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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from datetime import datetime, timedelta | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| st.set_page_config(layout="wide") | |
| df = pd.read_csv('last_results_8.csv') | |
| temp_data = pd.read_csv('temp_data(2).csv') | |
| temp_data['Data_Completa'] = pd.to_datetime(temp_data['Data_Completa']) | |
| temp_data.sort_values(['Instituição', 'Conta', 'Data_Completa'], inplace=True) | |
| temp_data['Últimos 12 meses'] = temp_data.groupby(['Instituição', 'Conta'])['Valor'].transform(lambda x: x.rolling(window=12, min_periods=1).sum()) | |
| last_dates = temp_data.groupby(['Instituição', 'Conta'])['Data_Completa'].transform(max) | |
| last_rows = temp_data[temp_data['Data_Completa'] == last_dates] | |
| ultimo_ano = last_rows[['Instituição', 'Conta', 'Últimos 12 meses']] | |
| image1 = 'images/rs_pmpa.PNG' | |
| title_html = """ | |
| <style> | |
| @font-face { | |
| font-family: 'Quicksand'; | |
| src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype'); | |
| } | |
| body { | |
| font-family: 'Quicksand', sans-serif; | |
| } | |
| .custom-title { | |
| color: darkgreen; | |
| font-size: 30px; | |
| font-weight: bold; | |
| } | |
| </style> | |
| <span class='custom-title'>PREVISÕES DE RECEITAS</span> | |
| """ | |
| # Set a fixed width for the sidebar | |
| st.markdown( | |
| """ | |
| <style> | |
| .sidebar .sidebar-content { | |
| width: 300px; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| with st.sidebar: | |
| st.image(image1, use_column_width=True) | |
| st.markdown(title_html, unsafe_allow_html=True) | |
| selected_instituicao = st.selectbox('Seleciona Instituição', df['Instituição'].unique()) | |
| selected_conta = st.selectbox('Seleciona Conta', df['Conta'].unique()) | |
| # Filter the DataFrame based on selected values | |
| #filtered_df = df[(df['Instituição'] == selected_instituicao) & (df['Conta'] == selected_conta)] | |
| # Initial filtering based on selected 'Instituição' | |
| instituicao_df = df[df['Instituição'] == selected_instituicao] | |
| # Container for adjusted DataFrame rows | |
| adjusted_rows = [] | |
| # Iterate through each unique 'Conta' within the selected 'Instituição' | |
| for conta in instituicao_df['Conta'].unique(): | |
| conta_df = instituicao_df[instituicao_df['Conta'] == selected_conta] | |
| # Check if 'Linear Regression' is available for this 'Conta' | |
| if len(conta_df['Modelo'].unique()) > 1 and "Linear Regression" in conta_df['Modelo'].unique(): | |
| lr_rows = conta_df[conta_df['Modelo'] == 'Linear Regression'] | |
| adjusted_rows.append(lr_rows) | |
| else: | |
| # If not, include all models' results for this 'Conta' | |
| adjusted_rows.append(conta_df) | |
| # Combine all adjusted rows back into a single DataFrame | |
| filtered_df = pd.concat(adjusted_rows) | |
| #col1, col2, col3 = st.columns(3) # This divides the page into three equal parts | |
| # Set custom width for columns | |
| col1_width = 500 | |
| col2_width = 500 | |
| col3_width = 500 | |
| col1, col2, col3 = st.columns([col1_width, col2_width, col3_width]) | |
| # Display the Forecasts values in the first column | |
| col1.header('Composição da RLIT') | |
| tab_df = df[df['Instituição'] == selected_instituicao] | |
| data = [] | |
| for conta in tab_df['Conta'].unique(): | |
| # Filter the DataFrame for the current 'Conta' | |
| conta_df = tab_df[tab_df['Conta'] == conta] | |
| if len(conta_df['Modelo'].unique()) > 1 and "Linear Regression" in conta_df['Modelo'].unique(): | |
| conta_df = conta_df[conta_df['Modelo'] == "Linear Regression"] | |
| # Initialize a variable to store the sum for the current 'Conta' | |
| conta_sum = 0.0 | |
| # Take the first 'Modelo' for simplicity | |
| modelo = conta_df['Modelo'].iloc[0] | |
| # Iterate over each row in the filtered DataFrame for the current 'Conta' | |
| for _, row in conta_df.iterrows(): | |
| lines = row['Forecasts'].split('\n') | |
| for line in lines[:-1]: # Skip the summary line | |
| if line.strip(): | |
| parts = line.split() | |
| value = parts[-1] | |
| try: | |
| conta_sum += float(value) | |
| except ValueError: | |
| print(f"Skipping line unable to convert to float: {line}") | |
| # Append the data to the list | |
