04_employment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 241,
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-16T15:41:04.369830Z",
     "end_time": "2023-07-16T15:41:04.373594Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "outputs": [
    {
     "data": {
      "text/plain": "   Main type of activity               Nationality      Occupational status  \\\n0                  Total                   Finland  Wage and salary earners   \n1                  Total                   Finland            Entrepreneurs   \n2                  Total                   Finland             Not employed   \n3                  Total  FOREIGN COUNTRIES, TOTAL  Wage and salary earners   \n4                  Total  FOREIGN COUNTRIES, TOTAL            Entrepreneurs   \n5                  Total  FOREIGN COUNTRIES, TOTAL             Not employed   \n6               Employed                   Finland  Wage and salary earners   \n7               Employed                   Finland            Entrepreneurs   \n8               Employed  FOREIGN COUNTRIES, TOTAL  Wage and salary earners   \n9               Employed  FOREIGN COUNTRIES, TOTAL            Entrepreneurs   \n10            Unemployed                   Finland             Not employed   \n11            Unemployed  FOREIGN COUNTRIES, TOTAL             Not employed   \n\n      Sex     2000     2001     2002     2003     2004     2005  ...     2012  \\\n0   Total  1927810  1938660  1947687  1952946  1968434  1986156  ...  2000812   \n1   Total   241110   238548   235679   233461   231938   232593  ...   238930   \n2   Total  1035231  1035224  1034826  1033750  1020077  1007238  ...   937180   \n3   Total    24792    27544    29468    30499    32050    35505  ...    69318   \n4   Total     2425     2720     2976     3171     3325     3669  ...     7709   \n5   Total    37190    40055    42033    43998    44079    45556  ...    75802   \n6   Total  1927810  1938660  1947687  1952946  1968434  1986156  ...  2000812   \n7   Total   241110   238548   235679   233461   231938   232593  ...   238930   \n8   Total    24792    27544    29468    30499    32050    35505  ...    69318   \n9   Total     2425     2720     2976     3171     3325     3669  ...     7709   \n10  Total   304184   299927   288457   289371   284626   269123  ...   258261   \n11  Total    12885    13845    13043    13642    13957    13858  ...    22664   \n\n       2013     2014     2015     2016     2017     2018     2019     2020  \\\n0   1963127  1937215  1923558  1935920  1988019  2018192  2004549  1918342   \n1    234952   229471   223293   225213   218695   225588   226099   223259   \n2    957543   966378   964849   930779   866957   811422   807158   879515   \n3     71342    73685    75962    80146    86164    93144    98181    94490   \n4      8004     8131     8438     9214     9519    10868    12109    13104   \n5     83075    89993    95045   100145    98197    95777    96865   107619   \n6   1963127  1937215  1923558  1935920  1988019  2018192  2004549  1918342   \n7    234952   229471   223293   225213   218695   225588   226099   223259   \n8     71342    73685    75962    80146    86164    93144    98181    94490   \n9      8004     8131     8438     9214     9519    10868    12109    13104   \n10   300902   331917   342841   323287   268157   230140   231343   316642   \n11    26960    30281    31693    32077    28034    26020    25743    34649   \n\n       2021  \n0   1988895  \n1    226073  \n2    789261  \n3    110360  \n4     14858  \n5    104241  \n6   1988895  \n7    226073  \n8    110360  \n9     14858  \n10   241117  \n11    29727  \n\n[12 rows x 26 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Main type of activity</th>\n      <th>Nationality</th>\n      <th>Occupational status</th>\n      <th>Sex</th>\n      <th>2000</th>\n      <th>2001</th>\n      <th>2002</th>\n      <th>2003</th>\n      <th>2004</th>\n      <th>2005</th>\n      <th>...</th>\n      <th>2012</th>\n      <th>2013</th>\n      <th>2014</th>\n      <th>2015</th>\n      <th>2016</th>\n      <th>2017</th>\n      <th>2018</th>\n      <th>2019</th>\n      <th>2020</th>\n      <th>2021</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Total</td>\n      <td>Finland</td>\n      <td>Wage and salary earners</td>\n      <td>Total</td>\n      <td>1927810</td>\n      <td>1938660</td>\n      <td>1947687</td>\n      <td>1952946</td>\n      <td>1968434</td>\n      <td>1986156</td>\n      <td>...