ADY.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sbd</th>\n",
       "      <th>toan</th>\n",
       "      <th>ngu_van</th>\n",
       "      <th>ngoai_ngu</th>\n",
       "      <th>vat_li</th>\n",
       "      <th>hoa_hoc</th>\n",
       "      <th>sinh_hoc</th>\n",
       "      <th>lich_su</th>\n",
       "      <th>dia_li</th>\n",
       "      <th>gdcd</th>\n",
       "      <th>hoi_dong</th>\n",
       "      <th>ma_tinh</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2.75</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Sở GDĐT Hà Nội</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       sbd  toan  ngu_van  ngoai_ngu  vat_li  hoa_hoc  sinh_hoc  lich_su  \\\n",
       "0  1000001   3.6     5.00        4.0     NaN      NaN       NaN     2.75   \n",
       "1  1000002   8.4     6.75        7.6     NaN      NaN       NaN     8.50   \n",
       "2  1000003   5.8     7.50        5.0     NaN      NaN       NaN     7.25   \n",
       "3  1000004   7.4     7.50        8.6     NaN      NaN       NaN     7.50   \n",
       "4  1000005   7.2     8.50        9.0     NaN      NaN       NaN     8.00   \n",
       "\n",
       "   dia_li  gdcd        hoi_dong  ma_tinh  \n",
       "0     6.0  8.75  Sở GDĐT Hà Nội        1  \n",
       "1     7.5  8.25  Sở GDĐT Hà Nội        1  \n",
       "2     5.5  8.75  Sở GDĐT Hà Nội        1  \n",
       "3     6.5  7.50  Sở GDĐT Hà Nội        1  \n",
       "4     8.5  8.25  Sở GDĐT Hà Nội        1  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"new.csv\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <th>toan</th>\n",
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       "      <th>ngoai_ngu</th>\n",
       "      <th>vat_li</th>\n",
       "      <th>hoa_hoc</th>\n",
       "      <th>sinh_hoc</th>\n",
       "      <th>lich_su</th>\n",
       "      <th>dia_li</th>\n",
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       "      <th>11</th>\n",
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       "      <th>19</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1000021</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "        sbd  toan  ngu_van  ngoai_ngu  vat_li  hoa_hoc  sinh_hoc  lich_su  \\\n",
       "8   1000009   2.6     5.25        NaN     NaN      NaN       NaN     5.25   \n",
       "11  1000012   6.4     7.50        NaN     NaN      NaN       NaN     4.75   \n",
       "18  1000019   6.8     7.50        NaN     NaN      NaN       NaN     7.25   \n",
       "19  1000020   NaN     6.75        NaN     NaN      NaN       NaN     8.50   \n",
       "20  1000021   8.0     9.00        NaN     NaN      NaN       NaN     7.00   \n",
       "\n",
       "    dia_li  gdcd        hoi_dong  ma_tinh  \n",
       "8     6.75   NaN  Sở GDĐT Hà Nội        1  \n",
       "11    5.00  8.00  Sở GDĐT Hà Nội        1  \n",
       "18    6.25  8.00  Sở GDĐT Hà Nội        1  \n",
       "19    8.00   NaN  Sở GDĐT Hà Nội        1  \n",
       "20    6.25  8.75  Sở GDĐT Hà Nội        1  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thi_sinh_co_chung_chi_ngoai_ngu = df[df.ngoai_ngu.isnull()]\n",
    "thi_sinh_co_chung_chi_ngoai_ngu.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"ngoai_ngu\"].fillna(10, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_22456\\206008121.py:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
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       "      <th>3</th>\n",
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       "      <td>7.50</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "       sbd  toan  ngu_van  ngoai_ngu  lich_su  dia_li  gdcd        hoi_dong  \\\n",
       "0  1000001   3.6     5.00        4.0     2.75     6.0  8.75  Sở GDĐT Hà Nội   \n",
       "1  1000002   8.4     6.75        7.6     8.50     7.5  8.25  Sở GDĐT Hà Nội   \n",
