├── China Annual GDP.xls
├── README.md
├── LICENSE
├── ISM Data.ipynb
├── Durable and Indurable Goods.ipynb
├── House Price Index.ipynb
├── Corporate Profit Cycle.ipynb
├── US Recession Shades.ipynb
├── Log Returns.ipynb
└── Bureau of Economic Analysis (BEA).ipynb


/China Annual GDP.xls:
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https://raw.githubusercontent.com/guowat/notesf/HEAD/China Annual GDP.xls


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/README.md:
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 1 | # Notes_For_Macroeconomic_Analysis
 2 | This a series of training notes for macroeconomic analysts who are practioners (such as analysts in banks, hedge funds, prop shops) or market observers. All notes are written by myself.
 3 | 
 4 | We all know there is a huge gap between textbook macroeconomics and practioning macreconomics, the latter is even esoteric for a new economics graduate. However, textbook knowledge are fundemental skills before any meaningful macroeconomic analysis can be formulated. 
 5 | 
 6 | # Prerequisite
 7 | It's perfectly alright if you do not have any formal economics education. I have seen the group of analysts with a wide range of educational backgroud, however they all walked through intermediate level textbooks by themselves. Here are recommendations of textbooks for new recruits. 
 8 | 1. Principles of Economics, Gregory Mankiw
 9 | 2. Microeconomic Theory: Basic Principles and Extensions, Walter Nicholson
10 | 3. Macroeconomics: Williamson
11 | 
12 | 


--------------------------------------------------------------------------------
/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2021 Weijie Chen
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/ISM Data.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {
 6 |     "toc": true
 7 |    },
 8 |    "source": [
 9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
10 |     "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
11 |    ]
12 |   },
13 |   {
14 |    "cell_type": "markdown",
15 |    "metadata": {},
16 |    "source": [
17 |     "ISM is shorted for Institute for Supply Management (ISM)."
18 |    ]
19 |   },
20 |   {
21 |    "cell_type": "code",
22 |    "execution_count": null,
23 |    "metadata": {},
24 |    "outputs": [],
25 |    "source": [
26 |     " "
27 |    ]
28 |   }
29 |  ],
30 |  "metadata": {
31 |   "kernelspec": {
32 |    "display_name": "Python 3",
33 |    "language": "python",
34 |    "name": "python3"
35 |   },
36 |   "language_info": {
37 |    "codemirror_mode": {
38 |     "name": "ipython",
39 |     "version": 3
40 |    },
41 |    "file_extension": ".py",
42 |    "mimetype": "text/x-python",
43 |    "name": "python",
44 |    "nbconvert_exporter": "python",
45 |    "pygments_lexer": "ipython3",
46 |    "version": "3.7.4"
47 |   },
48 |   "toc": {
49 |    "base_numbering": 1,
50 |    "nav_menu": {},
51 |    "number_sections": true,
52 |    "sideBar": true,
53 |    "skip_h1_title": false,
54 |    "title_cell": "Table of Contents",
55 |    "title_sidebar": "Contents",
56 |    "toc_cell": true,
57 |    "toc_position": {},
58 |    "toc_section_display": true,
59 |    "toc_window_display": false
60 |   }
61 |  },
62 |  "nbformat": 4,
63 |  "nbformat_minor": 2
64 | }
65 | 


