{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Visualización de datos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La visualización de datos es la representación gráfica de información y datos para facilitar la comprensión, análisis y toma de decisiones.\n", "\n", "- **Facilitar la comprensión**: Simplificar datos complejos para que sean comprensibles de un vistazo.\n", "- **Identificar patrones y tendencias**: Destacar relaciones y estructuras en los datos.\n", "- **Tomar decisiones informadas**: Proporcionar una base visual para decisiones fundamentadas.\n", "- **Comunicar información de manera efectiva**: Transmitir mensajes clave de manera clara y concisa.\n", "\n", "Caso contrario:\n", "

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\n", "\n", "Tomado de: [Analytics Vidhya](https://www.analyticsvidhya.com/blog/2021/06/data-visualization-techniques-you-must-know-as-a-data-scientist/)\n", "\n", "Otra forma de verlo, es que la visualización de datos es una forma de comprimir datos para que quepan en la memoria humana.\n", "\n", "Pero... ya habíamos visto formas de comprimir datos para nuestro entendimiento. ¿Recuerdas cuáles?\n", "\n", "Para ejemplificar éste punto, veamos dos ejemplos interesante." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "# import plotly.express as px\n", "\n", "sns.set()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Anscombe's quartet\n", "# https://matplotlib.org/stable/gallery/specialty_plots/anscombe.html\n", "\n", "x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]\n", "y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]\n", "y2 = [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]\n", "y3 = [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]\n", "x4 = [8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8]\n", "y4 = [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]\n", "\n", "G = ['I']*11 + ['II']*11 + ['III']*11 + ['IV']*11\n", "X = x*3 + x4\n", "Y = y1 + y2 + y3 + y4\n", "\n", "df = pd.DataFrame({'grupo': G, 'x': X, 'y': Y})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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grupoxy
0I108.04
1I86.95
2I137.58
3I98.81
4I118.33
5I149.96
6I67.24
7I44.26
8I1210.84
9I74.82
10I55.68
11II109.14
12II88.14
13II138.74
14II98.77
15II119.26
16II148.10
17II66.13
18II43.10
19II129.13
20II77.26
21II54.74
22III107.46
23III86.77
24III1312.74
25III97.11
26III117.81
27III148.84
28III66.08
29III45.39
30III128.15
31III76.42
32III55.73
33IV86.58
34IV85.76
35IV87.71
36IV88.84
37IV88.47
38IV87.04
39IV85.25
40IV1912.50
41IV85.56
42IV87.91
43IV86.89
\n", "
" ], "text/plain": [ " grupo x y\n", "0 I 10 8.04\n", "1 I 8 6.95\n", "2 I 13 7.58\n", "3 I 9 8.81\n", "4 I 11 8.33\n", "5 I 14 9.96\n", "6 I 6 7.24\n", "7 I 4 4.26\n", "8 I 12 10.84\n", "9 I 7 4.82\n", "10 I 5 5.68\n", "11 II 10 9.14\n", "12 II 8 8.14\n", "13 II 13 8.74\n", "14 II 9 8.77\n", "15 II 11 9.26\n", "16 II 14 8.10\n", "17 II 6 6.13\n", "18 II 4 3.10\n", "19 II 12 9.13\n", "20 II 7 7.26\n", "21 II 5 4.74\n", "22 III 10 7.46\n", "23 III 8 6.77\n", "24 III 13 12.74\n", "25 III 9 7.11\n", "26 III 11 7.81\n", "27 III 14 8.84\n", "28 III 6 6.08\n", "29 III 4 5.39\n", "30 III 12 8.15\n", "31 III 7 6.42\n", "32 III 5 5.73\n", "33 IV 8 6.58\n", "34 IV 8 5.76\n", "35 IV 8 7.71\n", "36 IV 8 8.84\n", "37 IV 8 8.47\n", "38 IV 8 7.04\n", "39 IV 8 5.25\n", "40 IV 19 12.50\n", "41 IV 8 5.56\n", "42 IV 8 7.91\n", "43 IV 8 6.89" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meanstd
xyxy
grupo
I97.53.322.03
II97.53.322.03
III97.53.322.03
IV97.53.322.03
\n", "
