Mobook_CIIP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3014 entries, 0 to 3013\n",
      "Data columns (total 17 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   REF_DATE                                    3014 non-null   object \n",
      " 1   GEO                                         3014 non-null   object \n",
      " 2   DGUID                                       3014 non-null   object \n",
      " 3   Canada's international investment position  3014 non-null   object \n",
      " 4   Geographic region                           3014 non-null   object \n",
      " 5   Currency                                    3014 non-null   object \n",
      " 6   UOM                                         3014 non-null   object \n",
      " 7   UOM_ID                                      3014 non-null   int64  \n",
      " 8   SCALAR_FACTOR                               3014 non-null   object \n",
      " 9   SCALAR_ID                                   3014 non-null   int64  \n",
      " 10  VECTOR                                      3014 non-null   object \n",
      " 11  COORDINATE                                  3014 non-null   object \n",
      " 12  VALUE                                       3003 non-null   float64\n",
      " 13  STATUS                                      11 non-null     object \n",
      " 14  SYMBOL                                      0 non-null      float64\n",
      " 15  TERMINATED                                  0 non-null      float64\n",
      " 16  DECIMALS                                    3014 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(11)\n",
      "memory usage: 400.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./36100412-eng/36100412.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3014 entries, 0 to 3013\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   REF_DATE                                    3014 non-null   object \n",
      " 1   GEO                                         3014 non-null   object \n",
      " 2   DGUID                                       3014 non-null   object \n",
      " 3   Canada's international investment position  3014 non-null   object \n",
      " 4   Geographic region                           3014 non-null   object \n",
      " 5   Currency                                    3014 non-null   object \n",
      " 6   UOM                                         3014 non-null   object \n",
      " 7   UOM_ID                                      3014 non-null   int64  \n",
      " 8   SCALAR_FACTOR                               3014 non-null   object \n",
      " 9   SCALAR_ID                                   3014 non-null   int64  \n",
      " 10  VECTOR                                      3014 non-null   object \n",
      " 11  COORDINATE                                  3014 non-null   object \n",
      " 12  VALUE                                       3003 non-null   float64\n",
      " 13  STATUS                                      11 non-null     object \n",
      " 14  DECIMALS                                    3014 non-null   int64  \n",
      "dtypes: float64(1), int64(3), object(11)\n",
      "memory usage: 353.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=[\"SYMBOL\", \"TERMINATED\"], inplace=True)\n",
    "#df.REF_DATE = pd.to_datetime(df.REF_DATE)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>REF_DATE</th>\n",
       "      <th>GEO</th>\n",
       "      <th>DGUID</th>\n",
       "      <th>Canada's international investment position</th>\n",
       "      <th>Geographic region</th>\n",
       "      <th>Currency</th>\n",
       "      <th>UOM</th>\n",
       "      <th>UOM_ID</th>\n",
       "      <th>SCALAR_FACTOR</th>\n",
       "      <th>SCALAR_ID</th>\n",
       "      <th>VECTOR</th>\n",
       "      <th>COORDINATE</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>DECIMALS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2015-04</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>2015-07</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>2015-10</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>2016-04</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>2016-07</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>2017-04</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>2017-07</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2016A000011124</td>\n",
       "      <td>Canadian portfolio investment, foreign equity ...</td>\n",
       "      <td>All countries</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>81</td>\n",
       "      <td>millions</td>\n",
       "      <td>6</td>\n",
       "      <td>v115115405</td>\n",
       "      <td>1.5.1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     REF_DATE     GEO           DGUID  \\\n",
       "29    2015-01  Canada  2016A000011124   \n",
       "173   2015-04  Canada  2016A000011124   \n",
       "317   2015-07  Canada  2016A000011124   \n",