       "2  1000003   5.8     7.50        5.0     7.25     5.5  8.75  Sở GDĐT Hà Nội   \n",
       "3  1000004   7.4     7.50        8.6     7.50     6.5  7.50  Sở GDĐT Hà Nội   \n",
       "4  1000005   7.2     8.50        9.0     8.00     8.5  8.25  Sở GDĐT Hà Nội   \n",
       "\n",
       "   ma_tinh  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "khoi_xa_hoi = df[(df[\"vat_li\"].isnull()) & (\n",
    "    df[\"hoa_hoc\"].isnull()) & (df[\"sinh_hoc\"].isnull())]\n",
    "khoi_xa_hoi.drop(columns=[\"vat_li\", \"hoa_hoc\", \"sinh_hoc\"], inplace=True)\n",
    "khoi_xa_hoi.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_22456\\250995334.py:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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       "      <th>22</th>\n",
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       "      <td>3.00</td>\n",
       "      <td>Sở GDĐT Hà Nội</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "        sbd  toan  ngu_van  ngoai_ngu  vat_li  hoa_hoc  sinh_hoc  \\\n",
       "10  1000011   6.4     7.25        7.6    3.50     8.00      3.00   \n",
       "14  1000015   8.6     7.25        9.6    8.25     3.25      4.25   \n",
       "15  1000016   7.8     5.75        8.4    4.25     8.00      4.75   \n",
       "16  1000017   8.6     6.25        7.0    7.00     3.50      3.75   \n",
       "22  1000023   6.8     6.00        6.8    2.50     5.50      3.00   \n",
       "\n",
       "          hoi_dong  ma_tinh  \n",
       "10  Sở GDĐT Hà Nội        1  \n",
       "14  Sở GDĐT Hà Nội        1  \n",
       "15  Sở GDĐT Hà Nội        1  \n",
       "16  Sở GDĐT Hà Nội        1  \n",
       "22  Sở GDĐT Hà Nội        1  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "khoi_tu_nhien = df[(df[\"lich_su\"].isnull()) & (\n",
    "    df[\"dia_li\"].isnull()) & (df[\"gdcd\"].isnull())]\n",
    "khoi_tu_nhien.drop(columns=[\"lich_su\", \"dia_li\", \"gdcd\"], inplace=True)\n",
    "khoi_tu_nhien.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sở GDĐT Hà Nội</td>\n",
       "      <td>1</td>\n",
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       "        sbd  toan  ngu_van  ngoai_ngu  vat_li  hoa_hoc  sinh_hoc  lich_su  \\\n",
       "8   1000009   2.6     5.25       10.0     NaN      NaN       NaN     5.25   \n",
       "13  1000014   5.8     5.50        6.4     NaN      NaN       NaN     8.50   \n",
       "19  1000020   NaN     6.75       10.0     NaN      NaN       NaN     8.50   \n",
       "43  1000044   2.6     3.00       10.0     NaN      NaN       NaN     2.00   \n",
       "59  1000060   8.2     7.50        7.2     NaN      NaN       NaN      NaN   \n",
       "\n",
       "    dia_li  gdcd        hoi_dong  ma_tinh  \n",
       "8     6.75   NaN  Sở GDĐT Hà Nội        1  \n",
       "13    8.00   NaN  Sở GDĐT Hà Nội        1  \n",
       "19    8.00   NaN  Sở GDĐT Hà Nội        1  \n",
       "43    4.50   NaN  Sở GDĐT Hà Nội        1  \n",
       "59     NaN   NaN  Sở GDĐT Hà Nội        1  "
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     "execution_count": 114,
     "metadata": {},
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   "source": [
    "thi_sinh_tu_do = df[((df[\"lich_su\"].isnull()) | (df[\"dia_li\"].isnull()) | (df[\"gdcd\"].isnull())) & (\n",
    "    (df[\"vat_li\"].isnull()) | (df[\"hoa_hoc\"].isnull()) | (df[\"sinh_hoc\"].isnull()))]\n",
    "thi_sinh_tu_do.head()\n"
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  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
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       "                  sbd      toan   ngu_van  ngoai_ngu    vat_li   hoa_hoc  \\\n",
       "ma_tinh                                                                    \n",
       "1        1.048806e+06  6.765761  6.652506   6.477440  6.840207  6.310386   \n",
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       "\n",
