{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#ipython"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandas: caricare i dati"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"DATA_PATH = '/usr/lib/python3/dist-packages/pandas/tests/data/tips.csv'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!cat /usr/lib/python3/dist-packages/pandas/tests/data/tips.csv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv(DATA_PATH)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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"\n",
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\n",
" \n",
" \n",
" \n",
" total_bill \n",
" tip \n",
" sex \n",
" smoker \n",
" day \n",
" time \n",
" size \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 16.99 \n",
" 1.01 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
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" 1 \n",
" 10.34 \n",
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" No \n",
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" Dinner \n",
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" 3 \n",
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" 3 \n",
" 23.68 \n",
" 3.31 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
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" 4 \n",
" 24.59 \n",
" 3.61 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
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" 5 \n",
" 25.29 \n",
" 4.71 \n",
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" No \n",
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" No \n",
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" 26.88 \n",
" 3.12 \n",
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" No \n",
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" Dinner \n",
" 4 \n",
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" 8 \n",
" 15.04 \n",
" 1.96 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
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" 9 \n",
" 14.78 \n",
" 3.23 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 10 \n",
" 10.27 \n",
" 1.71 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 11 \n",
" 35.26 \n",
" 5.00 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 12 \n",
" 15.42 \n",
" 1.57 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
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" 13 \n",
" 18.43 \n",
" 3.00 \n",
" Male \n",
" No \n",
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" Dinner \n",
" 4 \n",
" \n",
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" 14 \n",
" 14.83 \n",
" 3.02 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
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" 15 \n",
" 21.58 \n",
" 3.92 \n",
" Male \n",
" No \n",
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" Dinner \n",
" 2 \n",
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" 16 \n",
" 10.33 \n",
" 1.67 \n",
" Female \n",
" No \n",
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" Dinner \n",
" 3 \n",
" \n",
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" 3.71 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" \n",
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" 18 \n",
" 16.97 \n",
" 3.50 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 19 \n",
" 20.65 \n",
" 3.35 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 20 \n",
" 17.92 \n",
" 4.08 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 21 \n",
" 20.29 \n",
" 2.75 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 22 \n",
" 15.77 \n",
" 2.23 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 23 \n",
" 39.42 \n",
" 7.58 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 24 \n",
" 19.82 \n",
" 3.18 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 25 \n",
" 17.81 \n",
" 2.34 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 26 \n",
" 13.37 \n",
" 2.00 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 27 \n",
" 12.69 \n",
" 2.00 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 28 \n",
" 21.70 \n",
" 4.30 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 29 \n",
" 19.65 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 214 \n",
" 28.17 \n",
" 6.50 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 215 \n",
" 12.90 \n",
" 1.10 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 216 \n",
" 28.15 \n",
" 3.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 5 \n",
" \n",
" \n",
" 217 \n",
" 11.59 \n",
" 1.50 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 218 \n",
" 7.74 \n",
" 1.44 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 219 \n",
" 30.14 \n",
" 3.09 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 220 \n",
" 12.16 \n",
" 2.20 \n",
" Male \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 221 \n",
" 13.42 \n",
" 3.48 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 222 \n",
" 8.58 \n",
" 1.92 \n",
" Male \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 1 \n",
" \n",
" \n",
" 223 \n",
" 15.98 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Fri \n",
" Lunch \n",
" 3 \n",
" \n",
" \n",
" 224 \n",
" 13.42 \n",
" 1.58 \n",
" Male \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 225 \n",
" 16.27 \n",
" 2.50 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 226 \n",
" 10.09 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 227 \n",
" 20.45 \n",
" 3.00 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 228 \n",
" 13.28 \n",
" 2.72 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 229 \n",
" 22.12 \n",
" 2.88 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 230 \n",
" 24.01 \n",
" 2.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 231 \n",
" 15.69 \n",
" 3.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 232 \n",
" 11.61 \n",
" 3.39 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 233 \n",
" 10.77 \n",
" 1.47 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 234 \n",
" 15.53 \n",
" 3.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 235 \n",
" 10.07 \n",
" 1.25 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 236 \n",
" 12.60 \n",
" 1.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 237 \n",
" 32.83 \n",
" 1.17 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 238 \n",
" 35.83 \n",
" 4.67 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 239 \n",
" 29.03 \n",
" 5.92 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 240 \n",
" 27.18 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 241 \n",
