Arghoob_sale_report.py

#!/usr/bin/env python
# coding: utf-8

# In[15]:


import pandas as pd
import numpy as np
import os
import plotly.graph_objs as go
# import plotly.io as pio
import plotly.plotly as py
import plotly.figure_factory as ff
import dash
import dash_core_components as dcc
import dash_html_components as html
from  plotly.offline import plot
import plotly
# from IPython.display import SVG, display
# from IPython.display import Image
# from IPython.display import display, Math, Latex
import colorlover as cl
# from IPython.display import HTML
# from IPython.display import IFrame
# from plotly.offline import iplot, init_notebook_mode
# from IPython.display import YouTubeVideo
# init_notebook_mode(connected=True)

# import subprocess
# subprocess.check_call(['pip', 'install', 'numpy']) # install pkg
# subprocess.check_call(['pip', 'install',"--upgrade", 'numpy']) # upgrade pkg


# In[16]:


# sku = rawData.values[:189,0]
# upc = rawData.values[:189,1]
# Catalogue = rawData.values[:189,2]
# title = rawData.values[:189,3]
# label = rawData.values[:189,4]
# Cost_Price = rawData.values[:189,5]
# VSP = rawData.values[:189,6]


# In[17]:


# rawDatVal = []
# rawDatVal = rawData.values[:189,7:149]
# eachRaw = []
# allRawvalue = []
# BranchTotal = rawDatVal.sum(axis=0)

# for i in range(len(rawDatVal[:,0])):
#     for j in range(int((len(rawDatVal[0,:]))/3)):
        
#         k=3 * j 

#         eachRaw.append(rawDatVal[i,k])
#     eachRaw = np.array(eachRaw)
#     allRawvalue.append(eachRaw.sum())
#     eachRaw = []

# # rawData['Sum of sale for each goods'] = allRawvalue


# In[18]:



# eachRaw = []

# BranchTotal = rawDatVal.sum(axis=0)
# for j in range(int(len(rawDatVal[0,:])/3)):
#     k=3 * j 
#     sum = 0
#     for i in range(len(rawDatVal[:,0])):
#         sum += rawDatVal[i,k]
#     rawData.iloc[i+1,k] = sum
    

#     eachRaw = []
# # allRawvalue.append(np.array(allRawvalue).sum())
# # allRawvalue.append(np.array(allRawvalue).sum())


# In[19]:


# Virgin_Stock = []
# Virgin_Stock = pd.DataFrame(Virgin_Stock)
# Virgin_Stock['sku'] = sku
# Virgin_Stock['upc'] = upc
# Virgin_Stock['Catalogue'] = Catalogue
# Virgin_Stock['title'] = title
# Virgin_Stock['label'] = label
# Virgin_Stock['VMS Cost'] = Cost_Price
# Virgin_Stock['VSP'] = VSP


# Virgin_Stock[f1] = list(allRawvalue)


# In[20]:


# i = 1
# buf = np.zeros(len(Virgin_Stock.values[:,1]), dtype=int)



# ## i
# for j in range(i):
#     buf += np.array(Virgin_Stock.values[:,7+j], dtype = int)

# Virgin_Stock["TOTAL"] = buf
# ii = np.full(len(Virgin_Stock.values[:,1]) ,i )

# Virgin_Stock["AVG. WEEK"] = np.divide(Virgin_Stock["TOTAL"] , ii)
# Virgin_Stock['total VMS Cost'] = Virgin_Stock["TOTAL"] * Virgin_Stock['VMS Cost']
# Virgin_Stock['total V.S.P.'] = Virgin_Stock["TOTAL"] * Virgin_Stock['VSP']
# count = 0
# for head in list(rawData.head(0)):
#     if ".Sales Quantity" in head:
#         Virgin_Stock[head] = rawData[head]
#         try:
#             headNum = int(head.replace(".Sales Quantity",''))
#         except:
#             print(int(headNum)," can't be cast to int")
#         headNum = str(headNum)
#         Virgin_Stock['TOTAL' + headNum] = rawData.values[:189, count]
#         Virgin_Stock['VMS Cost' + headNum] = Virgin_Stock['VMS Cost'] * Virgin_Stock['TOTAL' + headNum]
#         Virgin_Stock['V.S.P.' + headNum] = Virgin_Stock['VSP'] * Virgin_Stock['TOTAL' + headNum]
#         Virgin_Stock['AVG. WEEK' + headNum] = np.divide(Virgin_Stock['TOTAL' + headNum] , ii)
#     count +=1
        


