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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| #st.set_page_config(layout="wide") | |
| df = pd.read_csv('last_results_5.csv') | |
| temp_data = pd.read_csv('temp_data(2).csv') | |
| 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)] | |
| #col1, col2, col3 = st.columns(3) # This divides the page into three equal parts | |
| # Set custom width for columns | |
| col1_width = 400 | |
| col2_width = 400 | |
| col1, col2 = st.columns([col1_width, col2_width]) | |
| # Display the Forecasts values in the first column | |
| col1.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') | |
| # Iterate through the lines and extract the values | |
| for line in lines[:-2]: | |
| period, value = line.split() | |
| num_float = float(value) | |
| monetary_value = f'R$ {num_float:,.2f}' # Adding commas for thousands separator | |
| col1.write(f"Período {period}: {monetary_value}") | |
| else: | |
| col1.warning('No data available for the selected filters.') | |
| # Display the Forecasts values as line plots in the second column | |
| col2.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') | |
| # Iterate through the lines and extract the values | |
| for line in lines[:-2]: | |
| period, value = line.split() | |
| num_float = float(value) | |
| monetary_value = f'R$ {num_float:,.2f}' # Adding commas for thousands separator | |
| data.append({'Period': int(period), '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 | |
| col2.line_chart(chart_data.set_index('Period')) | |
| else: | |
| col2.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 | |
| #col3.header('Resultados') | |
| 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] | |
| # 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, 'Valor Monetário': monetary_value}) | |
| # Convert the list to a DataFrame | |
| table_data = pd.DataFrame(data) | |
| # Calculate the grand total sum of all 'Conta' values | |
| total_sum = sum(float(row['Valor Monetário'].replace('R$ ', '').replace(',', '')) for row in data) | |
| # Append the "Total" row | |
| total_row = pd.DataFrame({'Conta': ['TOTAL (RLIT)'], 'Modelo': [''], 'Valor Monetário': [f'R$ {total_sum:,.2f}']}) | |
| table_data = pd.concat([table_data, total_row], ignore_index=True) | |
| # Calculate and append the rows for "Saúde (12% da RLIT)" and "Educação (25% da RLIT)" | |
| saude_value = total_sum * 0.15 | |
| educacao_value = total_sum * 0.25 | |
| saude_row = pd.DataFrame({'Conta': ['Saúde (15% da RLIT)'], 'Modelo': [''], 'Valor Monetário': [f'R$ {saude_value:,.2f}']}) | |
| educacao_row = pd.DataFrame({'Conta': ['Educação (25% da RLIT)'], 'Modelo': [''], 'Valor Monetário': [f'R$ {educacao_value:,.2f}']}) | |
| # Append these rows to the table data | |
| table_data = pd.concat([table_data, saude_row, educacao_row], ignore_index=True) | |
| # Convert 'Data_Completa' to datetime format to ensure correct processing | |
| temp_data['Data_Completa'] = pd.to_datetime(temp_data['Data_Completa']) | |
| # Assuming 'Data_Completa' is sorted, if not, you should sort it. | |
| # temp_data = temp_data.sort_values(by='Data_Completa', ascending=False) | |
| # Initialize an empty list to store data including the 'Último ano' sums | |
| data_with_last_year = [] | |
| # Iterate over each unique 'Instituição' and 'Conta' combination in 'df' | |
| for instituicao in df['Instituição'].unique(): | |
| for conta in df[df['Instituição'] == instituicao]['Conta'].unique(): | |
| # Filter temp_data for the current 'Instituição' and 'Conta' | |
| filtered_temp = temp_data[(temp_data['Instituição'] == instituicao) & (temp_data['Conta'] == conta)] | |
| # Get the last 12 periods of 'Data_Completa' | |
| last_12_periods = filtered_temp.nlargest(12, 'Data_Completa') | |
| # Calculate the sum of 'Valor' for these periods | |
| last_year_sum = last_12_periods['Valor'].sum() | |
| # Append this information to the data list | |
| data_with_last_year.append({ | |
| 'Instituição': instituicao, | |
| 'Conta': conta, | |
| 'Último ano': last_year_sum | |
| }) | |
| # Convert the list to a DataFrame | |
| last_year_data = pd.DataFrame(data_with_last_year) | |
| # Merge this DataFrame with your existing table data to add the 'Último ano' column | |
| # Assuming 'table_data' is your existing DataFrame that you want to add the column to | |
| # You might need to adjust column names or merge keys based on your actual data structure | |
| table_data = table_data.merge(last_year_data, on=['Instituição', 'Conta'], how='left') | |
| # Display the table | |
| st.table(table_data) | |
| else: | |
| col3.warning('No data available for the selected filters.') | |
| 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) |