" 22.67 \n",
" 2.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 242 \n",
" 17.82 \n",
" 1.75 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 243 \n",
" 18.78 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Thur \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
"
\n",
"
244 rows × 7 columns
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"1 10.34 1.66 Male No Sun Dinner 3\n",
"2 21.01 3.50 Male No Sun Dinner 3\n",
"3 23.68 3.31 Male No Sun Dinner 2\n",
"4 24.59 3.61 Female No Sun Dinner 4\n",
"5 25.29 4.71 Male No Sun Dinner 4\n",
"6 8.77 2.00 Male No Sun Dinner 2\n",
"7 26.88 3.12 Male No Sun Dinner 4\n",
"8 15.04 1.96 Male No Sun Dinner 2\n",
"9 14.78 3.23 Male No Sun Dinner 2\n",
"10 10.27 1.71 Male No Sun Dinner 2\n",
"11 35.26 5.00 Female No Sun Dinner 4\n",
"12 15.42 1.57 Male No Sun Dinner 2\n",
"13 18.43 3.00 Male No Sun Dinner 4\n",
"14 14.83 3.02 Female No Sun Dinner 2\n",
"15 21.58 3.92 Male No Sun Dinner 2\n",
"16 10.33 1.67 Female No Sun Dinner 3\n",
"17 16.29 3.71 Male No Sun Dinner 3\n",
"18 16.97 3.50 Female No Sun Dinner 3\n",
"19 20.65 3.35 Male No Sat Dinner 3\n",
"20 17.92 4.08 Male No Sat Dinner 2\n",
"21 20.29 2.75 Female No Sat Dinner 2\n",
"22 15.77 2.23 Female No Sat Dinner 2\n",
"23 39.42 7.58 Male No Sat Dinner 4\n",
"24 19.82 3.18 Male No Sat Dinner 2\n",
"25 17.81 2.34 Male No Sat Dinner 4\n",
"26 13.37 2.00 Male No Sat Dinner 2\n",
"27 12.69 2.00 Male No Sat Dinner 2\n",
"28 21.70 4.30 Male No Sat Dinner 2\n",
"29 19.65 3.00 Female No Sat Dinner 2\n",
".. ... ... ... ... ... ... ...\n",
"214 28.17 6.50 Female Yes Sat Dinner 3\n",
"215 12.90 1.10 Female Yes Sat Dinner 2\n",
"216 28.15 3.00 Male Yes Sat Dinner 5\n",
"217 11.59 1.50 Male Yes Sat Dinner 2\n",
"218 7.74 1.44 Male Yes Sat Dinner 2\n",
"219 30.14 3.09 Female Yes Sat Dinner 4\n",
"220 12.16 2.20 Male Yes Fri Lunch 2\n",
"221 13.42 3.48 Female Yes Fri Lunch 2\n",
"222 8.58 1.92 Male Yes Fri Lunch 1\n",
"223 15.98 3.00 Female No Fri Lunch 3\n",
"224 13.42 1.58 Male Yes Fri Lunch 2\n",
"225 16.27 2.50 Female Yes Fri Lunch 2\n",
"226 10.09 2.00 Female Yes Fri Lunch 2\n",
"227 20.45 3.00 Male No Sat Dinner 4\n",
"228 13.28 2.72 Male No Sat Dinner 2\n",
"229 22.12 2.88 Female Yes Sat Dinner 2\n",
"230 24.01 2.00 Male Yes Sat Dinner 4\n",
"231 15.69 3.00 Male Yes Sat Dinner 3\n",
"232 11.61 3.39 Male No Sat Dinner 2\n",
"233 10.77 1.47 Male No Sat Dinner 2\n",
"234 15.53 3.00 Male Yes Sat Dinner 2\n",
"235 10.07 1.25 Male No Sat Dinner 2\n",
"236 12.60 1.00 Male Yes Sat Dinner 2\n",
"237 32.83 1.17 Male Yes Sat Dinner 2\n",
"238 35.83 4.67 Female No Sat Dinner 3\n",
"239 29.03 5.92 Male No Sat Dinner 3\n",
"240 27.18 2.00 Female Yes Sat Dinner 2\n",
"241 22.67 2.00 Male Yes Sat Dinner 2\n",
"242 17.82 1.75 Male No Sat Dinner 2\n",
"243 18.78 3.00 Female No Thur Dinner 2\n",
"\n",
"[244 rows x 7 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" total_bill \n",
" tip \n",
" sex \n",
" smoker \n",
" day \n",
" time \n",
" size \n",
" \n",
" \n",
" \n",
" \n",
" 67 \n",
" 3.07 \n",
" 1.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 1 \n",
" \n",
" \n",
" 236 \n",
" 12.60 \n",
" 1.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 92 \n",
" 5.75 \n",
" 1.00 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 111 \n",
" 7.25 \n",
" 1.00 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 1 \n",
" \n",
" \n",
" 0 \n",
" 16.99 \n",
" 1.01 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 215 \n",
" 12.90 \n",
" 1.10 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 237 \n",
" 32.83 \n",
" 1.17 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 235 \n",
" 10.07 \n",
" 1.25 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 75 \n",
" 10.51 \n",
" 1.25 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 135 \n",
" 8.51 \n",
" 1.25 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 43 \n",
" 9.68 \n",
" 1.32 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 146 \n",
" 18.64 \n",
" 1.36 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 3 \n",
" \n",
" \n",
" 218 \n",
" 7.74 \n",
" 1.44 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 195 \n",
" 7.56 \n",
" 1.44 \n",
" Male \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 30 \n",
" 9.55 \n",
" 1.45 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 233 \n",
" 10.77 \n",
" 1.47 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 126 \n",
" 8.52 \n",
" 1.48 \n",
" Male \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 190 \n",
" 15.69 \n",
" 1.50 \n",
" Male \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 57 \n",
" 26.41 \n",
" 1.50 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 97 \n",
" 12.03 \n",
" 1.50 \n",
" Male \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 99 \n",
" 12.46 \n",
" 1.50 \n",
" Male \n",
" No \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 117 \n",
" 10.65 \n",
" 1.50 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 132 \n",
" 11.17 \n",
" 1.50 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 217 \n",
" 11.59 \n",
" 1.50 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 145 \n",
" 8.35 \n",
" 1.50 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 130 \n",
" 19.08 \n",
" 1.50 \n",
" Male \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 53 \n",
" 9.94 \n",
" 1.56 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 12 \n",
" 15.42 \n",
" 1.57 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 224 \n",
" 13.42 \n",
" 1.58 \n",
" Male \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 168 \n",
" 10.59 \n",
" 1.61 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 5 \n",
" 25.29 \n",
" 4.71 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 95 \n",
" 40.17 \n",
" 4.73 \n",
" Male \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 46 \n",
" 22.23 \n",
" 5.00 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 39 \n",
" 31.27 \n",
" 5.00 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 142 \n",
" 41.19 \n",
" 5.00 \n",
" Male \n",
" No \n",
" Thur \n",
" Lunch \n",
" 5 \n",
" \n",
" \n",
" 143 \n",
" 27.05 \n",
" 5.00 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 6 \n",
" \n",
" \n",
" 156 \n",
" 48.17 \n",
" 5.00 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 6 \n",
" \n",
" \n",
" 185 \n",
" 20.69 \n",
" 5.00 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 5 \n",
" \n",
" \n",
" 11 \n",
" 35.26 \n",
" 5.00 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 83 \n",
" 32.68 \n",
" 5.00 \n",
" Male \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 197 \n",
" 43.11 \n",
" 5.00 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 4 \n",
" \n",
" \n",
" 73 \n",
" 25.28 \n",
" 5.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 116 \n",
" 29.93 \n",
" 5.07 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 155 \n",
" 29.85 \n",
" 5.14 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 5 \n",
" \n",
" \n",
" 172 \n",
" 7.25 \n",
" 5.15 \n",
" Male \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 211 \n",
" 25.89 \n",
" 5.16 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 85 \n",
" 34.83 \n",
" 5.17 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 4 \n",
" \n",
" \n",
" 52 \n",
" 34.81 \n",
" 5.20 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 44 \n",
" 30.40 \n",
" 5.60 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 181 \n",
" 23.33 \n",
" 5.65 \n",
" Male \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" \n",
" \n",
" 88 \n",
" 24.71 \n",
" 5.85 \n",
" Male \n",
" No \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" \n",
" \n",
" 239 \n",
" 29.03 \n",
" 5.92 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 47 \n",
" 32.40 \n",
" 6.00 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 183 \n",
" 23.17 \n",
" 6.50 \n",
" Male \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 214 \n",
" 28.17 \n",
" 6.50 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
" 141 \n",
" 34.30 \n",
" 6.70 \n",
" Male \n",
" No \n",
" Thur \n",
" Lunch \n",
" 6 \n",
" \n",
" \n",
" 59 \n",
" 48.27 \n",
" 6.73 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 23 \n",
" 39.42 \n",
" 7.58 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 212 \n",
" 48.33 \n",
" 9.00 \n",
" Male \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" \n",
" \n",
" 170 \n",
" 50.81 \n",
" 10.00 \n",
" Male \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" \n",
" \n",
"
\n",
"
244 rows × 7 columns
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"67 3.07 1.00 Female Yes Sat Dinner 1\n",
"236 12.60 1.00 Male Yes Sat Dinner 2\n",
"92 5.75 1.00 Female Yes Fri Dinner 2\n",
"111 7.25 1.00 Female No Sat Dinner 1\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"215 12.90 1.10 Female Yes Sat Dinner 2\n",
"237 32.83 1.17 Male Yes Sat Dinner 2\n",
"235 10.07 1.25 Male No Sat Dinner 2\n",
"75 10.51 1.25 Male No Sat Dinner 2\n",
"135 8.51 1.25 Female No Thur Lunch 2\n",
"43 9.68 1.32 Male No Sun Dinner 2\n",
"146 18.64 1.36 Female No Thur Lunch 3\n",
"218 7.74 1.44 Male Yes Sat Dinner 2\n",
"195 7.56 1.44 Male No Thur Lunch 2\n",
"30 9.55 1.45 Male No Sat Dinner 2\n",
"233 10.77 1.47 Male No Sat Dinner 2\n",
"126 8.52 1.48 Male No Thur Lunch 2\n",
"190 15.69 1.50 Male Yes Sun Dinner 2\n",
"57 26.41 1.50 Female No Sat Dinner 2\n",
"97 12.03 1.50 Male Yes Fri Dinner 2\n",
"99 12.46 1.50 Male No Fri Dinner 2\n",
"117 10.65 1.50 Female No Thur Lunch 2\n",
"132 11.17 1.50 Female No Thur Lunch 2\n",
"217 11.59 1.50 Male Yes Sat Dinner 2\n",
"145 8.35 1.50 Female No Thur Lunch 2\n",
"130 19.08 1.50 Male No Thur Lunch 2\n",
"53 9.94 1.56 Male No Sun Dinner 2\n",
"12 15.42 1.57 Male No Sun Dinner 2\n",
"224 13.42 1.58 Male Yes Fri Lunch 2\n",
"168 10.59 1.61 Female Yes Sat Dinner 2\n",
".. ... ... ... ... ... ... ...\n",
"5 25.29 4.71 Male No Sun Dinner 4\n",
"95 40.17 4.73 Male Yes Fri Dinner 4\n",
"46 22.23 5.00 Male No Sun Dinner 2\n",
"39 31.27 5.00 Male No Sat Dinner 3\n",
"142 41.19 5.00 Male No Thur Lunch 5\n",
"143 27.05 5.00 Female No Thur Lunch 6\n",
"156 48.17 5.00 Male No Sun Dinner 6\n",
"185 20.69 5.00 Male No Sun Dinner 5\n",
"11 35.26 5.00 Female No Sun Dinner 4\n",
"83 32.68 5.00 Male Yes Thur Lunch 2\n",
"197 43.11 5.00 Female Yes Thur Lunch 4\n",
"73 25.28 5.00 Female Yes Sat Dinner 2\n",
"116 29.93 5.07 Male No Sun Dinner 4\n",
"155 29.85 5.14 Female No Sun Dinner 5\n",
"172 7.25 5.15 Male Yes Sun Dinner 2\n",
"211 25.89 5.16 Male Yes Sat Dinner 4\n",
"85 34.83 5.17 Female No Thur Lunch 4\n",
"52 34.81 5.20 Female No Sun Dinner 4\n",
"44 30.40 5.60 Male No Sun Dinner 4\n",
"181 23.33 5.65 Male Yes Sun Dinner 2\n",
"88 24.71 5.85 Male No Thur Lunch 2\n",
"239 29.03 5.92 Male No Sat Dinner 3\n",
"47 32.40 6.00 Male No Sun Dinner 4\n",
"183 23.17 6.50 Male Yes Sun Dinner 4\n",
"214 28.17 6.50 Female Yes Sat Dinner 3\n",
"141 34.30 6.70 Male No Thur Lunch 6\n",
"59 48.27 6.73 Male No Sat Dinner 4\n",
"23 39.42 7.58 Male No Sat Dinner 4\n",
"212 48.33 9.00 Male No Sat Dinner 4\n",
"170 50.81 10.00 Male Yes Sat Dinner 3\n",
"\n",
"[244 rows x 7 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.sort_values('tip')"
]
},
{
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"execution_count": 8,
"metadata": {
"collapsed": false
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"outputs": [
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"0 1.01\n",
"1 1.66\n",
"2 3.50\n",
"3 3.31\n",
"4 3.61\n",
"5 4.71\n",
"6 2.00\n",
"7 3.12\n",
"8 1.96\n",
"9 3.23\n",
"10 1.71\n",
"11 5.00\n",
"12 1.57\n",
"13 3.00\n",
"14 3.02\n",
"15 3.92\n",
"16 1.67\n",
"17 3.71\n",
"18 3.50\n",
"19 3.35\n",
"20 4.08\n",
"21 2.75\n",
"22 2.23\n",
"23 7.58\n",
"24 3.18\n",
"25 2.34\n",
"26 2.00\n",
"27 2.00\n",
"28 4.30\n",
"29 3.00\n",
" ... \n",
"214 6.50\n",
"215 1.10\n",
"216 3.00\n",
"217 1.50\n",
"218 1.44\n",
"219 3.09\n",
"220 2.20\n",
"221 3.48\n",
"222 1.92\n",
"223 3.00\n",
"224 1.58\n",
"225 2.50\n",
"226 2.00\n",
"227 3.00\n",
"228 2.72\n",
"229 2.88\n",
"230 2.00\n",
"231 3.00\n",
"232 3.39\n",
"233 1.47\n",
"234 3.00\n",
"235 1.25\n",
"236 1.00\n",
"237 1.17\n",
"238 4.67\n",
"239 5.92\n",
"240 2.00\n",
"241 2.00\n",
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"metadata": {
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"outputs": [
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"1 0.160542\n",
"2 0.166587\n",
"3 0.139780\n",
"4 0.146808\n",
"5 0.186240\n",
"6 0.228050\n",
"7 0.116071\n",
"8 0.130319\n",
"9 0.218539\n",
"10 0.166504\n",
"11 0.141804\n",
"12 0.101816\n",
"13 0.162778\n",
"14 0.203641\n",
"15 0.181650\n",
"16 0.161665\n",
"17 0.227747\n",
"18 0.206246\n",
"19 0.162228\n",
"20 0.227679\n",
"21 0.135535\n",
"22 0.141408\n",
"23 0.192288\n",
"24 0.160444\n",
"25 0.131387\n",
"26 0.149589\n",
"27 0.157604\n",
"28 0.198157\n",
"29 0.152672\n",
" ... \n",
"214 0.230742\n",
"215 0.085271\n",
"216 0.106572\n",
"217 0.129422\n",
"218 0.186047\n",
"219 0.102522\n",
"220 0.180921\n",
"221 0.259314\n",
"222 0.223776\n",
"223 0.187735\n",
"224 0.117735\n",
"225 0.153657\n",
"226 0.198216\n",
"227 0.146699\n",
"228 0.204819\n",
"229 0.130199\n",
"230 0.083299\n",
"231 0.191205\n",
"232 0.291990\n",
"233 0.136490\n",
"234 0.193175\n",
"235 0.124131\n",
"236 0.079365\n",
"237 0.035638\n",
"238 0.130338\n",
"239 0.203927\n",
"240 0.073584\n",
"241 0.088222\n",
"242 0.098204\n",
"243 0.159744\n",
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},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['perc_tip'] = data['tip'] / data['total_bill']"
]
},
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"metadata": {
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" 29 \n",
" 19.65 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.152672 \n",
" \n",
" \n",
" 32 \n",
" 15.06 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.199203 \n",
" \n",
" \n",
" 33 \n",
" 20.69 \n",
" 2.45 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" 0.118415 \n",
" \n",
" \n",
" 37 \n",
" 16.93 \n",
" 3.07 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" 0.181335 \n",
" \n",
" \n",
" 51 \n",
" 10.29 \n",
" 2.60 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" 0.252672 \n",
" \n",
" \n",
" 52 \n",
" 34.81 \n",
" 5.20 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" 0.149382 \n",
" \n",
" \n",
" 57 \n",
" 26.41 \n",
" 1.50 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.056797 \n",
" \n",
" \n",
" 66 \n",
" 16.45 \n",
" 2.47 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.150152 \n",
" \n",
" \n",
" 67 \n",
" 3.07 \n",
" 1.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 1 \n",
" 0.325733 \n",
" \n",
" \n",
" 71 \n",
" 17.07 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" 0.175747 \n",
" \n",
" \n",
" 72 \n",
" 26.86 \n",
" 3.14 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.116902 \n",
" \n",
" \n",
" 73 \n",
" 25.28 \n",
" 5.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.197785 \n",
" \n",
" \n",
" 74 \n",
" 14.73 \n",
" 2.20 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.149355 \n",
" \n",
" \n",
" 82 \n",
" 10.07 \n",
" 1.83 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 1 \n",
" 0.181728 \n",
" \n",
" \n",
" 85 \n",
" 34.83 \n",
" 5.17 \n",
" Female \n",
" No \n",
" Thur \n",
" Lunch \n",
" 4 \n",
" 0.148435 \n",
" \n",
" \n",
" 92 \n",
" 5.75 \n",
" 1.00 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" 0.173913 \n",
" \n",
" \n",
" 93 \n",
" 16.32 \n",
" 4.30 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" 0.263480 \n",
" \n",
" \n",
" 94 \n",
" 22.75 \n",
" 3.25 \n",
" Female \n",
" No \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" 0.142857 \n",
" \n",
" \n",
" 100 \n",
" 11.35 \n",
" 2.50 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" 0.220264 \n",
" \n",
" \n",
" 101 \n",
" 15.38 \n",
" 3.00 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Dinner \n",
" 2 \n",
" 0.195059 \n",
" \n",
" \n",
" 102 \n",
" 44.30 \n",
" 2.50 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" 0.056433 \n",
" \n",
" \n",
" 103 \n",
" 22.42 \n",
" 3.48 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.155219 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 155 \n",
" 29.85 \n",
" 5.14 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 5 \n",
" 0.172194 \n",
" \n",
" \n",
" 157 \n",
" 25.00 \n",
" 3.75 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" 0.150000 \n",
" \n",
" \n",
" 158 \n",
" 13.39 \n",
" 2.61 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" 0.194922 \n",
" \n",
" \n",
" 162 \n",
" 16.21 \n",
" 2.00 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" 0.123381 \n",
" \n",
" \n",
" 164 \n",
" 17.51 \n",
" 3.00 \n",
" Female \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" 0.171331 \n",
" \n",
" \n",
" 168 \n",
" 10.59 \n",
" 1.61 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.152030 \n",
" \n",
" \n",
" 169 \n",
" 10.63 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.188147 \n",
" \n",
" \n",
" 178 \n",
" 9.60 \n",
" 4.00 \n",
" Female \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" 0.416667 \n",
" \n",
" \n",
" 186 \n",
" 20.90 \n",
" 3.50 \n",
" Female \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" 0.167464 \n",
" \n",
" \n",
" 188 \n",
" 18.15 \n",
" 3.50 \n",
" Female \n",
" Yes \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" 0.192837 \n",
" \n",
" \n",
" 191 \n",
" 19.81 \n",
" 4.19 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" 0.211509 \n",
" \n",
" \n",
" 197 \n",
" 43.11 \n",
" 5.00 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 4 \n",
" 0.115982 \n",
" \n",
" \n",
" 198 \n",
" 13.00 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" 0.153846 \n",
" \n",
" \n",
" 201 \n",
" 12.74 \n",
" 2.01 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" 0.157771 \n",
" \n",
" \n",
" 202 \n",
" 13.00 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" 0.153846 \n",
" \n",
" \n",
" 203 \n",
" 16.40 \n",
" 2.50 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 2 \n",
" 0.152439 \n",
" \n",
" \n",
" 205 \n",
" 16.47 \n",
" 3.23 \n",
" Female \n",
" Yes \n",
" Thur \n",
" Lunch \n",
" 3 \n",
" 0.196114 \n",
" \n",
" \n",
" 209 \n",
" 12.76 \n",
" 2.23 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.174765 \n",
" \n",
" \n",
" 213 \n",
" 13.27 \n",
" 2.50 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.188395 \n",
" \n",
" \n",
" 214 \n",
" 28.17 \n",
" 6.50 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" 0.230742 \n",
" \n",
" \n",
" 215 \n",
" 12.90 \n",
" 1.10 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.085271 \n",
" \n",
" \n",
" 219 \n",
" 30.14 \n",
" 3.09 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 4 \n",
" 0.102522 \n",
" \n",
" \n",
" 221 \n",
" 13.42 \n",
" 3.48 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" 0.259314 \n",
" \n",
" \n",
" 223 \n",
" 15.98 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Fri \n",
" Lunch \n",
" 3 \n",
" 0.187735 \n",
" \n",
" \n",
" 225 \n",
" 16.27 \n",
" 2.50 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" 0.153657 \n",
" \n",
" \n",
" 226 \n",
" 10.09 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Fri \n",
" Lunch \n",
" 2 \n",
" 0.198216 \n",
" \n",
" \n",
" 229 \n",
" 22.12 \n",
" 2.88 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.130199 \n",
" \n",
" \n",
" 238 \n",
" 35.83 \n",
