def plot_h_cluster(dataframe, in1, in2):
if dataframe == "shipments":
pca_df = data.get_pca(dataframe)
else:
kmeans = Process()
all_df = kmeans.K_means(in1, in2)
pca_df = all_df[0]
chart = Chart()
return chart.plot_h_bar(pca_df)
def render_cluster_plots_worst(cluster_panel, dataframe, worst_worst, in1,
in2):
if dataframe == "shipments":
d = data.get_shp_cluster_name(cluster_panel, dataframe)
else:
kmeans = Process()
all_df = kmeans.K_means(in1, in2)
df = all_df[1]
kk = df.groupby('cluster')
d = kk.get_group(int(cluster_panel))
if worst_worst == "TOTAL KG":
details = data.get_buttom_kgs(d)
chart = Chart()
return chart.plot_h_top(details, False)
elif worst_worst == "TOTAL VOLUME":
details = data.get_buttom_volume(d)
chart = Chart()
return chart.plot_h_top(details, False)
elif worst_worst == "TOTAL PRICE":
details = data.get_buttom_price(d)
chart = Chart()
return chart.plot_h_top(details, False)
else:
details = data.get_buttom_shps(d)
chart = Chart()
return chart.plot_h_top(details, False)
def plot_pie(customer):
data = Data()
df = data.get_product_customer().groupby('CUST_NAME')
df_cust = df.get_group(customer)
chart = Chart()
return chart.plot_pie(df_cust, customer)
def plot_map(map_feature):
df = data.get_map_data_sender()
chart = Chart()
return chart.plot_map(df, map_feature)
def render_cluster_plots_products(name, filtre):
data = Data()
df = data.get_product_data(name)
chart = Chart()
return chart.plot_time_series(df, filtre)