       "         sinh_hoc   lich_su    dia_li      gdcd  \n",
       "ma_tinh                                          \n",
       "1        4.640575  6.476566  6.557574  7.926987  \n",
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   ],
   "source": [
    "prov = [\"Hà Nội\", \"TP. Hồ Chí Minh\", \"Hải Phòng\", \"Đà Nẵng\", \"Hà Giang\", \"Cao Bằng\", \"Lai Châu\", \"Lào Cai\", \"Tuyên Quang\", \"Lạng Sơn\", \"Bắc Kạn\", \"Thái Nguyên\", \"Yên Bái\", \"Sơn La\", \"Phú Thọ\", \"Vĩnh Phúc\", \"Quảng Ninh\", \"Bắc Giang\", \"Bắc Ninh\",None, \"Hải Dương\", \"Hưng Yên\", \"Hoà Bình\", \"Hà Nam\", \"Nam Định\", \"Thái Bình\", \"Ninh Bình\", \"Thanh Hoá\", \"Nghệ An\", \"Hà Tĩnh\", \"Quảng Bình\", \"Quảng Trị\", \"Thừa Thiên -Huế\",\n",
    "        \"Quảng Nam\", \"Quảng Ngãi\", \"Kon Tum\", \"Bình Định\", \"Gia Lai\", \"Phú Yên\", \"Đắk Lắk\", \"Khánh Hoà\", \"Lâm Đồng\", \"Bình Phước\", \"Bình Dương\", \"Ninh Thuận\", \"Tây Ninh\", \"Bình Thuận\", \"Đồng Nai\", \"Long An\", \"Đồng Tháp\", \"An Giang\", \"Bà Rịa-Vũng Tàu\", \"Tiền Giang\", \"Kiên Giang\", \"Cần Thơ\", \"Bến Tre\", \"Vĩnh Long\", \"Trà Vinh\", \"Sóc Trăng\", \"Bạc Liêu\", \"Cà Mau\", \"Điện Biên\", \"Đăk Nông\", \"Hậu Giang\"]\n",
    "val = df.groupby(\"ma_tinh\").mean()\n",
    "val.head()"
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  {
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   "execution_count": 226,
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   "source": [
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "fig = make_subplots(rows=1, cols=3)\n",
    "fig1 = px.box(val[\"toan\"])\n",
    "fig2 = px.box(val[\"ngu_van\"])\n",
    "fig3 = px.box(val[\"ngoai_ngu\"])\n",
    "fig.add_trace(fig1[\"data\"][0], row=1, col=1)\n",
    "fig.add_trace(fig2[\"data\"][0], row=1, col=2)\n",
    "fig.add_trace(fig3[\"data\"][0], row=1, col=3)\n",
    "fig.update_layout(height=720, width=1366, title_text=\"Box plot of mean score of 63 province\")"
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  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
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     "name": "stderr",
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     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_22456\\1753538804.py:3: FutureWarning:\n",
      "\n",
      "The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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    "meomeo = val.nsmallest(10, \"toan\")[\"toan\"]\n",
    "top10van = top10van.append(meomeo[::-1])\n",
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    "hoi_dong = [prov[i-1] for i in top10van.index]\n",
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    "top10van = val.nlargest(10, \"ngu_van\")[\"ngu_van\"]\n",
    "meomeo = val.nsmallest(10, \"ngu_van\")[\"ngu_van\"]\n",
    "top10van = top10van.append(meomeo[::-1])\n",
    "top10van\n",
    "hoi_dong = [prov[i-1] for i in top10van.index]\n",
    "fig = px.bar(x=hoi_dong, y=top10van.values, color=hoi_dong, text=top10van.values.round(2))\n",
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  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
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     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_22456\\1938207106.py:4: FutureWarning:\n",
      "\n",
      "The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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   "source": [
    "top10ngoai_ngu = val.nlargest(10, \"ngoai_ngu\")[\"ngoai_ngu\"]#+val.nsmallest(10, \"ngoai_ngu\")[\"ngoai_ngu\"]\n",
    "meomeo = val.nsmallest(10, \"ngoai_ngu\")[\"ngoai_ngu\"]\n",
    "top10ngoai_ngu = top10ngoai_ngu.append(meomeo[::-1])\n",
    "top10ngoai_ngu\n",
    "hoi_dong = [prov[i-1] for i in top10ngoai_ngu.index]\n",