</td>\n      <td>2000812</td>\n      <td>1963127</td>\n      <td>1937215</td>\n      <td>1923558</td>\n      <td>1935920</td>\n      <td>1988019</td>\n      <td>2018192</td>\n      <td>2004549</td>\n      <td>1918342</td>\n      <td>1988895</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Total</td>\n      <td>Finland</td>\n      <td>Entrepreneurs</td>\n      <td>Total</td>\n      <td>241110</td>\n      <td>238548</td>\n      <td>235679</td>\n      <td>233461</td>\n      <td>231938</td>\n      <td>232593</td>\n      <td>...</td>\n      <td>238930</td>\n      <td>234952</td>\n      <td>229471</td>\n      <td>223293</td>\n      <td>225213</td>\n      <td>218695</td>\n      <td>225588</td>\n      <td>226099</td>\n      <td>223259</td>\n      <td>226073</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Total</td>\n      <td>Finland</td>\n      <td>Not employed</td>\n      <td>Total</td>\n      <td>1035231</td>\n      <td>1035224</td>\n      <td>1034826</td>\n      <td>1033750</td>\n      <td>1020077</td>\n      <td>1007238</td>\n      <td>...</td>\n      <td>937180</td>\n      <td>957543</td>\n      <td>966378</td>\n      <td>964849</td>\n      <td>930779</td>\n      <td>866957</td>\n      <td>811422</td>\n      <td>807158</td>\n      <td>879515</td>\n      <td>789261</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Total</td>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Wage and salary earners</td>\n      <td>Total</td>\n      <td>24792</td>\n      <td>27544</td>\n      <td>29468</td>\n      <td>30499</td>\n      <td>32050</td>\n      <td>35505</td>\n      <td>...</td>\n      <td>69318</td>\n      <td>71342</td>\n      <td>73685</td>\n      <td>75962</td>\n      <td>80146</td>\n      <td>86164</td>\n      <td>93144</td>\n      <td>98181</td>\n      <td>94490</td>\n      <td>110360</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Total</td>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Entrepreneurs</td>\n      <td>Total</td>\n      <td>2425</td>\n      <td>2720</td>\n      <td>2976</td>\n      <td>3171</td>\n      <td>3325</td>\n      <td>3669</td>\n      <td>...</td>\n      <td>7709</td>\n      <td>8004</td>\n      <td>8131</td>\n      <td>8438</td>\n      <td>9214</td>\n      <td>9519</td>\n      <td>10868</td>\n      <td>12109</td>\n      <td>13104</td>\n      <td>14858</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Total</td>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Not employed</td>\n      <td>Total</td>\n      <td>37190</td>\n      <td>40055</td>\n      <td>42033</td>\n      <td>43998</td>\n      <td>44079</td>\n      <td>45556</td>\n      <td>...</td>\n      <td>75802</td>\n      <td>83075</td>\n      <td>89993</td>\n      <td>95045</td>\n      <td>100145</td>\n      <td>98197</td>\n      <td>95777</td>\n      <td>96865</td>\n      <td>107619</td>\n      <td>104241</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Employed</td>\n      <td>Finland</td>\n      <td>Wage and salary earners</td>\n      <td>Total</td>\n      <td>1927810</td>\n      <td>1938660</td>\n      <td>1947687</td>\n      <td>1952946</td>\n      <td>1968434</td>\n      <td>1986156</td>\n      <td>...</td>\n      <td>2000812</td>\n      <td>1963127</td>\n      <td>1937215</td>\n      <td>1923558</td>\n      <td>1935920</td>\n      <td>1988019</td>\n      <td>2018192</td>\n      <td>2004549</td>\n      <td>1918342</td>\n      <td>1988895</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Employed</td>\n      <td>Finland</td>\n      <td>Entrepreneurs</td>\n      <td>Total</td>\n      <td>241110</td>\n      <td>238548</td>\n      <td>235679</td>\n      <td>233461</td>\n      <td>231938</td>\n      <td>232593</td>\n      <td>...</td>\n      <td>238930</td>\n      <td>234952</td>\n      <td>229471</td>\n      <td>223293</td>\n      <td>225213</td>\n      <td>218695</td>\n      <td>225588</td>\n      <td>226099</td>\n      <td>223259</td>\n      <td>226073</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Employed</td>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Wage and salary earners</td>\n      <td>Total</td>\n      <td>24792</td>\n      <td>27544</td>\n      <td>29468</td>\n      <td>30499</td>\n      <td>32050</td>\n      <td>35505</td>\n      <td>...</td>\n      <td>69318</td>\n      <td>71342</td>\n      <td>73685</td>\n      <td>75962</td>\n      <td>80146</td>\n      <td>86164</td>\n      <td>93144</td>\n      <td>98181</td>\n      <td>94490</td>\n      <td>110360</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Employed</td>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Entrepreneurs</td>\n      <td>Total</td>\n      <td>2425</td>\n      <td>2720</td>\n      <td>2976</td>\n      <td>3171</td>\n      <td>3325</td>\n      <td>3669</td>\n      <td>...