diff --git a/notebooks/data/ans4.csv b/notebooks/data/ans4.csv new file mode 100644 index 000000000..8b18a6a55 --- /dev/null +++ b/notebooks/data/ans4.csv @@ -0,0 +1,45 @@ +dataset,id,x,y +I,0,10.0,8.04 +I,1,8.0,6.95 +I,2,13.0,7.58 +I,3,9.0,8.81 +I,4,11.0,8.33 +I,5,14.0,9.96 +I,6,6.0,7.24 +I,7,4.0,4.26 +I,8,12.0,10.84 +I,9,7.0,4.82 +I,10,5.0,5.68 +II,0,10.0,9.14 +II,1,8.0,8.14 +II,2,13.0,8.74 +II,3,9.0,8.77 +II,4,11.0,9.26 +II,5,14.0,8.1 +II,6,6.0,6.13 +II,7,4.0,3.1 +II,8,12.0,9.13 +II,9,7.0,7.26 +II,10,5.0,4.74 +III,0,10.0,7.46 +III,1,8.0,6.77 +III,2,13.0,12.74 +III,3,9.0,7.11 +III,4,11.0,7.81 +III,5,14.0,8.84 +III,6,6.0,6.08 +III,7,4.0,5.39 +III,8,12.0,8.15 +III,9,7.0,6.42 +III,10,5.0,5.73 +IV,0,8.0,6.58 +IV,1,8.0,5.76 +IV,2,8.0,7.71 +IV,3,8.0,8.84 +IV,4,8.0,8.47 +IV,5,8.0,7.04 +IV,6,8.0,5.25 +IV,7,19.0,12.5 +IV,8,8.0,5.56 +IV,9,8.0,7.91 +IV,10,8.0,6.89 diff --git a/notebooks/data/ans4.ipynb b/notebooks/data/ans4.ipynb new file mode 100644 index 000000000..ef639a3c2 --- /dev/null +++ b/notebooks/data/ans4.ipynb @@ -0,0 +1,420 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "ans4.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "MRiM3uK2kKnN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(font_scale=1.5)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "_IeJP_8mkQUr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 545 + }, + "outputId": "202d83eb-214e-4206-a917-8f65fb047ba0" + }, + "cell_type": "code", + "source": [ + "# Load the raw data\n", + "df = pd.read_csv('https://raw.githubusercontent.com/rgerkin/PythonDataScienceHandbook/master/notebooks/data/ans4.csv')\n", + "\n", + "# Renumber the indices\n", + "df['id'] = [x%11 for x in range(df.shape[0])]\n", + "\n", + "# Set new row names\n", + "df = df.set_index(['dataset','id'])\n", + "\n", + "df.head(15)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
xy
datasetid
I010.08.04
18.06.95
213.07.58
39.08.81
411.08.33
514.09.96
66.07.24
74.04.26
812.010.84
97.04.82
105.05.68
II010.09.14
18.08.14
213.08.74
39.08.77
\n", + "
" + ], + "text/plain": [ + " x y\n", + "dataset id \n", + "I 0 10.0 8.04\n", + " 1 8.0 6.95\n", + " 2 13.0 7.58\n", + " 3 9.0 8.81\n", + " 4 11.0 8.33\n", + " 5 14.0 9.96\n", + " 6 6.0 7.24\n", + " 7 4.0 4.26\n", + " 8 12.0 10.84\n", + " 9 7.0 4.82\n", + " 10 5.0 5.68\n", + "II 0 10.0 9.14\n", + " 1 8.0 8.14\n", + " 2 13.0 8.74\n", + " 3 9.0 8.77" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "mDi2i2cIkwLs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 370 + }, + "outputId": "05978bfd-1c78-419d-c067-f82cefa2e39d" + }, + "cell_type": "code", + "source": [ + "df.plot.scatter(x='x',y='y');" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAFhCAYAAABtSuN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "8KWifia0lEvf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 421 + }, + "outputId": "c4f4e8eb-9d2f-4344-c6b8-375004b36bc2" + }, + "cell_type": "code", + "source": [ + "# Enter your group number here:\n", + "group = '' # Enter I, II, III, or IV\n", + "\n", + "df.loc[group]" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
xy
id
010.08.04
18.06.95
213.07.58
39.08.81
411.08.33
514.09.96
66.07.24
74.04.26
812.010.84
97.04.82
105.05.68
\n", + "
" + ], + "text/plain": [ + " x y\n", + "id \n", + "0 10.0 8.04\n", + "1 8.0 6.95\n", + "2 13.0 7.58\n", + "3 9.0 8.81\n", + "4 11.0 8.33\n", + "5 14.0 9.96\n", + "6 6.0 7.24\n", + "7 4.0 4.26\n", + "8 12.0 10.84\n", + "9 7.0 4.82\n", + "10 5.0 5.68" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "metadata": { + "id": "DGu0O_n9lc9d", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "e29b6bfb-5ca7-4bbe-89a5-97d32c69cee4" + }, + "cell_type": "code", + "source": [ + "df.loc[group].mean().round(2)" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "x 9.0\n", + "y 7.5\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "metadata": { + "id": "q3sCvKMTmNtS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file