" 4.67 \n",
" Female \n",
" No \n",
" Sat \n",
" Dinner \n",
" 3 \n",
" 0.130338 \n",
" \n",
" \n",
" 240 \n",
" 27.18 \n",
" 2.00 \n",
" Female \n",
" Yes \n",
" Sat \n",
" Dinner \n",
" 2 \n",
" 0.073584 \n",
" \n",
" \n",
" 243 \n",
" 18.78 \n",
" 3.00 \n",
" Female \n",
" No \n",
" Thur \n",
" Dinner \n",
" 2 \n",
" 0.159744 \n",
" \n",
" \n",
"
\n",
"
87 rows × 8 columns
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size perc_tip\n",
"0 16.99 1.01 Female No Sun Dinner 2 0.059447\n",
"4 24.59 3.61 Female No Sun Dinner 4 0.146808\n",
"11 35.26 5.00 Female No Sun Dinner 4 0.141804\n",
"14 14.83 3.02 Female No Sun Dinner 2 0.203641\n",
"16 10.33 1.67 Female No Sun Dinner 3 0.161665\n",
"18 16.97 3.50 Female No Sun Dinner 3 0.206246\n",
"21 20.29 2.75 Female No Sat Dinner 2 0.135535\n",
"22 15.77 2.23 Female No Sat Dinner 2 0.141408\n",
"29 19.65 3.00 Female No Sat Dinner 2 0.152672\n",
"32 15.06 3.00 Female No Sat Dinner 2 0.199203\n",
"33 20.69 2.45 Female No Sat Dinner 4 0.118415\n",
"37 16.93 3.07 Female No Sat Dinner 3 0.181335\n",
"51 10.29 2.60 Female No Sun Dinner 2 0.252672\n",
"52 34.81 5.20 Female No Sun Dinner 4 0.149382\n",
"57 26.41 1.50 Female No Sat Dinner 2 0.056797\n",
"66 16.45 2.47 Female No Sat Dinner 2 0.150152\n",
"67 3.07 1.00 Female Yes Sat Dinner 1 0.325733\n",
"71 17.07 3.00 Female No Sat Dinner 3 0.175747\n",
"72 26.86 3.14 Female Yes Sat Dinner 2 0.116902\n",
"73 25.28 5.00 Female Yes Sat Dinner 2 0.197785\n",
"74 14.73 2.20 Female No Sat Dinner 2 0.149355\n",
"82 10.07 1.83 Female No Thur Lunch 1 0.181728\n",
"85 34.83 5.17 Female No Thur Lunch 4 0.148435\n",
"92 5.75 1.00 Female Yes Fri Dinner 2 0.173913\n",
"93 16.32 4.30 Female Yes Fri Dinner 2 0.263480\n",
"94 22.75 3.25 Female No Fri Dinner 2 0.142857\n",
"100 11.35 2.50 Female Yes Fri Dinner 2 0.220264\n",
"101 15.38 3.00 Female Yes Fri Dinner 2 0.195059\n",
"102 44.30 2.50 Female Yes Sat Dinner 3 0.056433\n",
"103 22.42 3.48 Female Yes Sat Dinner 2 0.155219\n",
".. ... ... ... ... ... ... ... ...\n",
"155 29.85 5.14 Female No Sun Dinner 5 0.172194\n",
"157 25.00 3.75 Female No Sun Dinner 4 0.150000\n",
"158 13.39 2.61 Female No Sun Dinner 2 0.194922\n",
"162 16.21 2.00 Female No Sun Dinner 3 0.123381\n",
"164 17.51 3.00 Female Yes Sun Dinner 2 0.171331\n",
"168 10.59 1.61 Female Yes Sat Dinner 2 0.152030\n",
"169 10.63 2.00 Female Yes Sat Dinner 2 0.188147\n",
"178 9.60 4.00 Female Yes Sun Dinner 2 0.416667\n",
"186 20.90 3.50 Female Yes Sun Dinner 3 0.167464\n",
"188 18.15 3.50 Female Yes Sun Dinner 3 0.192837\n",
"191 19.81 4.19 Female Yes Thur Lunch 2 0.211509\n",
"197 43.11 5.00 Female Yes Thur Lunch 4 0.115982\n",
"198 13.00 2.00 Female Yes Thur Lunch 2 0.153846\n",
"201 12.74 2.01 Female Yes Thur Lunch 2 0.157771\n",
"202 13.00 2.00 Female Yes Thur Lunch 2 0.153846\n",
"203 16.40 2.50 Female Yes Thur Lunch 2 0.152439\n",
"205 16.47 3.23 Female Yes Thur Lunch 3 0.196114\n",
"209 12.76 2.23 Female Yes Sat Dinner 2 0.174765\n",
"213 13.27 2.50 Female Yes Sat Dinner 2 0.188395\n",
"214 28.17 6.50 Female Yes Sat Dinner 3 0.230742\n",
"215 12.90 1.10 Female Yes Sat Dinner 2 0.085271\n",
"219 30.14 3.09 Female Yes Sat Dinner 4 0.102522\n",
"221 13.42 3.48 Female Yes Fri Lunch 2 0.259314\n",
"223 15.98 3.00 Female No Fri Lunch 3 0.187735\n",
"225 16.27 2.50 Female Yes Fri Lunch 2 0.153657\n",
"226 10.09 2.00 Female Yes Fri Lunch 2 0.198216\n",
"229 22.12 2.88 Female Yes Sat Dinner 2 0.130199\n",
"238 35.83 4.67 Female No Sat Dinner 3 0.130338\n",
"240 27.18 2.00 Female Yes Sat Dinner 2 0.073584\n",
"243 18.78 3.00 Female No Thur Dinner 2 0.159744\n",
"\n",
"[87 rows x 8 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data.sex == 'Female']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data[data.sex == 'Female'].to_csv('cameriere.csv')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
",total_bill,tip,sex,smoker,day,time,size,perc_tip\r\n",
"0,16.99,1.01,Female,No,Sun,Dinner,2,0.05944673337257211\r\n",
"4,24.59,3.61,Female,No,Sun,Dinner,4,0.14680764538430255\r\n",
"11,35.26,5.0,Female,No,Sun,Dinner,4,0.14180374361883155\r\n",
"14,14.83,3.02,Female,No,Sun,Dinner,2,0.20364126770060686\r\n",
"16,10.33,1.67,Female,No,Sun,Dinner,3,0.1616650532429816\r\n",
"18,16.97,3.5,Female,No,Sun,Dinner,3,0.20624631703005306\r\n",
"21,20.29,2.75,Female,No,Sat,Dinner,2,0.13553474618038444\r\n",
"22,15.77,2.23,Female,No,Sat,Dinner,2,0.14140773620798985\r\n",
"29,19.65,3.0,Female,No,Sat,Dinner,2,0.15267175572519084\r\n",
"32,15.06,3.0,Female,No,Sat,Dinner,2,0.199203187250996\r\n",
"33,20.69,2.45,Female,No,Sat,Dinner,4,0.11841469308844853\r\n",
"37,16.93,3.07,Female,No,Sat,Dinner,3,0.1813349084465446\r\n",
"51,10.29,2.6,Female,No,Sun,Dinner,2,0.2526724975704568\r\n",
"52,34.81,5.2,Female,No,Sun,Dinner,4,0.14938236139040506\r\n",
"57,26.41,1.5,Female,No,Sat,Dinner,2,0.05679666792881484\r\n",
"66,16.45,2.47,Female,No,Sat,Dinner,2,0.1501519756838906\r\n",
"67,3.07,1.0,Female,Yes,Sat,Dinner,1,0.32573289902280134\r\n",
"71,17.07,3.0,Female,No,Sat,Dinner,3,0.1757469244288225\r\n",
"72,26.86,3.14,Female,Yes,Sat,Dinner,2,0.11690245718540582\r\n",
"73,25.28,5.0,Female,Yes,Sat,Dinner,2,0.19778481012658228\r\n",
"74,14.73,2.2,Female,No,Sat,Dinner,2,0.14935505770536323\r\n",
"82,10.07,1.83,Female,No,Thur,Lunch,1,0.1817279046673287\r\n",
"85,34.83,5.17,Female,No,Thur,Lunch,4,0.14843525696238874\r\n",
"92,5.75,1.0,Female,Yes,Fri,Dinner,2,0.17391304347826086\r\n",
"93,16.32,4.3,Female,Yes,Fri,Dinner,2,0.26348039215686275\r\n",
"94,22.75,3.25,Female,No,Fri,Dinner,2,0.14285714285714285\r\n",
"100,11.35,2.5,Female,Yes,Fri,Dinner,2,0.22026431718061676\r\n",
"101,15.38,3.0,Female,Yes,Fri,Dinner,2,0.19505851755526657\r\n",
"102,44.3,2.5,Female,Yes,Sat,Dinner,3,0.05643340857787811\r\n",
"103,22.42,3.48,Female,Yes,Sat,Dinner,2,0.15521855486173058\r\n",
"104,20.92,4.08,Female,No,Sat,Dinner,2,0.1950286806883365\r\n",
"109,14.31,4.0,Female,Yes,Sat,Dinner,2,0.2795248078266946\r\n",
"111,7.25,1.0,Female,No,Sat,Dinner,1,0.13793103448275862\r\n",
"114,25.71,4.0,Female,No,Sun,Dinner,3,0.15558148580318942\r\n",
"115,17.31,3.5,Female,No,Sun,Dinner,2,0.20219526285384173\r\n",
"117,10.65,1.5,Female,No,Thur,Lunch,2,0.14084507042253522\r\n",
"118,12.43,1.8,Female,No,Thur,Lunch,2,0.14481094127111827\r\n",
"119,24.08,2.92,Female,No,Thur,Lunch,4,0.1212624584717608\r\n",
"121,13.42,1.68,Female,No,Thur,Lunch,2,0.12518628912071533\r\n",
"124,12.48,2.52,Female,No,Thur,Lunch,2,0.20192307692307693\r\n",
"125,29.8,4.2,Female,No,Thur,Lunch,6,0.14093959731543623\r\n",
"127,14.52,2.0,Female,No,Thur,Lunch,2,0.13774104683195593\r\n",
"128,11.38,2.0,Female,No,Thur,Lunch,2,0.17574692442882248\r\n",
"131,20.27,2.83,Female,No,Thur,Lunch,2,0.13961519486926494\r\n",
"132,11.17,1.5,Female,No,Thur,Lunch,2,0.13428827215756492\r\n",
"133,12.26,2.0,Female,No,Thur,Lunch,2,0.1631321370309951\r\n",
"134,18.26,3.25,Female,No,Thur,Lunch,2,0.17798466593647316\r\n",
"135,8.51,1.25,Female,No,Thur,Lunch,2,0.14688601645123384\r\n",
"136,10.33,2.0,Female,No,Thur,Lunch,2,0.1936108422071636\r\n",
"137,14.15,2.0,Female,No,Thur,Lunch,2,0.1413427561837456\r\n",
"139,13.16,2.75,Female,No,Thur,Lunch,2,0.20896656534954408\r\n",
"140,17.47,3.5,Female,No,Thur,Lunch,2,0.20034344590726963\r\n",
"143,27.05,5.0,Female,No,Thur,Lunch,6,0.18484288354898337\r\n",
"144,16.43,2.3,Female,No,Thur,Lunch,2,0.1399878271454656\r\n",
"145,8.35,1.5,Female,No,Thur,Lunch,2,0.17964071856287425\r\n",
"146,18.64,1.36,Female,No,Thur,Lunch,3,0.07296137339055794\r\n",
"147,11.87,1.63,Female,No,Thur,Lunch,2,0.13732097725358045\r\n",
"155,29.85,5.14,Female,No,Sun,Dinner,5,0.1721943048576214\r\n",
"157,25.0,3.75,Female,No,Sun,Dinner,4,0.15\r\n",
"158,13.39,2.61,Female,No,Sun,Dinner,2,0.19492158327109782\r\n",
"162,16.21,2.0,Female,No,Sun,Dinner,3,0.12338062924120913\r\n",
"164,17.51,3.0,Female,Yes,Sun,Dinner,2,0.17133066818960593\r\n",
"168,10.59,1.61,Female,Yes,Sat,Dinner,2,0.15203021718602455\r\n",
"169,10.63,2.0,Female,Yes,Sat,Dinner,2,0.18814675446848542\r\n",
"178,9.6,4.0,Female,Yes,Sun,Dinner,2,0.4166666666666667\r\n",
"186,20.9,3.5,Female,Yes,Sun,Dinner,3,0.1674641148325359\r\n",
"188,18.15,3.5,Female,Yes,Sun,Dinner,3,0.1928374655647383\r\n",
"191,19.81,4.19,Female,Yes,Thur,Lunch,2,0.21150933871781932\r\n",
"197,43.11,5.0,Female,Yes,Thur,Lunch,4,0.1159823706796567\r\n",
"198,13.0,2.0,Female,Yes,Thur,Lunch,2,0.15384615384615385\r\n",
"201,12.74,2.01,Female,Yes,Thur,Lunch,2,0.15777080062794346\r\n",
"202,13.0,2.0,Female,Yes,Thur,Lunch,2,0.15384615384615385\r\n",
"203,16.4,2.5,Female,Yes,Thur,Lunch,2,0.15243902439024393\r\n",
"205,16.47,3.23,Female,Yes,Thur,Lunch,3,0.19611414693381907\r\n",
"209,12.76,2.23,Female,Yes,Sat,Dinner,2,0.17476489028213166\r\n",
"213,13.27,2.5,Female,Yes,Sat,Dinner,2,0.18839487565938207\r\n",
"214,28.17,6.5,Female,Yes,Sat,Dinner,3,0.23074192403265883\r\n",
"215,12.9,1.1,Female,Yes,Sat,Dinner,2,0.08527131782945736\r\n",
"219,30.14,3.09,Female,Yes,Sat,Dinner,4,0.10252156602521566\r\n",
"221,13.42,3.48,Female,Yes,Fri,Lunch,2,0.2593144560357675\r\n",
"223,15.98,3.0,Female,No,Fri,Lunch,3,0.18773466833541927\r\n",
"225,16.27,2.5,Female,Yes,Fri,Lunch,2,0.15365703749231716\r\n",
"226,10.09,2.0,Female,Yes,Fri,Lunch,2,0.19821605550049554\r\n",
"229,22.12,2.88,Female,Yes,Sat,Dinner,2,0.13019891500904157\r\n",
"238,35.83,4.67,Female,No,Sat,Dinner,3,0.13033770583310075\r\n",
"240,27.18,2.0,Female,Yes,Sat,Dinner,2,0.07358351729212656\r\n",
"243,18.78,3.0,Female,No,Thur,Dinner,2,0.1597444089456869\r\n"
]
}
],
"source": [
"!cat cameriere.csv"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.16080258172250478"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['perc_tip'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Versione lenta:\n",
"#somma = 0\n",
"#for riga in data...\n",
"# somma += riga['perc_tip']\n",
"#\n",
"#media = somma / len(data)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"size\n",
"1 0.217292\n",
"2 0.165719\n",
"3 0.152157\n",
"4 0.145949\n",
"5 0.141495\n",
"6 0.156229\n",
"Name: perc_tip, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('size')['perc_tip'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"size sex \n",
"1 Female 0.215131\n",
" Male 0.223776\n",
"2 Female 0.170830\n",
" Male 0.162694\n",
"3 Female 0.159899\n",
" Male 0.147641\n",
"4 Female 0.132734\n",
" Male 0.150197\n",
"5 Female 0.172194\n",
" Male 0.133821\n",
"6 Female 0.162891\n",
" Male 0.149567\n",
"Name: perc_tip, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['size', 'sex'])['perc_tip'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"medie = data.groupby(['size', 'sex'])['perc_tip'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" sex \n",
" Female \n",
" Male \n",
" \n",
" \n",
" size \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 1 \n",
" 0.215131 \n",
" 0.223776 \n",
" \n",
" \n",
" 2 \n",
" 0.170830 \n",
" 0.162694 \n",
" \n",
" \n",
" 3 \n",
" 0.159899 \n",
" 0.147641 \n",
" \n",
" \n",
" 4 \n",
" 0.132734 \n",
" 0.150197 \n",
" \n",
" \n",
" 5 \n",
" 0.172194 \n",
" 0.133821 \n",
" \n",
" \n",
" 6 \n",
" 0.162891 \n",
" 0.149567 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"sex Female Male\n",
"size \n",
"1 0.215131 0.223776\n",
"2 0.170830 0.162694\n",
"3 0.159899 0.147641\n",
"4 0.132734 0.150197\n",
"5 0.172194 0.133821\n",
"6 0.162891 0.149567"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"medie.unstack('sex')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'\\\\begin{tabular}{lrr}\\n\\\\toprule\\nsex & Female & Male \\\\\\\\\\nsize & & \\\\\\\\\\n\\\\midrule\\n1 & 0.215131 & 0.223776 \\\\\\\\\\n2 & 0.170830 & 0.162694 \\\\\\\\\\n3 & 0.159899 & 0.147641 \\\\\\\\\\n4 & 0.132734 & 0.150197 \\\\\\\\\\n5 & 0.172194 & 0.133821 \\\\\\\\\\n6 & 0.162891 & 0.149567 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['size', 'sex'])['perc_tip'].mean().unstack().to_latex()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'\\\\begin{tabular}{lrr}\\n\\\\toprule\\nsex & Female & Male \\\\\\\\\\nsize & & \\\\\\\\\\n\\\\midrule\\n1 & 0.215131 & 0.223776 \\\\\\\\\\n2 & 0.170830 & 0.162694 \\\\\\\\\\n3 & 0.159899 & 0.147641 \\\\\\\\\\n4 & 0.132734 & 0.150197 \\\\\\\\\\n5 & 0.172194 & 0.133821 \\\\\\\\\\n6 & 0.162891 & 0.149567 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Versione \"leggibile\":\n",
"(data\n",
" .groupby(['size', 'sex'])\n",
" ['perc_tip']\n",
" .mean()\n",
" .unstack()\n",
