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secsilm/children.csv

Created June 16, 2017 02:43
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CSDN - 儿童统计
Raw
children.csv
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
no gend age high weight
6 0 10 1.46 38
18 0 11 1.56 48
17 0 11 1.5 40
7 1 10 1.48 39
12 1 10 1.43 43
26 1 12 1.64 60
15 0 10 1.48 39
45 0 10 1.43 35
21 1 11 1.55 46
27 1 11 1.55
9 1 11 1.46 40
27 1 13 1.59 55
4 0 11 1.52 42
5 1 10 1.43 43
10 0 12 1.6 53
14 1 12 1.59 42
8 1 11 14.8 40
3 0 11 1.55 44
20 0 10 1.44 37
19 0 12 1.62 56
1 1 12 1.6 55
2 1 12 1.62 53
11 0 11 1.55 55
16 0 10 1.44 38
13 1 11 1.46 41
40 1 12 1.62
25 1 11 1.55 48
Display the source blob
Display the rendered blob
Raw
children.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"children = pd.read_csv('children.csv', index_col=False, delim_whitespace=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>no</th>\n",
" <th>gend</th>\n",
" <th>age</th>\n",
" <th>high</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>39.0</td>\n",
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" <td>45</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
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" <td>35.0</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
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" <tr>\n",
" <th>11</th>\n",
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" <td>1.59</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.52</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>1.43</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1.60</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.59</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>14.80</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.44</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1.62</td>\n",
" <td>56.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.60</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.62</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.44</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.46</td>\n",
" <td>41.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.62</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>48.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" no gend age high weight\n",
"0 6 0 10 1.46 38.0\n",
"1 18 0 11 1.56 48.0\n",
"2 17 0 11 1.50 40.0\n",
"3 7 1 10 1.48 39.0\n",
"4 12 1 10 1.43 43.0\n",
"5 26 1 12 1.64 60.0\n",
"6 15 0 10 1.48 39.0\n",
"7 45 0 10 1.43 35.0\n",
"8 21 1 11 1.55 46.0\n",
"9 27 1 11 1.55 NaN\n",
"10 9 1 11 1.46 40.0\n",
"11 27 1 13 1.59 55.0\n",
"12 4 0 11 1.52 42.0\n",
"13 5 1 10 1.43 43.0\n",
"14 10 0 12 1.60 53.0\n",
"15 14 1 12 1.59 42.0\n",
"16 8 1 11 14.80 40.0\n",
"17 3 0 11 1.55 44.0\n",
"18 20 0 10 1.44 37.0\n",
"19 19 0 12 1.62 56.0\n",
"20 1 1 12 1.60 55.0\n",
"21 2 1 12 1.62 53.0\n",
"22 11 0 11 1.55 55.0\n",
"23 16 0 10 1.44 38.0\n",
"24 13 1 11 1.46 41.0\n",
"25 40 1 12 1.62 NaN\n",
"26 25 1 11 1.55 48.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"children"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>no</th>\n",
" <th>gend</th>\n",
" <th>age</th>\n",
" <th>high</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.46</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.56</td>\n",
" <td>48.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.50</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>1.48</td>\n",
" <td>39.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>1.43</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.64</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.48</td>\n",
" <td>39.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.43</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.46</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>13</td>\n",
" <td>1.59</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.52</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>1.43</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1.60</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.59</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>14.80</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.44</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1.62</td>\n",
" <td>56.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.60</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1.62</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1.44</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.46</td>\n",
" <td>41.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1.55</td>\n",
" <td>48.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" no gend age high weight\n",
"0 6 0 10 1.46 38.0\n",
"1 18 0 11 1.56 48.0\n",
"2 17 0 11 1.50 40.0\n",
"3 7 1 10 1.48 39.0\n",
"4 12 1 10 1.43 43.0\n",
"5 26 1 12 1.64 60.0\n",
"6 15 0 10 1.48 39.0\n",
"7 45 0 10 1.43 35.0\n",
"8 21 1 11 1.55 46.0\n",
"10 9 1 11 1.46 40.0\n",
"11 27 1 13 1.59 55.0\n",
"12 4 0 11 1.52 42.0\n",
"13 5 1 10 1.43 43.0\n",
"14 10 0 12 1.60 53.0\n",
"15 14 1 12 1.59 42.0\n",
"16 8 1 11 14.80 40.0\n",
"17 3 0 11 1.55 44.0\n",
"18 20 0 10 1.44 37.0\n",
"19 19 0 12 1.62 56.0\n",
"20 1 1 12 1.60 55.0\n",
"21 2 1 12 1.62 53.0\n",
"22 11 0 11 1.55 55.0\n",
"23 16 0 10 1.44 38.0\n",
"24 13 1 11 1.46 41.0\n",
"26 25 1 11 1.55 48.0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 丢弃所有含有缺失值的行\n",
"children.dropna(how='any', axis=0, inplace=True)\n",
"children"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 按照性别分组\n",
"groupby_gend = children.groupby('gend')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
" no gend age high weight\n",
"0 6 0 10 1.46 38.0\n",
"1 18 0 11 1.56 48.0\n",
"2 17 0 11 1.50 40.0\n",
"6 15 0 10 1.48 39.0\n",
"7 45 0 10 1.43 35.0\n",
"12 4 0 11 1.52 42.0\n",
"14 10 0 12 1.60 53.0\n",
"17 3 0 11 1.55 44.0\n",
"18 20 0 10 1.44 37.0\n",
"19 19 0 12 1.62 56.0\n",
"22 11 0 11 1.55 55.0\n",
"23 16 0 10 1.44 38.0\n",
"\n",
"1\n",
" no gend age high weight\n",
"3 7 1 10 1.48 39.0\n",
"4 12 1 10 1.43 43.0\n",
"5 26 1 12 1.64 60.0\n",
"8 21 1 11 1.55 46.0\n",
"10 9 1 11 1.46 40.0\n",
"11 27 1 13 1.59 55.0\n",
"13 5 1 10 1.43 43.0\n",
"15 14 1 12 1.59 42.0\n",
"16 8 1 11 14.80 40.0\n",
"20 1 1 12 1.60 55.0\n",
"21 2 1 12 1.62 53.0\n",
"24 13 1 11 1.46 41.0\n",
"26 25 1 11 1.55 48.0\n",
"\n"
]
}
],
"source": [
"# 查看每个性别的内容\n",
"for name, group in groupby_gend:\n",
" print(name)\n",
" print(group, end='\\n\\n')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x263e4f25630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x263e4f257f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 各分组的年龄分布\n",
"for name, group in groupby_gend:\n",
" plt.figure()\n",
" plt.hist(group.age)\n",
" plt.xlabel('age')\n",
" plt.title(name)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x263e4fdf710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x263e5034b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 各分组的身高分布\n",
"for name, group in groupby_gend:\n",
" plt.figure()\n",
" plt.hist(group.high)\n",
" plt.xlabel('high')\n",
" plt.title(name)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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pbrgO+FhE/G5mPkQVGK6lERIi4gXAi4GNwGHAszLzscZ9z4qII4A/A05uedwT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x263e523e630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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UOyRExGeB3wdOy8y/naHbg8CqjrZVwLq62xsb28L4+ETdhy0o27aNV9ecDKDx7YO978bG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\nSUWGBEmSVGRIkCRJRYYESZJU9P8AU2JyGDQ9l5gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x263e52efdd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 各分组的体重分布\n",
"for name, group in groupby_gend:\n",
" plt.figure()\n",
" plt.hist(group.weight)\n",
" plt.xlabel('weight')\n",
" plt.title(name)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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