file_processing_done.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Failu apstrāde ar Python\n",
    "**Python 3**\n",
    "\n",
    "```python\n",
    "from pathlib import Path\n",
    "p = Path('.')\n",
    "z = [x for x in p.iterdir() if x.is_file()]\n",
    "```\n",
    "\n",
    "**Python 2.7**\n",
    "```python\n",
    "import os\n",
    "for filename in os.listdir('.'):\n",
    "    if filename.endswith(\".txt\") or filename.endswith(\".md\"): \n",
    "        print(filename)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "p = Path('.')\n",
    "z = [x for x in p.iterdir() if x.is_file()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.is_file()` failiem\n",
    "\n",
    "`.is_dir()` mapēm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('2_nodarbiba.ipynb')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.ipynb'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[8].suffix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[8].suffixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "p = Path('./file_processing/')\n",
    "z = [x for x in p.iterdir() if x.is_file()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('./file_processing/2015.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Month</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Income</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1710</td>\n",
       "      <td>2980</td>\n",
       "      <td>1840</td>\n",
       "      <td>2663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>530</td>\n",
       "      <td>2532</td>\n",
       "      <td>1013</td>\n",
       "      <td>2354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1699</td>\n",
       "      <td>1153</td>\n",
       "      <td>1788</td>\n",
       "      <td>1515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Expenses  Income    e1    e2\n",
       "0      1      1710    2980  1840  2663\n",
       "1      2       530    2532  1013  2354\n",
       "2      3      1699    1153  1788  1515"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Month</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Income</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>957</td>\n",
       "      <td>2973</td>\n",
       "      <td>1075</td>\n",
       "      <td>2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1606</td>\n",
       "      <td>2591</td>\n",
       "      <td>828</td>\n",
       "      <td>2230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1994</td>\n",
       "      <td>2171</td>\n",
       "      <td>1445</td>\n",
       "      <td>1438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>745</td>\n",
       "      <td>1848</td>\n",
       "      <td>1529</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1397</td>\n",
       "      <td>1048</td>\n",
       "      <td>1878</td>\n",
       "      <td>2117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Expenses  Income    e1    e2\n",
       "7       8       957    2973  1075  2487\n",
       "8       9      1606    2591   828  2230\n",
       "9      10      1994    2171  1445  1438\n",
       "10     11       745    1848  1529  1033\n",
       "11     12      1397    1048  1878  2117"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Income</th>\n",
