import pandas as pd import numpy as np dataframe = pd.read_csv("/work/City_Sales_Data.csv") dataframe.head()

import plotly.express as px location_dict = { "A": "Los Angeles", "B": "Fresno", "C": "San Jose" } dataframe['Location_Mapped'] = dataframe['Location'].map(location_dict) data = dataframe.groupby(['Location_Mapped','Product line'])['Total'].agg(['sum', 'mean']).reset_index() data.columns = ['Location_Mapped','Product_Line','Total_Sales','Average_Sales'] data['Total_Sales'] = round(data['Total_Sales']) fig = px.bar(data, x="Location_Mapped", y="Total_Sales", color="Product_Line", text="Total_Sales", title="Stacked Column Chart - Sales by Location and Product") fig.show()

data = dataframe.groupby(['Location_Mapped','Gender','Customer type']).sum()['Total'].reset_index() data['Total'] = round(data['Total']) fig = px.bar(data, x="Location_Mapped", y="Total", color="Gender", facet_row="Customer type", text="Total",title="Stacked Column Chart - Sales by Location and Gender") fig.show()

#Convert the Object to a Datetime Object dataframe['Date']= pd.to_datetime(dataframe['Date'], dayfirst=True) #Create Month Column from Datetime Object ensuring Dayfirst = True dataframe['month'] = dataframe['Date'].dt.month dict_month = { 1: "Jan-19", 2: "Feb-19", 3: "Mar-19" } dataframe['month'] = dataframe['month'].map(dict_month) data = dataframe.groupby(['month','Product line']).sum()['Total'].reset_index() data['Total'] = round(data['Total']) fig = px.bar(data, x="month", y="Total", color="Product line", text="Total", title="Stacked Column Chart - Sales by Month and Product", category_orders=['Jan-19','Feb-19','Mar-19']) fig.show()