"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as go\n",
"\n",
"# Group the DataFrame by 'Año' and 'Orientación' and sum the 'NoAnimales' column (divided by 1 million)\n",
"grouped_df = df.groupby(['Año', 'Orientación'])['NoAnimales'].sum() / 1000000\n",
"\n",
"# Get unique years and orientations for the x-axis\n",
"years = df['Año'].unique()\n",
"orientations = df['Orientación'].unique()\n",
"\n",
"# Define custom colors for each orientation\n",
"color_palette = {\n",
" 'Carne': 'rgb(31, 119, 180)',\n",
" 'Doble Utilidad': 'rgb(255, 127, 14)',\n",
" 'Leche': 'rgb(44, 160, 44)'\n",
"}\n",
"\n",
"# Create a bar chart with grouped bars and custom colors\n",
"data = []\n",
"for orientation in orientations:\n",
" y_values = [grouped_df.loc[(year, orientation)] if (year, orientation) in grouped_df.index else 0 for year in years]\n",
" color = color_palette.get(orientation, 'rgb(0, 0, 0)') # Use custom color if available, else default to black\n",
" data.append(go.Bar(name=orientation, x=years, y=y_values, marker=dict(color=color)))\n",
"\n",
"# Set the layout of the chart\n",
"layout = go.Layout(title='Inventario bovino en Córdoba 2004-2009 por año', \n",
" title_x=0.5, #centrar el titulo\n",
" xaxis=dict(title='Year'),\n",
" yaxis=dict(title='NoAnimales (en millones)'))\n",
"\n",
"# Create the figure\n",
"fig = go.Figure(data=data, layout=layout)\n",
"\n",
"# Display the chart\n",
"fig.show()\n",
"\n",
"# Saving the interactive pie chart to a file\n",
"fig.write_html('inventario-bovino-cord-2004-2009.html')\n"
]
},
{
"cell_type": "markdown",
"id": "28613879",
"metadata": {},
"source": [
"# Pivot Table para productos agrícolas"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ea8d75a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Campos disponibles:\n",
"Index(['Año', 'Municipio', 'Area Sembrada', 'Area Cosechada',\n",
" 'Produccion (ton)', 'Rendimiento (ha/ton)', 'Producto'],\n",
" dtype='object')\n",
"Tabla de pivote:\n",
"Produccion (ton) 0.00 0.75 1.00 2.00 2.40 2.85 \n",
"Municipio \n",
"CHIMA 0.0 NaN NaN NaN NaN NaN \\\n",
"COTORRA 0.0 NaN NaN NaN NaN NaN \n",
"LORICA 0.0 NaN NaN NaN NaN NaN \n",
"MOMIL 0.0 0.75 1.066667 0.4 4.0 3.8 \n",
"PURISIMA 0.0 NaN NaN NaN NaN NaN \n",
"\n",
"Produccion (ton) 3.00 4.50 4.90 6.00 ... 22100.00 \n",
"Municipio ... \n",
"CHIMA NaN NaN NaN NaN ... NaN \\\n",
"COTORRA NaN NaN NaN NaN ... 4.73 \n",
"LORICA NaN NaN NaN NaN ... NaN \n",
"MOMIL 2.333333 1.5 0.54 2.0 ... NaN \n",
"PURISIMA NaN NaN NaN NaN ... NaN \n",
"\n",
"Produccion (ton) 22500.00 22550.00 25300.00 29800.00 30000.00 33359.20 \n",
"Municipio \n",
"CHIMA NaN 5.5 5.59 NaN NaN NaN \\\n",
"COTORRA NaN NaN NaN NaN NaN 4.6 \n",
"LORICA 25.0 NaN NaN 10.0 12.0 NaN \n",
"MOMIL NaN NaN NaN NaN NaN NaN \n",
"PURISIMA NaN NaN NaN NaN NaN NaN \n",
"\n",
"Produccion (ton) 34128.00 34200.00 52392.00 \n",
"Municipio \n",
"CHIMA NaN NaN NaN \n",
"COTORRA NaN NaN NaN \n",
"LORICA 12.0 12.0 12.0 \n",
"MOMIL NaN NaN NaN \n",
"PURISIMA NaN NaN NaN \n",
"\n",
"[5 rows x 515 columns]\n"
]
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Mostrar los campos disponibles\n",
"fields = data.columns\n",
"print(\"Campos disponibles:\")\n",
"print(fields)\n",
"\n",
"# Elegir los campos a incluir en la tabla y la gráfica\n",
"selected_fields = [\"Municipio\", \"Produccion (ton)\", \"Rendimiento (ha/ton)\"]\n",
"\n",
"# Filtrar el DataFrame con los campos seleccionados\n",
"filtered_data = data[selected_fields]\n",
"\n",
"# Crear la tabla de pivote\n",
"pivot_table = pd.pivot_table(filtered_data, values=\"Produccion (ton)\", index=\"Municipio\", columns=\"Produccion (ton)\")\n",
"\n",
"# Mostrar la tabla de pivote\n",
"print(\"Tabla de pivote:\")\n",
"print(pivot_table)\n",
"\n",
"# Crear la gráfica\n",
"chart_type = \"bar\" # Tipo de gráfica, puedes elegir entre \"bar\", \"line\", \"scatter\", etc.\n",
"chart_title = \"Gráfica de Producción por Rendimiento\" # Título de la gráfica\n",
"colors = [\"red\", \"green\", \"blue\", \"orange\"] # Colores para las barras\n",
"\n",
"try:\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), color=colors)\n",
" plt.title(chart_title)\n",
" plt.xlabel(\"Municipio\")\n",
" plt.ylabel(\"Producción (ton)\")\n",
" plt.show()\n",
"except ValueError as e:\n",
" print(f\"No se pudo generar la gráfica. Error: {str(e)}\")\n"
]
},
{
"cell_type": "markdown",
"id": "9020f17b",
"metadata": {},
"source": [
"## Visualización con listas desplegables en Ipython"
]
},
{
"cell_type": "markdown",
"id": "ca52fe5c",
"metadata": {},
"source": [
"## Versión 2 por verificar"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2009eb1f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Campos disponibles:\n",
"Index(['Año', 'Municipio', 'Area Sembrada', 'Area Cosechada',\n",
" 'Produccion (ton)', 'Rendimiento (ha/ton)', 'Producto'],\n",
" dtype='object')\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c87c7fd03d7d4a5a8e4ac7bb40f2678f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable 1:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccio…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5acafeb5148d4335926990e155ff3573",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable 2:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccio…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c372ea1fd40d4f5f90692a4396953583",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Tipo de gráfico:', options=('bar', 'line', 'scatter', 'area', 'pie', 'histogram', 'box',…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a3a6871ff31d4d67bcb33ede971455b7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Valores:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccion (…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" # Agregar una columna \"Producto\" con el nombre del archivo sin la extensión\n",
" df[\"Producto\"] = os.path.splitext(file)[0]\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Mostrar los campos disponibles\n",
"fields = data.columns\n",
"print(\"Campos disponibles:\")\n",
"print(fields)\n",
"\n",
"# Crear las listas desplegables para seleccionar las variables y el tipo de gráfico\n",
"variable1_dropdown = widgets.Dropdown(options=fields, description=\"Variable 1:\")\n",
"variable2_dropdown = widgets.Dropdown(options=fields, description=\"Variable 2:\")\n",
"chart_type_dropdown = widgets.Dropdown(options=[\"bar\", \"line\", \"scatter\", \"area\", \"pie\", \"histogram\", \"box\", \"bubble\", \"radar\", \"stacked_bar\", \"stacked_area\", \"polar\", \"violin\", \"heatmap\", \"treemap\", \"donut\", \"waterfall\", \"polar_area\", \"pareto\", \"network\"], description=\"Tipo de gráfico:\")\n",
"values_dropdown = widgets.Dropdown(options=fields, description=\"Valores:\")\n",
"\n",
"# Función para generar y mostrar el gráfico seleccionado\n",
"def generate_chart(change):\n",
" variable1 = variable1_dropdown.value\n",
" variable2 = variable2_dropdown.value\n",
" chart_type = chart_type_dropdown.value\n",
" values = values_dropdown.value\n",
" \n",
" # Filtrar el DataFrame con las variables seleccionadas\n",
" filtered_data = data[[variable1, variable2]]\n",
" \n",
" # Crear la tabla de pivote\n",
" pivot_table = pd.pivot_table(filtered_data, values=values, index=variable1)\n",
" \n",
" # Mostrar la tabla de pivote\n",
" print(\"Tabla de pivote:\")\n",
" print(pivot_table)\n",
" \n",
" # Crear la gráfica\n",
" chart_title = f\"Gráfica de {values} por {variable1}\" # Título de la gráfica\n",