       "461   2015-10  Canada  2016A000011124   \n",
       "605   2016-01  Canada  2016A000011124   \n",
       "749   2016-04  Canada  2016A000011124   \n",
       "893   2016-07  Canada  2016A000011124   \n",
       "1037  2016-10  Canada  2016A000011124   \n",
       "1181  2017-01  Canada  2016A000011124   \n",
       "1325  2017-04  Canada  2016A000011124   \n",
       "1469  2017-07  Canada  2016A000011124   \n",
       "\n",
       "             Canada's international investment position Geographic region  \\\n",
       "29    Canadian portfolio investment, foreign equity ...     All countries   \n",
       "173   Canadian portfolio investment, foreign equity ...     All countries   \n",
       "317   Canadian portfolio investment, foreign equity ...     All countries   \n",
       "461   Canadian portfolio investment, foreign equity ...     All countries   \n",
       "605   Canadian portfolio investment, foreign equity ...     All countries   \n",
       "749   Canadian portfolio investment, foreign equity ...     All countries   \n",
       "893   Canadian portfolio investment, foreign equity ...     All countries   \n",
       "1037  Canadian portfolio investment, foreign equity ...     All countries   \n",
       "1181  Canadian portfolio investment, foreign equity ...     All countries   \n",
       "1325  Canadian portfolio investment, foreign equity ...     All countries   \n",
       "1469  Canadian portfolio investment, foreign equity ...     All countries   \n",
       "\n",
       "             Currency      UOM  UOM_ID SCALAR_FACTOR  SCALAR_ID      VECTOR  \\\n",
       "29    Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "173   Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "317   Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "461   Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "605   Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "749   Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "893   Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "1037  Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "1181  Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "1325  Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "1469  Canadian dollar  Dollars      81      millions          6  v115115405   \n",
       "\n",
       "     COORDINATE  VALUE STATUS  DECIMALS  \n",
       "29      1.5.1.2    NaN     ..         0  \n",
       "173     1.5.1.2    NaN     ..         0  \n",
       "317     1.5.1.2    NaN     ..         0  \n",
       "461     1.5.1.2    NaN     ..         0  \n",
       "605     1.5.1.2    NaN     ..         0  \n",
       "749     1.5.1.2    NaN     ..         0  \n",
       "893     1.5.1.2    NaN     ..         0  \n",
       "1037    1.5.1.2    NaN     ..         0  \n",
       "1181    1.5.1.2    NaN     ..         0  \n",
       "1325    1.5.1.2    NaN     ..         0  \n",
       "1469    1.5.1.2    NaN     ..         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[~df.STATUS.isnull()][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3003 entries, 0 to 3013\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   REF_DATE                                    3003 non-null   object \n",
      " 1   GEO                                         3003 non-null   object \n",
      " 2   DGUID                                       3003 non-null   object \n",
      " 3   Canada's international investment position  3003 non-null   object \n",
      " 4   Geographic region                           3003 non-null   object \n",
      " 5   Currency                                    3003 non-null   object \n",
      " 6   UOM                                         3003 non-null   object \n",
      " 7   UOM_ID                                      3003 non-null   int64  \n",
      " 8   SCALAR_FACTOR                               3003 non-null   object \n",
      " 9   SCALAR_ID                                   3003 non-null   int64  \n",
      " 10  VECTOR                                      3003 non-null   object \n",
      " 11  COORDINATE                                  3003 non-null   object \n",
      " 12  VALUE                                       3003 non-null   float64\n",
      " 13  DECIMALS                                    3003 non-null   int64  \n",
      "dtypes: float64(1), int64(3), object(10)\n",
      "memory usage: 351.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[~df.VALUE.isnull()]\n",
    "df.drop(columns=\"STATUS\", inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"Canada's international investment position\":\"CIIP\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF_DATE               21\n",
       "GEO                     1\n",
       "DGUID                   1\n",
       "CIIP                   23\n",
       "Geographic region       1\n",
       "Currency                7\n",
       "UOM                     1\n",