# In[21]:


#  Virgin_Stock['VMS Cost'] * Virgin_Stock['TOTAL' + headNum]


# In[22]:


numListOfBranch = ['401 Co','402 Co','404 Co','405 Co','412 Co','416 Co','417 Co','423 Co',
                   '424 Co','425 Co','426 Co','429 Co','444 Co','490 Co',]
listOfBranch = ['401 Co', '401.VMS cost', '401.V.S.P.', '401.AVG. WEEK',
                '402 Co', '402.VMS cost', '402.V.S.P.', '402.AVG. WEEK',
                '404 Co', '404.VMS cost', '404.V.S.P.', '404.AVG. WEEK',
                '405 Co', '405.VMS cost', '405.V.S.P.', '405.AVG. WEEK',
                '412 Co', '412.VMS cost', '412.V.S.P.', '412.AVG. WEEK',
                '416 Co', '416.VMS cost', '416.V.S.P.', '416.AVG. WEEK',
                '417 Co', '417.VMS cost', '417.V.S.P.', '417.AVG. WEEK',
                '423 Co', '423.VMS cost', '423.V.S.P.', '423.AVG. WEEK',
                '424 Co', '424.VMS cost', '424.V.S.P.', '424.AVG. WEEK',
                '425 Co', '425.VMS cost', '425.V.S.P.', '425.AVG. WEEK',
                '426 Co', '426.VMS cost', '426.V.S.P.', '426.AVG. WEEK',
                '429 Co', '429.VMS cost', '429.V.S.P.', '429.AVG. WEEK',
                '444 Co', '444.VMS cost', '444.V.S.P.', '444.AVG. WEEK',
                '490 Co', '490.VMS cost', '490.V.S.P.', '490.AVG. WEEK']

productDetail = ['Sku', 'UPC', 'Catalogue N', 'Title', 'Label' ,'Cost Price' ,'V.S.P.']
header = productDetail.copy()
header.extend(listOfBranch.copy())


# In[23]:


# findID(str(748116), rawData)
# rawData


# In[24]:


# noStock(748116,rawData)


# In[25]:


# monthRawData.iloc[155]['Sku']


# In[26]:



# df = pd.read_csv('hg.csv')


# def generate_table(dataframe, max_rows=10):
#     return html.Table(
#         # Header
#         [html.Tr([html.Th(col) for col in dataframe.columns])] +

#         # Body
#         [html.Tr([
#             html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
#         ]) for i in range(min(len(dataframe), max_rows))]
#     )


# external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

# app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

# app.layout = html.Div(children=[
#     html.H4(children='US Agriculture Exports (2011)'),
#     generate_table(df)
# ])

# if __name__ == '__main__':
#     app.run_server(debug=True)


# In[27]:


df = pd.read_csv('toalOfMonth.csv')

branchTot = []
for col in numListOfBranch:
    branchTot.append(df[col].sum())


# In[28]:




trace = go.Table(
    header=dict(values=numListOfBranch),
    cells=dict(values=branchTot))

data = [trace] 
plot(data, filename = 'basic_table')


# In[29]:




trace = go.Table(
    header=dict(values=numListOfBranch,
                line = dict(color='#7D7F80'),
                fill = dict(color='#a1c3d1'),
                align = ['left'] * 5),
    cells=dict(values=branchTot,
               line = dict(color='#7D7F80'),
               fill = dict(color='#EDFAFF'),
               align = ['left'] * 5))

layout = dict(width=1000, height=600)
data = [trace]
fig = dict(data=data, layout=layout)
plot(fig, filename = 'styled_table')


# In[30]:




# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv')
df = pd.read_csv('toalOfMonth.csv')

trace = go.Table(
    header=dict(values=list(numListOfBranch),
                fill = dict(color='#C2D4FF'),
                align = ['left'] * len(branchTot)),
                cells=dict(values=branchTot,
                fill = dict(color='#F5F8FF'),
                align = ['left'] * len(branchTot)))

data = [trace] 
plot(data, filename = 'pandas_table')


# In[31]:




# values = [[['Salaries', 'Office', 'Merchandise', 'Legal', '<b>TOTAL<br>EXPENSES</b>']],
# [["Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus"
#   +" vim. Id usu aeterno adversarium, summo mollis timeam vel ad",
#   "Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus"
#   +" vim. Id usu aeterno adversarium, summo mollis timeam vel ad",
#   "Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus"
#   +" vim. Id usu aeterno adversarium, summo mollis timeam vel ad",
#   "Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus"
#   + " vim. Id usu aeterno adversarium, summo mollis timeam vel ad",
#   "Lorem ipsum dolor sit amet, tollit discere inermis pri ut. Eos ea iusto timeam, an prima laboramus"
#   +" vim. Id usu aeterno adversarium, summo mollis timeam vel ad"]]]