" colors = [\"red\", \"green\", \"blue\", \"orange\"] # Colores para las barras\n",
" \n",
" try:\n",
" if chart_type == \"pie\":\n",
" pivot_table.plot.pie(y=values, figsize=(10, 6), autopct='%1.1f%%', colors=colors)\n",
" else:\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), color=colors)\n",
" plt.title(chart_title)\n",
" plt.xlabel(variable1)\n",
" plt.ylabel(values)\n",
" plt.show()\n",
" except ValueError as e:\n",
" print(f\"No se pudo generar la gráfica. Error: {str(e)}\")\n",
"\n",
"# Asignar la función de generación de gráfico al evento \"change\" de las listas desplegables\n",
"variable1_dropdown.observe(generate_chart, 'value')\n",
"variable2_dropdown.observe(generate_chart, 'value')\n",
"chart_type_dropdown.observe(generate_chart, 'value')\n",
"values_dropdown.observe(generate_chart, 'value')\n",
"\n",
"# Mostrar las listas desplegables\n",
"display(variable1_dropdown, variable2_dropdown, chart_type_dropdown, values_dropdown)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "31741c4a",
"metadata": {},
"source": [
"## Visualización personalizada que agrupa y cuenta los campos de productos y los muestra por municipios con gráficos de pie o barras\n",
"\n",
"Sirve para ver la variedad de productos que produce un municipio"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4d15bb18",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Campos disponibles:\n",
"Index(['Año', 'Municipio', 'Area Sembrada', 'Area Cosechada',\n",
" 'Produccion (ton)', 'Rendimiento (ha/ton)', 'Producto'],\n",
" dtype='object')\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa133fdfb49a4dc8b4b49723159982b5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable 1:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccio…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c1debea9667c4e22af08ddc34a9d0876",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Tipo de gráfico:', options=('bar', 'pie'), value='bar')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tabla de recuento de productos por municipio:\n",
"Municipio\n",
"CHIMA 17\n",
"COTORRA 17\n",
"LORICA 17\n",
"MOMIL 24\n",
"PURISIMA 12\n",
"Name: Producto, dtype: int64\n"
]
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" # Agregar una columna \"Producto\" con el nombre del archivo sin la extensión\n",
" df[\"Producto\"] = os.path.splitext(file)[0]\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Mostrar los campos disponibles\n",
"fields = data.columns\n",
"print(\"Campos disponibles:\")\n",
"print(fields)\n",
"\n",
"# Crear las listas desplegables para seleccionar las variables y el tipo de gráfico\n",
"variable1_dropdown = widgets.Dropdown(options=fields, description=\"Variable 1:\")\n",
"chart_type_dropdown = widgets.Dropdown(options=[\"bar\", \"pie\"], description=\"Tipo de gráfico:\")\n",
"\n",
"# Función para generar y mostrar el gráfico seleccionado\n",
"def generate_chart(change):\n",
" variable1 = variable1_dropdown.value\n",
" chart_type = chart_type_dropdown.value\n",
" \n",
" # Realizar un recuento de los productos por municipio\n",
" product_counts = data.groupby(\"Municipio\")[\"Producto\"].nunique()\n",
" \n",
" # Mostrar la tabla de recuento\n",
" print(\"Tabla de recuento de productos por municipio:\")\n",
" print(product_counts)\n",
" \n",
" # Crear la gráfica\n",
" chart_title = f\"Gráfico de {chart_type} de distribución de productos por municipio\" # Título de la gráfica\n",
" \n",
" try:\n",
" if chart_type == \"bar\":\n",
" product_counts.plot(kind=chart_type, figsize=(10, 6))\n",
" elif chart_type == \"pie\":\n",
" product_counts.plot(kind=chart_type, figsize=(10, 6), autopct='%1.1f%%')\n",
" plt.title(chart_title)\n",
" plt.xlabel(\"Municipio\")\n",
" plt.ylabel(\"Cantidad de productos\")\n",
" plt.show()\n",
" except ValueError as e:\n",
" print(f\"No se pudo generar la gráfica. Error: {str(e)}\")\n",
"\n",
"# Asignar la función de generación de gráfico al evento \"change\" de las listas desplegables\n",
"variable1_dropdown.observe(generate_chart, 'value')\n",
"chart_type_dropdown.observe(generate_chart, 'value')\n",
"\n",
"# Mostrar las listas desplegables\n",
"display(variable1_dropdown, chart_type_dropdown)\n"
]
},
{
"cell_type": "markdown",
"id": "0663cd42",
"metadata": {},
"source": [
"## FUNCIONAL:\n",
"\n",
"ver en el eje x (independiente) como variables por años y por municipios, en el eje y (dependiente) las columnas a elegir por lista desplegable: 'Area Sembrada', 'Area Cosechada', 'Produccion (ton)', 'Rendimiento (ha/ton)' , para un grupo determinado de productos que pueda seleccionar de un listado de todos de productos"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0e92436a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Campos disponibles:\n",
"Index(['Año', 'Municipio', 'Area Sembrada', 'Area Cosechada',\n",
" 'Produccion (ton)', 'Rendimiento (ha/ton)', 'Producto'],\n",
" dtype='object')\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ebeb5293b174cb680d5b1d8e5c3f3b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable:', options=('Area Sembrada', 'Area Cosechada', 'Produccion (ton)', 'Rendimiento…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2dda3d5f76f84fd0a58c7a097fc7bdd2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"SelectMultiple(description='Productos:', options=('yuca', 'maiz-blanco-tecnificado', 'pepino-cohombro', 'arroz…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "25f0260aa2174e6fb7db9b55eac827f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Tipo de gráfico:', options=('bar', 'line', 'scatter'), value='bar')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tabla de pivote:\n",
"Producto arroz-secano-manual\n",
"Año Municipio \n",
"2007.0 CHIMA 16.00\n",
" COTORRA 180.00\n",
" LORICA 345.00\n",
" MOMIL 61.00\n",
" PURISIMA 80.00\n",
"2008.0 CHIMA 38.00\n",
" COTORRA 210.00\n",
" LORICA 350.00\n",
" MOMIL 42.00\n",
" PURISIMA 40.00\n",
"2009.0 CHIMA 25.00\n",
" COTORRA 150.00\n",
" LORICA 332.00\n",
" MOMIL 88.00\n",
" PURISIMA 40.00\n",
"2010.0 CHIMA 82.00\n",
" COTORRA 350.00\n",
" LORICA 637.00\n",
" MOMIL 57.00\n",
" PURISIMA 0.00\n",
"2011.0 CHIMA 40.00\n",
" COTORRA 350.00\n",
" LORICA 646.00\n",
" MOMIL 45.00\n",
" PURISIMA 40.00\n",
"2012.0 CHIMA 224.75\n",
" COTORRA 300.00\n",
" LORICA 637.00\n",
" MOMIL 28.90\n",
" PURISIMA 0.00\n",
"2013.0 CHIMA 204.75\n",
" COTORRA 600.00\n",
" LORICA 500.00\n",
" MOMIL 60.00\n",
"2014.0 CHIMA 188.00\n",
" COTORRA 600.00\n",
" LORICA 500.00\n",
" MOMIL 176.00\n",
"2015.0 CHIMA 38.00\n",
" COTORRA 95.00\n",
" LORICA 300.00\n",
" MOMIL 270.00\n",
"2016.0 CHIMA 40.00\n",
" LORICA 250.00\n",
" MOMIL 110.00\n",
" PURISIMA 21.00\n",
"2017.0 CHIMA 72.00\n",
" COTORRA 337.80\n",
" LORICA 620.00\n",
" MOMIL 0.00\n",
" PURISIMA 22.00\n",
"2018.0 CHIMA 73.31\n",
" COTORRA 341.37\n",
" LORICA 100.00\n",
" PURISIMA 22.62\n"
]
},
{
"data": {
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",
"text/plain": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, HTML\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" # Agregar la columna \"Producto\" con el nombre del archivo sin extensión\n",
" df[\"Producto\"] = os.path.splitext(file)[0]\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Mostrar los campos disponibles\n",
"fields = data.columns\n",
"print(\"Campos disponibles:\")\n",
"print(fields)\n",
"\n",
"# Obtener la lista de productos\n",