       "      <th>Expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2980</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2532</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1153</td>\n",
       "      <td>1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1427</td>\n",
       "      <td>1327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1610</td>\n",
       "      <td>861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1732</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1432</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2973</td>\n",
       "      <td>957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2591</td>\n",
       "      <td>1606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2171</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1848</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1048</td>\n",
       "      <td>1397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Income  Expenses\n",
       "0     2980      1710\n",
       "1     2532       530\n",
       "2     1153      1699\n",
       "3     1427      1327\n",
       "4     1610       861\n",
       "5     1732      1165\n",
       "6     1432       956\n",
       "7     2973       957\n",
       "8     2591      1606\n",
       "9     2171      1994\n",
       "10    1848       745\n",
       "11    1048      1397"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Income', 'Expenses']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. veids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "aaa = list(df['Expenses'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14947"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(aaa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. veids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14947"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Expenses'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1245.5833333333333"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Expenses'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1246.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Expenses'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "p = Path('./file_processing/')\n",
    "z = [x for x in p.iterdir() if x.is_file()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "z1 = [x for x in z if x.suffix == '.xlsx']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('file_processing/2015.xlsx'),\n",
       " WindowsPath('file_processing/2016.xlsx'),\n",
       " WindowsPath('file_processing/2017.xlsx'),\n",
       " WindowsPath('file_processing/2018.xlsx'),\n",
       " WindowsPath('file_processing/2019.xlsx')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [pd.read_excel(x) for x in z1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Month</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Income</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>2686</td>\n",
       "      <td>1814</td>\n",
       "      <td>2905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1143</td>\n",
       "      <td>1455</td>\n",
       "      <td>863</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1944</td>\n",
       "      <td>1326</td>\n",
       "      <td>1299</td>\n",
       "      <td>1857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1983</td>\n",
       "      <td>1473</td>\n",
       "      <td>950</td>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1935</td>\n",
