{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mushroom Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Data Set Desciption\n", " - http://archive.ics.uci.edu/ml/datasets/Mushroom\n", " - Attribute Information\n", " - 0 - classes (target attribute): edible=e, poisonous=p\n", " - 1 - cap-shape: bell=b, conical=c, convex=x, flat=f, knobbed=k, sunken=s\n", " - 2 - cap-surface: fibrous=f, grooves=g, scaly=y, smooth=s\n", " - 3 - cap-color: brown=n, buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y\n", " - 4 - bruises: bruises=t, no=f\n", " - 5 - odor: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, none=n, pungent=p, spicy=s\n", " - 6 - gill-attachment: attached=a, descending=d, free=f, notched=n\n", " - 7 - gill-spacing: close=c,crowded=w,distant=d\n", " - 8 - gill-size: broad=b, narrow=n\n", " - 9 - gill-color: black=k, brown=n, buff=b, chocolate=h, gray=g, green=r, orange=o, pink=p, purple=u, red=e, white=w, yellow=y\n", " - 10 - stalk-shape: enlarging=e, tapering=t\n", " - 11 - stalk-root: bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z, rooted=r, missing=?\n", " - 12 - stalk-surface-above-ring: fibrous=f, scaly=y, silky=k, smooth=s\n", " - 13 - stalk-surface-below-ring: fibrous=f, scaly=y, silky=k, smooth=s\n", " - 14 - stalk-color-above-ring: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y\n", " - 15 - stalk-color-below-ring: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y\n", " - 16 - veil-type: partial=p, universal=u\n", " - 17 - veil-color: brown=n, orange=o, white=w, yellow=y\n", " - 18 - ring-number: none=n, one=o, two=t\n", " - 19 - ring-type: cobwebby=c, evanescent=e, flaring=f, large=l, none=n, pendant=p, sheathing=s, zone=z\n", " - 20 - spore-print-color: black=k, brown=n, buff=b, chocolate=h, green=r, orange=o, purple=u, white=w, yellow=y\n", " - 21 - population: abundant=a, clustered=c, numerous=n, scattered=s, several=v, solitary=y\n", " - 22 - habitat: grasses=g, leaves=l, meadows=m, paths=p, urban=u, waste=w, woods=d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Pandas DataFame 다루기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1) Loading Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import urllib2\n", "from scipy import stats\n", "from pandas import Series, DataFrame\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "path = 'http://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data'\n", "raw_csv = urllib2.urlopen(path)\n", "col_names = range(23)\n", "df = pd.read_csv(raw_csv, names = col_names)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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1.000000 \n", "\n", "[8 rows x 23 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_edible.describe()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/yhhan/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: \n", "The default value for 'return_type' will change to 'axes' in a future release.\n", " To use the future behavior now, set return_type='axes'.\n", " To keep the previous behavior and silence this warning, set return_type='dict'.\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(15, 4)\n", "df_edible.boxplot(ax=ax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 \\\n", "count 4208.0 4208.000000 4208.000000 4208.000000 4208.000000 \n", "mean 1.0 0.293156 0.366286 0.224757 0.346008 \n", "std 0.0 0.300591 0.265197 0.192212 0.475752 \n", "min 1.0 0.000000 0.000000 0.000000 0.000000 \n", "25% 1.0 0.000000 0.000000 0.000000 0.000000 \n", "50% 1.0 0.200000 0.333333 0.222222 0.000000 \n", "75% 1.0 0.600000 0.666667 0.333333 1.000000 \n", "max 1.0 0.800000 0.666667 1.000000 1.000000 \n", "\n", " 5 6 7 8 9 \\\n", "count 4208.000000 4208.000000 4208.000000 4208.000000 4208.000000 \n", "mean 0.339354 0.045627 0.285171 0.931559 0.291912 \n", "std 0.078467 0.208700 0.451550 0.252531 0.205210 \n", "min 0.125000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.375000 0.000000 0.000000 1.000000 0.090909 \n", "50% 0.375000 0.000000 0.000000 1.000000 0.272727 \n", "75% 0.375000 0.000000 1.000000 1.000000 0.363636 \n", "max 0.375000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " ... 13 14 15 16 \\\n", "count ... 4208.000000 4208.000000 4208.000000 4208.0 \n", "mean ... 0.103295 0.101236 0.113118 0.0 \n", "std ... 0.239945 0.188588 0.213440 0.0 \n", "min ... 0.000000 0.000000 0.000000 0.0 \n", "25% ... 0.000000 0.000000 0.000000 0.0 \n", "50% ... 0.000000 0.000000 0.000000 0.0 \n", "75% ... 0.000000 0.125000 0.125000 0.0 \n", "max ... 1.000000 0.750000 0.875000 0.0 \n", "\n", " 17 18 19 20 21 \\\n", "count 4208.000000 4208.000000 4208.000000 4208.000000 4208.000000 \n", "mean 0.022814 0.062738 0.068441 0.157319 0.496198 \n", "std 0.110257 0.165648 0.129258 0.211734 0.323201 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 0.200000 \n", "50% 0.000000 0.000000 0.000000 0.125000 0.600000 \n", "75% 0.000000 0.000000 0.250000 0.125000 0.800000 \n", "max 0.666667 0.500000 0.750000 1.000000 1.000000 \n", "\n", " 22 \n", "count 4208.000000 \n", "mean 0.416033 \n", "std 0.240334 \n", "min 0.000000 \n", "25% 0.166667 \n", "50% 0.500000 \n", "75% 0.500000 \n", "max 1.000000 \n", "\n", "[8 rows x 23 columns]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_poisonous.describe()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/yhhan/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: \n", "The default value for 'return_type' will change to 'axes' in a future release.\n", " To use the future behavior now, set return_type='axes'.\n", " To keep the previous behavior and silence this warning, set return_type='dict'.\n", " app.launch_new_instance()\n" ] }, { "data": { "image/png": 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