| data.append({'Conta': conta, 'Modelo': modelo, 'Próximos 12 meses': conta_sum}) | |
| # Convert the list to a DataFrame | |
| table_data = pd.DataFrame(data) | |
| total_sum = table_data['Próximos 12 meses'].sum() | |
| labels = table_data['Conta'] | |
| sizes = [(i / total_sum) * 100 for i in table_data['Próximos 12 meses']] | |
| fig1, ax1 = plt.subplots() | |
| ax1.pie(sizes, labels=labels, autopct='%1.1f%%',) | |
| ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle. | |
| col1.pyplot(fig1) | |
| # Display the Forecasts values in the first column | |
| col2.header('Valores previstos') | |
| if not filtered_df.empty: | |
| data_string = filtered_df['Forecasts'].iloc[0] | |
| # Split the string into lines | |
| lines = data_string.split('\n') | |
| mes = 0 | |
| # Iterate through the lines and extract the values | |
| for line in lines[:-1]: # Skip the last two lines which might not contain forecast data | |
| period, value = line.split() | |
| num_float = float(value) | |
| monetary_value = f'R$ {num_float:,.2f}' # Adding commas for thousands separator | |
| mes += 1 | |
| col2.write(f"Mês {mes}: {monetary_value}") | |
| else: | |
| col2.warning('No data available for the selected filters.') | |
| # Display the Forecasts values as line plots in the second column | |
| col3.header('Gráfico com previsões') | |
| if not filtered_df.empty: | |
| data_string = filtered_df['Forecasts'].iloc[0] | |
| # Create a list to store data for each period | |
| data = [] | |
| # Split the string into lines | |
| lines = data_string.split('\n') | |
| mes = 0 | |
| # Iterate through the lines and extract the values | |
| for line in lines[:-1]: | |
| period, value = line.split() | |
| num_float = float(value) | |
| monetary_value = f'R$ {num_float:,.2f}' # Adding commas for thousands separator | |
| mes += 1 | |
| data.append({'Period': int(mes), 'Monetary Value': num_float}) | |
| # Create a DataFrame from the list | |
| chart_data = pd.DataFrame(data) | |
| # Sort the DataFrame by 'Period' | |
| chart_data = chart_data.sort_values(by='Period') | |
| # Display line chart with "period" on X-axis and "Monetary Value" on Y-axis | |
| col3.line_chart(chart_data.set_index('Period')) | |
| else: | |
| col3.warning('No data available for the selected filters.') | |
| # Display the table in the third column | |
| #col3 = st.columns(1) # You can use st.columns(1) to create a single column layout | |
| col4_width = 750 | |
| col5_width = 750 | |
| col4, col5 = st.columns([col4_width, col5_width]) | |
| if not filtered_df.empty: | |
| # Filter the DataFrame for the selected institution | |
| tab_df = df[df['Instituição'] == selected_instituicao] | |
| # Create an empty list to store data | |
| data = [] | |
| # Iterate through each unique 'Conta' in the filtered DataFrame | |
| for conta in tab_df['Conta'].unique(): | |
| # Filter the DataFrame for the current 'Conta' | |
| conta_df = tab_df[tab_df['Conta'] == conta] | |
| if len(conta_df['Modelo'].unique()) > 1 and "Linear Regression" in conta_df['Modelo'].unique(): | |
| conta_df = conta_df[conta_df['Modelo'] == "Linear Regression"] | |
| # Initialize a variable to store the sum for the current 'Conta' | |
| conta_sum = 0.0 | |
| # Take the first 'Modelo' for simplicity | |
| modelo = conta_df['Modelo'].iloc[0] | |
| # Iterate over each row in the filtered DataFrame for the current 'Conta' | |
| for _, row in conta_df.iterrows(): | |
| lines = row['Forecasts'].split('\n') | |
| for line in lines[:-1]: # Skip the summary line | |
| if line.strip(): | |
| parts = line.split() | |
| value = parts[-1] | |
| try: | |
| conta_sum += float(value) | |
| except ValueError: | |
| print(f"Skipping line unable to convert to float: {line}") | |
| # Format the sum as a monetary value | |
| monetary_value = f'R$ {conta_sum:,.2f}' | |
| # Append the data to the list | |
| data.append({'Conta': conta, 'Modelo': modelo, 'Próximos 12 meses': monetary_value}) | |
| # Convert the list to a DataFrame | |
| table_data = pd.DataFrame(data) | |
| last_df = ultimo_ano[ultimo_ano['Instituição'] == selected_instituicao] | |
| last_df.drop(['Instituição'], axis=1, inplace=True) | |
| print(last_df) | |
| last_sum = last_df.iloc[:,-1].sum() | |
| def format_currency(x): | |
| return "R${:,.2f}".format(x) | |
