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November 27, 2020 20:10
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mi_primer_cuaderno.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "mi_primer_cuaderno.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/GEJ1/521795f7b0e2e1bfffbae82d3c1eb9cc/mi_primer_cuaderno.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "weZWbVdx_cyC" | |
| }, | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "from matplotlib import pyplot as plt\n", | |
| "from scipy import stats" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "oY2wF9xTyahN", | |
| "cellView": "form", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 336 | |
| }, | |
| "outputId": "1f392050-0924-4d89-d2a8-058b43351f60" | |
| }, | |
| "source": [ | |
| "# @title Video para musicalizar el aprendizaje. Dale doble click a esta celda para ver el código.\n", | |
| "\n", | |
| "from IPython.display import HTML\n", | |
| "\n", | |
| "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/D2yymMhjRu8\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/D2yymMhjRu8\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 48 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "A7Ho3iQ84Tzv" | |
| }, | |
| "source": [ | |
| "# Montamos nuestro drive en Colab" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ooWsd1ApUvlp", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "0fd69056-9346-47e1-b814-87e37db29f16" | |
| }, | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Mounted at /content/drive\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Je3auXOf4Zua" | |
| }, | |
| "source": [ | |
| "# Cargamos nuestro archivo\n", | |
| "### Podes descargarlo de Github desde [acá](https://github.com/GEJ1/Tutoriales_jsPsych/blob/master/go-no-go.csv)\n", | |
| "\n", | |
| "### O si no están muy familiarizados con git pueden bajar el .zip desde [acá](https://minhaskamal.github.io/DownGit/#/home?url=https:%2F%2Fgithub.com%2FGEJ1%2FTutoriales_jsPsych%2Fblob%2Fmaster%2Fgo-no-go.csv), pero no se olviden de descompromirlo porque lo necesitamos en formato .csv" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "YnVtWROy_3FM" | |
| }, | |
| "source": [ | |
| "# Creamos nuestro DataFrame con los datos de nuestro experimento\n", | |
| "# Esta es la salida del experimento de go no go, tal cual como sale de la página donde tenemos subido el experimento.\n", | |
| "df = pd.read_csv('/content/drive/My Drive/datos_de_mi_experimento/go-no-go.csv')" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "4uhw8blVZf--", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "5713e116-b6d5-4f0d-b375-f543cf476be2" | |
| }, | |
| "source": [ | |
| "# Dimensiones descriptas en formato (filas, columnas)\n", | |
| "df.shape" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(24, 21)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "rjZ8uqdxAy8K", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 564 | |
| }, | |
| "outputId": "fb381608-e20e-4a2a-ead3-e508b85421fb" | |
| }, | |
| "source": [ | |
| "#Miramos los primeros 5 valores\n", | |
| "df.head()" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>run_id</th>\n", | |
| " <th>condition</th>\n", | |
| " <th>rt</th>\n", | |
| " <th>stimulus</th>\n", | |
| " <th>key_press</th>\n", | |
| " <th>trial_type</th>\n", | |
| " <th>trial_index</th>\n", | |
| " <th>time_elapsed</th>\n", | |
| " <th>internal_node_id</th>\n", | |
| " <th>success</th>\n", | |
| " <th>test_part</th>\n", | |
| " <th>correct_response</th>\n", | |
| " <th>correct</th>\n", | |
| " <th>recorded_at</th>\n", | |
| " <th>ip</th>\n", | |
| " <th>user_agent</th>\n", | |
| " <th>device</th>\n", | |
| " <th>browser</th>\n", | |
| " <th>browser_version</th>\n", | |
| " <th>platform</th>\n", | |
| " <th>platform_version</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2033.0200000025798</td>\n", | |
| " <td>Welcome to the experiment. Press any key to be...</td>\n", | |
| " <td>32</td>\n", | |
| " <td>html-keyboard-response</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2087</td>\n", | |
| " <td>0.0-0.0</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>2020-09-21 03:38:08</td>\n", | |
| " <td>181.164.53.97</td>\n", | |
| " <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n", | |
| " <td>WebKit</td>\n", | |
| " <td>Chrome</td>\n", | |
| " <td>85.0.4183.102</td>\n", | |
| " <td>Windows</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>fullscreen</td>\n", | |
| " <td>1</td>\n", | |
| " <td>4559</td>\n", | |
| " <td>0.0-1.0</td>\n", | |
| " <td>true</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>2020-09-21 03:38:08</td>\n", | |
| " <td>181.164.53.97</td>\n", | |