</td>\n      <td>7709</td>\n      <td>8004</td>\n      <td>8131</td>\n      <td>8438</td>\n      <td>9214</td>\n      <td>9519</td>\n      <td>10868</td>\n      <td>12109</td>\n      <td>13104</td>\n      <td>14858</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Unemployed</td>\n      <td>Finland</td>\n      <td>Not employed</td>\n      <td>Total</td>\n      <td>304184</td>\n      <td>299927</td>\n      <td>288457</td>\n      <td>289371</td>\n      <td>284626</td>\n      <td>269123</td>\n      <td>...</td>\n      <td>258261</td>\n      <td>300902</td>\n      <td>331917</td>\n      <td>342841</td>\n      <td>323287</td>\n      <td>268157</td>\n      <td>230140</td>\n      <td>231343</td>\n      <td>316642</td>\n      <td>241117</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Unemployed</td>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Not employed</td>\n      <td>Total</td>\n      <td>12885</td>\n      <td>13845</td>\n      <td>13043</td>\n      <td>13642</td>\n      <td>13957</td>\n      <td>13858</td>\n      <td>...</td>\n      <td>22664</td>\n      <td>26960</td>\n      <td>30281</td>\n      <td>31693</td>\n      <td>32077</td>\n      <td>28034</td>\n      <td>26020</td>\n      <td>25743</td>\n      <td>34649</td>\n      <td>29727</td>\n    </tr>\n  </tbody>\n</table>\n<p>12 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filepath = os.path.join('data', 'employment_statistics', '001_115e_2021_20230716-132338.csv')\n",
    "employment_df = pd.read_csv(filepath)\n",
    "display(employment_df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-16T15:41:04.373969Z",
     "end_time": "2023-07-16T15:41:04.383483Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "outputs": [
    {
     "data": {
      "text/plain": "                  Nationality                     type  year   number\n0                     Finland  Wage and salary earners  2000  1927810\n1                     Finland            Entrepreneurs  2000   241110\n3    FOREIGN COUNTRIES, TOTAL  Wage and salary earners  2000    24792\n4    FOREIGN COUNTRIES, TOTAL            Entrepreneurs  2000     2425\n6                     Finland  Wage and salary earners  2001  1938660\n..                        ...                      ...   ...      ...\n124  FOREIGN COUNTRIES, TOTAL            Entrepreneurs  2020    13104\n126                   Finland  Wage and salary earners  2021  1988895\n127                   Finland            Entrepreneurs  2021   226073\n129  FOREIGN COUNTRIES, TOTAL  Wage and salary earners  2021   110360\n130  FOREIGN COUNTRIES, TOTAL            Entrepreneurs  2021    14858\n\n[88 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Nationality</th>\n      <th>type</th>\n      <th>year</th>\n      <th>number</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Finland</td>\n      <td>Wage and salary earners</td>\n      <td>2000</td>\n      <td>1927810</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Finland</td>\n      <td>Entrepreneurs</td>\n      <td>2000</td>\n      <td>241110</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Wage and salary earners</td>\n      <td>2000</td>\n      <td>24792</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Entrepreneurs</td>\n      <td>2000</td>\n      <td>2425</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Finland</td>\n      <td>Wage and salary earners</td>\n      <td>2001</td>\n      <td>1938660</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Entrepreneurs</td>\n      <td>2020</td>\n      <td>13104</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>Finland</td>\n      <td>Wage and salary earners</td>\n      <td>2021</td>\n      <td>1988895</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>Finland</td>\n      <td>Entrepreneurs</td>\n      <td>2021</td>\n      <td>226073</td>\n    </tr>\n    <tr>\n      <th>129</th>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Wage and salary earners</td>\n      <td>2021</td>\n      <td>110360</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>FOREIGN COUNTRIES, TOTAL</td>\n      <td>Entrepreneurs</td>\n      <td>2021</td>\n      <td>14858</td>\n    </tr>\n  </tbody>\n</table>\n<p>88 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entrepreneur_df = employment_df.loc[employment_df['Main type of activity']=='Total'].copy(deep=True)\n",
    "entrepreneur_df['type'] = entrepreneur_df['Occupational status'] + '/' + entrepreneur_df['Nationality']\n",
    "entrepreneur_df = entrepreneur_df.drop(columns=['Main type of activity', 'Sex', 'Nationality', 'Occupational status'])\n",
    "df_melt = pd.melt(entrepreneur_df, id_vars='type', var_name='year', value_name='number')\n",
    "df_melt[['type', 'Nationality']] = df_melt['type'].str.split(\"/\", expand = True)\n",
    "df_melt = df_melt[['Nationality', 'type', 'year', 'number']]\n",
    "df_melt = df_melt.loc[df_melt['type'] != 'Not employed']\n",
