Lesson6 案例 美国婴儿名字数据.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "### 本案例是针对groupby方法的练习案例，groupby方法讲解见Lesson6\n",
    "  - 读取1880-2106年数据时，使用了concat()方法将各年数据整合为一张表\n",
    "  - 本练习包含可视化练习，使用matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1891894 entries, 0 to 1891893\n",
      "Data columns (total 4 columns):\n",
      "name         object\n",
      "gender       object\n",
      "frequency    int64\n",
      "year         int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 57.7+ MB\n",
      "None\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1891889</th>\n",
       "      <td>Zolton</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891890</th>\n",
       "      <td>Zurich</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891891</th>\n",
       "      <td>Zyahir</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891892</th>\n",
       "      <td>Zyel</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891893</th>\n",
       "      <td>Zylyn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "           name gender  frequency  year\n",
       "1891889  Zolton      M          5  2016\n",
       "1891890  Zurich      M          5  2016\n",
       "1891891  Zyahir      M          5  2016\n",
       "1891892    Zyel      M          5  2016\n",
       "1891893   Zylyn      M          5  2016"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#读取1880-2016年婴儿姓名使用信息，由于信息表按年统计，因此需要将多年数据整合到一张表中\n",
    "years=range(1880,2017)\n",
    "columns=[\"name\",\"gender\",\"frequency\"]\n",
    "pieces=[] #用于存储循环读取的每一年的数据\n",
    "\n",
    "#使用循环读取每年数据，并将各年数据都存入pieces中,pieces列表的各元素表示各年的数据\n",
    "for year in years:\n",
    "    path=\"/Users/dengsudden/Documents/python_course/names/yob%d.txt\" %year\n",
    "    frame=pd.read_csv(path,names=columns)\n",
    "    frame[\"year\"]=year\n",
    "    pieces.append(frame)\n",
    "\n",
    "#中pieces列表中填入各年数据后，需要使用concat方法将pieces各元素整合在一起\n",
    "baby_names=pd.concat(pieces,ignore_index=True)\n",
    "\n",
    "print(baby_names.info())\n",
    "baby_names.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  gender\n",
       "1880  F           942\n",
       "      M          1058\n",
       "1881  F           938\n",
       "      M           997\n",
       "1882  F          1028\n",
       "      M          1099\n",
       "1883  F          1054\n",
       "      M          1030\n",
       "1884  F          1172\n",
       "      M          1125\n",
       "1885  F          1197\n",
       "      M          1097\n",
       "1886  F          1282\n",
       "      M          1110\n",
       "1887  F          1306\n",
       "      M          1067\n",
       "1888  F          1474\n",
       "      M          1177\n",
       "1889  F          1479\n",
       "      M          1111\n",
       "1890  F          1534\n",
       "      M          1161\n",
       "1891  F          1533\n",
       "      M          1127\n",
       "1892  F          1661\n",
       "      M          1260\n",
       "1893  F          1652\n",
       "      M          1179\n",
       "1894  F          1702\n",
       "      M          1239\n",
       "                ...  \n",
       "2002  F         18080\n",
       "      M         12480\n",
       "2003  F         18427\n",
       "      M         12753\n",
       "2004  F         18825\n",
       "      M         13220\n",
       "2005  F         19181\n",
       "      M         13362\n",
       "2006  F         20047\n",
       "      M         14032\n",
       "2007  F         20558\n",
       "      M         14388\n",
       "2008  F         20454\n",
       "      M         14611\n",
       "2009  F         20175\n",
       "      M         14522\n",
       "2010  F         19810\n",
       "      M         14253\n",
       "2011  F         19554\n",
       "      M         14339\n",
       "2012  F         19486\n",
       "      M         14229\n",
       "2013  F         19219\n",
       "      M         14034\n",
       "2014  F         19169\n",
       "      M         14037\n",
       "2015  F         19054\n",
       "      M         14009\n",
       "2016  F         18757\n",
       "      M         14111\n",
       "Length: 274, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#小小练习一下，用groupby统计每年出生的男女婴儿数\n",
    "#如果要同时按几列来分组，需要使用[]\n",
    "baby_names.groupby([\"year\",\"gender\"]).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 统计不同年份、不同性别中，各名字出现次数所占分组名字出现次数的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "      <th>pct</th>\n",
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       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>7.764419</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>2.861790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>2.201292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>2.130957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>1.918850</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        name gender  frequency  year       pct\n",
       "0       Mary      F       7065  1880  7.764419\n",
       "1       Anna      F       2604  1880  2.861790\n",
       "2       Emma      F       2003  1880  2.201292\n",
       "3  Elizabeth      F       1939  1880  2.130957\n",
       "4     Minnie      F       1746  1880  1.918850"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#先自定义函数，统计分组中各名字占比\n",
    "def add_pct(group):\n",
    "    group[\"pct\"]=group.frequency/group.frequency.sum()*100\n",
    "    return group\n",
    "\n",
    "baby_names=baby_names.groupby([\"year\",\"gender\"]).apply(add_pct)\n",
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 增加一个变量ranked，表示各年份性别分组下，各名字在分组中出现频率的排名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "      <td>Mary</td>\n",
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       "      <td>7.764419</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>2.861790</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>2.201292</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>2.130957</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>1.918850</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        name gender  frequency  year       pct  ranked\n",
       "0       Mary      F       7065  1880  7.764419     1.0\n",