| " <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n", | |
| " <td>WebKit</td>\n", | |
| " <td>Chrome</td>\n", | |
| " <td>85.0.4183.102</td>\n", | |
| " <td>Windows</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>13162.125000002561</td>\n", | |
| " <td><p>In this experiment, a circle will appear in...</td>\n", | |
| " <td>32</td>\n", | |
| " <td>html-keyboard-response</td>\n", | |
| " <td>2</td>\n", | |
| " <td>17722</td>\n", | |
| " <td>0.0-2.0</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>2020-09-21 03:38:08</td>\n", | |
| " <td>181.164.53.97</td>\n", | |
| " <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n", | |
| " <td>WebKit</td>\n", | |
| " <td>Chrome</td>\n", | |
| " <td>85.0.4183.102</td>\n", | |
| " <td>Windows</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td><div style=\"font-size:60px;\">+</div></td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>html-keyboard-response</td>\n", | |
| " <td>3</td>\n", | |
| " <td>20227</td>\n", | |
| " <td>0.0-3.0-0.0</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>fixation</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>2020-09-21 03:38:08</td>\n", | |
| " <td>181.164.53.97</td>\n", | |
| " <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n", | |
| " <td>WebKit</td>\n", | |
| " <td>Chrome</td>\n", | |
| " <td>85.0.4183.102</td>\n", | |
| " <td>Windows</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>466.5249999961816</td>\n", | |
| " <td>img/orange.png</td>\n", | |
| " <td>74</td>\n", | |
| " <td>image-keyboard-response</td>\n", | |
| " <td>4</td>\n", | |
| " <td>20695</td>\n", | |
| " <td>0.0-3.0-1.0</td>\n", | |
| " <td>\"</td>\n", | |
| " <td>test</td>\n", | |
| " <td>j</td>\n", | |
| " <td>true</td>\n", | |
| " <td>2020-09-21 03:38:08</td>\n", | |
| " <td>181.164.53.97</td>\n", | |
| " <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n", | |
| " <td>WebKit</td>\n", | |
| " <td>Chrome</td>\n", | |
| " <td>85.0.4183.102</td>\n", | |
| " <td>Windows</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " run_id condition ... platform platform_version\n", | |
| "0 38 1 ... Windows 10.0\n", | |
| "1 38 1 ... Windows 10.0\n", | |
| "2 38 1 ... Windows 10.0\n", | |
| "3 38 1 ... Windows 10.0\n", | |
| "4 38 1 ... Windows 10.0\n", | |
| "\n", | |
| "[5 rows x 21 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ofclV2vzzEmL", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 297 | |
| }, | |
| "outputId": "09ee3130-ac8c-4214-f882-7e82a3873152" | |
| }, | |
| "source": [ | |
| "#Comando para estadísticas basicas\n", | |
| "df.describe()" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>run_id</th>\n", | |
| " <th>condition</th>\n", | |
| " <th>trial_index</th>\n", | |
| " <th>time_elapsed</th>\n", | |
| " <th>platform_version</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>24.0</td>\n", | |
| " <td>24.0</td>\n", | |
| " <td>24.000000</td>\n", | |
| " <td>24.000000</td>\n", | |
| " <td>24.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>11.500000</td>\n", | |
| " <td>25968.416667</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>7.071068</td>\n", | |
| " <td>8578.981114</td>\n", | |
| " <td>0.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2087.000000</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.750000</td>\n", | |
| " <td>22796.750000</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>11.500000</td>\n", | |
| " <td>28205.000000</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>17.250000</td>\n", | |
| " <td>31630.000000</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>23.000000</td>\n", | |
| " <td>35553.000000</td>\n", | |
| " <td>10.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " run_id condition trial_index time_elapsed platform_version\n", | |
| "count 24.0 24.0 24.000000 24.000000 24.0\n", | |
| "mean 38.0 1.0 11.500000 25968.416667 10.0\n", | |
| "std 0.0 0.0 7.071068 8578.981114 0.0\n", | |
| "min 38.0 1.0 0.000000 2087.000000 10.0\n", | |
| "25% 38.0 1.0 5.750000 22796.750000 10.0\n", | |
| "50% 38.0 1.0 11.500000 28205.000000 10.0\n", | |
| "75% 38.0 1.0 17.250000 31630.000000 10.0\n", | |
| "max 38.0 1.0 23.000000 35553.000000 10.0" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "krm6lg4ybs6M", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "ccb8c46a-90eb-4c68-f413-fd85f81e9067" | |
| }, | |
| "source": [ | |
| "# Chequeamos el tipo de las columnas\n", | |
| "\n", | |
| "print(df['run_id'].dtypes)\n", | |
| "print(df['condition'].dtypes)\n", | |
| "print(df['trial_index'].dtypes)\n", | |
| "print(df['time_elapsed'].dtypes)\n", | |
| "print(df['platform_version'].dtypes) " | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "int64\n", | |
| "int64\n", | |
