{
"cells": [
{
"cell_type": "markdown",
"id": "f74ee357",
"metadata": {},
"source": [
"# Gráficos y Visualizaciones"
]
},
{
"cell_type": "markdown",
"id": "ab18f3d5",
"metadata": {},
"source": [
"## 1. Preparación de datos"
]
},
{
"cell_type": "markdown",
"id": "a04ee435",
"metadata": {},
"source": [
"### Importación de Librerías"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c5e6fe34",
"metadata": {},
"outputs": [],
"source": [
"# --- Importacion de librerias genéricas\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"# --- Específicas para los gráficos\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline \n",
"# --- Estilo de visualizacion de matplotlib\n",
"mpl.style.use('bmh') \n",
"# bmh es el nombre del estilo que usaremos\n",
"#\n",
"# El separador presentado abajo es multi OS.\n",
"separador = os.path.sep"
]
},
{
"cell_type": "markdown",
"id": "8a2bc9ed",
"metadata": {},
"source": [
"### Adquisición del conjunto de datos"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0be3a8fa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tamaño del df: (38, 12)\n",
"primeros registros...\n"
]
},
{
"data": {
"text/html": [
"
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" | \n",
" Jan | \n",
" Feb | \n",
" Mar | \n",
" Apr | \n",
" May | \n",
" Jun | \n",
" Jul | \n",
" Aug | \n",
" Sep | \n",
" Oct | \n",
" Nov | \n",
" Dec | \n",
"
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" \n",
" Year | \n",
" | \n",
" | \n",
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" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 1965 | \n",
" 0.029 | \n",
" 0.069 | \n",
" 0.000 | \n",
" 21.667 | \n",
" 17.859 | \n",
" 102.111 | \n",
" 606.071 | \n",
" 402.521 | \n",
" 69.511 | \n",
" 5.249 | \n",
" 16.232 | \n",
" 22.075 | \n",
"
\n",
" \n",
" 1966 | \n",
" 0.905 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 2.981 | \n",
" 63.008 | \n",
" 94.088 | \n",
" 481.942 | \n",
" 59.386 | \n",
" 150.624 | \n",
" 1.308 | \n",
" 41.214 | \n",
" 4.132 | \n",
"
\n",
" \n",
" 1967 | \n",
" 0.248 | \n",
" 3.390 | \n",
" 1.320 | \n",
" 13.482 | \n",
" 11.116 | \n",
" 251.314 | \n",
" 780.006 | \n",
" 181.069 | \n",
" 183.757 | \n",
" 50.404 | \n",
" 8.393 | \n",
" 37.685 | \n",
"
\n",
" \n",
" 1968 | \n",
" 0.318 | \n",
" 3.035 | \n",
" 1.704 | \n",
" 23.307 | \n",
" 7.441 | \n",
" 179.872 | \n",
" 379.354 | \n",
" 171.979 | \n",
" 219.884 | \n",
" 73.997 | \n",
" 23.326 | \n",
" 2.020 | \n",
"
\n",
" \n",
" 1969 | \n",
" 0.248 | \n",
" 2.524 | \n",
" 0.334 | \n",
" 4.569 | \n",
" 6.213 | \n",
" 393.682 | \n",
" 678.354 | \n",
" 397.335 | \n",
" 205.413 | \n",
" 24.014 | \n",
" 24.385 | \n",
" 1.951 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Jan Feb Mar Apr May Jun Jul Aug Sep \\\n",
"Year \n",
"1965 0.029 0.069 0.000 21.667 17.859 102.111 606.071 402.521 69.511 \n",
"1966 0.905 0.000 0.000 2.981 63.008 94.088 481.942 59.386 150.624 \n",
"1967 0.248 3.390 1.320 13.482 11.116 251.314 780.006 181.069 183.757 \n",
"1968 0.318 3.035 1.704 23.307 7.441 179.872 379.354 171.979 219.884 \n",
"1969 0.248 2.524 0.334 4.569 6.213 393.682 678.354 397.335 205.413 \n",
"\n",
" Oct Nov Dec \n",
"Year \n",
"1965 5.249 16.232 22.075 \n",
"1966 1.308 41.214 4.132 \n",
"1967 50.404 8.393 37.685 \n",
"1968 73.997 23.326 2.020 \n",
"1969 24.014 24.385 1.951 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# El archivo de Lluvias:\n",
"# Comparativa Enero-Febrero\n",
"archivoLluvias=\"datasets\"+str(separador)+\"pune_1965_to_2002.csv\"\n",
"df_lluvias = pd.read_csv(archivoLluvias)\n",
"# Ponemos como index el año, y lo reflejamos en el eje x\n",
"df_lluvias.index = df_lluvias[\"Year\"]\n",
"df_lluvias = df_lluvias.drop(\"Year\", axis=\"columns\") \n",
"print('Tamaño del df: ',df_lluvias.shape)\n",
"print('primeros registros...')\n",
"df_lluvias.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b2dc24ec",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" Jan | \n",