       "UOM_ID                  1\n",
       "SCALAR_FACTOR           1\n",
       "SCALAR_ID               1\n",
       "VECTOR                143\n",
       "COORDINATE            143\n",
       "VALUE                2716\n",
       "DECIMALS                1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Total assets', 'Direct investment assets',\n",
       "       'Canadian portfolio investment',\n",
       "       'Canadian portfolio investment, foreign debt securities',\n",
       "       'Canadian portfolio investment, foreign equity and investment fund shares',\n",
       "       'Official international reserves', 'Other Canadian investment',\n",
       "       'Other Canadian investment, loans',\n",
       "       'Other Canadian investment, currency and deposits',\n",
       "       'Other Canadian investment, trade credits and advances',\n",
       "       'Other Canadian investment, other accounts receivable',\n",
       "       'Total liabilities', 'Direct investment liabilities',\n",
       "       'Foreign portfolio investment',\n",
       "       'Foreign portfolio investment, Canadian debt securities',\n",
       "       'Foreign portfolio investment, Canadian equity and investment fund shares',\n",
       "       'Other foreign investment', 'Other foreign investment, loans',\n",
       "       'Other foreign investment, currency and deposits',\n",
       "       'Other foreign investment, special drawing rights',\n",
       "       'Other foreign investment, trade credits and advances',\n",
       "       'Other foreign investment, other accounts payable',\n",
       "       \"Canada's net international investment position\"], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.CIIP.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_rows = [\"Total assets\", \"Total liabilities\"]\n",
    "total_cols = [\"REF_DATE\", \"CIIP\", \"VALUE\", \"Currency\"]\n",
    "total_df = df.loc[df.CIIP.isin(total_rows)][total_cols]\n",
    "total_df = total_df.loc[total_df.Currency != \"All currencies\"]\n",
    "total_df.set_index(\"REF_DATE\", inplace=True)\n",
    "total_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 252 entries, 2015-01 to 2020-01\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   CIIP      252 non-null    object \n",
      " 1   VALUE     252 non-null    float64\n",
      " 2   Currency  252 non-null    object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "total_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1224 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1 = total_df.groupby(by=[\"CIIP\", \"Currency\"]).mean().unstack(\"CIIP\")\n",
    "fig1 =g.plot.pie(subplots=True, figsize=(17, 17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>CIIP</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REF_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment assets</td>\n",
       "      <td>17233.0</td>\n",
       "      <td>Canadian dollar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment assets</td>\n",
       "      <td>758364.0</td>\n",
       "      <td>United States dollar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment assets</td>\n",
       "      <td>182770.0</td>\n",
       "      <td>Euro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment assets</td>\n",
       "      <td>133762.0</td>\n",
       "      <td>United Kingdom pound sterling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment assets</td>\n",
       "      <td>10224.0</td>\n",
       "      <td>Japanese yen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CIIP     VALUE                       Currency\n",
       "REF_DATE                                                                   \n",
       "2015-01   Direct investment assets   17233.0                Canadian dollar\n",
       "2015-01   Direct investment assets  758364.0           United States dollar\n",
       "2015-01   Direct investment assets  182770.0                           Euro\n",
       "2015-01   Direct investment assets  133762.0  United Kingdom pound sterling\n",
       "2015-01   Direct investment assets   10224.0                   Japanese yen"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_rows = [\"Direct investment assets\", \"Canadian portfolio investment\", \"Official international reserves\", ...,\n",
    "             \"Other Canadian investment\"]\n",
    "asset_cols = [\"REF_DATE\", \"CIIP\", \"VALUE\", \"Currency\"]\n",
    "asset_df = df.loc[df.CIIP.isin(asset_rows)][asset_cols]\n",
    "asset_df = asset_df.loc[asset_df.Currency != \"All currencies\"]\n",
    "asset_df.set_index(\"REF_DATE\", inplace=True)\n",
    "asset_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 483 entries, 2015-01 to 2020-01\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   CIIP      483 non-null    object \n",
      " 1   VALUE     483 non-null    float64\n",
      " 2   Currency  483 non-null    object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 15.1+ KB\n"