       "1       Anna      F       2604  1880  2.861790     2.0\n",
       "2       Emma      F       2003  1880  2.201292     3.0\n",
       "3  Elizabeth      F       1939  1880  2.130957     4.0\n",
       "4     Minnie      F       1746  1880  1.918850     5.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names[\"ranked\"]=baby_names.groupby([\"year\",\"gender\"])[\"frequency\"].rank(ascending=False)\n",
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化-每年最流行的男孩女孩名字使用次数所占当年出生男孩女孩总数百分比的变化趋势\n",
    "  - matplotlib画出各年rank1的名字占比变化情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format=\"retina\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#布尔索引选取数据中的男孩女孩\n",
    "dfm=baby_names[baby_names.gender==\"M\"]\n",
    "dff=baby_names[baby_names.gender==\"F\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "      <th>942</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9655</td>\n",
       "      <td>1880</td>\n",
       "      <td>8.738268</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>8769</td>\n",
       "      <td>1881</td>\n",
       "      <td>8.704327</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9557</td>\n",
       "      <td>1882</td>\n",
       "      <td>8.406488</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7116</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>8894</td>\n",
       "      <td>1883</td>\n",
       "      <td>8.500674</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9318</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9388</td>\n",
       "      <td>1884</td>\n",
       "      <td>8.203210</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      name gender  frequency  year       pct  ranked\n",
       "942   John      M       9655  1880  8.738268     1.0\n",
       "2938  John      M       8769  1881  8.704327     1.0\n",
       "4963  John      M       9557  1882  8.406488     1.0\n",
       "7116  John      M       8894  1883  8.500674     1.0\n",
       "9318  John      M       9388  1884  8.203210     1.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每年最流行的男孩名字\n",
    "rank1m=dfm[dfm.ranked==1]\n",
    "rank1m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "      <th>year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>7.764419</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>6919</td>\n",
       "      <td>1881</td>\n",
       "      <td>7.524496</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>8148</td>\n",
       "      <td>1882</td>\n",
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       "    <tr>\n",
       "      <th>6062</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>8012</td>\n",
       "      <td>1883</td>\n",
       "      <td>7.133318</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8146</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>9217</td>\n",
       "      <td>1884</td>\n",
       "      <td>7.143854</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name gender  frequency  year       pct  ranked\n",
       "0     Mary      F       7065  1880  7.764419     1.0\n",
       "2000  Mary      F       6919  1881  7.524496     1.0\n",
       "3935  Mary      F       8148  1882  7.555148     1.0\n",
       "6062  Mary      F       8012  1883  7.133318     1.0\n",
       "8146  Mary      F       9217  1884  7.143854     1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每年最流行的女孩名字\n",
    "rank1f=dff[dff.ranked==1]\n",
    "rank1f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12e0076a0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O9BT5Vf0YFvlf7PEM0vcjmQEcRmh2VGm42cmEq6PLujebQbvd8NAEZANCu+ZS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\np5SzqltsAYqIiIhUQbt2cOSRcNNNJWV33gmXXgoNGyYXl9QPsSb/7v5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JNt8cvv0293Hl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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d51ecc0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 287,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制折线图\n",
    "plt.plot(rank1m.year,rank1m.pct,color=\"blue\",linewidth=2)\n",
    "\n",
    "#plt.fill_between设置填充线与坐标轴之间的空间\n",
    "plt.fill_between(rank1m.year,rank1m.pct,color=\"blue\",alpha=0.2)\n",
    "\n",
    "#设置坐标轴区间范围\n",
    "plt.xlim(1880,2016)\n",
    "plt.ylim(0,9)\n",
    "\n",
    "#美化图：给图添加标题，调整字体大小等\n",
    "plt.title(\"Popularity of 1# boys'name by year\",size=18,color=\"blue\")\n",
    "plt.xlabel(\"Year\",size=16)\n",
    "plt.ylabel(\"% of male births\",size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x118d911d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f6BJ17rvjzl+URDST+6u5LPNGioc/XJrSKdbz5hDDflWvlizvG8QX/HbEj9u6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\nTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c469cc0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 289,
       "width": 392
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rank1f.year,rank1f.pct,color=\"red\",linewidth=2)\n",
    "plt.fill_between(rank1f.year,rank1f.pct,color=\"red\",alpha=0.2)\n",
    "plt.xlim(1880,2016)\n",
    "plt.ylim(0,9)\n",
    "plt.title(\"Popularity of 1# girls'name by year\",size=20,color=\"red\")\n",
    "plt.xlabel(\"Year\",size=16)\n",
    "plt.ylabel(\"% of female births\",size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据以上输出结果，我们发现，不论是男孩还是女孩，每年最流行的名字所占当年分组出生人数比例都在下降\n",
    "  - 说明民众的思想更加自主，越来越个性化\n",
    "  - 还有一个原因可能是每年可选择的姓名数越来越多了，下面我们用统计结果来证明\n",
    "      - 思路：按年份、性别分组，统计各分组下的姓名数量，然后根据按年份，绘制不同性别各年度可选姓名数量的趋势图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>gender</th>\n",
       "      <th>name_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>M</td>\n",
       "      <td>1058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1881</td>\n",
       "      <td>F</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1881</td>\n",
       "      <td>M</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1882</td>\n",
       "      <td>F</td>\n",
       "      <td>1028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year gender  name_count\n",
       "0  1880      F         942\n",
       "1  1880      M        1058\n",
       "2  1881      F         938\n",
       "3  1881      M         997\n",