"product_list = data[\"Producto\"].unique()\n",
"\n",
"# Crear las listas desplegables para seleccionar las variables y el tipo de gráfico\n",
"variable_dropdown = widgets.Dropdown(options=[\"Area Sembrada\", \"Area Cosechada\", \"Produccion (ton)\", \"Rendimiento (ha/ton)\"], description=\"Variable:\")\n",
"product_dropdown = widgets.SelectMultiple(options=product_list, description=\"Productos:\")\n",
"chart_type_dropdown = widgets.Dropdown(options=[\"bar\", \"line\", \"scatter\"], description=\"Tipo de gráfico:\")\n",
"\n",
"# Función para generar y mostrar el gráfico seleccionado\n",
"def generate_chart(change):\n",
" variable = variable_dropdown.value\n",
" chart_type = chart_type_dropdown.value\n",
" products_selected = product_dropdown.value\n",
" \n",
" # Filtrar el DataFrame por los productos seleccionados\n",
" filtered_data = data[data[\"Producto\"].isin(products_selected)]\n",
" \n",
" # Crear la tabla de pivote\n",
" pivot_table = pd.pivot_table(filtered_data, values=variable, index=[\"Año\", \"Municipio\"], columns=\"Producto\")\n",
" \n",
" # Mostrar la tabla de pivote\n",
" table_html = pivot_table.to_html()\n",
" display(HTML(f\"
Tabla de pivote:
{table_html}\"))\n",
" \n",
" # Crear la gráfica\n",
" chart_title = f\"Gráfico de {chart_type} de {variable} por Año y Municipio\" # Título de la gráfica\n",
" \n",
" try:\n",
" if chart_type == \"bar\":\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6))\n",
" elif chart_type == \"line\":\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), marker=\"o\")\n",
" elif chart_type == \"scatter\":\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), marker=\"o\")\n",
" plt.title(chart_title)\n",
" plt.xlabel(\"Año, Municipio\")\n",
" plt.ylabel(variable)\n",
" plt.show()\n",
" except ValueError as e:\n",
" print(f\"No se pudo generar la gráfica. Error: {str(e)}\")\n",
"\n",
"# Asignar la función de generación de gráfico al evento \"change\" de las listas desplegables\n",
"variable_dropdown.observe(generate_chart, 'value')\n",
"product_dropdown.observe(generate_chart, 'value')\n",
"chart_type_dropdown.observe(generate_chart, 'value')\n",
"\n",
"# Mostrar las listas desplegables\n",
"display(variable_dropdown, product_dropdown, chart_type_dropdown)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d4d2533",
"metadata": {},
"outputs": [],
"source": [
"## El mismo anterior pero agrupados por colores"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d90915a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Campos disponibles:\n",
"Index(['Año', 'Municipio', 'Area Sembrada', 'Area Cosechada',\n",
" 'Produccion (ton)', 'Rendimiento (ha/ton)', 'Producto'],\n",
" dtype='object')\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e45b73fd9bb4e3989a01ebba31f1b13",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable:', options=('Area Sembrada', 'Area Cosechada', 'Produccion (ton)', 'Rendimiento…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1125bf7286bb4c0a929d5d87754ab135",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"SelectMultiple(description='Productos:', options=('yuca', 'maiz-blanco-tecnificado', 'pepino-cohombro', 'arroz…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ff2d48b214594dd5b0c4fd09331c79e8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Tipo de gráfico:', options=('bar', 'line', 'scatter'), value='bar')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tabla de pivote:\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n"
]
},
{
"ename": "TypeError",
"evalue": "no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:773\u001b[0m, in \u001b[0;36mWidget._handle_msg\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 772\u001b[0m _put_buffers(state, data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m], msg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffers\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m--> 773\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;66;03m# Handle a state request.\u001b[39;00m\n\u001b[1;32m 776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequest_state\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:650\u001b[0m, in \u001b[0;36mWidget.set_state\u001b[0;34m(self, sync_data)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send(msg, buffers\u001b[38;5;241m=\u001b[39mecho_buffers)\n\u001b[1;32m 647\u001b[0m \u001b[38;5;66;03m# The order of these context managers is important. Properties must\u001b[39;00m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;66;03m# be locked when the hold_trait_notification context manager is\u001b[39;00m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;66;03m# released and notifications are fired.\u001b[39;00m\n\u001b[0;32m--> 650\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock_property(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msync_data), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhold_trait_notifications():\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m sync_data:\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys:\n",
"File \u001b[0;32m/usr/lib/python3.10/contextlib.py:142\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1502\u001b[0m, in \u001b[0;36mHasTraits.hold_trait_notifications\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m changes \u001b[38;5;129;01min\u001b[39;00m cache\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 1501\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m change \u001b[38;5;129;01min\u001b[39;00m changes:\n\u001b[0;32m-> 1502\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget_selection.py:236\u001b[0m, in \u001b[0;36m_Selection._propagate_index\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value:\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m \u001b[38;5;241m=\u001b[39m value\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:732\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 732\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:721\u001b[0m, in \u001b[0;36mTraitType.set\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 717\u001b[0m silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 719\u001b[0m \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[0;32m--> 721\u001b[0m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_value\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1505\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[0;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, old_value, new_value):\n\u001b[0;32m-> 1505\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1506\u001b[0m \u001b[43m \u001b[49m\u001b[43mBunch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1507\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1508\u001b[0m \u001b[43m \u001b[49m\u001b[43mold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1509\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1510\u001b[0m \u001b[43m \u001b[49m\u001b[43mowner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1511\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchange\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1512\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1513\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[3], line 73\u001b[0m, in \u001b[0;36mgenerate_chart\u001b[0;34m(change)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chart_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 73\u001b[0m \u001b[43mpivot_table\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchart_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m chart_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mline\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 75\u001b[0m pivot_table\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39mchart_type, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m), marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_core.py:975\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 972\u001b[0m label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m 973\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[0;32m--> 975\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m 69\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[1;32m 70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:446\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 445\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args_adjust()\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_plot_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_plot()\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:632\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[0;32m--> 632\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno numeric data to plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m numeric_data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_to_ndarray)\n",
"\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tabla de pivote:\n",
"Producto pepino-cohombro\n",
"Año Municipio \n",
"2015.0 LORICA 0.00\n",
"2016.0 LORICA 120.00\n",
" MOMIL 0.75\n",
"2017.0 LORICA 150.00\n",
"2019.0 LORICA 71.50\n",
"2020.0 LORICA 75.00\n",
"2021.0 LORICA 90.00\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" # Agregar la columna \"Producto\" con el nombre del archivo sin extensión\n",
" df[\"Producto\"] = os.path.splitext(file)[0]\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Mostrar los campos disponibles\n",
"fields = data.columns\n",
"print(\"Campos disponibles:\")\n",
"print(fields)\n",
"\n",
"# Obtener la lista de productos\n",
"product_list = data[\"Producto\"].unique()\n",
"\n",
"# Crear las listas desplegables para seleccionar las variables y el tipo de gráfico\n",
"variable_dropdown = widgets.Dropdown(options=[\"Area Sembrada\", \"Area Cosechada\", \"Produccion (ton)\", \"Rendimiento (ha/ton)\"], description=\"Variable:\")\n",
"product_dropdown = widgets.SelectMultiple(options=product_list, description=\"Productos:\")\n",
"chart_type_dropdown = widgets.Dropdown(options=[\"bar\", \"line\", \"scatter\"], description=\"Tipo de gráfico:\")\n",
"\n",
"# Función para generar y mostrar el gráfico seleccionado\n",
"def generate_chart(change):\n",
" variable = variable_dropdown.value\n",
" chart_type = chart_type_dropdown.value\n",
" products_selected = product_dropdown.value\n",
" \n",
" # Filtrar el DataFrame por los productos seleccionados\n",
" filtered_data = data[data[\"Producto\"].isin(products_selected)]\n",
" \n",
" # Crear la tabla de pivote\n",
" pivot_table = pd.pivot_table(filtered_data, values=variable, index=[\"Año\", \"Municipio\"], columns=\"Producto\")\n",
" \n",
" # Mostrar la tabla de pivote\n",
" print(\"Tabla de pivote:\")\n",
" print(pivot_table)\n",
" \n",
" # Crear la gráfica\n",
" chart_title = f\"Gráfico de {chart_type} de {variable} por Año y Municipio\" # Título de la gráfica\n",
" \n",
" try:\n",
" if chart_type == \"bar\":\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6))\n",
" elif chart_type == \"line\":\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), marker=\"o\")\n",
" elif chart_type == \"scatter\":\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), marker=\"o\")\n",
" \n",
" plt.title(chart_title)\n",
" plt.xlabel(\"Año\")\n",
" plt.ylabel(variable)\n",
" plt.legend(loc='upper right', title=\"Municipio\")\n",
" plt.show()\n",
" except ValueError as e:\n",
" print(f\"No se pudo generar la gráfica. Error: {str(e)}\")\n",
"\n",
"# Asignar la función de generación de gráfico al evento \"change\" de las listas desplegables\n",
"variable_dropdown.observe(generate_chart, 'value')\n",
"product_dropdown.observe(generate_chart, 'value')\n",
"chart_type_dropdown.observe(generate_chart, 'value')\n",
"\n",
"# Mostrar las listas desplegables\n",
"display(variable_dropdown, product_dropdown, chart_type_dropdown)\n"
]
},
{
"cell_type": "markdown",
"id": "f088cc1d",
"metadata": {},
"source": [
"# Afinando listas desplegables para elegir el index"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e7df1560",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Campos disponibles:\n",
"Index(['Año', 'Municipio', 'Area Sembrada', 'Area Cosechada',\n",
" 'Produccion (ton)', 'Rendimiento (ha/ton)', 'Producto'],\n",
" dtype='object')\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42873fd77bb9475ca6a3a0cc40675333",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable 1:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccio…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e10fa95b033e444193b55315ddf68c13",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Variable 2:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccio…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1b4151668f2d48e78445d63898b79c06",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Tipo de gráfico:', options=('bar', 'line', 'scatter', 'area', 'pie', 'histogram', 'box',…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1db0163ea0a14cf7b6e665d6504aaefa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Valores:', options=('Año', 'Municipio', 'Area Sembrada', 'Area Cosechada', 'Produccion (…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "450aa811a37443299ff88a0352901fad",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Índice:', options=('Variable 1:', 'Variable 2:'), value='Variable 1:')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "KeyError",
"evalue": "'Variable 1:'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:773\u001b[0m, in \u001b[0;36mWidget._handle_msg\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 772\u001b[0m _put_buffers(state, data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m], msg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffers\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m--> 773\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;66;03m# Handle a state request.\u001b[39;00m\n\u001b[1;32m 776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequest_state\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:650\u001b[0m, in \u001b[0;36mWidget.set_state\u001b[0;34m(self, sync_data)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send(msg, buffers\u001b[38;5;241m=\u001b[39mecho_buffers)\n\u001b[1;32m 647\u001b[0m \u001b[38;5;66;03m# The order of these context managers is important. Properties must\u001b[39;00m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;66;03m# be locked when the hold_trait_notification context manager is\u001b[39;00m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;66;03m# released and notifications are fired.\u001b[39;00m\n\u001b[0;32m--> 650\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock_property(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msync_data), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhold_trait_notifications():\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m sync_data:\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys:\n",