    "fig = px.bar(x=hoi_dong, y=top10ngoai_ngu.values, color=hoi_dong, text=top10ngoai_ngu.values.round(2))\n",
    "fig.update_layout(title=\"Top 10 province with the highest and lowest average in foreign language\", xaxis_title=\"Province\",yaxis_title=\"Average score\",showlegend=False)"
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      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
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      "\n",
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       "        sbd  toan  ngu_van  ngoai_ngu  vat_li  hoa_hoc  sinh_hoc  \\\n",
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       "          hoi_dong  ma_tinh       avg  \n",
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    "khoi_tu_nhien[\"avg\"] = (khoi_tu_nhien[\"vat_li\"] + khoi_tu_nhien.hoa_hoc + khoi_tu_nhien.sinh_hoc) / 3\n",
    "khoi_tu_nhien.head()"
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   "metadata": {},
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    "val_tn = khoi_tu_nhien.groupby(\"ma_tinh\").mean()"
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   "execution_count": 257,
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       "       sbd  toan  ngu_van  ngoai_ngu  lich_su  dia_li  gdcd        hoi_dong  \\\n",
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       "4  1000005   7.2     8.50        9.0     8.00     8.5  8.25  Sở GDĐT Hà Nội   \n",
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    "khoi_xa_hoi[\"avg\"] = (khoi_xa_hoi.lich_su + khoi_xa_hoi.dia_li + khoi_xa_hoi.gdcd) / 3\n",
    "khoi_xa_hoi.head()"
   ]
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  {
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   "metadata": {},
   "outputs": [],
   "source": [
    "val_xh = khoi_xa_hoi.groupby(\"ma_tinh\").mean()"
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  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
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        },
        "xaxis": {
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          "text": "Average score of three subject in each category"
         }
        },
        "yaxis": {
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "import numpy as np\n",
    "x0 = val_tn[\"avg\"]\n",
    "x1 = val_xh[\"avg\"]\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Histogram(\n",
    "    x=x0,\n",
    "    histnorm='percent',\n",
    "    name='Science',\n",
    "    nbinsx=20,\n",
    "    marker_color='#EB89B5',\n",
    "    opacity=0.75\n",
    "))\n",
    "fig.add_trace(go.Histogram(\n",
    "    x=x1,\n",
    "    histnorm='percent',\n",
    "    name='Social',\n",
    "    nbinsx=20,\n",
    "    marker_color='#330C73',\n",
    "    opacity=0.75\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Histogram to compare social vs science', \n",
    "    xaxis_title_text='Average score of three subject in each category',\n",
    "    yaxis_title_text='Count',\n",
    "    bargap=0.2,\n",
    "    bargroupgap=0.1\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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    "tn = val_tn.nlargest(10, \"avg\")[\"avg\"]\n",
    "meomeo = val_tn.nsmallest(10, \"avg\")[\"avg\"]\n",
    "tn = tn.append(meomeo[::-1])\n",
    "hoi_dong = [prov[i-1] for i in tn.index]\n",
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   "source": [
    "tn = val_xh.nlargest(10, \"avg\")[\"avg\"]\n",
    "meomeo = val_xh.nsmallest(10, \"avg\")[\"avg\"]\n",
    "tn = tn.append(meomeo[::-1])\n",
    "hoi_dong = [prov[i-1] for i in tn.index]\n",
    "fig = px.bar(x=hoi_dong, y=tn.values, color=hoi_dong, text=tn.values.round(2))\n",
    "fig.update_layout(title=\"Top 10 province with the highest and lowest average in social\", xaxis_title=\"Province\",yaxis_title=\"Average score\",showlegend=False)"
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