     ]
    }
   ],
   "source": [
    "asset_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g2 = asset_df.loc[asset_df.Currency==\"United States dollar\"][:]\n",
    "g2 = g2.groupby(by=[g2.index, \"CIIP\"]).sum().unstack(\"CIIP\")\n",
    "fig2 = g2.plot.bar(y=\"VALUE\", stacked=True, figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>CIIP</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REF_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment liabilities</td>\n",
       "      <td>1141383.0</td>\n",
       "      <td>Canadian dollar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment liabilities</td>\n",
       "      <td>51367.0</td>\n",
       "      <td>United States dollar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Direct investment liabilities</td>\n",
       "      <td>35927.0</td>\n",
       "      <td>Other currencies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Foreign portfolio investment</td>\n",
       "      <td>1030744.0</td>\n",
       "      <td>Canadian dollar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01</th>\n",
       "      <td>Foreign portfolio investment</td>\n",
       "      <td>617187.0</td>\n",
       "      <td>United States dollar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   CIIP      VALUE              Currency\n",
       "REF_DATE                                                                \n",
       "2015-01   Direct investment liabilities  1141383.0       Canadian dollar\n",
       "2015-01   Direct investment liabilities    51367.0  United States dollar\n",
       "2015-01   Direct investment liabilities    35927.0      Other currencies\n",
       "2015-01    Foreign portfolio investment  1030744.0       Canadian dollar\n",
       "2015-01    Foreign portfolio investment   617187.0  United States dollar"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liab_rows = [\"Direct investment liabilities\", \"Foreign portfolio investment\", \"Other foreign investment\"]\n",
    "liab_cols = [\"REF_DATE\", \"CIIP\", \"VALUE\", \"Currency\"]\n",
    "liab_df = df.loc[df.CIIP.isin(liab_rows)][liab_cols]\n",
    "liab_df = liab_df.loc[liab_df.Currency != \"All currencies\"]\n",
    "liab_df.set_index(\"REF_DATE\", inplace=True)\n",
    "liab_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 315 entries, 2015-01 to 2020-01\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   CIIP      315 non-null    object \n",
      " 1   VALUE     315 non-null    float64\n",
      " 2   Currency  315 non-null    object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 9.8+ KB\n"
     ]
    }
   ],
   "source": [
    "liab_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3 = liab_df.loc[liab_df.Currency!=\"Canadian dollar\"][:]\n",
    "g3 = g3.groupby(by=[g3.index, \"CIIP\"]).sum().unstack(\"CIIP\")\n",
    "fig3 = g3.plot.bar(y=\"VALUE\", stacked=True, figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g4 = liab_df.groupby(by=[liab_df.index, \"Currency\"]).sum().unstack(\"Currency\")\n",
    "fig4 = g4.plot.bar(y=\"VALUE\", stacked=True, figsize=(10, 7))\n",
    "plt.legend(loc='upper left');\n",
    "plt.title(\"Canada's liabilities\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('VALUE',               'Canadian dollar'),\n",
       "            ('VALUE',                          'Euro'),\n",
       "            ('VALUE',                  'Japanese yen'),\n",
       "            ('VALUE',              'Other currencies'),\n",
       "            ('VALUE', 'United Kingdom pound sterling'),\n",
       "            ('VALUE',          'United States dollar')],\n",
       "           names=[None, 'Currency'])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF_DATE\n",
       "2015-01    2323460.0\n",
       "2015-04    2324619.0\n",
       "2015-07    2210650.0\n",
       "2015-10    2240761.0\n",
       "2016-01    2258621.0\n",
       "2016-04    2351189.0\n",
       "2016-07    2422674.0\n",
       "2016-10    2460154.0\n",
       "2017-01    2515185.0\n",
       "2017-04    2517882.0\n",
       "2017-07    2549649.0\n",
       "2017-10    2615428.0\n",
       "2018-01    2527114.0\n",
       "2018-04    2644846.0\n",
       "2018-07    2619181.0\n",
       "2018-10    2517673.0\n",
       "2019-01    2744962.0\n",
       "2019-04    2749209.0\n",
       "2019-07    2780756.0\n",
       "2019-10    2864167.0\n",
       "2020-01    2493874.0\n",
       "Name: (VALUE, Canadian dollar), dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g4.loc[:][('VALUE', 'Canadian dollar')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Canadian dollar', 'Euro', 'Japanese yen', 'Other currencies',\n",
       "       'United Kingdom pound sterling', 'United States dollar'],\n",
       "      dtype='object', name='Currency')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g4.loc[:][\"VALUE\"].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}