| "int64\n", | |
| "int64\n", | |
| "float64\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "giUaKnLbUkrj" | |
| }, | |
| "source": [ | |
| "# Eliminamos las primeras 3 filas que no tienen datos que nos sirvan para este analisis\n", | |
| "df.drop([0, 1, 2], inplace=True )" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "K1TC7kCXENXz" | |
| }, | |
| "source": [ | |
| "# Filtro los tiempos de respuesta para estímulos naranjas\n", | |
| "df['rt_naranjas'] = df[df['stimulus'].str.contains(\"orange\")]['rt']" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-AuuXfwXEBD-" | |
| }, | |
| "source": [ | |
| "# Filtro los tiempos de respuesta para estímulos azules\n", | |
| "df['rt_azules'] = df[df['stimulus'].str.contains(\"blue\")]['rt']" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "NGQH3C4DUcRD", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "28951f3f-f991-4bd6-90bb-86a239a1cc40" | |
| }, | |
| "source": [ | |
| "# ¿Por qué no observamos los tiempos de respuesta en las estadísticas? \n", | |
| "# ¿ Qué tipo de datos contiene la columna \"rt\" ?\n", | |
| "df['rt_naranjas'].dtype" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "dtype('O')" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 16 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "WaHs7atzIlFA" | |
| }, | |
| "source": [ | |
| "# Preparo los naranjas y azules para utilizarlos eliminando los NaN y\n", | |
| "# asegurándome que sean los valores sean tipo numérico.\n", | |
| "naranjas = df['rt_naranjas'].dropna().astype(float)\n", | |
| "azules = df['rt_azules'].dropna().astype(float)\n" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "G0Xgpt16LaPT", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "outputId": "42c05cf3-7358-4e7d-ff45-b84beae9cd97" | |
| }, | |
| "source": [ | |
| "# Plor para los azules\n", | |
| "fig2, ax2 = plt.subplots()\n", | |
| "ax2.set_title('boxplot')\n", | |
| "ax2.boxplot(azules)\n", | |
| "\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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ramSmB86IuEtnqiTjVTU67HVIc+W2jCR1yLhLUoeMu3RiW4a9AOlkuOcuSR3ynbskdci4S1KHjLs0gyQfTvJEkj3DXot0Moy7NLOPAm8a9iKkk2XcpRlU1ReBp4a9DulkGXdJ6pBxl6QOGXdJ6pBxl6QOGXdpBkm2Af8KXJrkQJKxYa9Jmgv//IAkdch37pLUIeMuSR0y7pLUIeMuSR0y7pLUIeMuSR0y7pLUof8Hn2yixLeD0VIAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "PigTxB1HMTCH", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "outputId": "9f19bb88-3f3d-48ee-de56-accaac5b30e0" | |
| }, | |
| "source": [ | |
| "fig2, ax2 = plt.subplots()\n", | |
| "ax2.set_title('boxplot')\n", | |
| "ax2.boxplot(naranjas)\n", | |
| "\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_3XkrU2qObfG", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "96677a35-c36e-4d81-c7e7-bcfca3ac461f" | |
| }, | |
| "source": [ | |
| "azules" | |
| ], | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "6 464.150\n", | |
| "8 617.920\n", | |
| "14 444.515\n", | |
| "16 936.490\n", | |
| "22 572.165\n", | |
| "Name: rt_azules, dtype: float64" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vanGwSrOMFwL", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "outputId": "77fdaa49-a6bb-448e-9275-326c72078fd2" | |
| }, | |
| "source": [ | |
| "data = [azules,naranjas]\n", | |
| "fig1, ax = plt.subplots()\n", | |
| "ax.set_title('Tiempo de reacción')\n", | |
| "ax.boxplot(data)\n", | |
| "\n", | |
| "# etiqueta eje x \n", | |
| "ax.set_xticklabels(['azules', 'naranjas'])\n", | |
| "\n", | |
| "# Estilo y colores de las cajas\n", | |
| "bp = ax.boxplot(data, patch_artist = True) \n", | |
| "colors = ['#0000FF', '#f44611'] \n", | |
| " \n", | |
| "for patch, color in zip(bp['boxes'], colors): \n", | |
| " patch.set_facecolor(color) \n", | |
| "\n", | |
| "# Estilos de los puntos \n", | |
| "for flier in bp['fliers']: \n", | |
| " flier.set(marker ='x', \n", | |
| " color ='#e7298a', \n", | |
| " alpha = 0.5) \n", | |
| " \n", | |
| "# Color y grosor de las líneas que marcan las medianas \n", | |
| "for median in bp['medians']: \n", | |
| " median.set(color ='black', \n", | |
| " linewidth = 3) \n", | |
| "# Mostrar el gráfico\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "qcEbC4E8E76M", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "fdaebe68-296a-4381-cab4-b839cf826de8" | |
| }, | |
| "source": [ | |
| "# Hacemos un t-test de naranjas vs. azules\n", | |
| "\n", | |
| "stats.ttest_ind(naranjas,azules)\n" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Ttest_indResult(statistic=-1.7964682563327923, pvalue=0.1101458715478464)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 76 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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