" ], "text/plain": [ " mean std \n", " x y x y\n", "grupo \n", "I 9 7.5 3.32 2.03\n", "II 9 7.5 3.32 2.03\n", "III 9 7.5 3.32 2.03\n", "IV 9 7.5 3.32 2.03" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filas = ['grupo']\n", "valores = ['x', 'y']\n", "aggfunc = ['mean', 'std']\n", "\n", "df.pivot_table(index=filas, values=valores, aggfunc=aggfunc).round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por lo tanto ¿Concluimos que la data del los sets I, II, III y IV son iguales?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualice df by grupo in diferent scatter plots\n", "\n", "fig, ax = plt.subplots(2, 2, figsize=(6, 6), sharex=True, sharey=True)\n", "ax[0, 0].scatter(x, y1)\n", "ax[0, 0].set_title('Grupo I')\n", "ax[0, 1].scatter(x, y2)\n", "ax[0, 1].set_title('Grupo II')\n", "ax[1, 0].scatter(x, y3)\n", "ax[1, 0].set_title('Grupo III')\n", "ax[1, 1].scatter(x4, y4)\n", "ax[1, 1].set_title('Grupo IV')\n", "fig.tight_layout()\n", "plt.xlim(0, 20)\n", "plt.ylim(0, 14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Conclusión?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos otro ejemplo." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
meanstd
x11.511.8
x21.521.8
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" ], "text/plain": [ " mean std\n", "x1 1.51 1.8\n", "x2 1.52 1.8" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(123)\n", "\n", "# create a bimodal random data\n", "x1 = np.concatenate([np.random.normal(0, 1, 10000), np.random.normal(3, 1, 10000)])\n", "x2 = np.random.normal(x1.mean(), x1.std(), 20000)\n", "\n", "data = [[x1.mean(), x1.std()], [x2.mean(), x2.std()]]\n", "\n", "\n", "pd.DataFrame(data, index=['x1', 'x2'], columns=['mean', 'std']).round(2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Son iguales?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ejemplo dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a trabajar con datos de:\n", "\n", "https://www.kaggle.com/datasets/dansbecker/melbourne-housing-snapshot" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('https://raw.githubusercontent.com/alejo-acosta/pmdb-material/master/data/melb_data.csv')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcode...BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
0Abbotsford85 Turner St2h1480000.0SBiggin3/12/20162.53067.0...1.01.0202.0NaNNaNYarra-37.7996144.9984Northern Metropolitan4019.0
1Abbotsford25 Bloomburg St2h1035000.0SBiggin4/02/20162.53067.0...1.00.0156.079.01900.0Yarra-37.8079144.9934Northern Metropolitan4019.0
2Abbotsford5 Charles St3h1465000.0SPBiggin4/03/20172.53067.0...2.00.0134.0150.01900.0Yarra-37.8093144.9944Northern Metropolitan4019.0
3Abbotsford40 Federation La3h850000.0PIBiggin4/03/20172.53067.0...2.01.094.0NaNNaNYarra-37.7969144.9969Northern Metropolitan4019.0
4Abbotsford55a Park St4h1600000.0VBNelson4/06/20162.53067.0...1.02.0120.0142.02014.0Yarra-37.8072144.9941Northern Metropolitan4019.0
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5 rows × 21 columns

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" ], "text/plain": [ " Suburb Address Rooms Type Price Method SellerG \\\n", "0 Abbotsford 85 Turner St 2 h 1480000.0 S Biggin \n", "1 Abbotsford 25 Bloomburg St 2 h 1035000.0 S Biggin \n", "2 Abbotsford 5 Charles St 3 h 1465000.0 SP Biggin \n", "3 Abbotsford 40 Federation La 3 h 850000.0 PI Biggin \n", "4 Abbotsford 55a Park St 4 h 1600000.0 VB Nelson \n", "\n", " Date Distance Postcode ... Bathroom Car Landsize BuildingArea \\\n", "0 3/12/2016 2.5 3067.0 ... 1.0 1.0 202.0 NaN \n", "1 4/02/2016 2.5 3067.0 ... 1.0 0.0 156.0 79.0 \n", "2 4/03/2017 2.5 3067.0 ... 2.0 0.0 134.0 150.0 \n", "3 4/03/2017 2.5 3067.0 ... 2.0 1.0 94.0 NaN \n", "4 4/06/2016 2.5 3067.0 ... 1.0 2.0 120.0 142.0 \n", "\n", " YearBuilt CouncilArea Lattitude Longtitude Regionname \\\n", "0 NaN Yarra -37.7996 144.9984 Northern Metropolitan \n", "1 1900.0 Yarra -37.8079 144.9934 Northern Metropolitan \n", "2 1900.0 Yarra -37.8093 144.9944 Northern Metropolitan \n", "3 NaN Yarra -37.7969 144.9969 Northern Metropolitan \n", "4 2014.0 Yarra -37.8072 144.9941 Northern Metropolitan \n", "\n", " Propertycount \n", "0 4019.0 \n", "1 4019.0 \n", "2 4019.0 \n", "3 4019.0 \n", "4 4019.0 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 13580 entries, 0 to 13579\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Suburb 13580 non-null object \n", " 1 Address 13580 non-null object \n", " 2 Rooms 13580 non-null int64 \n", " 3 Type 13580 non-null object \n", " 4 Price 13580 non-null float64\n", " 5 Method 13580 non-null object \n", " 6 SellerG 13580 non-null object \n", " 7 Date 13580 non-null object \n", " 8 Distance 13580 non-null float64\n", " 9 Postcode 13580 non-null float64\n", " 10 Bedroom2 13580 non-null float64\n", " 11 Bathroom 13580 non-null float64\n", " 12 Car 13518 non-null float64\n", " 13 Landsize 13580 non-null float64\n", " 14 BuildingArea 7130 non-null float64\n", " 15 YearBuilt 8205 non-null float64\n", " 16 CouncilArea 12211 non-null object \n", " 17 Lattitude 13580 non-null float64\n", " 18 Longtitude 13580 non-null float64\n", " 19 Regionname 13580 non-null object \n", " 20 Propertycount 13580 non-null float64\n", "dtypes: float64(12), int64(1), object(8)\n", "memory usage: 2.2+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RoomsPriceDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltLattitudeLongtitudePropertycount
count13580.0013580.0013580.0013580.0013580.0013580.0013518.0013580.007130.008205.0013580.0013580.0013580.00
mean2.941075684.0810.143105.302.911.531.61558.42151.971964.68-37.81145.007454.42
std0.96639310.725.8790.680.970.690.963990.67541.0137.270.080.104378.58
min1.0085000.000.003000.000.000.000.000.000.001196.00-38.18144.43249.00
25%2.00650000.006.103044.002.001.001.00177.0093.001940.00-37.86144.934380.00
50%3.00903000.009.203084.003.001.002.00440.00126.001970.00-37.80145.006555.00
75%3.001330000.0013.003148.003.002.002.00651.00174.001999.00-37.76145.0610331.00
max10.009000000.0048.103977.0020.008.0010.00433014.0044515.002018.00-37.41145.5321650.00
\n", "
" ], "text/plain": [ " Rooms Price Distance Postcode Bedroom2 Bathroom Car \\\n", "count 13580.00 13580.00 13580.00 13580.00 13580.00 13580.00 13518.00 \n", "mean 2.94 1075684.08 10.14 3105.30 2.91 1.53 1.61 \n", "std 0.96 639310.72 5.87 90.68 0.97 0.69 0.96 \n", "min 1.00 85000.00 0.00 3000.00 0.00 0.00 0.00 \n", "25% 2.00 650000.00 6.10 3044.00 2.00 1.00 1.00 \n", "50% 3.00 903000.00 9.20 3084.00 3.00 1.00 2.00 \n", "75% 3.00 1330000.00 13.00 3148.00 3.00 2.00 2.00 \n", "max 10.00 9000000.00 48.10 3977.00 20.00 8.00 10.00 \n", "\n", " Landsize BuildingArea YearBuilt Lattitude Longtitude \\\n", "count 13580.00 7130.00 8205.00 13580.00 13580.00 \n", "mean 558.42 151.97 1964.68 -37.81 145.00 \n", "std 3990.67 541.01 37.27 0.08 0.10 \n", "min 0.00 0.00 1196.00 -38.18 144.43 \n", "25% 177.00 93.00 1940.00 -37.86 144.93 \n", "50% 440.00 126.00 1970.00 -37.80 145.00 \n", "75% 651.00 174.00 1999.00 -37.76 145.06 \n", "max 433014.00 44515.00 2018.00 -37.41 145.53 \n", "\n", " Propertycount \n", "count 13580.00 \n", "mean 7454.42 \n", "std 4378.58 \n", "min 249.00 \n", "25% 4380.00 \n", "50% 6555.00 \n", "75% 10331.00 \n", "max 21650.00 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe().round(2)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Price'].hist()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "lista = ['CHUQUIMARCA/ARGUELLO DANIELA NICOLE','FREIRE/VARGAS MELANY BELÉN','GUAMAN/TIPAN JUAN FRANCISCO','PAREDES/ERAZO MARÍA JOSÉ','GONZALEZ/MENDEZ ARIAN MARCELO','PANCHANA/COTO STEPHANO ALBERTO','RAMIREZ/NAVARRO MARTIN RICARDO','VASCONEZ/CELI GABRIELA VIVIANA','VERDEZOTO/BARBA EVELYN ADRIANA','ANAGUANO/PERALVO ALAN ARIEL','DAVILA/JIMENEZ CAROLINA ESTEFANIA','TERAN/TOSCANO STEFANO MATEO','CHICAIZA/JAGUACO BRAYAN JAIR','CARTAGENOVA/ECHEVERRIA ISAAC  ','GONZALEZ/MENDEZ ARIEL MARTIN','MORALES/VELEZ DILAN ALEJANDRO','PILLIGUA/PEÑAHERRERA FRANCISCO XAVIER',]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'FREIRE/VARGAS MELANY BELÉN'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.choice(lista, 1)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gráficos interactivos - Plotly" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "gapminder = px.data.gapminder()\n", "tips = px.data.tips()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "day=%{x}
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