       "4  1882      F        1028"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_count=baby_names.groupby([\"year\",\"gender\"]).size() #.size()返回一个series\n",
    "#使用to_frame将series转换为dataframe\n",
    "name_count=name_count.to_frame(name=\"name_count\").reset_index()\n",
    "name_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>gender</th>\n",
       "      <th>name_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>M</td>\n",
       "      <td>1058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1881</td>\n",
       "      <td>M</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1882</td>\n",
       "      <td>M</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1883</td>\n",
       "      <td>M</td>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1884</td>\n",
       "      <td>M</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year gender  name_count\n",
       "1  1880      M        1058\n",
       "3  1881      M         997\n",
       "5  1882      M        1099\n",
       "7  1883      M        1030\n",
       "9  1884      M        1125"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_countm=name_count[name_count.gender==\"M\"]\n",
    "name_countf=name_count[name_count.gender==\"F\"]\n",
    "name_countm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制男孩女孩各年名字数量趋势图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1327d4400>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7czLhS8Z95M8XlfQ/Qu3Xli0JaGL+kFh+l/xhlKvpcWAPQk3XGynSf0Eo3+ru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\n/wf23QTSrC6XlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134571cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 290,
       "width": 410
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(name_countm.year,name_countm.name_count,color=\"blue\",linewidth=2)\n",
    "plt.fill_between(name_countm.year,name_countm.name_count,color=\"blue\",alpha=0.2)\n",
    "plt.xlim(1880,2016)\n",
    "plt.ylim(0,17000)\n",
    "plt.title(\"Number of boy's name by year\",size=20,color=\"blue\")\n",
    "plt.xlabel(\"Year\",size=18)\n",
    "plt.ylabel(\"Number of names\",size=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1330816a0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Cms5DQY2IiNSH2bfJn1BjcjS+TUQ6GrNu5E8A8RH508Bn4RhCd7Cc5Dpg0oEp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GyzH2MxurHEwfP18ltUPS5uKhvp/Hst5cesPSNiapd1PGdPoMmDboD65seU/s3vAv4QEVcCC4Gd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\nRpIkSVKt/X9vyTvujaBiyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ec7df98>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 290,
       "width": 410
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(name_countf.year,name_countf.name_count,color=\"red\",linewidth=2)\n",
    "plt.fill_between(name_countf.year,name_countf.name_count,color=\"red\",alpha=0.2)\n",
    "plt.xlim(1880,2016)\n",
    "plt.ylim(0,21000)\n",
    "plt.title(\"Number of girl's name by year\",size=20,color=\"red\")\n",
    "plt.xlabel(\"Year\",size=18)\n",
    "plt.ylabel(\"Number of names\",size=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 以上验证可以看到，不论男女，可选名字数量逐年递增，这可能也是导致每年最流行的名字占比当年出生人口比例逐年下降的因素之一。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 作业\n",
    "  - 分别找出2015, 2016年最流行的前10个男孩和女孩名字\n",
    "    - 思路：\n",
    "    - groupby().apply()，\n",
    "    - 使用ranked列，取ranked值小于11的数据\n",
    "    - 或者使用sort_values()方法对pct列大小排序，取最大的10行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "      <th>pct</th>\n",
       "      <th>ranked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1845022</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>15049</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.789058</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877786</th>\n",
       "      <td>Mason</td>\n",
       "      <td>M</td>\n",
       "      <td>15192</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.807796</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845021</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>15863</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.831738</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877785</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>15668</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.833106</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845020</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>15914</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.834412</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845019</th>\n",
       "      <td>Mason</td>\n",
       "      <td>M</td>\n",
       "      <td>16591</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.869909</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845018</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>18330</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.961089</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877784</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>18138</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.964441</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877783</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>19015</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.011074</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845017</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>19594</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.027364</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name gender  frequency  year       pct  ranked\n",
       "1845022    Ethan      M      15049  2015  0.789058     6.0\n",
       "1877786    Mason      M      15192  2016  0.807796     4.0\n",
       "1845021  William      M      15863  2015  0.831738     5.0\n",
       "1877785  William      M      15668  2016  0.833106     3.0\n",
       "1845020    Jacob      M      15914  2015  0.834412     4.0\n",
       "1845019    Mason      M      16591  2015  0.869909     3.0\n",
       "1845018     Liam      M      18330  2015  0.961089     2.0\n",
       "1877784     Liam      M      18138  2016  0.964441     2.0\n",
       "1877783     Noah      M      19015  2016  1.011074     1.0\n",
