df["StoreID"] == storeID]
title = title + " - Shop" + str(storeID)
if daily:
df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y')
df['Day'] = df['Date'].dt.weekday_name
sb.distplot(a=df[df["Day"] == "Monday"][target],
label="Monday").set_title(title)
sb.distplot(a=df[df["Day"] == "Tuesday"][target],
label="Tuesday").set_title(title)
sb.distplot(a=df[df["Day"] == "Wednesday"][target],
label="Wednesday").set_title(title)
sb.distplot(a=df[df["Day"] == "Thursday"][target],
label="Thursday").set_title(title)
sb.distplot(a=df[df["Day"] == "Friday"][target],
label="Friday").set_title(title)
sb.distplot(a=df[df["Day"] == "Saturday"][target],
label="Saturday").set_title(title)
sb.distplot(a=df[df["Day"] == "Sunday"][target],
label="Sunday").set_title(title)
plt.legend()
elif shoptype:
sb.distplot(a=df[df["StoreType_Hyper Market"] == 1][target],
label="Hyper Market").set_title(title)
sb.distplot(a=df[df["StoreType_Super Market"] == 1][target],
label="Super Market").set_title(title)
sb.distplot(a=df[df["StoreType_Standard Market"] == 1][target],
label="Standard Market").set_title(title)
sb.distplot(a=df[df["StoreType_Shopping Center"] == 1][target],
label="Shopping Center").set_title(title)
plt.legend()
elif cloudcover:
sb.distplot(a=df[df["CloudCover"] == 0][target],
label="0").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 1][target],
label="1").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 2][target],
label="2").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 3][target],
label="3").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 4][target],
label="4").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 5][target],
label="5").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 6][target],
label="6").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 7][target],
label="7").set_title(title)
sb.distplot(a=df[df["CloudCover"] == 8][target],
label="8").set_title(title)
plt.legend()
elif events:
sb.distplot(a=df[df["Events_none"] == 1][target],
label="None").set_title(title)
sb.distplot(a=df[df["Events_Fog"] == 1][target],
label="Fog").set_title(title)
sb.distplot(a=df[df["Events_Rain"] == 1][target],
label="Rain").set_title(title)
sb.distplot(a=df[df["Events_Thunderstorm"] == 1][target],
label="Thunderstorm").set_title(title)
sb.distplot(a=df[df["Events_Snow"] == 1][target],
label="Snow").set_title(title)
sb.distplot(a=df[df["Events_Hail"] == 1][target],
label="Hail").set_title(title)
plt.legend()
elif region:
sb.distplot(a=df[df["Region"] == 0][target],
label="0").set_title(title)
sb.distplot(a=df[df["Region"] == 1][target],
label="1").set_title(title)
sb.distplot(a=df[df["Region"] == 2][target],
label="2").set_title(title)
sb.distplot(a=df[df["Region"] == 3][target],
label="3").set_title(title)
sb.distplot(a=df[df["Region"] == 4][target],
label="4").set_title(title)
sb.distplot(a=df[df["Region"] == 5][target],
label="5").set_title(title)
sb.distplot(a=df[df["Region"] == 6][target],
label="6").set_title(title)
sb.distplot(a=df[df["Region"] == 7][target],
label="7").set_title(title)
sb.distplot(a=df[df["Region"] == 8][target],
label="8").set_title(title)
sb.distplot(a=df[df["Region"] == 9][target],
label="9").set_title(title)
sb.distplot(a=df[df["Region"] == 10][target],
label="10").set_title(title)
plt.legend()
else:
if storeID == -1:
sb.boxplot(x="StoreID", y=target, data=df).set_title(title)
plt.legend()
elif storeID == -2:
df = df[df["StoreType_Shopping Center"] == 1]
for id in values_of(df, "StoreID"):
sb.distplot(a=df[df["StoreID"] == id][target],
hist=False,
label=str(id)).set_title(title)
plt.legend()
elif storeID == -3:
df1 = df[df["StoreID"] == 1307]
df2 = df[df["StoreID"] == 1330]
df = pd.concat([df1, df2])
sb.distplot(a=df[df["StoreID"] == 1307]["NumberOfSales"],
hist=False,
label="1307 - Sales").set_title(title)
sb.distplot(a=df[df["StoreID"] == 1307]["NumberOfCustomers"],
hist=False,
label="1307 - Customers").set_title(title)
sb.distplot(a=df[df["StoreID"] == 1330]["NumberOfSales"],
hist=False,
label="1330 - Sales").set_title(title)
sb.distplot(a=df[df["StoreID"] == 1330]["NumberOfCustomers"],
hist=False,
label="1330 - Customers").set_title(title)
plt.legend()
else:
sb.distplot(a=df[target]).set_title(title)
fig = plt.gcf()
fig.set_size_inches(18, 9)
if show:
plt.show()
if save:
fig.savefig("frequencypershop.png")
# 'Min_VisibilitykM>CloudCover': 0.8677897336390897,
# 'Min_VisibilitykM>Events': 0.9578408890456871,
# 'Min_VisibilitykM>Max_Gust_SpeedKm_h': 0.8377138825189627,
# 'Min_VisibilitykM>Max_VisibilityKm': 1.0,
# 'Min_VisibilitykM>Mean_VisibilityKm': 1.0}
print("MISS CORRELATIONS: ")
print(misses)
print(
"###################################################################")
ds = imp.full_preprocess(ds)
print("PREPROCESSING COMPLETE")
print(
"###################################################################")
st.missing(ds, list(ds), printa=True)
print(
"###################################################################")
for attr in d.nominal_only(ds):
print("VALUES OF " + attr + ": " + str(d.values_of(ds, attr)))
print(
"###################################################################")
for attr in d.numeric_only(ds):
print("DISTINCT NUMBER OF " + attr + ": " +
str(len(d.values_of(ds, attr))))
for attr in list(d.numeric_only(ds)):
gr.scatterplot(ds, attr, 'NumberOfSales')