"File \u001b[0;32m/usr/lib/python3.10/contextlib.py:142\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1502\u001b[0m, in \u001b[0;36mHasTraits.hold_trait_notifications\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m changes \u001b[38;5;129;01min\u001b[39;00m cache\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 1501\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m change \u001b[38;5;129;01min\u001b[39;00m changes:\n\u001b[0;32m-> 1502\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget_selection.py:236\u001b[0m, in \u001b[0;36m_Selection._propagate_index\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value:\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m \u001b[38;5;241m=\u001b[39m value\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:732\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 732\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:721\u001b[0m, in \u001b[0;36mTraitType.set\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 717\u001b[0m silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 719\u001b[0m \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[0;32m--> 721\u001b[0m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_value\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1505\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[0;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, old_value, new_value):\n\u001b[0;32m-> 1505\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1506\u001b[0m \u001b[43m \u001b[49m\u001b[43mBunch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1507\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1508\u001b[0m \u001b[43m \u001b[49m\u001b[43mold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1509\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1510\u001b[0m \u001b[43m \u001b[49m\u001b[43mowner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1511\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchange\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1512\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1513\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[5], line 63\u001b[0m, in \u001b[0;36mgenerate_chart\u001b[0;34m(change)\u001b[0m\n\u001b[1;32m 60\u001b[0m filtered_data \u001b[38;5;241m=\u001b[39m data[[variable1, variable2]]\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# Crear la tabla de pivote\u001b[39;00m\n\u001b[0;32m---> 63\u001b[0m pivot_table \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpivot_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiltered_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# Mostrar la tabla de pivote\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTabla de pivote:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:97\u001b[0m, in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 94\u001b[0m table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 97\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43m__internal_pivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 104\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:166\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 164\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(values)\n\u001b[0;32m--> 166\u001b[0m grouped \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m agged \u001b[38;5;241m=\u001b[39m grouped\u001b[38;5;241m.\u001b[39magg(aggfunc)\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(agged, ABCDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(agged\u001b[38;5;241m.\u001b[39mcolumns):\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:8262\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m 8259\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou have to supply one of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mby\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlevel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 8260\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[0;32m-> 8262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8263\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8264\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8265\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8266\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8267\u001b[0m \u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8268\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8269\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8270\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8271\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8272\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:931\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropna \u001b[38;5;241m=\u001b[39m dropna\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 931\u001b[0m grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 932\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 936\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 938\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 939\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 941\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m 942\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/groupby/grouper.py:985\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[1;32m 983\u001b[0m in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 985\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m 986\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 987\u001b[0m \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m 988\u001b[0m exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
"\u001b[0;31mKeyError\u001b[0m: 'Variable 1:'"
]
},
{
"ename": "KeyError",
"evalue": "'Producto'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:773\u001b[0m, in \u001b[0;36mWidget._handle_msg\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 772\u001b[0m _put_buffers(state, data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m], msg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffers\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m--> 773\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;66;03m# Handle a state request.\u001b[39;00m\n\u001b[1;32m 776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequest_state\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:650\u001b[0m, in \u001b[0;36mWidget.set_state\u001b[0;34m(self, sync_data)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send(msg, buffers\u001b[38;5;241m=\u001b[39mecho_buffers)\n\u001b[1;32m 647\u001b[0m \u001b[38;5;66;03m# The order of these context managers is important. Properties must\u001b[39;00m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;66;03m# be locked when the hold_trait_notification context manager is\u001b[39;00m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;66;03m# released and notifications are fired.\u001b[39;00m\n\u001b[0;32m--> 650\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock_property(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msync_data), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhold_trait_notifications():\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m sync_data:\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys:\n",