# trace0 = go.Table(
#   columnorder = [1,2],
#   columnwidth = [80,400],
#   header = dict(
#     values = [['<b>EXPENSES</b><br>as of July 2017'],
#                   ['<b>DESCRIPTION</b>']],
#     line = dict(color = '#506784'),
#     fill = dict(color = '#119DFF'),
#     align = ['left','center'],
#     font = dict(color = 'white', size = 12),
#     height = 40
#   ),
#   cells = dict(
#     values = values,
#     line = dict(color = '#506784'),
#     fill = dict(color = ['#25FEFD', 'white']),
#     align = ['left', 'center'],
#     font = dict(color = '#506784', size = 12),
#     height = 30
#     ))

# data = [trace0]

# plot(data, filename = "Row and Column Size")


# In[32]:




# headerColor = 'grey'
# rowEvenColor = 'lightgrey'
# rowOddColor = 'white'

# trace0 = go.Table(
#   header = dict(
#     values = [['<b>EXPENSES</b>'],
#                   ['<b>Q1</b>'],
#                   ['<b>Q2</b>'],
#                   ['<b>Q3</b>'],
#                   ['<b>Q4</b>']],
#     line = dict(color = '#506784'),
#     fill = dict(color = headerColor),
#     align = ['left','center'],
#     font = dict(color = 'white', size = 12)
#   ),
#   cells = dict(
#     values = [
#       [['Salaries', 'Office', 'Merchandise', 'Legal', '<b>TOTAL</b>']],
#       [[1200000, 20000, 80000, 2000, 12120000]],
#       [[1300000, 20000, 70000, 2000, 130902000]],
#       [[1300000, 20000, 120000, 2000, 131222000]],
#       [[1400000, 20000, 90000, 2000, 14102000]]],
#     line = dict(color = '#506784'),
#     fill = dict(color = [rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]),
#     align = ['left', 'center'],
#     font = dict(color = '#506784', size = 11)
#     ))

# data = [trace0]

# plot(data, filename = "alternating row colors")
if len(cl.scales['7']['seq']['Blues']) <= 14:
    color = cl.scales['7']['seq']['Blues']
    color.extend(cl.scales['7']['seq']['Blues'])
    len(color)


# In[33]:



colors = cl.scales['7']['seq']['Blues']
len(colors)

print(len(colors))

data = {'Branch' : list(numListOfBranch),
        'Total' : list(branchTot),
        'Color' : list(colors[:14])}
df = pd.DataFrame(data)


trace0 = go.Table(
  header = dict(
    values = ["Total Sale", "<b>Branch</b>"],
    line = dict(color = 'white'),
    fill = dict(color = 'white'),
    align = ['center'],
    font = dict(color = 'black', size = 12)
  ),
  cells = dict(
    values = [df.Total, df.Branch],
    line = dict(color = [df.Color]),
    fill = dict(color = [df.Color]),
    align = 'center',
    font = dict(color = 'black', size = 11)
    ))

data = [trace0]

plot(data, filename = "row variable color")


# In[34]:



# colors = cl.scales['9']['seq']['Reds']
# a = np.random.randint(low=0, high=9, size=10)
# b = np.random.randint(low=0, high=9, size=10)
# c = np.random.randint(low=0, high=9, size=10)


# trace0 = go.Table(
#   header = dict(
#     values = numListOfBranch,
#     line = dict(color = 'white'),
#     fill = dict(color = 'white'),
#     align = 'center',
#     font = dict(color = 'black', size = 12)
#   ),
#   cells = dict(
#     values =branchTot,
#     line = dict(color = [np.array(colors)[a],np.array(colors)[b],
#                         np.array(colors)[c]]),
#     fill = dict(color = [np.array(colors)[a],np.array(colors)[b],
#                         np.array(colors)[c]]),
#     align = 'center',
#     font = dict(color = 'white', size = 11)
#     ))

# data = [trace0]

# plot(data, filename = "cell variable color")


# In[35]:



# df = pd.read_csv("hg.csv")
# ff = df[['Sku','Catalogue N','Cost Price','V.S.P.','total sale of Goods','Status']]

# table = ff.create_table(df)
# # print(table)
# # table
# plot(table)
# pio.write_image(table, 'table.webp')
# pio.write_image(table, 'table.pdf')


