{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gráfico de barras\n",
"\n",
"***\n",
"Un gráfico de barras esta compuesto por rectangulos que van desde el eje X hasta una altura Y. \n",
"\n",
"Así es como se produce un gráfico de barras en `altair`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# primero importamos las bibliotecas necesarias\n",
"# estos alias son convenciones populares\n",
"# pero no son requisitos estrictos\n",
"\n",
"import pandas as pd\n",
"import altair as alt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si estas trabajando con este __notebook__ en un __jupyter notebook__ y no el __jupyter lab__ requerirás ejecutar el siguiente código también."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alt.renderers.enable(\"notebook\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Los datos con los que vamos a trabajar son de [INEGI](https://inegi.org.mx). En este caso, el número de personas en el país desde 1910."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"datos = pd.read_csv(\"../../datos/poblacion.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Puedes explorar el __DataFrame__ con el método `.head()` para observar las primeras líneas."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Periodo | \n",
" Número de personas | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1910 | \n",
" 15160369 | \n",
"
\n",
" \n",
" 1 | \n",
" 1921 | \n",
" 14334780 | \n",
"
\n",
" \n",
" 2 | \n",
" 1930 | \n",
" 16552722 | \n",
"
\n",
" \n",
" 3 | \n",
" 1940 | \n",
" 19653552 | \n",
"
\n",
" \n",
" 4 | \n",
" 1950 | \n",
" 25791017 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Periodo Número de personas\n",
"0 1910 15160369\n",
"1 1921 14334780\n",
"2 1930 16552722\n",
"3 1940 19653552\n",
"4 1950 25791017"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datos.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nota que por default `.head()` sólo muestra las primeras 5 líneas pero puedes pasarle cualquier número entero como parametro."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Periodo | \n",
" Número de personas | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1910 | \n",
" 15160369 | \n",
"
\n",
" \n",
" 1 | \n",
" 1921 | \n",
" 14334780 | \n",
"
\n",
" \n",
" 2 | \n",
" 1930 | \n",
" 16552722 | \n",
"
\n",
" \n",
" 3 | \n",
" 1940 | \n",
" 19653552 | \n",
"
\n",
" \n",
" 4 | \n",
" 1950 | \n",
" 25791017 | \n",
"
\n",
" \n",
" 5 | \n",
" 1960 | \n",
" 34923129 | \n",
"
\n",
" \n",
" 6 | \n",
" 1970 | \n",
" 48225238 | \n",
"
\n",
" \n",
" 7 | \n",
" 1980 | \n",
" 66846833 | \n",
"
\n",
" \n",
" 8 | \n",
" 1990 | \n",
" 81249645 | \n",
"
\n",
" \n",
" 9 | \n",
" 1995 | \n",
" 91158290 | \n",
"
\n",
" \n",
"
\n",
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"
],
"text/plain": [
" Periodo Número de personas\n",
"0 1910 15160369\n",
"1 1921 14334780\n",
"2 1930 16552722\n",
"3 1940 19653552\n",
"4 1950 25791017\n",
"5 1960 34923129\n",
"6 1970 48225238\n",
"7 1980 66846833\n",
"8 1990 81249645\n",
"9 1995 91158290"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datos.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualización"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
"config": {
"view": {
"height": 300,
"width": 400
}
},
"data": {
"name": "data-cf3d660dcf7d8fe1659bd5f2e283e349"
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"datasets": {
"data-cf3d660dcf7d8fe1659bd5f2e283e349": [
{
"Número de personas": 15160369,
"Periodo": 1910
},
{
"Número de personas": 14334780,
"Periodo": 1921
},
{
"Número de personas": 16552722,
"Periodo": 1930
},
{
"Número de personas": 19653552,
"Periodo": 1940
},
{
"Número de personas": 25791017,
"Periodo": 1950
},
{
"Número de personas": 34923129,
"Periodo": 1960
},
{
"Número de personas": 48225238,
"Periodo": 1970
},
{
"Número de personas": 66846833,
"Periodo": 1980
},
{
"Número de personas": 81249645,
"Periodo": 1990
},
{
"Número de personas": 91158290,
"Periodo": 1995
},
{
"Número de personas": 97483412,
"Periodo": 2000
},
{
"Número de personas": 103263388,
"Periodo": 2005
},
{
"Número de personas": 112336538,
"Periodo": 2010
},
{
"Número de personas": 119938473,
"Periodo": 2015
}
]
},
"encoding": {
"x": {
"field": "Periodo",
"type": "quantitative"
},
"y": {
"field": "Número de personas",
"type": "quantitative"
}
},
"mark": "bar"
},
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",
"text/plain": [
"\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(datos).mark_bar().encode(\n",
" x = \"Periodo:Q\",\n",
" y = \"Número de personas:Q\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nota que utilizamos \n",
"```python\n",
" x = \"Periodo:Q\",\n",
" y = \"Número de personas:Q\",\n",
"```\n",
"\n",
"en nuestro gráfico. La __Q__ le esta indicando a `altair` que interprete los valores de la columna \"Periodo\" y \"Número de personas\" como datos de tipo __cuantitativo__ (Q).\n",
"\n",
"Modifica el valor de __Y__ en este gráfico para que sea interpretado como tipo _nominal_ (__N__), _ordinal_ (__O__), y _temporal_ (__T__) para ver como cambia tu gráfico."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Nominal\n",
"alt.Chart(datos).mark_bar().encode(\n",
" x = \"Periodo:Q\",\n",
" y = \"\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Ordinal\n",
"alt.Chart(datos).mark_bar().encode(\n",
" x = \"\",\n",
" y = \"\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Temporal\n",
"alt.Chart(datos)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bonus\n",
"¿Cómo modificarías tu gráfico para que las barras sean horizontales?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Horizontal"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}