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Created November 27, 2015 05:10
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ch01ex
{
"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": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <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",
" <td>NaN</td>\n",
" <td>6.3750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13585</th>\n",
" <td>12570</td>\n",
" <td>1</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5181.311509</td>\n",
" <td>6205.829154</td>\n",
" <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>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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5181.311509</td>\n",
" <td>6205.829154</td>\n",
" <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",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5181.311509</td>\n",
" <td>6205.829154</td>\n",
" <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",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4670.540953</td>\n",
" <td>5795.692880</td>\n",
" <td>6269.200989</td>\n",
" <td>1</td>\n",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4670.540953</td>\n",
" <td>5795.692880</td>\n",
" <td>6269.200989</td>\n",
" <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",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4670.540953</td>\n",
" <td>5795.692880</td>\n",
" <td>6269.200989</td>\n",
" <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",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4670.540953</td>\n",
" <td>5795.692880</td>\n",
" <td>6269.200989</td>\n",
" <td>1</td>\n",
" <td>78</td>\n",
" <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",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4670.540953</td>\n",
" <td>5795.692880</td>\n",
" <td>6269.200989</td>\n",
" <td>1</td>\n",
" <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": 3,
"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": 4,
"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",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.birthord.value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print value counts for <tt>prglngth</tt> and compare to results published in the [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=PREG&section=A&subSec=8016&srtLabel=611931)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print value counts for <tt>agepreg</tt> and compare to results published in the [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=PREG&section=A&subSec=8016&srtLabel=611935).\n",
"\n",
"Looking at this data, please remember my comments in the book about the obligation to approach data with consideration for the context and respect for the respondents."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the mean birthweight."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.265628457623368"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.totalwgt_lb.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new column named <tt>totalwgt_kg</tt> that contains birth weight in kilograms. Compute its mean. Remember that when you create a new column, you have to use dictionary syntax, not dot notation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look through the codebook and find a variable, other than the ones mentioned in the book, that you find interesting. Compute values counts, means, or other statistics."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a boolean Series."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"4 True\n",
"5 True\n",
"6 True\n",
"7 True\n",
"8 True\n",
"9 True\n",
"10 True\n",
"11 True\n",
"12 True\n",
"13 False\n",
"14 False\n",
"15 True\n",
"16 True\n",
"17 True\n",
"18 False\n",
"19 True\n",
"20 True\n",
"21 True\n",
"22 False\n",
"23 True\n",
"24 True\n",
"25 True\n",
"26 True\n",
"27 True\n",
"28 True\n",
"29 True\n",
" ... \n",
"13563 True\n",
"13564 True\n",
"13565 True\n",
"13566 True\n",
"13567 False\n",
"13568 False\n",
"13569 True\n",
"13570 True\n",
"13571 True\n",
"13572 True\n",
"13573 True\n",
"13574 True\n",
"13575 False\n",
"13576 True\n",
"13577 False\n",
"13578 True\n",
"13579 True\n",
"13580 False\n",
"13581 True\n",
"13582 False\n",
"13583 False\n",
"13584 True\n",
"13585 False\n",
"13586 False\n",
"13587 False\n",
"13588 True\n",
"13589 False\n",
"13590 False\n",
"13591 True\n",
"13592 True\n",
"Name: outcome, dtype: bool"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.outcome == 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use a boolean Series to select the records for the pregnancies that ended in live birth."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9148"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"live = df[df.outcome == 1]\n",
"len(live)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count the number of live births with <tt>birthwgt_lb</tt> between 0 and 5 pounds (including both). The result should be 1125."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1125"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(live[(live.birthwgt_lb >= 0) & (live.birthwgt_lb <= 5)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count the number of live births with <tt>birthwgt_lb</tt> between 9 and 95 pounds (including both). The result should be 798 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use <tt>birthord</tt> to select the records for first babies and others. How many are there of each?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4413, 4735)"
]
},
"execution_count": 9,
"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": [
"Compute the mean weight for first babies and others."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.201094430437772"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firsts.totalwgt_lb.mean()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.325855614973262"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"others.totalwgt_lb.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the mean <tt>prglngth</tt> for first babies and others. Compute the difference in means, expressed in hours."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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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