# In[36]:


# import tkinter
# from tkinter import messagebox
# root = tkinter.Tk()
# root.withdraw()
# df = pd.read_csv("hg.csv")
# # messagebox.showinfo(str(df.std()))
# # messagebox.showinfo(str(df.mean()))
# # messagebox.showinfo(str(df.info()))
# # print(df.mean())
# # print(df.info())

# top = tkinter.Tk()
# def STD():
#     messagebox.showinfo('STD',str(df.std()))
# def mean():
#     messagebox.showinfo('Mean',str(df.mean()))
# def info():
#     messagebox.showinfo('Info', str(df.info()))

# B1 = tkinter.Button(top, text = 'STD', command = STD)
# B1.pack()



# B2 = tkinter.Button(top, text = 'Mean', command = mean)
# B2.pack()



# B3 = tkinter.Button(top,text = 'Info', command = info)
# B3.pack()

# top.mainloop()


# In[37]:



data = pd.read_csv('toalOfMonth.csv')

branchTot = []
for col in numListOfBranch:
    branchTot.append(data[col].sum())
    
data = [go.Bar(x=numListOfBranch,
            y=branchTot)]


plot(data, filename='jupyter-basic_bar')


# In[38]:




data = pd.read_csv('toalOfMonth.csv')

branchTot = []
for col in numListOfBranch:
    branchTot.append(data[col].sum())
N = 100
x = numListOfBranch
y = branchTot
colors = np.random.rand(N)
sz = np.random.rand(N)*30

fig = go.Figure()
fig.add_scatter(x=x,
                y=y,
                mode='markers',
                marker={'size': sz,
                        'color': colors,
                        'opacity': 0.6,
                        'colorscale': 'Viridis'
                       });
plot(fig)


# In[40]:



# data = pd.read_csv('toalOfMonth.csv')

# branchTot = []
# for col in numListOfBranch:
#     branchTot.append(data[col].sum())

# x = numListOfBranch
# y = branchTot
# colors = np.random.rand(N)
  
# data = go.Figure()
# data.add_bar(x=x,
#                 y=y,
#                 marker={
#                         'color': colors,
#                         'opacity': 0.6,
#                         'colorscale': 'Viridis'
#                        })
# pio.write_image(data, 'fig1.jpeg')
# img_bytes = pio.to_image(data, format='svg')
# pio.write_image(data, 'fig1.webp')
# display(SVG(img_bytes))


# In[42]:


# img_bytes = pio.to_image(data, format='png', width=600, height=400, scale=2)
# Image(img_bytes)


# In[43]:


# %%time
# pio.write_image(data, 'fig1.jpeg')
# img_bytes = pio.to_image(data, format='svg')

# display(SVG(img_bytes))


# In[44]:



# df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

# trace_women = go.Bar(x=df.School,
#                   y=df.Women,
#                   name='Women',
#                   marker=dict(color='#ffcdd2'))

# trace_men = go.Bar(x=df.School,
#                 y=df.Men,
#                 name='Men',
#                 marker=dict(color='#A2D5F2'))

# trace_gap = go.Bar(x=df.School,
#                 y=df.Gap,
#                 name='Gap',
#                 marker=dict(color='#59606D'))

# data = [trace_women, trace_men, trace_gap]

# layout = go.Layout(title="Average Earnings for Graduates",
#                 xaxis=dict(title='School'),
#                 yaxis=dict(title='Salary (in thousands)'))

# fig = go.Figure(data=data, layout=layout)

# plot(fig, sharing='private', filename='jupyter-styled_bar')


# In[45]:



# mapbox_access_token = 'dfg'

# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv')
# site_lat = df.lat
# site_lon = df.lon
# locations_name = df.text

# data = [
#     go.Scattermapbox(
#         lat=site_lat,
#         lon=site_lon,
#         mode='markers',
#         marker=dict(
#             size=17,
#             color='rgb(255, 0, 0)',
#             opacity=0.7
#         ),
#         text=locations_name,
#         hoverinfo='text'
#     ),
#     go.Scattermapbox(
#         lat=site_lat,
#         lon=site_lon,
#         mode='markers',
#         marker=dict(
#             size=8,
#             color='rgb(242, 177, 172)',
#             opacity=0.7
#         ),
#         hoverinfo='none'
#     )]


# layout = go.Layout(
#     title='Nuclear Waste Sites on Campus',
#     autosize=True,
#     hovermode='closest',
#     showlegend=False,
#     mapbox=dict(
#         accesstoken=mapbox_access_token,
#         bearing=0,
#         center=dict(
#             lat=38,
#             lon=-94
#         ),
#         pitch=0,
#         zoom=3,
#         style='light'
#     ),
# )