       "1845017     Noah      M      19594  2015  1.027364     1.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort_values方法\n",
    "\n",
    "#先将男女分组\n",
    "baby_namesm=baby_names[baby_names.gender==\"M\"]\n",
    "baby_namesf=baby_names[baby_names.gender==\"F\"]\n",
    "\n",
    "bm1516=baby_namesm[baby_namesm.year>2014]\n",
    "bm1516.sort_values(by=\"pct\")[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>frequency</th>\n",
       "      <th>year</th>\n",
       "      <th>pct</th>\n",
       "      <th>ranked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1845017</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>19594</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.027364</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845018</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>18330</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.961089</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845019</th>\n",
       "      <td>Mason</td>\n",
       "      <td>M</td>\n",
       "      <td>16591</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.869909</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845020</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>15914</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.834412</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845021</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>15863</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.831738</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845022</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>15049</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.789058</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845023</th>\n",
       "      <td>James</td>\n",
       "      <td>M</td>\n",
       "      <td>14773</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.774587</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845024</th>\n",
       "      <td>Alexander</td>\n",
       "      <td>M</td>\n",
       "      <td>14507</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.760639</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845025</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>14396</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.754819</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845026</th>\n",
       "      <td>Benjamin</td>\n",
       "      <td>M</td>\n",
       "      <td>13670</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.716753</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877783</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>19015</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.011074</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877784</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>18138</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.964441</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877785</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>15668</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.833106</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877786</th>\n",
       "      <td>Mason</td>\n",
       "      <td>M</td>\n",
       "      <td>15192</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.807796</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877787</th>\n",
       "      <td>James</td>\n",
       "      <td>M</td>\n",
       "      <td>14776</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.785676</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877788</th>\n",
       "      <td>Benjamin</td>\n",
       "      <td>M</td>\n",
       "      <td>14569</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.774669</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877789</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>14416</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.766534</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877790</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>13998</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.744308</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877791</th>\n",
       "      <td>Elijah</td>\n",
       "      <td>M</td>\n",
       "      <td>13764</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.731865</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877792</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>13758</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.731546</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name gender  frequency  year       pct  ranked\n",
       "1845017       Noah      M      19594  2015  1.027364     1.0\n",
       "1845018       Liam      M      18330  2015  0.961089     2.0\n",
       "1845019      Mason      M      16591  2015  0.869909     3.0\n",
       "1845020      Jacob      M      15914  2015  0.834412     4.0\n",
       "1845021    William      M      15863  2015  0.831738     5.0\n",
       "1845022      Ethan      M      15049  2015  0.789058     6.0\n",
       "1845023      James      M      14773  2015  0.774587     7.0\n",
       "1845024  Alexander      M      14507  2015  0.760639     8.0\n",
       "1845025    Michael      M      14396  2015  0.754819     9.0\n",
       "1845026   Benjamin      M      13670  2015  0.716753    10.0\n",
       "1877783       Noah      M      19015  2016  1.011074     1.0\n",
       "1877784       Liam      M      18138  2016  0.964441     2.0\n",
       "1877785    William      M      15668  2016  0.833106     3.0\n",
       "1877786      Mason      M      15192  2016  0.807796     4.0\n",
       "1877787      James      M      14776  2016  0.785676     5.0\n",
       "1877788   Benjamin      M      14569  2016  0.774669     6.0\n",
       "1877789      Jacob      M      14416  2016  0.766534     7.0\n",
       "1877790    Michael      M      13998  2016  0.744308     8.0\n",
       "1877791     Elijah      M      13764  2016  0.731865     9.0\n",
       "1877792      Ethan      M      13758  2016  0.731546    10.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#通过ranked筛选方法\n",
    "\n",
    "bm=bm1516[bm1516.ranked<=10]\n",
    "bm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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