| last_df['Últimos 12 meses'] = last_df['Últimos 12 meses'].apply(format_currency) | |
| table_data = pd.merge(table_data, last_df) | |
| print(table_data) | |
| try: | |
| # Calculate the grand total sum of 'Próximos 12 meses' and 'Últimos 12 meses' values | |
| total_sum = sum(float(row['Próximos 12 meses'].replace('R$ ', '').replace(',', '')) for row in data) | |
| total_sum_prev = last_sum | |
| # Append the "Total" row | |
| total_row = pd.DataFrame({ | |
| 'Conta': ['TOTAL (RLIT)'], | |
| 'Modelo': [''], | |
| 'Próximos 12 meses': [f'R$ {total_sum:,.2f}'], | |
| 'Últimos 12 meses': [f'R$ {total_sum_prev:,.2f}'] | |
| }) | |
| table_data = pd.concat([table_data, total_row], ignore_index=True) | |
| # Additional rows calculations and appending | |
| # Assuming percentages for health and education as previously mentioned | |
| saude_value = total_sum * 0.15 | |
| educacao_value = total_sum * 0.25 | |
| saude_value_prev = total_sum_prev * 0.15 | |
| educacao_value_prev = total_sum_prev * 0.25 | |
| saude_row = pd.DataFrame({'Conta': ['Saúde (15% da RLIT)'], 'Modelo': [''], 'Próximos 12 meses': [f'R$ {saude_value:,.2f}'], 'Últimos 12 meses': [f'R$ {saude_value_prev:,.2f}']}) | |
| educacao_row = pd.DataFrame({'Conta': ['Educação (25% da RLIT)'], 'Modelo': [''], 'Próximos 12 meses': [f'R$ {educacao_value:,.2f}'], 'Últimos 12 meses': [f'R$ {educacao_value_prev:,.2f}']}) | |
| # Append these rows to the table data | |
| table_data = pd.concat([table_data, saude_row, educacao_row], ignore_index=True) | |
| table_data.fillna('-', inplace=True) | |
| # Display the table using Streamlit | |
| st.table(table_data) | |
| except Exception as e: | |
| st.error(f"Error in processing data: {str(e)}") | |
| else: | |
| col4.warning('No data available for the selected filters.') | |
| col5.header('Realizado X Previsto') | |
| for conta in tab_df['Conta'].unique(): | |
| # Filter the DataFrame for the current 'Conta' | |
| conta_df = tab_df[tab_df['Conta'] == conta] | |
| if len(conta_df['Modelo'].unique()) > 1 and "Linear Regression" in conta_df['Modelo'].unique(): | |
| conta_df = conta_df[conta_df['Modelo'] == "Linear Regression"] | |
| # Initialize a variable to store the sum for the current 'Conta' | |
| conta_sum = 0.0 | |
| # Take the first 'Modelo' for simplicity | |
| modelo = conta_df['Modelo'].iloc[0] | |
| # Iterate over each row in the filtered DataFrame for the current 'Conta' | |
| for _, row in conta_df.iterrows(): | |
| lines = row['Forecasts'].split('\n') | |
| for line in lines[:-1]: # Skip the summary line | |
| if line.strip(): | |
| parts = line.split() | |
| value = parts[-1] | |
| try: | |
| conta_sum += float(value) | |
| except ValueError: | |
| print(f"Skipping line unable to convert to float: {line}") | |
| # Append the data to the list | |
| data.append({'Conta': conta, 'Modelo': modelo, 'Próximos 12 meses': conta_sum}) | |
| last_df = ultimo_ano[ultimo_ano['Instituição'] == selected_instituicao] | |
| last_df.drop(['Instituição'], axis=1, inplace=True) | |
| print(last_df) | |
| last_sum = last_df.iloc[:,-1].sum() | |
| total_sum = sum(float(row['Próximos 12 meses'].replace('R$ ', '').replace(',', '')) for row in data) | |
| total_sum_prev = last_sum | |
| saude_value = total_sum * 0.15 | |
| educacao_value = total_sum * 0.25 | |
| saude_value_prev = total_sum_prev * 0.15 | |
| educacao_value_prev = total_sum_prev * 0.25 | |
| data = { | |
| "Últimos 12 meses": [saude_value_prev, educacao_value_prev], # Placeholder data for 'Last 12 Months' | |
| "Próximos 12 meses": [saude_value, educacao_value] # Placeholder data for 'Next 12 Months' | |
| } | |
| # Define the index names | |
| index_names = ["Saúde", "Educação"] # 'Health' and 'Education' | |
| df = pd.DataFrame(data, index=index_names).reset_index().melt(id_vars='index', var_name='Period', value_name='Value') | |
| # Create the bar chart | |
| fig = px.bar(df, x='index', y='Value', color='Period', barmode='group') | |
| col5.write(fig) | |
| st.markdown(""" | |
| <b>Observação:</b> Previsões realizadas com dados extraídos do Relatório Resumido de Execução Orçamentária (RREO) até o 6º bimestre de 2023 no Sistema de Informações Contábeis e Fiscais do Setor Público Brasileiro (SICONFI). | |
| [Link](https://siconfi.tesouro.gov.br/) | |
| """, unsafe_allow_html=True) |