# fig = dict(data=data, layout=layout)

# plot(fig, filename='jupyter-Nuclear Waste Sites on American Campuses')


# In[46]:



# s = np.linspace(0, 2 * np.pi, 240)
# t = np.linspace(0, np.pi, 240)
# tGrid, sGrid = np.meshgrid(s, t)

# r = 2 + np.sin(7 * sGrid + 5 * tGrid)  # r = 2 + sin(7s+5t)
# x = r * np.cos(sGrid) * np.sin(tGrid)  # x = r*cos(s)*sin(t)
# y = r * np.sin(sGrid) * np.sin(tGrid)  # y = r*sin(s)*sin(t)
# z = r * np.cos(tGrid)                  # z = r*cos(t)

# surface = go.Surface(x=x, y=y, z=z)
# data = [surface]

# layout = go.Layout(
#     title='Parametric Plot',
#     scene=dict(
#         xaxis=dict(
#             gridcolor='rgb(255, 255, 255)',
#             zerolinecolor='rgb(255, 255, 255)',
#             showbackground=True,
#             backgroundcolor='rgb(230, 230,230)'
#         ),
#         yaxis=dict(
#             gridcolor='rgb(255, 255, 255)',
#             zerolinecolor='rgb(255, 255, 255)',
#             showbackground=True,
#             backgroundcolor='rgb(230, 230,230)'
#         ),
#         zaxis=dict(
#             gridcolor='rgb(255, 255, 255)',
#             zerolinecolor='rgb(255, 255, 255)',
#             showbackground=True,
#             backgroundcolor='rgb(230, 230,230)'
#         )
#     )
# )

# fig = go.Figure(data=data, layout=layout)
# plot(fig, filename='jupyter-parametric_plot')


# In[47]:




# data = [dict(
#         visible = False,
#         line=dict(color='#00CED1', width=6),
#         name = '𝜈 = '+str(step),
#         x = np.arange(0,10,0.01),
#         y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]
# data[10]['visible'] = True

# steps = []
# for i in range(len(data)):
#     step = dict(
#         method = 'restyle',
#         args = ['visible', [False] * len(data)],
#     )
#     step['args'][1][i] = True # Toggle i'th trace to "visible"
#     steps.append(step)

# sliders = [dict(
#     active = 10,
#     currentvalue = {"prefix": "Frequency: "},
#     pad = {"t": 50},
#     steps = steps
# )]

# layout = dict(sliders=sliders)
# fig = dict(data=data, layout=layout)

# plot(fig, filename='Sine Wave Slider')


# In[48]:




# YouTubeVideo("wupToqz1e2g")
# # YouTubeVideo('')


# In[49]:





# display(Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx'))


# In[50]:


# IFrame(src= "https://dash-simple-apps.plotly.host/dash-tableplot/", width="100%", height="850px", frameBorder="0")


# In[51]:



# IFrame(src= "https://dash-simple-apps.plotly.host/dash-tableplot/code", width="100%", height=500, frameBorder="0")


# In[52]:


# toalOfMonth = pd.read_csv('toalOfMonth.csv')

# branchTot = []
# for col in numListOfBranch:
#     branchTot.append(toalOfMonth[col].sum())
    
# np.random.seed(42)
# random_x = np.random.randint(1,101,100)
# random_y = np.random.randint(1,101,100)


# In[53]:


data = [go.Box(y = branchTot, name = 'sale distribution',
               marker = dict(
                   size = 12,
                   color = 'rgb(120,56,98)',
                   symbol = 'pentagon',
                   line = {'width':2}
               ),
               boxmean=True, boxpoints = 'all')]
# boxmean = 'sd'
# boxpoints = 'outliers'
# boxpoints = 'suspectedoutliers
# boxpoints = False

layout = go.Layout(
    title = "sale distribution",
    xaxis = {'title':''},
    yaxis = dict(title = 'sale values'),
    hovermode = 'closest'
)

fig = go.Figure(data=data,layout=layout)
plot(fig, filename = "sale distribution of each branch")


# In[54]:


data = [go.Bar(x = numListOfBranch, y = branchTot,
               marker = dict(
                   color = 'rgb(18,95,200)',
                   line = {'width':2}
               ))]

layout = go.Layout(
    title = "sale distribution",
    xaxis = {'title':'Branches'},
    yaxis = dict(title = 'sale values'),
    hovermode = 'closest'
    
)

fig = go.Figure(data = data, layout = layout)

plot(fig, filename = 'sale amount of each branch')


# In[56]:


# IFrame(src= "https://dash-simple-apps.plotly.host/dash-boxplot/",
#        width="100%", height="650px", frameBorder="0")


# In[ ]: