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Created November 28, 2015 12:41
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Think Stats 2, 1장 연습문제 해답
Raw
chap01soln-kor
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"통계적 사고 (2판) 연습문제 ([thinkstats2.com](thinkstats2.com), [think-stat.xwmooc.org](http://think-stat.xwmooc.org))<br>\n",
"Allen Downey / 이광춘(xwMOOC)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>2580.967613</td>\n",
" <td>2925.167116</td>\n",
" <td>5075.164946</td>\n",
" <td>2</td>\n",
" <td>61</td>\n",
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" <td>6.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13585</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
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" <td>NaN</td>\n",
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" <td>40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13586</th>\n",
" <td>12570</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
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" <td>11325.017623</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13587</th>\n",
" <td>12570</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>0</td>\n",
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" <td>5181.311509</td>\n",
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" <td>11325.017623</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13588</th>\n",
" <td>12571</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
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" <td>78</td>\n",
" <td>NaN</td>\n",
" <td>6.1875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13589</th>\n",
" <td>12571</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>5795.692880</td>\n",
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" <td>1</td>\n",
" <td>78</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13590</th>\n",
" <td>12571</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
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" <td>1</td>\n",
" <td>78</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13591</th>\n",
" <td>12571</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>6</td>\n",
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" <td>NaN</td>\n",
" <td>7.5000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13592</th>\n",
" <td>12571</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>6</td>\n",
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" <td>78</td>\n",
" <td>NaN</td>\n",
" <td>7.5000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13593 rows × 244 columns</p>\n",
"</div>"
],
"text/plain": [
" caseid pregordr howpreg_n howpreg_p moscurrp nowprgdk pregend1 \\\n",
"0 1 1 NaN NaN NaN NaN 6 \n",
"1 1 2 NaN NaN NaN NaN 6 \n",
"2 2 1 NaN NaN NaN NaN 5 \n",
"3 2 2 NaN NaN NaN NaN 6 \n",
"4 2 3 NaN NaN NaN NaN 6 \n",
"5 6 1 NaN NaN NaN NaN 6 \n",
"6 6 2 NaN NaN NaN NaN 6 \n",
"7 6 3 NaN NaN NaN NaN 6 \n",
"8 7 1 NaN NaN NaN NaN 5 \n",
"9 7 2 NaN NaN NaN NaN 5 \n",
"10 12 1 NaN NaN NaN NaN 5 \n",
"11 14 1 NaN NaN NaN NaN 6 \n",
"12 14 2 NaN NaN NaN NaN 6 \n",
"13 14 3 NaN NaN NaN NaN 3 \n",
"14 15 1 NaN NaN NaN NaN 1 \n",
"15 15 2 NaN NaN NaN NaN 6 \n",
"16 15 3 NaN NaN NaN NaN 6 \n",
"17 18 1 NaN NaN NaN NaN 5 \n",
"18 18 2 NaN NaN NaN NaN 1 \n",
"19 21 1 NaN NaN NaN NaN 6 \n",
"20 21 2 NaN NaN NaN NaN 6 \n",
"21 23 1 NaN NaN NaN NaN 6 \n",
"22 23 2 NaN NaN NaN NaN 1 \n",
"23 24 1 NaN NaN NaN NaN 6 \n",
"24 24 2 NaN NaN NaN NaN 6 \n",
"25 24 3 NaN NaN NaN NaN 6 \n",
"26 28 1 NaN NaN NaN NaN 6 \n",
"27 31 1 NaN NaN NaN NaN 6 \n",
"28 31 2 NaN NaN NaN NaN 6 \n",
"29 31 3 NaN NaN NaN NaN 6 \n",
"... ... ... ... ... ... ... ... \n",
"13563 12547 2 NaN NaN NaN NaN 6 \n",
"13564 12547 3 NaN NaN NaN NaN 6 \n",
"13565 12550 1 NaN NaN NaN NaN 6 \n",
"13566 12551 1 NaN NaN NaN NaN 5 \n",
"13567 12554 1 NaN NaN NaN NaN 3 \n",
"13568 12554 2 NaN NaN NaN NaN 4 \n",
"13569 12556 1 NaN NaN NaN NaN 6 \n",
"13570 12556 2 NaN NaN NaN NaN 6 \n",
"13571 12556 3 NaN NaN NaN NaN 6 \n",
"13572 12556 4 NaN NaN NaN NaN 6 \n",
"13573 12561 1 NaN NaN NaN NaN 6 \n",
"13574 12561 2 NaN NaN NaN NaN 6 \n",
"13575 12564 1 NaN NaN NaN NaN 3 \n",
"13576 12565 1 NaN NaN NaN NaN 6 \n",
"13577 12565 2 35 1 8 NaN NaN \n",
"13578 12566 1 NaN NaN NaN NaN 6 \n",
"13579 12566 2 NaN NaN NaN NaN 6 \n",
"13580 12568 1 NaN NaN NaN NaN 1 \n",
"13581 12568 2 NaN NaN NaN NaN 5 \n",
"13582 12568 3 NaN NaN NaN NaN 4 \n",
"13583 12569 1 NaN NaN NaN NaN 3 \n",
"13584 12569 2 NaN NaN NaN NaN 6 \n",
"13585 12570 1 NaN NaN NaN NaN 3 \n",
"13586 12570 2 NaN NaN NaN NaN 3 \n",
"13587 12570 3 NaN NaN NaN NaN 3 \n",
"13588 12571 1 NaN NaN NaN NaN 6 \n",
"13589 12571 2 NaN NaN NaN NaN 3 \n",
"13590 12571 3 NaN NaN NaN NaN 3 \n",
"13591 12571 4 NaN NaN NaN NaN 6 \n",
"13592 12571 5 NaN NaN NaN NaN 6 \n",
"\n",
" pregend2 nbrnaliv multbrth ... laborfor_i religion_i \\\n",
"0 NaN 1 NaN ... 0 0 \n",
"1 NaN 1 NaN ... 0 0 \n",
"2 NaN 3 5 ... 0 0 \n",
"3 NaN 1 NaN ... 0 0 \n",
"4 NaN 1 NaN ... 0 0 \n",
"5 NaN 1 NaN ... 0 0 \n",
"6 NaN 1 NaN ... 0 0 \n",
"7 NaN 1 NaN ... 0 0 \n",
"8 NaN 1 NaN ... 0 0 \n",
"9 NaN 1 NaN ... 0 0 \n",
"10 NaN 1 NaN ... 0 0 \n",
"11 NaN 1 NaN ... 0 0 \n",
"12 NaN 1 NaN ... 0 0 \n",
"13 NaN NaN NaN ... 0 0 \n",
"14 NaN NaN NaN ... 0 0 \n",
"15 NaN 1 NaN ... 0 0 \n",
"16 NaN 1 NaN ... 0 0 \n",
"17 NaN 1 NaN ... 0 0 \n",
"18 NaN NaN NaN ... 0 0 \n",
"19 NaN 1 NaN ... 0 0 \n",
"20 NaN 1 NaN ... 0 0 \n",
"21 NaN 1 NaN ... 0 0 \n",
"22 NaN NaN NaN ... 0 0 \n",
"23 NaN 1 NaN ... 0 0 \n",
"24 NaN 1 NaN ... 0 0 \n",
"25 NaN 1 NaN ... 0 0 \n",
"26 NaN 1 NaN ... 0 0 \n",
"27 NaN 1 NaN ... 0 0 \n",
"28 NaN 1 NaN ... 0 0 \n",
"29 NaN 1 NaN ... 0 0 \n",
"... ... ... ... ... ... ... \n",
"13563 NaN 1 NaN ... 0 0 \n",
"13564 NaN 1 NaN ... 0 0 \n",
"13565 NaN 1 NaN ... 0 0 \n",
"13566 NaN 1 NaN ... 0 0 \n",
"13567 NaN NaN NaN ... 0 0 \n",
"13568 NaN NaN NaN ... 0 0 \n",
"13569 NaN 1 NaN ... 0 0 \n",
"13570 NaN 1 NaN ... 0 0 \n",
"13571 NaN 1 NaN ... 0 0 \n",
"13572 NaN 1 NaN ... 0 0 \n",
"13573 NaN 1 NaN ... 0 0 \n",
"13574 NaN 1 NaN ... 0 0 \n",
"13575 NaN NaN NaN ... 0 0 \n",
"13576 NaN 1 NaN ... 0 0 \n",
"13577 NaN NaN NaN ... 0 0 \n",
"13578 NaN 1 NaN ... 0 0 \n",
"13579 NaN 1 NaN ... 0 0 \n",
"13580 NaN NaN NaN ... 0 0 \n",
"13581 NaN 1 NaN ... 0 0 \n",
"13582 NaN NaN NaN ... 0 0 \n",
"13583 NaN NaN NaN ... 0 0 \n",
"13584 NaN 1 NaN ... 0 0 \n",
"13585 NaN NaN NaN ... 0 0 \n",
"13586 NaN NaN NaN ... 0 0 \n",
"13587 NaN NaN NaN ... 0 0 \n",
"13588 NaN 1 NaN ... 0 0 \n",
"13589 NaN NaN NaN ... 0 0 \n",
"13590 NaN NaN NaN ... 0 0 \n",
"13591 NaN 1 NaN ... 0 0 \n",
"13592 NaN 1 NaN ... 0 0 \n",
"\n",
" metro_i basewgt adj_mod_basewgt finalwgt secu_p sest \\\n",
"0 0 3410.389399 3869.349602 6448.271112 2 9 \n",
"1 0 3410.389399 3869.349602 6448.271112 2 9 \n",
"2 0 7226.301740 8567.549110 12999.542264 2 12 \n",
"3 0 7226.301740 8567.549110 12999.542264 2 12 \n",
"4 0 7226.301740 8567.549110 12999.542264 2 12 \n",
"5 0 4870.926435 5325.196999 8874.440799 1 23 \n",
"6 0 4870.926435 5325.196999 8874.440799 1 23 \n",
"7 0 4870.926435 5325.196999 8874.440799 1 23 \n",
"8 0 3409.579565 3787.539000 6911.879921 2 14 \n",
"9 0 3409.579565 3787.539000 6911.879921 2 14 \n",
"10 0 3612.781968 4146.013572 6909.331618 1 31 \n",
"11 0 2418.069494 2810.302771 3039.904507 2 56 \n",
"12 0 2418.069494 2810.302771 3039.904507 2 56 \n",
"13 0 2418.069494 2810.302771 3039.904507 2 56 \n",
"14 0 1667.816099 3200.862017 5553.495599 1 33 \n",
"15 0 1667.816099 3200.862017 5553.495599 1 33 \n",
"16 0 1667.816099 3200.862017 5553.495599 1 33 \n",
"17 0 2957.257457 3404.403067 4153.371741 2 14 \n",
"18 0 2957.257457 3404.403067 4153.371741 2 14 \n",
"19 0 3408.342437 3965.763949 7237.122630 1 48 \n",
"20 0 3408.342437 3965.763949 7237.122630 1 48 \n",
"21 0 6210.373020 8120.841310 13533.382043 2 64 \n",
"22 0 6210.373020 8120.841310 13533.382043 2 64 \n",
"23 0 3409.573258 4068.628645 7424.840414 1 27 \n",
"24 0 3409.573258 4068.628645 7424.840414 1 27 \n",
"25 0 3409.573258 4068.628645 7424.840414 1 27 \n",
"26 0 3407.794208 3808.343516 6949.846082 2 57 \n",
"27 0 3405.679025 4272.084519 5211.943113 1 2 \n",
"28 0 3405.679025 4272.084519 5211.943113 1 2 \n",
"29 0 3405.679025 4272.084519 5211.943113 1 2 \n",
"... ... ... ... ... ... ... \n",
"13563 0 3453.545517 6628.022524 11499.619080 1 52 \n",
"13564 0 3453.545517 6628.022524 11499.619080 1 52 \n",
"13565 0 3080.452699 3745.326058 5268.550165 1 79 \n",
"13566 0 2418.538866 3653.453268 3951.940400 2 75 \n",
"13567 0 1914.676604 2177.957240 2764.045534 2 75 \n",
"13568 0 1914.676604 2177.957240 2764.045534 2 75 \n",
"13569 0 2474.619764 3250.573384 3965.699528 1 44 \n",
"13570 0 2474.619764 3250.573384 3965.699528 1 44 \n",
"13571 0 2474.619764 3250.573384 3965.699528 1 44 \n",