" .to_latex())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# $\\to$ Slide"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# matplotlib: visualizzare i dati"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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5YBt9W1bjwfa13Y5jjAkCVviD2K8nPM3X6kaU4s07m1vzNWOMT+wYf5C61Hzt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\nAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot([1,3,2])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar([0,1,2], [1,3,2])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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uN66/fVINvci6Nw2iNDUSRTA5QycN3ZskKcqOv7ywiLUBcCtFnaRo4BND9/VY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\nSd7dBGQaNHVhUXkXx7//hmork295+VWjXwT1vPQjM4EQVjsDDJJUB3I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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['total_bill'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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GyyWxBd3AQ+4JQRhqL7o9tnYwkqJZtsV+GHqK5MKTgp0wu9JEw1N7ow4D3LZY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XcDyPY6dg+AO9T3dwD4S6zLGaa6zgEwDcDGvuoKYCGAd8BVPmcC+CLW5Y9A3X8L4GcG505Q270L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\nAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data[['total_bill', 'tip']].plot()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.groupby('size')['perc_tip'].mean().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.groupby(['size', 'sex'])['perc_tip'].mean().unstack().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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i+/qrroKfftK/RU6U0gDE7+e4CLW9Xhrs3EkZ4OvixXHkVlX144/V0DRrBsuX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\nxAh9E69aVSWnhwxRSYqcDBi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Tsltmnkz58ipvffXVGosAuPFGlejOya5d2kJz/HhwOon6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\nrkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"donna = (data['sex'] == 'Female')\n",
"data.plot(kind='scatter', x='perc_tip', y='total_bill',\n",
" c=donna, edgecolor='r'\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# $\\to$ Slide"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statsmodels"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/pietro/.local/lib/python3.5/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"res = sm.OLS.from_formula('tip ~ total_bill + sex + day + size', data=data).fit()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results \n",
"\n",
" Dep. Variable: tip R-squared: 0.469 \n",
" \n",
"\n",
" Model: OLS Adj. R-squared: 0.456 \n",
" \n",
"\n",
" Method: Least Squares F-statistic: 34.93 \n",
" \n",
"\n",
" Date: Sun, 29 Oct 2017 Prob (F-statistic): 4.04e-30 \n",
" \n",
"\n",
" Time: 09:24:20 Log-Likelihood: -347.66 \n",
" \n",
"\n",
" No. Observations: 244 AIC: 709.3 \n",
" \n",
"\n",
" Df Residuals: 237 BIC: 733.8 \n",
" \n",
"\n",
" Df Model: 6 \n",
" \n",
"\n",
" Covariance Type: nonrobust \n",
" \n",
"
\n",
"\n",
"\n",
" coef std err t P>|t| [0.025 0.975] \n",
" \n",
"\n",
" Intercept 0.7601 0.289 2.633 0.009 0.191 1.329 \n",
" \n",
"\n",
" sex[T.Male] -0.0326 0.141 -0.231 0.818 -0.311 0.245 \n",
" \n",
"\n",
" day[T.Sat] -0.1202 0.261 -0.461 0.645 -0.634 0.394 \n",
" \n",
"\n",
" day[T.Sun] -0.0064 0.269 -0.024 0.981 -0.536 0.523 \n",
" \n",
"\n",
" day[T.Thur] -0.0789 0.269 -0.293 0.770 -0.609 0.451 \n",
" \n",
"\n",
" total_bill 0.0932 0.009 10.019 0.000 0.075 0.112 \n",
" \n",
"\n",
" size 0.1867 0.087 2.137 0.034 0.015 0.359 \n",
" \n",
"
\n",
"\n",
"\n",
" Omnibus: 26.163 Durbin-Watson: 2.094 \n",
" \n",
"\n",
" Prob(Omnibus): 0.000 Jarque-Bera (JB): 49.406 \n",
" \n",
"\n",
" Skew: 0.571 Prob(JB): 1.87e-11 \n",
" \n",
"\n",
" Kurtosis: 4.886 Cond. No. 161. \n",
" \n",
"
"
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: tip R-squared: 0.469\n",
"Model: OLS Adj. R-squared: 0.456\n",
"Method: Least Squares F-statistic: 34.93\n",
"Date: Sun, 29 Oct 2017 Prob (F-statistic): 4.04e-30\n",
"Time: 09:24:20 Log-Likelihood: -347.66\n",
"No. Observations: 244 AIC: 709.3\n",
"Df Residuals: 237 BIC: 733.8\n",
"Df Model: 6 \n",
"Covariance Type: nonrobust \n",
"===============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 0.7601 0.289 2.633 0.009 0.191 1.329\n",
"sex[T.Male] -0.0326 0.141 -0.231 0.818 -0.311 0.245\n",
"day[T.Sat] -0.1202 0.261 -0.461 0.645 -0.634 0.394\n",
"day[T.Sun] -0.0064 0.269 -0.024 0.981 -0.536 0.523\n",
"day[T.Thur] -0.0789 0.269 -0.293 0.770 -0.609 0.451\n",
"total_bill 0.0932 0.009 10.019 0.000 0.075 0.112\n",
"size 0.1867 0.087 2.137 0.034 0.015 0.359\n",
"==============================================================================\n",
"Omnibus: 26.163 Durbin-Watson: 2.094\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 49.406\n",
"Skew: 0.571 Prob(JB): 1.87e-11\n",
"Kurtosis: 4.886 Cond. No. 161.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.summary()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Sun', 'Sat', 'Thur', 'Fri'], dtype=object)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['day'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# $\\to$ Slide"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf = MLPClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" total_bill \n",
" tip \n",
" sex \n",
" smoker \n",
" day \n",
" time \n",
" size \n",
" perc_tip \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 16.99 \n",
" 1.01 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" 0.059447 \n",
" \n",
" \n",
" 1 \n",
" 10.34 \n",
" 1.66 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" 0.160542 \n",
" \n",
" \n",
" 2 \n",
" 21.01 \n",
" 3.50 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 3 \n",
" 0.166587 \n",
" \n",
" \n",
" 3 \n",
" 23.68 \n",
" 3.31 \n",
" Male \n",
" No \n",
" Sun \n",
" Dinner \n",
" 2 \n",
" 0.139780 \n",
" \n",
" \n",
" 4 \n",
" 24.59 \n",
" 3.61 \n",
" Female \n",
" No \n",
" Sun \n",
" Dinner \n",
" 4 \n",
" 0.146808 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size perc_tip\n",
"0 16.99 1.01 Female No Sun Dinner 2 0.059447\n",
"1 10.34 1.66 Male No Sun Dinner 3 0.160542\n",
"2 21.01 3.50 Male No Sun Dinner 3 0.166587\n",
"3 23.68 3.31 Male No Sun Dinner 2 0.139780\n",
"4 24.59 3.61 Female No Sun Dinner 4 0.146808"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data['sex'] = data['sex'] == 'Female'\n",
"data['smoker'] = data['smoker'] == 'Yes'\n",
"data['time'] = data['time'] == 'Dinner'"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" total_bill \n",
" tip \n",
" sex \n",
" smoker \n",
" day \n",
" time \n",
" size \n",
" perc_tip \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 16.99 \n",
" 1.01 \n",
" True \n",
" False \n",
" Sun \n",
" True \n",
" 2 \n",
" 0.059447 \n",
" \n",
" \n",
" 1 \n",
" 10.34 \n",
" 1.66 \n",
" False \n",
" False \n",
" Sun \n",
" True \n",
" 3 \n",
" 0.160542 \n",
" \n",
" \n",
" 2 \n",
" 21.01 \n",
" 3.50 \n",
" False \n",
" False \n",
" Sun \n",
" True \n",
" 3 \n",
" 0.166587 \n",
" \n",
" \n",
" 3 \n",