"File \u001b[0;32m/usr/lib/python3.10/contextlib.py:142\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1502\u001b[0m, in \u001b[0;36mHasTraits.hold_trait_notifications\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m changes \u001b[38;5;129;01min\u001b[39;00m cache\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 1501\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m change \u001b[38;5;129;01min\u001b[39;00m changes:\n\u001b[0;32m-> 1502\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget_selection.py:236\u001b[0m, in \u001b[0;36m_Selection._propagate_index\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value:\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m \u001b[38;5;241m=\u001b[39m value\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:732\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 732\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:721\u001b[0m, in \u001b[0;36mTraitType.set\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 717\u001b[0m silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 719\u001b[0m \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[0;32m--> 721\u001b[0m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_value\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1505\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[0;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, old_value, new_value):\n\u001b[0;32m-> 1505\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1506\u001b[0m \u001b[43m \u001b[49m\u001b[43mBunch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1507\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1508\u001b[0m \u001b[43m \u001b[49m\u001b[43mold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1509\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1510\u001b[0m \u001b[43m \u001b[49m\u001b[43mowner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1511\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchange\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1512\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1513\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[5], line 63\u001b[0m, in \u001b[0;36mgenerate_chart\u001b[0;34m(change)\u001b[0m\n\u001b[1;32m 60\u001b[0m filtered_data \u001b[38;5;241m=\u001b[39m data[[variable1, variable2]]\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# Crear la tabla de pivote\u001b[39;00m\n\u001b[0;32m---> 63\u001b[0m pivot_table \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpivot_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiltered_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# Mostrar la tabla de pivote\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTabla de pivote:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:97\u001b[0m, in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 94\u001b[0m table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 97\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43m__internal_pivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 104\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:143\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m values:\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(i)\n\u001b[1;32m 145\u001b[0m to_filter \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m keys \u001b[38;5;241m+\u001b[39m values:\n",
"\u001b[0;31mKeyError\u001b[0m: 'Producto'"
]
},
{
"ename": "KeyError",
"evalue": "'Variable 1:'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:773\u001b[0m, in \u001b[0;36mWidget._handle_msg\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 772\u001b[0m _put_buffers(state, data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m], msg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffers\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m--> 773\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;66;03m# Handle a state request.\u001b[39;00m\n\u001b[1;32m 776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequest_state\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:650\u001b[0m, in \u001b[0;36mWidget.set_state\u001b[0;34m(self, sync_data)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send(msg, buffers\u001b[38;5;241m=\u001b[39mecho_buffers)\n\u001b[1;32m 647\u001b[0m \u001b[38;5;66;03m# The order of these context managers is important. Properties must\u001b[39;00m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;66;03m# be locked when the hold_trait_notification context manager is\u001b[39;00m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;66;03m# released and notifications are fired.\u001b[39;00m\n\u001b[0;32m--> 650\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock_property(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msync_data), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhold_trait_notifications():\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m sync_data:\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys:\n",
"File \u001b[0;32m/usr/lib/python3.10/contextlib.py:142\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1502\u001b[0m, in \u001b[0;36mHasTraits.hold_trait_notifications\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m changes \u001b[38;5;129;01min\u001b[39;00m cache\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 1501\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m change \u001b[38;5;129;01min\u001b[39;00m changes:\n\u001b[0;32m-> 1502\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget_selection.py:236\u001b[0m, in \u001b[0;36m_Selection._propagate_index\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value:\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m \u001b[38;5;241m=\u001b[39m value\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:732\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 732\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:721\u001b[0m, in \u001b[0;36mTraitType.set\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 717\u001b[0m silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 719\u001b[0m \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[0;32m--> 721\u001b[0m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_value\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1505\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[0;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, old_value, new_value):\n\u001b[0;32m-> 1505\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1506\u001b[0m \u001b[43m \u001b[49m\u001b[43mBunch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1507\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1508\u001b[0m \u001b[43m \u001b[49m\u001b[43mold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1509\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1510\u001b[0m \u001b[43m \u001b[49m\u001b[43mowner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1511\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchange\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1512\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1513\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[5], line 63\u001b[0m, in \u001b[0;36mgenerate_chart\u001b[0;34m(change)\u001b[0m\n\u001b[1;32m 60\u001b[0m filtered_data \u001b[38;5;241m=\u001b[39m data[[variable1, variable2]]\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# Crear la tabla de pivote\u001b[39;00m\n\u001b[0;32m---> 63\u001b[0m pivot_table \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpivot_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiltered_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# Mostrar la tabla de pivote\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTabla de pivote:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:97\u001b[0m, in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 94\u001b[0m table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 97\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43m__internal_pivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 104\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:166\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 164\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(values)\n\u001b[0;32m--> 166\u001b[0m grouped \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m agged \u001b[38;5;241m=\u001b[39m grouped\u001b[38;5;241m.