"13572 0 2474.619764 3250.573384 3965.699528 1 44 \n",
"13573 0 2418.089703 2698.650781 4497.301527 1 10 \n",
"13574 0 2418.089703 2698.650781 4497.301527 1 10 \n",
"13575 0 1820.850938 2129.214067 2768.191208 2 44 \n",
"13576 0 3195.641221 3834.241709 6652.409365 1 78 \n",
"13577 0 3195.641221 3834.241709 6652.409365 1 78 \n",
"13578 0 2080.317155 2422.820274 2627.548587 2 2 \n",
"13579 0 2080.317155 2422.820274 2627.548587 2 2 \n",
"13580 0 2734.687353 4258.980140 7772.212858 2 28 \n",
"13581 0 2734.687353 4258.980140 7772.212858 2 28 \n",
"13582 0 2734.687353 4258.980140 7772.212858 2 28 \n",
"13583 0 2580.967613 2925.167116 5075.164946 2 61 \n",
"13584 0 2580.967613 2925.167116 5075.164946 2 61 \n",
"13585 0 5181.311509 6205.829154 11325.017623 2 40 \n",
"13586 0 5181.311509 6205.829154 11325.017623 2 40 \n",
"13587 0 5181.311509 6205.829154 11325.017623 2 40 \n",
"13588 0 4670.540953 5795.692880 6269.200989 1 78 \n",
"13589 0 4670.540953 5795.692880 6269.200989 1 78 \n",
"13590 0 4670.540953 5795.692880 6269.200989 1 78 \n",
"13591 0 4670.540953 5795.692880 6269.200989 1 78 \n",
"13592 0 4670.540953 5795.692880 6269.200989 1 78 \n",
"\n",
" cmintvw totalwgt_lb \n",
"0 NaN 8.8125 \n",
"1 NaN 7.8750 \n",
"2 NaN 9.1250 \n",
"3 NaN 7.0000 \n",
"4 NaN 6.1875 \n",
"5 NaN 8.5625 \n",
"6 NaN 9.5625 \n",
"7 NaN 8.3750 \n",
"8 NaN 7.5625 \n",
"9 NaN 6.6250 \n",
"10 NaN 7.8125 \n",
"11 NaN 7.0000 \n",
"12 NaN 4.0000 \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN 7.6875 \n",
"16 NaN 7.5000 \n",
"17 NaN 6.3125 \n",
"18 NaN NaN \n",
"19 NaN 8.7500 \n",
"20 NaN 8.1875 \n",
"21 NaN 5.5625 \n",
"22 NaN NaN \n",
"23 NaN 6.7500 \n",
"24 NaN 7.3750 \n",
"25 NaN 6.8125 \n",
"26 NaN 8.1250 \n",
"27 NaN 7.1250 \n",
"28 NaN 6.0625 \n",
"29 NaN 7.4375 \n",
"... ... ... \n",
"13563 NaN 7.6875 \n",
"13564 NaN 7.6250 \n",
"13565 NaN 8.1250 \n",
"13566 NaN 7.5000 \n",
"13567 NaN NaN \n",
"13568 NaN NaN \n",
"13569 NaN 5.8125 \n",
"13570 NaN 6.6875 \n",
"13571 NaN 6.0000 \n",
"13572 NaN 5.8125 \n",
"13573 NaN 6.5625 \n",
"13574 NaN 6.1250 \n",
"13575 NaN NaN \n",
"13576 NaN 6.4375 \n",
"13577 NaN NaN \n",
"13578 NaN 6.0000 \n",
"13579 NaN 7.0000 \n",
"13580 NaN NaN \n",
"13581 NaN 6.3750 \n",
"13582 NaN NaN \n",
"13583 NaN NaN \n",
"13584 NaN 6.3750 \n",
"13585 NaN NaN \n",
"13586 NaN NaN \n",
"13587 NaN NaN \n",
"13588 NaN 6.1875 \n",
"13589 NaN NaN \n",
"13590 NaN NaN \n",
"13591 NaN 7.5000 \n",
"13592 NaN 7.5000 \n",
"\n",
"[13593 rows x 244 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nsfg\n",
"df = nsfg.ReadFemPreg()\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<tt>birthord</tt>에 대한 빈도수를 출력하고 [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=PREG&section=A&subSec=8016&srtLabel=611933) 게시된 결과값과 비교하시오."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 4413\n",
"2 2874\n",
"3 1234\n",
"4 421\n",
"5 126\n",
"6 50\n",
"7 20\n",
"8 7\n",
"9 2\n",
"10 1\n",
"Name: birthord, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.birthord.value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<tt>prglngth</tt>에 대한 빈도수를 출력하고 [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=PREG&section=A&subSec=8016&srtLabel=611931) 게시된 결과값과 비교하시오."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 15\n",
"1 9\n",
"2 78\n",
"3 151\n",
"4 412\n",
"5 181\n",
"6 543\n",
"7 175\n",
"8 409\n",
"9 594\n",
"10 137\n",
"11 202\n",
"12 170\n",
"13 446\n",
"14 29\n",
"15 39\n",
"16 44\n",
"17 253\n",
"18 17\n",
"19 34\n",
"20 18\n",
"21 37\n",
"22 147\n",
"23 12\n",
"24 31\n",
"25 15\n",
"26 117\n",
"27 8\n",
"28 38\n",
"29 23\n",
"30 198\n",
"31 29\n",
"32 122\n",
"33 50\n",
"34 60\n",
"35 357\n",
"36 329\n",
"37 457\n",
"38 609\n",
"39 4744\n",
"40 1120\n",
"41 591\n",
"42 328\n",
"43 148\n",
"44 46\n",
"45 10\n",
"46 1\n",
"47 1\n",
"48 7\n",
"50 2\n",
"Name: prglngth, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.prglngth.value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<tt>agepreg</tt>에 대한 빈도수를 출력하고 [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=PREG&section=A&subSec=8016&srtLabel=611935)에 게시된 결과값과 비교하시오.\n",
"\n",
"이 데이터를 살펴보고, 응답자에 대한 존경과 맥락을 고려해서 데이터에 접근하는에 필요한 의무에 관해서 저자가 언급한 논평을 기억하라."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10.33 1\n",
"10.50 1\n",
"10.83 1\n",
"10.91 1\n",
"11.08 1\n",
"11.41 1\n",
"11.50 1\n",
"11.75 2\n",
"12.08 1\n",
"12.25 1\n",
"12.41 2\n",
"12.50 1\n",
"12.66 2\n",
"12.75 1\n",
"12.83 2\n",
"12.91 1\n",
"13.00 2\n",
"13.08 1\n",
"13.16 1\n",
"13.25 4\n",
"13.33 1\n",
"13.50 3\n",
"13.58 2\n",
"13.66 5\n",
"13.75 4\n",
"13.83 5\n",
"13.91 5\n",
"14.00 5\n",
"14.08 7\n",
"14.16 12\n",
" ..\n",
"40.50 6\n",
"40.58 4\n",
"40.66 4\n",
"40.75 2\n",
"40.83 4\n",
"40.91 4\n",
"41.00 2\n",
"41.08 2\n",
"41.16 2\n",
"41.25 6\n",
"41.33 1\n",
"41.41 2\n",
"41.58 4\n",
"41.75 3\n",
"41.83 1\n",
"41.91 4\n",
"42.08 2\n",
"42.16 1\n",
"42.25 1\n",
"42.33 1\n",
"42.41 1\n",
"42.50 2\n",
"42.58 2\n",
"42.75 2\n",
"43.00 3\n",
"43.16 1\n",
"43.25 4\n",
"43.58 3\n",
"43.91 1\n",
"44.08 1\n",
"Name: agepreg, dtype: int64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.agepreg.value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"평균 출생체중(birthweight)을 계산하시오."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.265628457623368"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.totalwgt_lb.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"킬로그램으로 출생체중 정보를 담는 <tt>totalwgt_kg</tt>로 불리는 새로운 칼럼을 생성하라. 평균도 계산하시오. 새로운 칼럼을 생성할 때, 점표기법이 아닌 딕셔너리 구문을 사용하는 것을 기억하라."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.3025583898288033"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['totalwgt_kg'] = df.totalwgt_lb / 2.2\n",
"df.totalwgt_kg.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"코드북(codebook)을 살펴보고 책에서 언급된 것이 아닌 본인이 관심있는 변수를 찾아내라. 그리고 그 변수의 빈도수, 평균, 다른 통계량을 계산하시오."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([ u'caseid', u'pregordr', u'howpreg_n',\n",