" 23.68 \n",
" 3.31 \n",
" False \n",
" False \n",
" Sun \n",
" True \n",
" 2 \n",
" 0.139780 \n",
" \n",
" \n",
" 4 \n",
" 24.59 \n",
" 3.61 \n",
" True \n",
" False \n",
" Sun \n",
" True \n",
" 4 \n",
" 0.146808 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size perc_tip\n",
"0 16.99 1.01 True False Sun True 2 0.059447\n",
"1 10.34 1.66 False False Sun True 3 0.160542\n",
"2 21.01 3.50 False False Sun True 3 0.166587\n",
"3 23.68 3.31 False False Sun True 2 0.139780\n",
"4 24.59 3.61 True False Sun True 4 0.146808"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['good_tip'] = data['perc_tip'] > data['perc_tip'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = data.drop(['good_tip', 'day', 'perc_tip', 'tip'], axis=1)\n",
"y = data['good_tip']"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" total_bill \n",
" tip \n",
" sex \n",
" smoker \n",
" day \n",
" time \n",
" size \n",
" perc_tip \n",
" good_tip \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 16.99 \n",
" 1.01 \n",
" True \n",
" False \n",
" Sun \n",
" True \n",
" 2 \n",
" 0.059447 \n",
" False \n",
" \n",
" \n",
" 1 \n",
" 10.34 \n",
" 1.66 \n",
" False \n",
" False \n",
" Sun \n",
" True \n",
" 3 \n",
" 0.160542 \n",
" False \n",
" \n",
" \n",
" 2 \n",
" 21.01 \n",
" 3.50 \n",
" False \n",
" False \n",
" Sun \n",
" True \n",
" 3 \n",
" 0.166587 \n",
" True \n",
" \n",
" \n",
" 3 \n",
" 23.68 \n",
" 3.31 \n",
" False \n",
" False \n",
" Sun \n",
" True \n",
" 2 \n",
" 0.139780 \n",
" False \n",
" \n",
" \n",
" 4 \n",
" 24.59 \n",
" 3.61 \n",
" True \n",
" False \n",
" Sun \n",
" True \n",
" 4 \n",
" 0.146808 \n",
" False \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size perc_tip good_tip\n",
"0 16.99 1.01 True False Sun True 2 0.059447 False\n",
"1 10.34 1.66 False False Sun True 3 0.160542 False\n",
"2 21.01 3.50 False False Sun True 3 0.166587 True\n",
"3 23.68 3.31 False False Sun True 2 0.139780 False\n",
"4 24.59 3.61 True False Sun True 4 0.146808 False"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"res = clf.fit(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.56557377049180324"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# STO BARANDO! STO BARANDO!\n",
"res.score(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier, export_graphviz"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tree = DecisionTreeClassifier(max_depth=4)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res = tree.fit(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.64754098360655743"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.score(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dot_data = export_graphviz(tree, out_file=None,\n",
" feature_names=x.columns,\n",
" filled=True, rounded=True,\n",
" special_characters=True)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphviz\n",
"graph = graphviz.Source(dot_data) "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Tree \n",
" \n",
"\n",
"0 \n",
" \n",
"total_bill ≤ 22.325 \n",
"gini = 0.4924 \n",
"samples = 244 \n",
"value = [137, 107] \n",
" \n",
"\n",
"1 \n",
" \n",
"total_bill ≤ 10.335 \n",
"gini = 0.4996 \n",
"samples = 169 \n",
"value = [82, 87] \n",
" \n",
"\n",
"0->1 \n",
" \n",
" \n",
"True \n",
" \n",
"\n",
"14 \n",
" \n",
"total_bill ≤ 48.3 \n",
"gini = 0.3911 \n",
"samples = 75 \n",
"value = [55, 20] \n",
" \n",
"\n",
"0->14 \n",
" \n",
" \n",
"False \n",
" \n",
"\n",
"2 \n",
" \n",
"smoker ≤ 0.5 \n",
"gini = 0.375 \n",
"samples = 24 \n",
"value = [6, 18] \n",
" \n",
"\n",
"1->2 \n",
" \n",
" \n",
" \n",
"\n",
"7 \n",
" \n",
"total_bill ≤ 20.47 \n",
"gini = 0.4988 \n",
"samples = 145 \n",
"value = [76, 69] \n",
" \n",
"\n",
"1->7 \n",
" \n",
" \n",
" \n",
"\n",
"3 \n",
" \n",
"time ≤ 0.5 \n",
"gini = 0.4567 \n",
"samples = 17 \n",
"value = [6, 11] \n",
" \n",
"\n",
"2->3 \n",
" \n",
" \n",
" \n",
"\n",
"6 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [0, 7] \n",
" \n",
"\n",
"2->6 \n",
" \n",
" \n",
" \n",
"\n",
"4 \n",
" \n",
"gini = 0.2188 \n",
"samples = 8 \n",
"value = [1, 7] \n",
" \n",
"\n",
"3->4 \n",
" \n",
" \n",
" \n",
"\n",
"5 \n",
" \n",
"gini = 0.4938 \n",
"samples = 9 \n",
"value = [5, 4] \n",
" \n",
"\n",
"3->5 \n",
" \n",
" \n",
" \n",
"\n",
"8 \n",
" \n",
"total_bill ≤ 19.815 \n",
"gini = 0.4949 \n",
"samples = 129 \n",
"value = [71, 58] \n",
" \n",
"\n",
"7->8 \n",
" \n",
" \n",
" \n",
"\n",
"11 \n",
" \n",
"total_bill ≤ 20.67 \n",
"gini = 0.4297 \n",
"samples = 16 \n",
"value = [5, 11] \n",
" \n",
"\n",
"7->11 \n",
" \n",
" \n",
" \n",
"\n",
"9 \n",
" \n",
"gini = 0.4988 \n",
"samples = 122 \n",
"value = [64, 58] \n",
" \n",
"\n",
"8->9 \n",
" \n",
" \n",
" \n",
"\n",
"10 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
" \n",
"\n",
"8->10 \n",
" \n",
" \n",
" \n",
"\n",
"12 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [0, 3] \n",
" \n",
"\n",
"11->12 \n",
" \n",
" \n",
" \n",
"\n",
"13 \n",
" \n",
"gini = 0.4734 \n",
"samples = 13 \n",
"value = [5, 8] \n",
" \n",
"\n",
"11->13 \n",
" \n",
" \n",
" \n",
"\n",
"15 \n",
" \n",
"total_bill ≤ 34.465 \n",
"gini = 0.3716 \n",
"samples = 73 \n",
"value = [55, 18] \n",
" \n",
"\n",
"14->15 \n",
" \n",
" \n",
" \n",
"\n",
"22 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
" \n",
"\n",
"14->22 \n",
" \n",
" \n",
" \n",
"\n",
"16 \n",
" \n",
"size ≤ 3.5 \n",
"gini = 0.4271 \n",
"samples = 55 \n",
"value = [38, 17] \n",
" \n",
"\n",
"15->16 \n",
" \n",
" \n",
" \n",
"\n",
"19 \n",
" \n",
"smoker ≤ 0.5 \n",
"gini = 0.1049 \n",
"samples = 18 \n",
"value = [17, 1] \n",
" \n",
"\n",
"15->19 \n",
" \n",
" \n",
" \n",
"\n",
"17 \n",
" \n",
"gini = 0.3343 \n",
"samples = 33 \n",
"value = [26, 7] \n",
" \n",
"\n",
"16->17 \n",
" \n",
" \n",
" \n",
"\n",
"18 \n",
" \n",
"gini = 0.4959 \n",
"samples = 22 \n",
"value = [12, 10] \n",
" \n",
"\n",
"16->18 \n",
" \n",
" \n",
" \n",
"\n",
"20 \n",
" \n",
"gini = 0.1975 \n",
"samples = 9 \n",
"value = [8, 1] \n",
" \n",
"\n",
"19->20 \n",
" \n",
" \n",
" \n",
"\n",
"21 \n",
" \n",
"gini = 0.0 \n",
"samples = 9 \n",
"value = [9, 0] \n",
" \n",
"\n",
"19->21 \n",
" \n",
" \n",
" \n",
" \n",
" \n"
],
"text/plain": [
""
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"forest = RandomForestClassifier(max_depth=4)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res = forest.fit(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.70081967213114749"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.score(x, y)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}