import streamlit as st import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets import warnings warnings.filterwarnings("ignore") import datetime from datetime import date import seaborn as sns import calendar import matplotlib.dates as mdates import plotly.express as px ####### Load Dataset ##################### data_value = pd.read_excel(r'C:\Users\stani\Desktop\Webapp\st_zagora\data_for_python2.xlsx') df_value = pd.DataFrame(data_value) #df['data'] = pd.to_datetime(df['data']).dt.date df_value['data'] = pd.to_datetime(df_value.data, format='%Y-%m-%d') df_value['year'] = pd.DatetimeIndex(df_value['data']).year #print(df['year'].unique()) year_float=df_value['year'].unique() df_value['quarter'] = df_value['data'].dt.quarter quarter= df_value['quarter'] year=list(map(str,year_float)) st.set_page_config(layout="wide") st.markdown("## Finance Performance Indicators in Dairy Farm") ## Main Title st.markdown("## The results for choiced period for criteries are:") #df['quarter'] = df['data'].dt.to_period('Q') #st.write(df['quarter']) ################# Scatter Chart Logic ################# st.sidebar.markdown("### Chooice year :") axis_year = st.sidebar.multiselect(label ="Chooice year", options=year, default=['2019', '2020', '2021']) choice_value = list(map(int, axis_year)) ################# Histogram Logic ######################## st.sidebar.markdown("### Chooice quater :") axis_quater = st.sidebar.multiselect( 'Chooice quarter', ['Quarter 1', 'Quarter 2', 'Quarter 3', 'Quarter 4'], default= ['Quarter 1', 'Quarter 2', 'Quarter 3', 'Quarter 4']) i = 0 while i < len(axis_quater): if axis_quater[i] == 'Quarter 1': axis_quater[i] = 1 # replace pant with ishan if axis_quater[i] == 'Quarter 2': axis_quater[i] = 2 if axis_quater[i] == 'Quarter 3': axis_quater[i] = 3 if axis_quater[i] == 'Quarter 4': axis_quater[i] = 4 i += 1 choice_quater=axis_quater df1_value=df_value[df_value['year'].isin(choice_value) & df_value['quarter'].isin(choice_quater)] #st.write(df1_value) #production_cost_for_period=df1_value.groupby(df1_value.data.dt.year)['production cost'].sum() #litter_period=df1_value.groupby(df1_value.data.dt.year)['litters'].sum() production_cost_for_period=df1_value['production cost'].sum() litter_period=df1_value['litters'].sum() production_cost_per_litter=round(production_cost_for_period/litter_period,2) cost_forage=df1_value['forage'].sum() income_milk=df1_value['income milk'].sum() ratio_milk_forage=income_milk/cost_forage total_income_sum = df1_value['total_income'].sum() month_flow_sum = df1_value['month_flow'].sum() ratio_profit_revenue = (month_flow_sum/total_income_sum) ratio_profit_revenue = round(ratio_profit_revenue) profit_month_sum = df1_value['profit_month'].sum() ratio_profit_litter=round(profit_month_sum/litter_period,2) #st.write(ratio_profit_litter) data=pd.read_excel(r'C:\Users\stani\Desktop\Webapp\st_zagora\criteria1.xlsx') df = pd.DataFrame(data) #data_for_table = [['Production cost per litter', production_cost_per_litter], ['Ratio Milk-Forage', ratio_milk_forage], ['Ratio Profit-Revenue', ratio_profit_revenue], ['Profit per Litter', ratio_profit_litter]] #data_for_table = [[production_cost_per_litter], [ratio_milk_forage], [ratio_profit_revenue], [ratio_profit_litter]] # Create the pandas DataFrame #df_table = pd.DataFrame(data_for_table, columns=['Criteria', 'Value'], index=['Production cost per litter', 'Ratio Milk-Forage', 'Ratio Profit-Revenue', 'Profit per Litter', ]) d = {'Criteria': pd.Series(['Production cost per litter', 'Ratio Milk-Forage', 'Ratio Profit-Revenue', 'Profit per Litter'], index=['Production cost per litter', 'Ratio Milk-Forage', 'Ratio Profit-Revenue', 'Profit per Litter']), 'Value': pd.Series([production_cost_per_litter, ratio_milk_forage, ratio_profit_revenue, ratio_profit_litter], index=['Production cost per litter', 'Ratio Milk-Forage', 'Ratio Profit-Revenue', 'Profit per Litter'])} df_table = pd.DataFrame(d) blankIndex=[''] * len(df_table) df_table.index=blankIndex # Using DataFrame.to_string() to print without index #df_table2 = df_table.to_string(index=False) st.table(df_table) index=df.columns loss = df.iloc[0] zero = df.iloc[1] low = df.iloc[2] average = df.iloc[3] high = df.iloc[4] N = 1 ind = np.arange(N) # the x locations for the groups width = 0.35 # the width of the bars: can also be len(x) sequence #st.write(df_value) df2 = pd.DataFrame({"low": low, "average": average, "high": high}, index=index) axis_criteria = st.sidebar.multiselect( 'Chooice criteria', ['production_cost', 'milk_forage', 'profit_revenue', 'profit_litter'], default= ['production_cost', 'milk_forage', 'profit_revenue', 'profit_litter']) i = 0 container1 = st.container() col1, col2 = st.columns(2) container2 = st.container() col3, col4 = st.columns(2) while i < len(axis_criteria): with container1: with col1: #if((axis_criteria[i] == 'production_cost') & (axis_criteria[i] == 'milk_forage')) if axis_criteria[i] == 'production_cost': axis_criteria[i] = 'production_cost' bar_fig1 = plt.figure(figsize=(6,4)) bar_ax1 = bar_fig1.add_subplot(111) y1list = df["production_cost"].tolist() plt.savefig("output1.jpg") bar_ax1.bar(ind, y1list[2], width, color='green', align='center', label='Low') bar_ax1.bar(ind, y1list[3], width, color='orange', bottom=y1list[2], align='center', label='Average') bar_ax1.bar(ind, y1list[4], width, color='red', bottom=y1list[2]+y1list[3], align='center', label='High') #st.write(y1list) production_cost_per_litter = production_cost_per_litter.astype(float) bar_ax1.plot(production_cost_per_litter, 'o-', lw=2, color='k', label='Calculate value') bar_ax1.set_ylabel('Lev') bar_ax1.set_title('Production cost per litter milk') bar_ax1.set_xticks(ind) bar_ax1.set_xlabel('Production cost') bar_ax1.legend() plt.show() st.pyplot(bar_fig1) # replace pant with ishan with col2: if axis_criteria[i] == 'milk_forage': axis_criteria[i] = 'milk_forage' bar_fig2 = plt.figure(figsize=(6,4)) bar_ax2 = bar_fig2.add_subplot(111) y2list = df["milk_forage"].tolist() #st.write(y2list) plt.savefig("output2.jpg") bar_ax2.bar(ind, y2list[2], width, color='r', align='center', label='Low') bar_ax2.bar(ind, y2list[3], width, color='orange', bottom=y2list[2], align='center', label='Average') bar_ax2.bar(ind, y2list[4], width, color='g', bottom=y2list[2]+y2list[3], align='center', label='High') #production_cost_per_litter = production_cost_per_litter.astype(float) bar_ax2.plot(ratio_milk_forage, 'o-', lw=2, color='k', label='Calculate value') bar_ax2.set_ylabel('Ratio') bar_ax2.set_title('Ratio Milk-Forage') bar_ax2.set_xticks(ind) bar_ax2.set_xlabel('Milk Forage') bar_ax2.legend() plt.show() st.pyplot(bar_fig2) #y2 = df2.loc[["milk_forage"]] #y2.plot.bar(stacked=True).figure, plt.xticks(rotation=0) #st.write(milk_forage) with container2: with col3: if axis_criteria[i] == 'profit_revenue': axis_criteria[i] = 'profit_revenue' bar_fig3 = plt.figure(figsize=(6,4)) bar_ax3 = bar_fig3.add_subplot(111) #y3 = df2.loc[["profit_revenue"]] #y3.plot.bar(stacked=True).figure, plt.xticks(rotation=0) # profit_revenue= df['profit_revenue'] y3list = df["profit_revenue"].tolist() #st.write(y3list) plt.savefig("output3.jpg") bar_ax3.bar(ind, y3list[0], width, color='r', align='center') bar_ax3.bar(ind, y3list[1], width, color='r', bottom=y3list[0], align='center', label='Loss') bar_ax3.bar(ind, y3list[2], width, color='orange', bottom=y3list[0]+y3list[1], align='center', label='Low') bar_ax3.bar(ind, y3list[3], width, color='yellow', bottom=y3list[0]+y3list[1]+y3list[2], align='center', label='Average') bar_ax3.bar(ind, y3list[4], width, color='green', bottom=y3list[0]+y3list[1]+y3list[2]+y3list[3], align='center', label='High') #production_cost_per_litter = production_cost_per_litter.astype(float) bar_ax3.plot(ratio_profit_revenue, 'o-', lw=2, color='k', label='Calculate value') bar_ax3.set_ylabel('Percent') bar_ax3.set_title('Ratio Profit-Revenue') bar_ax3.set_xticks(ind) bar_ax3.set_xlabel('Profit Revenue') bar_ax3.legend() plt.show() st.pyplot(bar_fig3) with col4: if axis_criteria[i] == 'profit_litter': axis_criteria[i] = 'profit_litter' bar_fig4 = plt.figure(figsize=(6,4)) bar_ax4 = bar_fig4.add_subplot(111) y4list = df["profit_litter"].tolist() #st.write(y4list) plt.savefig("output4.jpg") bar_ax4.bar(ind, y4list[0], width, color='r', align='center') bar_ax4.bar(ind, y4list[1], width, color='r', bottom=y4list[0], align='center', label='Loss') bar_ax4.bar(ind, y4list[2], width, color='orange', bottom=y4list[0]+y4list[1], align='center', label='Low') bar_ax4.bar(ind, y4list[3], width, color='yellow', bottom=y4list[0]+y4list[1]+y4list[2], align='center', label='Average') bar_ax4.bar(ind, y4list[4], width, color='green', bottom=y4list[0]+y4list[1]+y4list[2]+y4list[3], align='center', label='High') #production_cost_per_litter = production_cost_per_litter.astype(float) bar_ax4.plot(ratio_profit_litter, 'o-', lw=2, color='k', label='Calculate value') bar_ax4.set_ylabel('Lev') bar_ax4.set_title('Profit per Litter') bar_ax4.set_xticks(ind) bar_ax4.set_xlabel('Profit per Litter') bar_ax4.legend() plt.show() st.pyplot(bar_fig4) #y4 = df2.loc[["profit_litter"]] #y4.plot.bar(stacked=True).figure, plt.xticks(rotation=0) # profit_litter= df['profit_litter'] i += 1 #df3 = df2.loc[["production_cost", "milk_forage"]] #choice_criteria=axis_criteria #df3 = df2.loc[choice_criteria] #st.write(choice_criteria) #for i in choice_criteria: # if(i == "production_cost"): ##bar_fig1 # with col2: #elif(i == "milk_forage"): #elif(i == "profit_revenue"): #else: #ax1=y1.plot.bar(stacked=True).figure, plt.xticks(rotation=0) #st.pyplot(y1.plot.bar(stacked=True).figure, plt.xticks(rotation=0)) #ax2=y2.plot.bar(stacked=True).figure, plt.xticks(rotation=0) #y1.plot.bar(stacked=True).figure, plt.xticks(rotation=0) #st.bar_chart(y1) #y2.plot.bar(stacked=True).figure, plt.xticks(rotation=0) #st.bar_chart(y2) #plt.show() #st.pyplot.title('Income from milk in farm', fontsize=25) #st.pyplot(ax1) #st.pyplot(fig, ax1, ax2) #fig, ax = plt.subplots(figsize=(15,8)) #ax.plot(ax.get_xticks(),#profit_per_litter, # linestyle='-', # marker='o', linewidth=2.0) #st.pyplot(fig) #st.write(df1) #plt.bar(x_axis_name, percent_Medicines_costs, color='r') # y1.plot.bar(stacked=True).figure, plt.xticks(rotation=0) # bar_fig1 # bar_fig2 # y2.plot.bar(stacked=True).figure, plt.xticks(rotation=0)