" u'howpreg_p', u'moscurrp', u'nowprgdk',\n",
" u'pregend1', u'pregend2', u'nbrnaliv',\n",
" u'multbrth',\n",
" ...\n",
" u'religion_i', u'metro_i', u'basewgt',\n",
" u'adj_mod_basewgt', u'finalwgt', u'secu_p',\n",
" u'sest', u'cmintvw', u'totalwgt_lb',\n",
" u'totalwgt_kg'],\n",
" dtype='object', length=245)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"부울 시리즈(boolean Series)를 생성하시오."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Count:', 1 83\n",
"2 168\n",
"3 199\n",
"4 91\n",
"5 14\n",
"6 12\n",
"9 1\n",
"Name: npostsmk, dtype: int64)\n",
"('Mean:', 2.6954225352112675)\n"
]
}
],
"source": [
"print('Count:', df.npostsmk.value_counts().sort_index()) ## 임신기간동안 흡연\n",
"print('Mean:', df.npostsmk.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"부울 시리즈를 사용해서 정상출산 임신에 대한 레코드를 선택하시오."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9148"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"live = df[df.outcome == 1]\n",
"len(live)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<tt>birthwgt_lb</tt> 변수에 0 에서 5 파운드(0과 5도 모두 포함) 사이 정상출산 빈도수를 계산하시오. 결과는 1125 가 되어야만 된다."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1125"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(live[(live.birthwgt_lb >= 0) & (live.birthwgt_lb <= 5)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<tt>birthwgt_lb</tt> 변수에 9 에서 95 파운드(9과 95도 모두 포함) 사이 정상출산 빈도수를 계산하시오. 결과는 798 가 되어야만 된다."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"798"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(live[(live.birthwgt_lb >=9)&(live.birthwgt_lb <=95)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<tt>birthord</tt> 변수를 사용해서, 첫번째 아이와 첫째가 아닌 아이에 대한 레코드를 선택하시오. 첫번째 아이와 첫째가 아닌 아이는 얼마나 되는가?"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4413, 4735)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firsts = df[df.birthord==1]\n",
"others = df[df.birthord>1]\n",
"len(firsts), len(others)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"첫번째 아이와 첫째가 아닌 아이에 대한 평균 체중을 계산하시오."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.201094430437772"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firsts.totalwgt_lb.mean()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.325855614973262"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"others.totalwgt_lb.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"변수 <tt>prglngth</tt>으로 첫째 아이와 첫째가 아닌 아이에 대한 평균임신기간을 계산하시오. 시간으로 표시된, 평균에 차이를 계산하시오."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Firsts Mean: ', 38.60095173351461)\n",
"('Others Mean: ', 38.52291446673706)\n",
"('Diff: ', 0.07803726677754952)\n"
]
}
],
"source": [
"print('Firsts Mean: ', firsts.prglngth.mean())\n",
"print('Others Mean: ', others.prglngth.mean())\n",
"print('Diff: ', firsts.prglngth.mean()-others.prglngth.mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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