    "display(df_melt)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-16T15:41:04.384299Z",
     "end_time": "2023-07-16T15:41:04.392423Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_wage_earners_breakdown(input_df: pd.DataFrame) -> None:\n",
    "    # Data\n",
    "    grouped_employment = input_df.copy(deep=True)\n",
    "    grouped_employment = grouped_employment[grouped_employment['type']=='Wage and salary earners']\n",
    "    grouped_employment['normalized_values'] = grouped_employment.groupby(['year'])['number'].transform(lambda x: (x * 100 / x.sum()))\n",
    "    # Figure\n",
    "    fig, ax = plt.subplots(figsize=(16, 9), facecolor='w')\n",
    "    sns.barplot(data=grouped_employment, x=\"year\", y=\"normalized_values\", hue='Nationality', palette='Blues_r', errorbar=None, ax=ax)\n",
    "    # Labels\n",
    "    plt.suptitle(f\"Labour Force Break Down by Nationality\", y=0.91, fontsize=15)\n",
    "    ax.set(xlabel='Year', ylabel=f'Percentage of people within labour force.')\n",
    "    ax.set_yticks(range(5, 105, 5))\n",
    "    ax.yaxis.set_major_formatter(ticker.PercentFormatter(decimals=1))\n",
    "    ax.grid(axis='x')\n",
    "    # Author\n",
    "    ax.text(x=0.99, y=0.005, s='© Github/Geometrein', va='bottom', ha='right',\n",
    "        transform=ax.transAxes, color='#303030', fontsize=12, alpha=0.7\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_wage_earners_breakdown(input_df=df_melt)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-16T15:41:04.395245Z",
     "end_time": "2023-07-16T15:41:04.563903Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_entrepreneur_breakdown(input_df: pd.DataFrame) -> None:\n",
    "    # Data\n",
    "    grouped_employment = input_df.copy(deep=True)\n",
    "    grouped_employment = grouped_employment[grouped_employment['type']=='Entrepreneurs']\n",
    "    grouped_employment['normalized_values'] = grouped_employment.groupby(['year'])['number'].transform(lambda x: (x * 100 / x.sum()))\n",
    "    # Figure\n",
    "    fig, ax = plt.subplots(figsize=(16, 9), facecolor='w')\n",
    "    sns.barplot(data=grouped_employment, x=\"year\", y=\"normalized_values\", hue='Nationality', palette='Blues_r', errorbar=None, ax=ax)\n",
    "    # Labels\n",
    "    plt.suptitle(f\"Entrepreneurs Break Down by Nationality\", y=0.91, fontsize=15)\n",
    "    ax.set(xlabel='Year', ylabel=f'Percentage of people within labour force.')\n",
    "    ax.set_yticks(range(5, 105, 5))\n",
    "    ax.yaxis.set_major_formatter(ticker.PercentFormatter(decimals=1))\n",
    "    ax.grid(axis='x')\n",
    "    # Author\n",
    "    ax.text(x=0.99, y=0.005, s='© Github/Geometrein', va='bottom', ha='right',\n",
    "        transform=ax.transAxes, color='#303030', fontsize=12, alpha=0.7\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "plot_entrepreneur_breakdown(input_df=df_melt)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-16T15:41:04.564724Z",
     "end_time": "2023-07-16T15:41:04.730764Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_entrepreneur_ratio(input_df: pd.DataFrame) -> None:\n",
    "    # Data\n",
    "    grouped_employment = input_df.copy(deep=True)\n",
    "    grouped_employment['total_workers'] = grouped_employment.groupby(['year', 'Nationality'])['number'].transform(lambda x: x.sum())\n",
    "    grouped_employment['ratio'] = (grouped_employment['number'] / grouped_employment['total_workers']) * 100\n",
    "    grouped_employment = grouped_employment[grouped_employment['type']=='Entrepreneurs']\n",
    "    #print(grouped_employment)\n",
    "    grouped_employment['normalized_values'] = grouped_employment.groupby(['year'])['number'].transform(lambda x: (x * 100 / x.sum()))\n",
    "    # Figure\n",
    "    fig, ax = plt.subplots(figsize=(16, 9), facecolor='w')\n",
    "    sns.barplot(data=grouped_employment, x=\"year\", y=\"ratio\", hue='Nationality', palette='Blues_r', errorbar=None, ax=ax)\n",
    "    # Labels\n",
    "    plt.suptitle(f\"Percentage of Entrepreneurs within the Employed Population\", y=0.91, fontsize=15)\n",
    "    ax.set(xlabel='Year', ylabel=f'Percentage of people within labour force.')\n",
    "    ax.set_yticks(range(5, 21, 5))\n",
    "    ax.yaxis.set_major_formatter(ticker.PercentFormatter(decimals=1))\n",
    "    ax.grid(axis='x')\n",
    "    # Author\n",
    "    ax.text(x=0.99, y=0.005, s='© Github/Geometrein', va='bottom', ha='right',\n",
    "        transform=ax.transAxes, color='#303030', fontsize=12, alpha=0.7\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "plot_entrepreneur_ratio(input_df=df_melt)"
   ],
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