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/Durable and Indurable Goods.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {
 6 |     "toc": true
 7 |    },
 8 |    "source": [
 9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
10 |     "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
11 |    ]
12 |   },
13 |   {
14 |    "cell_type": "code",
15 |    "execution_count": 1,
16 |    "metadata": {},
17 |    "outputs": [],
18 |    "source": [
19 |     "from pandas_datareader import data, wb\n",
20 |     "import pandas_datareader as pdr\n",
21 |     "import matplotlib.pyplot as plt\n",
22 |     "import datetime as dt\n",
23 |     "import pandas as pd\n",
24 |     "from pandas.plotting import register_matplotlib_converters\n",
25 |     "register_matplotlib_converters() # Allow matplotlib have access to timestamp \n",
26 |     "import matplotlib.ticker as mplticker"
27 |    ]
28 |   },
29 |   {
30 |    "cell_type": "markdown",
31 |    "metadata": {},
32 |    "source": [
33 |     "Household spending on durable goods $(\\geq 3y)$ is most sensitive to business cycle. Because durable goods do not bring utility at one time, but rather brings utility in longer run, consumer does not suffer from great reduction of utility even if they cut down the durable good consumption.\n",
34 |     "\n",
35 |     "It usually have higher value than non-durable goods, more likely to be financed by personal credit."
36 |    ]
37 |   }
38 |  ],
39 |  "metadata": {
40 |   "kernelspec": {
41 |    "display_name": "Python 3",
42 |    "language": "python",
43 |    "name": "python3"
44 |   },
45 |   "language_info": {
46 |    "codemirror_mode": {
47 |     "name": "ipython",
48 |     "version": 3
49 |    },
50 |    "file_extension": ".py",
51 |    "mimetype": "text/x-python",
52 |    "name": "python",
53 |    "nbconvert_exporter": "python",
54 |    "pygments_lexer": "ipython3",
55 |    "version": "3.7.4"
56 |   },
57 |   "toc": {
58 |    "base_numbering": 1,
59 |    "nav_menu": {},
60 |    "number_sections": true,
61 |    "sideBar": true,
62 |    "skip_h1_title": false,
63 |    "title_cell": "Table of Contents",
64 |    "title_sidebar": "Contents",
65 |    "toc_cell": true,
66 |    "toc_position": {},
67 |    "toc_section_display": true,
68 |    "toc_window_display": false
69 |   }
70 |  },
71 |  "nbformat": 4,
72 |  "nbformat_minor": 2
73 | }
74 | 