\u001b[39magg(aggfunc)\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(agged, ABCDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(agged\u001b[38;5;241m.\u001b[39mcolumns):\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:8262\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m 8259\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou have to supply one of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mby\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlevel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 8260\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[0;32m-> 8262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8263\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8264\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8265\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8266\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8267\u001b[0m \u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8268\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8269\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8270\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8271\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8272\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:931\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropna \u001b[38;5;241m=\u001b[39m dropna\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 931\u001b[0m grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 932\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 936\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 938\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 939\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 941\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m 942\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/groupby/grouper.py:985\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[1;32m 983\u001b[0m in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 985\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m 986\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 987\u001b[0m \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m 988\u001b[0m exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
"\u001b[0;31mKeyError\u001b[0m: 'Variable 1:'"
]
},
{
"ename": "KeyError",
"evalue": "'Produccion (ton)'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:773\u001b[0m, in \u001b[0;36mWidget._handle_msg\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m 772\u001b[0m _put_buffers(state, data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer_paths\u001b[39m\u001b[38;5;124m'\u001b[39m], msg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffers\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m--> 773\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;66;03m# Handle a state request.\u001b[39;00m\n\u001b[1;32m 776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequest_state\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:650\u001b[0m, in \u001b[0;36mWidget.set_state\u001b[0;34m(self, sync_data)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send(msg, buffers\u001b[38;5;241m=\u001b[39mecho_buffers)\n\u001b[1;32m 647\u001b[0m \u001b[38;5;66;03m# The order of these context managers is important. Properties must\u001b[39;00m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;66;03m# be locked when the hold_trait_notification context manager is\u001b[39;00m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;66;03m# released and notifications are fired.\u001b[39;00m\n\u001b[0;32m--> 650\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock_property(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msync_data), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhold_trait_notifications():\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m sync_data:\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys:\n",
"File \u001b[0;32m/usr/lib/python3.10/contextlib.py:142\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1502\u001b[0m, in \u001b[0;36mHasTraits.hold_trait_notifications\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m changes \u001b[38;5;129;01min\u001b[39;00m cache\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 1501\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m change \u001b[38;5;129;01min\u001b[39;00m changes:\n\u001b[0;32m-> 1502\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget_selection.py:236\u001b[0m, in \u001b[0;36m_Selection._propagate_index\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value:\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m \u001b[38;5;241m=\u001b[39m value\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:732\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 732\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:721\u001b[0m, in \u001b[0;36mTraitType.set\u001b[0;34m(self, obj, value)\u001b[0m\n\u001b[1;32m 717\u001b[0m silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 719\u001b[0m \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[1;32m 720\u001b[0m \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[0;32m--> 721\u001b[0m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_value\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1505\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[0;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, old_value, new_value):\n\u001b[0;32m-> 1505\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1506\u001b[0m \u001b[43m \u001b[49m\u001b[43mBunch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1507\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1508\u001b[0m \u001b[43m \u001b[49m\u001b[43mold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mold_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1509\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1510\u001b[0m \u001b[43m \u001b[49m\u001b[43mowner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1511\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchange\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1512\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1513\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/ipywidgets/widgets/widget.py:701\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m--> 