" Feb | \n",
" Mar | \n",
" Apr | \n",
" May | \n",
" Jun | \n",
" Jul | \n",
" Aug | \n",
" Sep | \n",
" Oct | \n",
" Nov | \n",
" Dec | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
" 38.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.294368 | \n",
" 1.101132 | \n",
" 1.677184 | \n",
" 12.381237 | \n",
" 25.059789 | \n",
" 337.096395 | \n",
" 430.010395 | \n",
" 277.088342 | \n",
" 201.111711 | \n",
" 73.245263 | \n",
" 25.223474 | \n",
" 6.069632 | \n",
"
\n",
" \n",
" std | \n",
" 0.640510 | \n",
" 1.741219 | \n",
" 2.486516 | \n",
" 13.671071 | \n",
" 22.451708 | \n",
" 171.666565 | \n",
" 177.976444 | \n",
" 132.245356 | \n",
" 123.736320 | \n",
" 62.936775 | \n",
" 31.806180 | \n",
" 11.725716 | \n",
"
\n",
" \n",
" min | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.061000 | \n",
" 0.508000 | \n",
" 94.088000 | \n",
" 84.936000 | \n",
" 59.386000 | \n",
" 44.574000 | \n",
" 1.078000 | \n",
" 0.260000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 2.291750 | \n",
" 7.005250 | \n",
" 226.180250 | \n",
" 322.461000 | \n",
" 183.152750 | \n",
" 105.936000 | \n",
" 21.970500 | \n",
" 3.732750 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.008000 | \n",
" 0.247500 | \n",
" 0.596000 | \n",
" 5.489500 | \n",
" 18.144500 | \n",
" 312.100000 | \n",
" 415.079500 | \n",
" 243.230500 | \n",
" 180.966500 | \n",
" 49.830500 | \n",
" 14.686000 | \n",
" 0.496500 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.248000 | \n",
" 1.948500 | \n",
" 2.076000 | \n",
" 19.796500 | \n",
" 33.066000 | \n",
" 412.568250 | \n",
" 555.284250 | \n",
" 401.224500 | \n",
" 242.433500 | \n",
" 115.655750 | \n",
" 37.006750 | \n",
" 4.151500 | \n",
"
\n",
" \n",
" max | \n",
" 3.013000 | \n",
" 8.410000 | \n",
" 9.619000 | \n",
" 53.266000 | \n",
" 80.539000 | \n",
" 773.737000 | \n",
" 780.006000 | \n",
" 541.579000 | \n",
" 613.522000 | \n",
" 225.904000 | \n",
" 122.809000 | \n",
" 37.685000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Jan Feb Mar Apr May Jun \\\n",
"count 38.000000 38.000000 38.000000 38.000000 38.000000 38.000000 \n",
"mean 0.294368 1.101132 1.677184 12.381237 25.059789 337.096395 \n",
"std 0.640510 1.741219 2.486516 13.671071 22.451708 171.666565 \n",
"min 0.000000 0.000000 0.000000 0.061000 0.508000 94.088000 \n",
"25% 0.000000 0.000000 0.000000 2.291750 7.005250 226.180250 \n",
"50% 0.008000 0.247500 0.596000 5.489500 18.144500 312.100000 \n",
"75% 0.248000 1.948500 2.076000 19.796500 33.066000 412.568250 \n",
"max 3.013000 8.410000 9.619000 53.266000 80.539000 773.737000 \n",
"\n",
" Jul Aug Sep Oct Nov Dec \n",
"count 38.000000 38.000000 38.000000 38.000000 38.000000 38.000000 \n",
"mean 430.010395 277.088342 201.111711 73.245263 25.223474 6.069632 \n",
"std 177.976444 132.245356 123.736320 62.936775 31.806180 11.725716 \n",
"min 84.936000 59.386000 44.574000 1.078000 0.260000 0.000000 \n",
"25% 322.461000 183.152750 105.936000 21.970500 3.732750 0.000000 \n",
"50% 415.079500 243.230500 180.966500 49.830500 14.686000 0.496500 \n",
"75% 555.284250 401.224500 242.433500 115.655750 37.006750 4.151500 \n",
"max 780.006000 541.579000 613.522000 225.904000 122.809000 37.685000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lluvias.describe()"
]
},
{
"cell_type": "markdown",
"id": "cba83393",
"metadata": {},
"source": [
"## 2. Gráficas con Matplotlib"
]
},
{
"cell_type": "markdown",
"id": "3be534c9",
"metadata": {},
"source": [
"### 2.1. Gráficas de linea"
]
},
{
"cell_type": "markdown",
"id": "ddbd28a1",
"metadata": {},
"source": [
"#### Gráficas elementales"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "27661020",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gráficos de linea:\n"
]
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
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