       "      <td>1823</td>\n",
       "      <td>1619</td>\n",
       "      <td>1764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1938</td>\n",
       "      <td>2154</td>\n",
       "      <td>1125</td>\n",
       "      <td>2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1270</td>\n",
       "      <td>2409</td>\n",
       "      <td>1060</td>\n",
       "      <td>2489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1694</td>\n",
       "      <td>2550</td>\n",
       "      <td>1507</td>\n",
       "      <td>1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>575</td>\n",
       "      <td>1080</td>\n",
       "      <td>1837</td>\n",
       "      <td>2888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>745</td>\n",
       "      <td>2539</td>\n",
       "      <td>1192</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1373</td>\n",
       "      <td>2928</td>\n",
       "      <td>1959</td>\n",
       "      <td>2387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1030</td>\n",
       "      <td>1054</td>\n",
       "      <td>1827</td>\n",
       "      <td>2427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Expenses  Income    e1    e2\n",
       "0       1      1125    2686  1814  2905\n",
       "1       2      1143    1455   863  1255\n",
       "2       3      1944    1326  1299  1857\n",
       "3       4      1983    1473   950  1218\n",
       "4       5      1935    1823  1619  1764\n",
       "5       6      1938    2154  1125  2673\n",
       "6       7      1270    2409  1060  2489\n",
       "7       8      1694    2550  1507  1620\n",
       "8       9       575    1080  1837  2888\n",
       "9      10       745    2539  1192  2685\n",
       "10     11      1373    2928  1959  2387\n",
       "11     12      1030    1054  1827  2427"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "yearly_income = []\n",
    "yearly_expenses = []\n",
    "for df in dfs:\n",
    "    yearly_income.append(df['Income'].sum())\n",
    "    yearly_expenses.append(df['Expenses'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[23497, 23316, 22524, 23477, 23159]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14947, 16438, 15259, 16755, 15293]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_expenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(['2015', '2016', '2017', '2018', '2019'], yearly_income)\n",
    "# ax.plot(range(2015, 2020), yearly_income)\n",
    "ax.set(xlabel='years (s)', ylabel='Euros')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E9SN9/5UZGyP8etoIDp6s4YkV9p81kszPLaRn5wS+OXaA26H4pGuHeGZMSOft7YcoKK9yO5yQFsifCM8BU70bRGQKMA24WFVHAI85Hx0Fvqqqo4AZwAtnLesOVR3tPMqdtpnAcVXNBh4Hfh+YzTBnW1tUwcf7TzDLD4MKjU3vyS2XpvH0ymJKjlb7KULjpt2HKnl/VzkzJqTTMcH9cu+++u5V6STGxTA/z86NnE/AkoiqrgTOvrxhDvA7Va11pil3nreo6gFnmnygg4gkXmAV04DFzuuXgWslnM7ehbH5eUV+HVTo59cPIz5W+M2bO/yyPOOup/IK6Rgfy90TQqPcu6+SkxKZPm4gr28po/T4abfDCVkSyCtjnENMb6rqSOf9VuANPHsoNcBPVHXjWfPcCsxW1S8573OBZKAReAV4WFXVOUw2VVVLnekKgStU9QujH4nILGAWQGpq6pglS5b4tD1VVVUkJYVm2YZg2XuqkYc+rOHWIfHclJlw3mnb0l/Liut4aXc9PxyTyCW94vwRatiJhO9XxZkmfrryDNcMjOOOiy70O7B9gtFfzduTMyCOu4YHdnuCoT19NmXKlM2qOvYLH6hqwB5AOrDd6/124A+AAOOAYpxE5nw+AigEsrza0pznLsBy4G7nfT7Q32u6QiD5QjGNGTNGfbVixQqf540U3/vzRzril+/oidN1F5y2Lf1VW9+oUx5boZMf/UBr6hvaEWH4ioTv19yl2zXr529p6fHTAV9XsPrrn/+6VYf86zItP1UTlPUFUnv6DNikLfxNDfZlE6XAq05MG4AmIAVARPoDrzlJ4rODkKpa5jxXAn/Gk3yalzXAmTcO6MYXD58ZP9pbUe0p9z7e/4MKJcTF8NBXR1BScZpnVhX7ddkmOI47AzvdfInnru9IMXtyFnWNTTy7xr6XLQl2EnkduAZARIYACcBREekOvAX8XFXXNE8sInEi0pxk4oGb8OzNACzFcxIe4FbgAydbmgBpHlRo5lWBGVRo8pBefHl4Kv/7QQEHT54JyDpM4Dy/di9n6hu5L8QLLbZVZq8kbhjVlxfW7rV6by0I5CW+LwJrgaEiUioiM4FFQKZzPmMJMMP5w/89IBv4xVmX8iYC74rIJ8BWoAx42lnFQiBZRAqAHwEPBmpbjKfc+0ubSvnGmDR6B3BQoV/cOJxGVX67zAavCidn6hpZvLaEa4b1ZmifLm6H43f352RRWdvAH9fZEM9nC9gZTFWdfo6P7mxh2oeBh88x/ZhzLL8GuM236ExbPfdhMfWNTdw7MbCDCg1M7sTsyVn84f093HHFQMZnJgd0fcY/Xtq0n2PVdT6VwAkHI/p1Y8rQXixcXcw9V2WE9aXL/hZ+t5KaoKusqef5tXu5fmTryr2315zJWaR178jcpfk0NNqY16GuobGJp1cVMWZQDy5P7+l2OAHzwJRsjlXXsWTjPrdDCSmWRMwFvbhhH5VBHFSoY0Is/3bjRew6VMmf1tt/2FD31raDlB4/E/KDTrXX2PSejMvoyYKVRdQ12I+bZpZEzHnVNjTyzKpirspO5uL+rS/33l5TR/bhquxk/nP5biqqaoO2XtM2qsq83EIG907i2mG93Q4n4O7PyeLgyRpe31Lmdighw5KIOa/Xt5RRXlkb9F+ZIsLcr47gdF0jjy3fHdR1m9bL/fQIuw5VMmtSJjHtLIETDiYP6cWIfl2Zn1dIY5NdDAqWRMx5NDYpT+UVMTLNt3Lv7TU4tQszrkxnycb9fFJ6IujrNxc2P7eQvt06MG10mtuhBIWI8MCUbIqOVvPO9kNuhxMSLImYc3pvxyGKjla7OqjQD740mOTOiTy0NJ8m++UXUrbsO8764mPMvDqDhLjo+VPylRF9yOzVmSdsCF3Akog5h+Zj3e0t995eXTvE87OpQ9my7wSvfFTqWhzmi+bnFdK1Qxy3jxvodihBFRsjzJmcxY6Dp8j99Ijb4bjOkohp0dqiCj4uPemXcu/t9Y3L+nPpwO78/p1dnKqxO4ZDQeGRKpbvOMzdE9JJSoy+gplfuzSNtO4dedLGwbEkYlo2L7fQr+Xe2yMmRvjVzSOoqK7jf/6+x+1wDLAgr4iE2Bi+c1W626G4Ij42hlmTMtlYcpwNxdFdss+SiPmC7WUnWbXnKPdcnU6H+NC4M/fi/t25/fIBLP6whD2HK90OJ6odPlXDa1vKuG1sf1KSwr88uq++OXYAyZ0Ton5UTksi5gvm5xWSlBjHHVeE1qBCP7luKJ0SYpn7t3w7oemiRauLaWhqYtbEyL658EI6JsRyz9UZ5H16hO1lJ90OxzWWRMzn7K2oZtm2gwEp995eyUmJ/Pi6oawpqLDLK11y8kw9f1q/jxsv7sfA5E5uh+O6uyYMoktiHPNyo3cIXUsi5nMWrAxsuff2uuOKgQzr04WH39rJmbpGt8OJOn9ct5eq2gbumxTYQpzhomuHeO6+chDLth+k8EiV2+G4wpKI+cyRylr+ujnw5d7bIy42hrk3j6DsxBnm5UXvrz831NQ38uyaEiYOTmFkWje3wwkZ370qg8S4GOZH6d6IJRHzmWfXBKfce3uNz0zmq5f0Y35eIfsqTrsdTtR45aNSjlbVMifCCy22VUpSIrdfPpDXtpRRdiL6BlOzJGIAT7n3F9YFr9x7e/3LDcOIFeE3b+1wO5So0NikPL2yiIv7d2NClo3xcrZZzuG9p1cWuRxJ8FkSMQD8eX1wy723V99uHfneNdm8t+MweXbXcMC9s/0QJRWnXS2BE8r6de/I1y9L48UN+zgaZVWnLYkYahsaWbg6+OXe2+sfJmaQntyJXy3Nt/EdAkhVmZ9XSEZKZ74yoo/b4YSs+yZnUdfYxKLVxW6HElSWRAyvfeROuff2SoyL5aGvjqDoaDXPromu/7jB9GFhBdvKTnLvRPdL4ISyrF5J3DCyLy+s3RtV5XkCmkREZJGIlIvI9rPavy8iu0UkX0Qe9Wr/uYgUOJ99xat9qtNWICIPerVniMh6EdkjIn8RkYRAbk8kamxSFqx0r9x7e00Z1ptrh/XmD+/v4fCpGrfDiUjz8zwlcL5+WXSUe2+POTlZVNY28MLavW6HEjSB3hN5Dpjq3SAiU4BpwMWqOgJ4zGkfDtwOjHDmeVJEYkUkFngCuB4YDkx3pgX4PfC4qg4GjgMzA7w9EWd5vvvl3tvrFzcNp75R+d3bu9wOJeKEYgmcUDYyrRs5Q3uxaHVx1NzHFNAkoqorgbOrk80Bfqeqtc405U77NGCJqtaqajFQAIxzHgWqWqSqdcASYJp4/uJdA7zszL8Y+FogtyfSNB/rdrvce3ulp3Tm3kkZvLaljE0l0V0Mz9/m5xXSJTGOO8eHVgmcUPbAlGwqquv4y8Z9bocSFG7UcB4CTBSRR4Aa4CequhFIA9Z5TVfqtAHsP6v9CiAZOKGqDS1M/zkiMguYBZCamkpubq5PgVdVVfk8byjaUdHIx6U1zBiewKqVeX5ffjD76+JYpWcH4Ud/Ws/cKzsQE4Z7VaH2/So/3cRbn5zh+ox4Plq3xu1wviDU+svbkB4x/OG9nfSvLSEuhM4jBaLP3EgicUAPYDxwOfCSiGQCLfW00vLekp5n+i82qi4AFgCMHTtWc3Jy2h41kJubi6/zhqKFC9eTkqT8fPqUgByqCHZ/1fU6wPdf3MKBjplh+cs51L5f//raNuJjS5k7fVJIVjAItf76nL7lfOfZjRzrms03xw5wO5rPBKLP3Lg6qxR4VT02AE1AitPu3dv9gQPnaT8KdBeRuLPaTStE4rHumy7uy/jMnjy2fDfHq+vcDiesNZfA+fploVsCJ5RNHtKLEf26Mj+3kMYIH9bZjSTyOp5zGYjIECABT0JYCtwuIokikgEMBjYAG4HBzpVYCXhOvi9VTy3wFcCtznJnAG8EdUvCWCQe6xYR5t48gsqaBh5bvtvtcMLacx96SuDMskKLPhER7s/JpuhodcRXnA70Jb4vAmuBoSJSKiIzgUVApnPZ7xJghrNXkg+8BOwA3gEeUNVG55zH94B3gZ3AS860AD8DfiQiBXjOkSwM5PZEiuZy798eP5CuHUKr3Ht7DevTlbvGD+LPG/ZF9RgP7VHlXKL6leHhUQInVE0d2YfMlM48mVsQ0ePftCqJiEiWiCQ6r3NE5B9F5IK3NqvqdFXtq6rxqtpfVReqap2q3qmqI1X1MlX9wGv6R1Q1S1WHqurbXu3LVHWI89kjXu1FqjpOVbNV9bbmK77M+YV6uff2+uGXh9CzUwJzl9rgVb54cf0+TtU0MDsnvG4+DTWxMcLsnCzyD5yK6NI8rd0TeQVoFJFsPL/2M4A/BywqEzDllTUhX+69vbp1jOenU4eyae9xXt9a5nY4YaWuoYmFq4sZn9mT0QPCpwROqPra6DT6devAkysit0x8a5NIk3NY6Rbgv1X1h0D43lgQxZ5bU+Ic647sX5m3jRnAJf278e/LdlFV23DhGQwAr28t49CpmrArgROqEuJimDUpkw0lx9hQHJn3MLU2idSLyHQ8J6/fdNoi62B6FPAu956R0tntcAIqJsZzkr28spb/9/4et8MJC01NylN5hVzUtyuTh/RyO5yI8a3LB5LcOYEncwvcDiUgWptEvgtMAB5R1WLn6qk/Bi4sEwjhVu69vS4d2IPbxvRn0ZriqB26tC3+vvMwhUeqmT05M2xL4ISijgmx3HN1Brm7j0TkxR6tSiKqugP4CbBNREYCpar6u4BGZvwqXMu9t9dPpw6jQ1ysnWS/AFVlXl4h/Xt05MZRdqTa3+4cP4guiXHMi8AhdFt7dVYOsAdPIcQngU9FZFIA4zJ+1lzufc7kbLdDCapeXRL5py8PYdWeoyzfcdjtcELWhuJjbNl3glmTMomLtREi/K1bx3jumjCIZdsPUhRhe8Wt/bb8J3Cdqk5W1UnAV4DHAxeW8afGJuUpp9z7VdnRN7Tp3RMGMSQ1id+8uYOa+uiorNpW8/MK6dk5gdvGhE6Jjkhzz9UZJMTGMD8vsvZGWptE4lX1s1uAVfVT7MR62Fief4jio9XMmZwdlce642NjmHvzCEqPn+GpvOgbA/tCdh06xYrdR/jOlel0TIiMEjihKCUpkenjBvLqR2WUnTjjdjh+09oksklEFjo3GuaIyNPA5kAGZvyj+Vh3enInpo6M3qFNr8xK4cZRfXkyt4DS46fdDiekPJVXRKeEWO6eEDklcELVvU4ZmadXRs6PmdYmkTlAPvCPwA/wlCaZHaigjP+sLazgk9KTzJqUFfVDm/7LjRchAo+8tdPtUEJG6fHTLP34ALdfPpDunWxg0EBL696RWy5NY8nGfRytiowCGxdMIs7IggtV9b9U9euqeouqPm4lRsLDPBva9DNp3TvyQE42b28/xJqCo26HExKeWVWMAP8wMTJL4ISi2TlZ1DY08eyaYrdD8YsLJhFVbQR62fjl4ae53PvMqzMiptx7e907KZOBPTvx0NJ86hub3A7HVcer6/jLxv3cPLof/bp3dDucqJHVK4nrR/bh+Q/3cqqm3u1w2q21h7NKgDUi8gsR+VHzI4BxGT+Y55R7v2P8QLdDCRkd4mP5xU3DKSivYvGHJW6H46rFa0s4U98YNTefhpL7c7KpdKolh7vWJpEDeMqdxABdvB4mRJUcrebtbQe5Y/ygiCv33l5fuqg3k4f04n/+vofyyhq3w3HF6boGFn9YwrXDejMk1f4rB9vItG5MHtKLRauLOVMX3pedt/aO9V+19Ah0cMZ3C1Z5yr3fc1W626GEHBHhoa8Op6ahkd+/HZ2DV720cT/HT9czx8q9u+aBKdlUVNfx0qb9bofSLq29Y32FiHxw9iPQwRnflFfW8PLmUr4xpn/Elntvr8xeScy8OpNXPipl897jbocTVPWNTTy9qpixg3owNr2n2+FErXEZPbk8vQdP5RVS1xC+5+daezjrJ8A/O49fAFuBTYEKyrTP/5V7t6FNz+f712ST2jWRuUvzI34cbG9vfnKAshNn7FxICLh/SjYHTtbwRhiPe9Paw1mbvR5rVPVHwBUBjs34oLnc+w0j+0Z8uff26pwYx7/ccBHbyk6G/SGF1lJVnsorYnDvJK4Z1tvtcKJezpBeDO/blXl5hcAe16QAAB4CSURBVGH7Q6a1h7N6ej1SROQrwHlvfxaRRSJS7oyl3tw2V0TKRGSr87jBab/Dq22riDSJyGjns1wR2e31WW+nPVFE/iIiBSKyXkTSfeyDiBJt5d7b6+ZL+jEuvSf/8e5uTp4O/8stLyR39xF2HarkvslZxET5zaehQES4f0oWRUeqeTf/kNvh+KS1h7M24zl8tRlYC/wYmHmBeZ4DprbQ/riqjnYeywBU9U/NbcBdQImqbvWa5w6vecqdtpnAcVXNxlMM8vet3JaI1Vzu/ersFEb17+Z2OGFBxDN41YnTdfzXe5F/kn1eXiF9u3Xg5kv6uR2KcVzvHDV4YkVBWA5X0NrDWRmqmuk8D1bV61R19QXmWQn4Mh7kdODFVkw3DVjsvH4ZuFaisbqgl+Zy77YX0jbD+3XljisG8cK6vew8eMrtcALmo33H2VB8jJlXZ5AQZ+XeQ0VsjDBnchb5B06R9+kRt8NpMzlf5hORn6rqo87r21T1r16f/VZV/+W8C/ccYnpTVUc67+cC3wFO4dmz+bGqHj9rnkJgmqpud97nAslAI/AK8LCqqnOYbKqqlnrNd4WqfqGehYjMAmYBpKamjlmyZMn5wj6nqqoqkpKSfJo30JpU+fmqM3SMEx6a0CEkqvWGcn+drapOeXDVadKSYnhwnDv9F+j++sNHNew+3sh/Tu5Ehzj3vx/tFU7frwtpaFJ+uvIMvToKP78icNUD2tNnU6ZM2ayqY7/wgaqe8wF81NLrlt6fY/50YLvX+1QgFs8e0CPAorOmvwLYdlZbmvPcBVgO3O28zwf6e01XCCRfKKYxY8aor1asWOHzvIG27JMDOuhnb+qbHx9wO5TPhHJ/teSP60p00M/e1De2lrmy/kD2157DlZr+4Jv62Lu7AraOYAu379eFLFpdpIN+9qZuKK4I2Dra02fAJm3hb+qF9mnlHK9ben9BqnpYVRtVtQl4Ghh31iS3c9ahLFUtc54rgT97zVMKDAAQkTigG74dPgt7auXe/eL2ywcyMq0rj7y1g+raBrfD8asFKwtJiI1hxpXpbodizuH2ywfSs3MCT64ocDuUNrlQEtFzvG7p/QWJiPfgzbcA3lduxQC3AUu82uJEJMV5HQ/c5DXPUmCG8/pW4AMnW0YdK/fuH7Exwq9uHsHhU7X8b5j9Rz6fQydreG1LGd8cO4CUpES3wzHn0DEhlplXZ7Bi9xHyD5x0O5xWu1ASuURETolIJXCx87r5/ajzzSgiL+K5kmuoiJSKyEzgURHZJiKfAFOAH3rNMgkoVVXv0VoSgXed6bcCZXj2YAAWAskiUgD8CHiwVVscgeblFdKri5V794cxg3ry9cvSeGZVEcVHq90Oxy8Wri6iSbGbT8PAneMHkZQYx5O54TOEbtz5PlRVn+uHq+r0FpoXnmf6XGD8WW3VwJhzTF+DZ88lqjWXe//Z1GFW7t1PHrx+GMvzD/Prv+Xz7HfPPuIaXk6erufP6/dx46i+DOjZye1wzAV06xjPXRMGMT+vkKIjVWT2Cv0LB+w6vzBn5d79r3eXDvzg2sGs2H2E93cedjucdvnj+r1U1zVy32TbCwkX91yVQUJsDPPzwmNvxJJIGLNy74Ez48p0snp15tdv7qCmPjxLddfUN/LsmmImDenFiH5282m46NUlkdsvH8BrW8o4cOKM2+FckCWRMLZgVRFxsVbuPRAS4mKYe/MI9lacZuHq8BzG9OXNpRytqmO27YWEnXsnZaIKT68quvDELrMkEqY+K/d+mZV7D5SJg3vxlRGp/O8HBWHxi9BbY5Py9KoiLunfjQmZyW6HY9qof49OfO3SNF7csI+Kqlq3wzkvSyJh6tk1JTQ0NnGfXXETUP9243CaVPntsp1uh9Imb28/yN6K08yenBUS1QtM282enEVtQxPPrilxO5TzsiQShk7V1PPHtXu5fmRf0q3ce0AN6NmJ2ZOzePOTg3xY+IWKOiFJVZmfV0hGSmeuG2E3n4ar7N5JTB3Rh8VrSzhVE7oVpi2JhKE/r99HZa2Vew+WOTlZ9O/RkV8t3UFDY+iPQLemoILtZaeYNSnTbj4Nc/fnZFNZ08Af1+11O5RzsiQSZmrqrdx7sHWIj+XfbhzO7sOVvBDC/5mbzXduPr3lUrv5NNyN6t+NSUN6sXBVMWfqQvMqQUsiYea1LWUcsXLvQfeVEalMHJzCf733KUdD+ETnttKTrC44yj1XZdjNpxHigZwsKqrrQnb0TUsiYaSxSVmwsohRad24KtuuuAkmEeGhr47gTF0j//FO6A5eNd9uPo044zJ6MnZQDxasLKI+BA+nWhIJI+/mH6L4aLVdceOS7N5JfPeqdF7avJ+P959wO5wvKDlazdvb7ebTSCMiPDAlm7ITZ3hj6wG3w/kCSyJhovmKGyv37q5/vHYwKUmJ/HJpPk1NoVU0esGqIuJi7ObTSJQztBcX9e3Kk7kFNIbY986SSJj40Mq9h4QuHeJ5cOowPt5/gpc/KnU7nM98dvPpmDS7+TQCiQj352RRdKSa5fmH3A7ncyyJhIn5Vu49ZNxyaRqXDezOo+/sCpnr959bU0J9YxP3TrSbTyPVDaP6kp7ciSdyCwiloZMsiYSBbaWecu92xU1oiIkRfj1tJBXVdfz3e3vcDofKmnpeWLeXqSP6hEXpcOOb2BhhTk4W28tOsXJP6Nz4akkkDMxfaVfchJqRad2YPm4gi9eWsPtQpauxvLhhH5U1dvNpNLjl0v707daBJ0Jo5E1LIiHOyr2Hrn++bihJiXHMXZrv2uGF2gbPzacTMpO5ZEB3V2IwwZMQF8O9EzPZUHyMTSXH3A4HsCQS8qzce+jq0TmBn1w3hLVFFSzb5s7Jzje2HODwqVpm59heSLS4fdwAenZOCJkhdAOWRERkkYiUi8h2r7a5IlImIludxw1Oe7qInPFqn+81zxhnXPYCEfmDODdIiEhPEXlPRPY4zz0CtS1usXLvoe/bVwzior5deeStHZyuawjqupualPkrCxnetyuTBqcEdd3GPZ0S4rjnqnQ+2FVO/oGTbocT0D2R54CpLbQ/rqqjnccyr/ZCr/bZXu3zgFnAYOfRvMwHgfdVdTDwvvM+oli599AXGyP86uYRHDhZw7wg/zJcvuMwRUequW9ypt18GmXumpBOUmJc0L9zLQlYElHVlUC7DtqJSF+gq6quVc9B5+eBrzkfTwMWO68Xe7VHBCv3Hj7GZfRk2uh+PLWyiH0Vp4OyzuabTwf07MiNo/oGZZ0mdHTrGM+d4wfx1raDFB2pcjWWOBfW+T0RuRvYBPxYVY877RkisgU4Bfybqq4C0gDvO7pKnTaAVFU9CKCqB0Wk97lWKCKz8OzNkJqaSm5urk+BV1VV+TxvWy0rqqOytoHLk44HbZ3+Fsz+cltO9ybe0SZ+sHglP7jMt0OPbemvXcca2bq/hruGJ7B61Uqf1hfuoun71ZKLYpQ4gbl/WcM9IxNbNU9A+kxVA/YA0oHtXu9TgVg8e0CPAIuc9kQg2Xk9BtgPdAUuB/7uNf9E4G/O6xNnret4a2IaM2aM+mrFihU+z9sWZ+oadOzD7+kdT68LyvoCJVj9FSqeXFGgg372pq7Yddin+dvSXzMWrdfLfr1cz9Q1+LSuSBBt36+W/OL1bZr9L29p2fHTrZq+PX0GbNIW/qYG9eosVT2sqo2q2gQ8DYxz2mtVtcJ5vRkoBIbg2fPo77WI/kBzBbLDzuGu5sNe5cHZisBrLvc+x664CSv3XJ1ORkpnfvW3HdQ2BG7sh50HT5G7+wjfuTLdbj6NcrMmZaIKT68qci2GoCaR5j/6jluA7U57LxGJdV5n4jmBXqSew1WVIjLeuSrrbuANZ/6lwAzn9Qyv9rDW2KQ8lVfIqLRuXJll5d7DSWJcLA99dTjFR6tZtLokYOt5Kq+QTgmx3DVhUMDWYcJD/x6dmDY6jSUb9lPh0jg3gbzE90VgLTBUREpFZCbwqHO57ifAFOCHzuSTgE9E5GPgZWC2qjaflJ8DPAMU4NlDedtp/x3wZRHZA3zZeR/23s0/REnFaebkWLn3cJQztDdfuiiV//fBHg6drPH78vcfO83fPjnI9HED6d4pwe/LN+FnTk4mNQ2NPPdhiSvrD9iJdVWd3kLzwnNM+wrwyjk+2wSMbKG9Ari2PTGGGlVlXm4hGSmd+coIK/cern5503C+9Hge//72Tv7n9kv9uuyFq4sRYObVGX5drglf2b27MHVEH577sIRZkzLpEuTKFnbHegj5sLCCbWUnmTUp08q9h7GByZ24b1Imb2w9wIZi/5WmOFZdx5KN+5g2Oo1+3Tv6bbkm/N2fk01lTQN/XLcv6Ou2JBJC5uV6yr3fcqmVew939+dk069bBx5amu+3QYQWf1hCTX0Tsyfbzafm80b178bEwSksXF1ETX3gLupoiSWRELGt9CSrC44y82or9x4JOibE8q83DmfnwVP8ef3edi/vdF0Di9eW8KWLejM4tUv7AzQR54Ep2RytquOlTfuDul5LIiFifp6n3Pu3r7By75HihlF9mJCZzGPLP+VYdV27lrVkw35OnK63cu/mnK7I6MmYQT14Kq+I+samoK3XkkgIKD5azdvbD3LnBCv3HklEhF9NG0FVbQOPLd/t83LqG5tYuLqYy9N7MDa9px8jNJFERHhgShZlJ86wdOuBC8/gJ5ZEQsCClZ5y79+1cu8RZ0hqF+6eMIgXN+xje5lvFVf/9vEByk6csb0Qc0FThvZmWJ8uPJlbQJOfzsVdiCURl5WfquGVzaXcOqY/vbtYufdI9E9fGkJy5wR++cb2Nv/HVlWeyitiSGoSU4aeszycMUDz3kg2hUeqWb4jOGPcWBJx2aI1JTQ0NTFrol1xE6m6dYznp1OH8dG+E7y2paxN867YXc7uw5XcNymLGLvs27TCDaP6kp7ciSdWFAZlxE1LIi46VVPPn9bt5fpRVu490t16WX9GD+jOv7+9i8qa+lbPNz+3iH7dOnDz6H4BjM5EktgYYfbkLLaVnWTVnqMBX58lERf9ad0+KmsbmGPHuiNejDN4VUV1LX94f0+r5tm89zgbSo4xc2Im8bH2X9W03i2XpdGnaweeWFEQ8HXZN9MlNfWNLFpTzMTBKYxM6+Z2OCYILhnQnW+OGcCza0ooKK+84PTz8wrp1jGe2y8fEIToTCRJjIvl3kmZrC8+xqYS/1VNaIklEZe8+pGn3LtdcRNd/nnqUDomxPKrv+047/HqgvJK3ttxmBkTBtE50Y2x40y4mz5uAD06xfNkgIfQtSTigsYmZcHKQi7ub+Xeo01KUiI/+vIQVu05yrv5h8853fy8IjrExzDjyvTgBWciSqeEOO65KoMPdpWz48CpgK3HkogL3tnuKfc+e7KVe49Gd40fxNDULjz81o4W6xwdPHmGN7aW8c2xA0hOat2wp8a05O4J6SQlxjEvL3B7I5ZEgkxVmZ9n5d6jWVxsDHNvHkHp8TPMb+E/98JVxTQp3GuXfZt26tYpnjvGD+StTw5QfLQ6IOuwJBJkawqs3LuBCVnJ3HRxX+blFrL/2OnP2k+erufFDfu46eK+DOjZycUITaSYeXUGcbExPBWgvRFLIkE2P6+Q3l0S+fplVu492v3rjRcRI8LDb+34rO2FdSVU1zVy3yS74ML4R+8uHfjW2AG88lEpx2r8X5jRkkgQNZd7v+fqDBLjrNx7tOvbrSPfuyabd/MPs2rPEeoalWfXlDB5SC+G9+vqdngmgsyalEmvpEQOVfv/DnZLIkE0P6+QLh3iuMPKvRvHP0zMYFByJ+YuzSdvfwMV1XV22bfxuwE9O7HqZ9cwPNn/P14DlkREZJGIlIvIdq+2uSJSJiJbnccNTvuXRWSziGxznq/xmidXRHZ7zdPbaU8Ukb+ISIGIrBeR9EBtiz8UH61m2faD3Dl+UNDHQDahKzEull/eNJzCI9Us2V3HJQO6Mz7Tyr0b/wvUOdhA7ok8B0xtof1xVR3tPJY5bUeBr6rqKGAG8MJZ89zhNU+50zYTOK6q2cDjwO/9vwn+s2BlEfFW7t204NqLUpkytBeNCnMmZ9pl3yasBCyJqOpKoFX326vqFlVtHkUlH+ggIhe6QH4asNh5/TJwrYTo/z4r924u5HffuJjpwxK4brhd9m3Cixv1FL4nIncDm4Afq+rxsz7/BrBFVWu92p4VkUbgFeBh9dSLSAP2A6hqg4icBJLx7NV8jojMAmYBpKamkpub61PgVVVVPs370u466hubuCThiM/rDke+9le0uiqllpUr89wOI2zY96vtAtJnqhqwB5AObPd6nwrE4tkDegRYdNb0I4BCIMurLc157gIsB+523ucD/b2mKwSSLxTTmDFj1FcrVqxo8zwnz9TpyF++o/f/abPP6w1XvvRXNLP+ahvrr7ZrT58Bm7SFv6lBvTpLVQ+raqOqNgFPA+OaPxOR/sBrTpIo9JqnzHmuBP7sNU8pMMCZNw7oRisPnwWTlXs3xkSyoCYREenr9fYWYLvT3h14C/i5qq7xmj5ORFKc1/HATc3zAEvxnIQHuBX4wMmWIaOmvpGFq63cuzEmcgXsnIiIvAjkACkiUgo8BOSIyGhAgRLgPmfy7wHZwC9E5BdO23VANfCuk0Bigb/j2YMBWAi8ICIFePZAbg/Utvjq1Y/KOFpVy5zJo90OxRhjAiJgSURVp7fQvPAc0z4MPHyORY05xzw1wG2+RRd43uXeJ1i5d2NMhLI71gOkudz7HCv3boyJYJZEAkBVmZdXQEZKZ66zcu/GmAhmSSQA1hRUsL3sFPdZuXdjTISzJBIA8/IK6N0lkVus3LsxJsJZEvGzT0pPsKagwsq9G2OigiURP7Ny78aYaGJJxI+Kj1bz9vZDVu7dGBM1LIn4kZV7N8ZEG0sifmLl3o0x0ciSiJ8sWlNCQ1MTsyZmuh2KMcYEjSURPzhVU8+f1u3l+lF9SU/p7HY4xhgTNJZE/MDKvRtjopUlkXaycu/GmGhmSaSd/q/cu+2FGGOijyWRdrBy78aYaGdJpB2s3LsxJtpZEvGRlXs3xhhLIj6zcu/GGBPgJCIii0SkXES2e7XNFZEyEdnqPG7w+uznIlIgIrtF5Cte7VOdtgIRedCrPUNE1ovIHhH5i4gkBHJ7vFm5d2OMCfyeyHPA1BbaH1fV0c5jGYCIDAduB0Y48zwpIrEiEgs8AVwPDAemO9MC/N5Z1mDgODAzoFvjaC73PtPKvRtjolxAk4iqrgSOtXLyacASVa1V1WKgABjnPApUtUhV64AlwDTxnMm+BnjZmX8x8DW/bsA5NJd7/7aVezfGRLk4l9b7PRG5G9gE/FhVjwNpwDqvaUqdNoD9Z7VfASQDJ1S1oYXpP0dEZgGzAFJTU8nNzfUp6KqqKpa89QFvbzvDjZnxbF63xqflRIuqqiqf+zoaWX+1jfVX2wWiz9xIIvOA3wDqPP8ncA/Q0tlppeW9JT3P9F9sVF0ALAAYO3as5uTktDlogNzcXNYc60l8XBkPTZ9Mry6JPi0nWuTm5uJrX0cj66+2sf5qu0D0WdCTiKoebn4tIk8DbzpvS4EBXpP2Bw44r1tqPwp0F5E4Z2/Ee/qAOFHTxCuby7htbH9LIMYYgwuX+IpIX6+3twDNV24tBW4XkUQRyQAGAxuAjcBg50qsBDwn35eqqgIrgFud+WcAbwQy9uV7Gzzl3idZuXdjjIEA74mIyItADpAiIqXAQ0COiIzGc+ipBLgPQFXzReQlYAfQADygqo3Ocr4HvAvEAotUNd9Zxc+AJSLyMLAFWBiobTlVU8+K/fXcMKovg5Kt3LsxxkCAk4iqTm+h+Zx/6FX1EeCRFtqXActaaC/Cc/VWwP1x3V7ONMBsK7RojDGfsTvWW6lXUiIT0+Ks3Lsxxnhx6xLfsHPb2AH0qip0OwxjjAkptidijDHGZ5ZEjDHG+MySiDHGGJ9ZEjHGGOMzSyLGGGN8ZknEGGOMzyyJGGOM8ZklEWOMMT4TTx3D6CEiR4C9Ps6egqd6sGkd66+2sf5qG+uvtmtPnw1S1V5nN0ZdEmkPEdmkqmPdjiNcWH+1jfVX21h/tV0g+swOZxljjPGZJRFjjDE+syTSNgvcDiDMWH+1jfVX21h/tZ3f+8zOiRhjjPGZ7YkYY4zxmSURY4wxPovqJCIiA0RkhYjsFJF8EfmB095TRN4TkT3Ocw+nfZiIrBWRWhH5yVnLKhGRbSKyVUQ2ubE9gebn/uouIi+LyC5neRPc2KZA8ld/ichQ53vV/DglIv/k1nYFkp+/Yz90lrFdRF4UkQ5ubFMg+bm/fuD0VX5bvl9RfU5ERPoCfVX1IxHpAmwGvgZ8Bzimqr8TkQeBHqr6MxHpDQxypjmuqo95LasEGKuqEXvzk5/7azGwSlWfEZEEoJOqngj2NgWSP/vLa5mxQBlwhar6etNsyPJXn4lIGrAaGK6qZ0TkJWCZqj4X/K0KHD/210hgCTAOqAPeAeao6p4LxRDVeyKqelBVP3JeVwI7gTRgGrDYmWwxng5HVctVdSNQ70K4rvNXf4lIV2ASsNCZri7SEggE7Pt1LVAYiQkE/N5ncUBHEYkDOgEHAhx+0Pmxvy4C1qnqaVVtAPKAW1oTQ1QnEW8ikg5cCqwHUlX1IHj+kYDerViEAstFZLOIzApUnKGinf2VCRwBnhWRLSLyjIh0DmC4rvPD96vZ7cCL/o4vFLWnz1S1DHgM2AccBE6q6vJAxuu2dn7HtgOTRCRZRDoBNwADWrNeSyKAiCQBrwD/pKqnfFzMVap6GXA98ICITPJbgCHGD/0VB1wGzFPVS4Fq4EE/hhhS/PT9wjnsdzPwV3/FFqra22fOOYBpQAbQD+gsInf6N8rQ0d7+UtWdwO+B9/AcyvoYaGjNvFGfREQkHk/n/0lVX3WaDzvHGpuPOZZfaDmqesB5Lgdew3NsMeL4qb9KgVJVXe+8fxlPUok4/vp+Oa4HPlLVw/6PNHT4qc++BBSr6hFVrQdeBa4MVMxu8uPfsIWqepmqTgKOARc8HwJRnkRERPAcl9+pqv/l9dFSYIbzegbwxgWW09k5qYVzWOY6PLuHEcVf/aWqh4D9IjLUaboW2OHncF3nr/7yMp0IP5Tlxz7bB4wXkU7OMq/Fc74govjzO+acdEdEBgJfp7XfNVWN2gdwNZ5zGZ8AW53HDUAy8D6eTPw+0NOZvg+eX9GngBPO6654jvF/7DzygX91e9tCub+cz0YDm5xlvY7n6hHXtzGE+6sTUAF0c3u7wqjPfgXswvOD7gUg0e3tC/H+WoXnx9zHwLWtjSGqL/E1xhjTPlF9OMsYY0z7WBIxxhjjM0sixhhjfGZJxBhjjM8siRhjjPGZJRFjwpCIfE1Efnmez0eJyHNBDMlEKUsixoQQp0pva/wUePJcH6rqNqC/c+OYMQFjScQYH4jIb5rHbnDePyIi/+i8/mcR2Sgin4jIr7ymed0p0JnvXaRTRKpE5Ncish6YICK/E5EdzvwtlYMfAtSqM+yAiNzmjAPxsYis9Jr0b3gKNhoTMHazoTE+cCqmvqqql4lIDJ47g8cBY4BbgfsAwVN+4lFVXSkiPVX1mIh0BDYCk1W1QkQU+JaqviQiPYG1wDBVVRHprmeVyReR7wIjVfXHzvttwFRVLfOeXkSuAh5U1a8GvENM1LI9EWN8oKolQIWIXIqnVtoWVa1wXl8HbAE+AoYBg53Z/lFEPgbW4Smz3dzeiKeAHnjKUdQAz4jI14HTLay+L55S+s3WAM+JyL2A9+GwcjwVbI0JmDi3AzAmjD2DZwS5PsAip02Af1fVp7wnFJEcPJVlJ6jqaRHJBZqHa61R1UYAVW0QkXF4CgbeDnwPuOas9Z4BujW/UdXZInIFcCOwVURGOwmtgzOtMQFjeyLG+O41YCpwOfCu0/YucI8zvgMikuZUR+2GZzjS0yIyDBjf0gKd+bqp6jLgn/AUqjzbTiDba54sVV2vqr8EjvJ/gwkNIQKrSZvQYnsixvhIVetEZAVwwmtPYrmIXASs9VTppgq4E89AP7NF5BNgN55DWi3pArwhIh3w7NX8sIVpVgL/KSKinpOa/yEig53p38dThRVgCvCWHzbVmHOyE+vG+Mg5of4RcJuqtmoAHz+u+3+Av6nq38/xeSKecbKvVs+Y2cYEhB3OMsYHIjIcKADeD3YCcfwWzxgj5zIQz5VZlkBMQNmeiDHGGJ/ZnogxxhifWRIxxhjjM0sixhhjfGZJxBhjjM8siRhjjPHZ/weqWCx3Vcc4zwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(['2015', '2016', '2017', '2018', '2019'], yearly_expenses)\n",
    "# ax.plot(range(2015, 2020), yearly_income)\n",
    "ax.set(xlabel='years (s)', ylabel='Euros')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['2015', '2016', '2017', '2018', '2019']\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "ax.plot(labels, yearly_income)\n",
    "ax.plot(labels, yearly_expenses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "ye = np.array(yearly_expenses)\n",
    "yi = np.array(yearly_income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8550, 6878, 7265, 6722, 7866], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yi - ye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# pd.read_excel(...)\n",
    "# df[...].sum()\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(range(1,10), range(1,10))\n",
    "\n",
    "# ax.set(xlabel='years (s)', ylabel='Euros')\n",
    "# ax.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dokumentācija\n",
    "* Pandas https://pandas.pydata.org/pandas-docs/stable/\n",
    "* Matplotlib https://matplotlib.org/gallery/index.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}