701\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotify_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1517\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[0;34m(self, change)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change):\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1517\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_notify_observers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchange\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/traitlets/traitlets.py:1564\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1562\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1564\u001b[0m \u001b[43mc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[5], line 63\u001b[0m, in \u001b[0;36mgenerate_chart\u001b[0;34m(change)\u001b[0m\n\u001b[1;32m 60\u001b[0m filtered_data \u001b[38;5;241m=\u001b[39m data[[variable1, variable2]]\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# Crear la tabla de pivote\u001b[39;00m\n\u001b[0;32m---> 63\u001b[0m pivot_table \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpivot_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiltered_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# Mostrar la tabla de pivote\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTabla de pivote:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:97\u001b[0m, in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 94\u001b[0m table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 97\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43m__internal_pivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 104\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:143\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m values:\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(i)\n\u001b[1;32m 145\u001b[0m to_filter \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m keys \u001b[38;5;241m+\u001b[39m values:\n",
"\u001b[0;31mKeyError\u001b[0m: 'Produccion (ton)'"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" # Agregar una columna \"Producto\" con el nombre del archivo sin la extensión\n",
" df[\"Producto\"] = os.path.splitext(file)[0]\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Mostrar los campos disponibles\n",
"fields = data.columns\n",
"print(\"Campos disponibles:\")\n",
"print(fields)\n",
"\n",
"# Crear las listas desplegables para seleccionar las variables y el tipo de gráfico\n",
"variable1_dropdown = widgets.Dropdown(options=fields, description=\"Variable 1:\")\n",
"variable2_dropdown = widgets.Dropdown(options=fields, description=\"Variable 2:\")\n",
"chart_type_dropdown = widgets.Dropdown(options=[\"bar\", \"line\", \"scatter\", \"area\", \"pie\", \"histogram\", \"box\", \"bubble\", \"radar\", \"stacked_bar\", \"stacked_area\", \"polar\", \"violin\", \"heatmap\", \"treemap\", \"donut\", \"waterfall\", \"polar_area\", \"pareto\", \"network\"], description=\"Tipo de gráfico:\")\n",
"values_dropdown = widgets.Dropdown(options=fields, description=\"Valores:\")\n",
"index_dropdown = widgets.Dropdown(options=[variable1_dropdown.description, variable2_dropdown.description], description=\"Índice:\")\n",
"\n",
"# Función para generar y mostrar el gráfico seleccionado\n",
"def generate_chart(change):\n",
" variable1 = variable1_dropdown.value\n",
" variable2 = variable2_dropdown.value\n",
" chart_type = chart_type_dropdown.value\n",
" values = values_dropdown.value\n",
" index = index_dropdown.value\n",
" \n",
" # Filtrar el DataFrame con las variables seleccionadas\n",
" filtered_data = data[[variable1, variable2]]\n",
" \n",
" # Crear la tabla de pivote\n",
" pivot_table = pd.pivot_table(filtered_data, values=values, index=index)\n",
" \n",
" # Mostrar la tabla de pivote\n",
" print(\"Tabla de pivote:\")\n",
" print(pivot_table)\n",
" \n",
" # Crear la gráfica\n",
" chart_title = f\"Gráfica de {values} por {index}\" # Título de la gráfica\n",
" colors = [\"red\", \"green\", \"blue\", \"orange\"] # Colores para las barras\n",
" \n",
" try:\n",
" if chart_type == \"pie\":\n",
" pivot_table.plot.pie(y=values, figsize=(10, 6), autopct='%1.1f%%', colors=colors)\n",
" else:\n",
" pivot_table.plot(kind=chart_type, figsize=(10, 6), color=colors)\n",
" plt.title(chart_title)\n",
" plt.xlabel(index)\n",
" plt.ylabel(values)\n",
" plt.show()\n",
" except ValueError as e:\n",
" print(f\"No se pudo generar la gráfica. Error: {str(e)}\")\n",
"\n",
"# Asignar la función de generación de gráfico al evento \"change\" de las listas desplegables\n",
"variable1_dropdown.observe(generate_chart, 'value')\n",
"variable2_dropdown.observe(generate_chart, 'value')\n",
"chart_type_dropdown.observe(generate_chart, 'value')\n",
"values_dropdown.observe(generate_chart, 'value')\n",
"index_dropdown.observe(generate_chart, 'value')\n",
"\n",
"# Mostrar las listas desplegables\n",
"display(variable1_dropdown, variable2_dropdown, chart_type_dropdown, values_dropdown, index_dropdown)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "17430fb4",
"metadata": {},
"source": [
"# ¿Cuáles son los productos que en volumen de toneladas que se producen en todos los municipios?"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7a732a75",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tabla de productos que se producen en todos los municipios:\n",
"Empty DataFrame\n",
"Columns: [Producto]\n",
"Index: []\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Directorio que contiene los archivos CSV\n",
"directory = \"./\"\n",
"\n",
"# Obtener la lista de archivos CSV en el directorio\n",
"csv_files = [file for file in os.listdir(directory) if file.endswith(\".csv\")]\n",
"\n",
"# Verificar si hay archivos CSV en el directorio\n",
"if len(csv_files) == 0:\n",
" print(\"No se encontraron archivos CSV en el directorio especificado.\")\n",
" exit()\n",
"\n",
"# Cargar los datos de los archivos CSV en un DataFrame\n",
"dfs = []\n",
"\n",
"for file in csv_files:\n",
" file_path = os.path.join(directory, file)\n",
" try:\n",
" df = pd.read_csv(file_path)\n",
" # Agregar una columna \"Producto\" con el nombre del archivo sin la extensión\n",
" df[\"Producto\"] = os.path.splitext(file)[0]\n",
" dfs.append(df)\n",
" except pd.errors.EmptyDataError:\n",
" print(f\"El archivo {file} está vacío y no se puede cargar.\")\n",
"\n",
"# Verificar si se cargaron datos en el DataFrame\n",
"if len(dfs) == 0:\n",
" print(\"No se pudo cargar ningún archivo CSV con datos.\")\n",
" exit()\n",
"\n",
"# Concatenar los DataFrames en uno solo\n",
"data = pd.concat(dfs)\n",
"\n",
"# Calcular el volumen de toneladas por producto y municipio\n",
"volumen_por_producto_municipio = data.groupby([\"Producto\", \"Municipio\"])[\"Produccion (ton)\"].sum().reset_index()\n",
"\n",
"# Filtrar los productos que se producen en todos los municipios\n",
"productos_en_todos_municipios = volumen_por_producto_municipio.groupby(\"Producto\").filter(lambda x: len(x) == len(data[\"Municipio\"].unique()))\n",
"\n",
"# Obtener la lista de productos\n",
"productos = productos_en_todos_municipios[\"Producto\"].unique()\n",
"\n",
"# Mostrar los productos que se producen en todos los municipios en una tabla\n",
"tabla_productos = pd.DataFrame({\"Producto\": productos})\n",
"print(\"Tabla de productos que se producen en todos los municipios:\")\n",
"print(tabla_productos)\n",
"\n",
"# Mostrar los productos que se producen en todos los municipios en un gráfico de barras\n",
"plt.figure(figsize=(10, 6))\n",
"plt.barh(tabla_productos[\"Producto\"], 1)\n",
"plt.xlabel(\"Cantidad de municipios\")\n",
"plt.ylabel(\"Producto\")\n",
"plt.title(\"Productos que se producen en todos los municipios\")\n",
"plt.show()\n"
]
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