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/House Price Index.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "toc": true
  7 |    },
  8 |    "source": [
  9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
 10 |     "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 10,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "import pandas_datareader as pdr\n",
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "import pandas as pd\n",
 22 |     "import datetime as dt"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 17,
 28 |    "metadata": {
 29 |     "scrolled": true
 30 |    },
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/html": [
 35 |        "<div>\n",
 36 |        "<style scoped>\n",
 37 |        "    .dataframe tbody tr th:only-of-type {\n",
 38 |        "        vertical-align: middle;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe tbody tr th {\n",
 42 |        "        vertical-align: top;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe thead tr th {\n",
 46 |        "        text-align: left;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe thead tr:last-of-type th {\n",
 50 |        "        text-align: right;\n",
 51 |        "    }\n",
 52 |        "</style>\n",
 53 |        "<table border=\"1\" class=\"dataframe\">\n",
 54 |        "  <thead>\n",
 55 |        "    <tr>\n",
 56 |        "      <th>UNIT</th>\n",
 57 |        "      <th colspan=\"10\" halign=\"left\">Quarterly index</th>\n",
 58 |        "      <th>...</th>\n",
 59 |        "      <th colspan=\"10\" halign=\"left\">Quarterly rate of change</th>\n",
 60 |        "    </tr>\n",
 61 |        "    <tr>\n",
 62 |        "      <th>GEO</th>\n",
 63 |        "      <th>Austria</th>\n",
 64 |        "      <th>Belgium</th>\n",
 65 |        "      <th>Bulgaria</th>\n",
 66 |        "      <th>Cyprus</th>\n",
 67 |        "      <th>Czechia</th>\n",
 68 |        "      <th>Germany (until 1990 former territory of the FRG)</th>\n",
 69 |        "      <th>Denmark</th>\n",
 70 |        "      <th>Estonia</th>\n",
 71 |        "      <th>Greece</th>\n",
 72 |        "      <th>Spain</th>\n",
 73 |        "      <th>...</th>\n",
 74 |        "      <th>Latvia</th>\n",
 75 |        "      <th>Malta</th>\n",
 76 |        "      <th>Netherlands</th>\n",
 77 |        "      <th>Poland</th>\n",
 78 |        "      <th>Portugal</th>\n",
 79 |        "      <th>Romania</th>\n",
 80 |        "      <th>Sweden</th>\n",
 81 |        "      <th>Slovenia</th>\n",
 82 |        "      <th>Slovakia</th>\n",
 83 |        "      <th>United Kingdom</th>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>FREQ</th>\n",
 87 |        "      <th>Quarterly</th>\n",
 88 |        "      <th>Quarterly</th>\n",
 89 |        "      <th>Quarterly</th>\n",
 90 |        "      <th>Quarterly</th>\n",
 91 |        "      <th>Quarterly</th>\n",
 92 |        "      <th>Quarterly</th>\n",
 93 |        "      <th>Quarterly</th>\n",
 94 |        "      <th>Quarterly</th>\n",
 95 |        "      <th>Quarterly</th>\n",
 96 |        "      <th>Quarterly</th>\n",
 97 |        "      <th>...</th>\n",
 98 |        "      <th>Quarterly</th>\n",
 99 |        "      <th>Quarterly</th>\n",
100 |        "      <th>Quarterly</th>\n",
101 |        "      <th>Quarterly</th>\n",
102 |        "      <th>Quarterly</th>\n",
103 |        "      <th>Quarterly</th>\n",
104 |        "      <th>Quarterly</th>\n",
105 |        "      <th>Quarterly</th>\n",
106 |        "      <th>Quarterly</th>\n",
107 |        "      <th>Quarterly</th>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <th>TIME_PERIOD</th>\n",
111 |        "      <th></th>\n",
112 |        "      <th></th>\n",
113 |        "      <th></th>\n",
114 |        "      <th></th>\n",
115 |        "      <th></th>\n",
116 |        "      <th></th>\n",
117 |        "      <th></th>\n",
118 |        "      <th></th>\n",
119 |        "      <th></th>\n",
120 |        "      <th></th>\n",
121 |        "      <th></th>\n",
122 |        "      <th></th>\n",
123 |        "      <th></th>\n",
124 |        "      <th></th>\n",
125 |        "      <th></th>\n",
126 |        "      <th></th>\n",
127 |        "      <th></th>\n",
128 |        "      <th></th>\n",
129 |        "      <th></th>\n",
130 |        "      <th></th>\n",
131 |        "      <th></th>\n",
132 |        "    </tr>\n",
133 |        "  </thead>\n",
134 |        "  <tbody>\n",
135 |        "    <tr>\n",
136 |        "      <td>2005-01-01</td>\n",
137 |        "      <td>NaN</td>\n",
138 |        "      <td>69.68</td>\n",
139 |        "      <td>76.01</td>\n",
140 |        "      <td>93.41</td>\n",
141 |        "      <td>NaN</td>\n",
142 |        "      <td>84.2</td>\n",
143 |        "      <td>75.49</td>\n",
144 |        "      <td>50.59</td>\n",
145 |        "      <td>NaN</td>\n",
146 |        "      <td>NaN</td>\n",
147 |        "      <td>...</td>\n",
148 |        "      <td>NaN</td>\n",
149 |        "      <td>NaN</td>\n",
150 |        "      <td>NaN</td>\n",
151 |        "      <td>NaN</td>\n",
152 |        "      <td>NaN</td>\n",
153 |        "      <td>NaN</td>\n",
154 |        "      <td>NaN</td>\n",
155 |        "      <td>NaN</td>\n",
156 |        "      <td>NaN</td>\n",
157 |        "      <td>NaN</td>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <td>2005-04-01</td>\n",
161 |        "      <td>NaN</td>\n",
162 |        "      <td>71.24</td>\n",
163 |        "      <td>79.26</td>\n",
164 |        "      <td>95.38</td>\n",
165 |        "      <td>NaN</td>\n",
166 |        "      <td>82.7</td>\n",
167 |        "      <td>79.46</td>\n",
168 |        "      <td>57.07</td>\n",
169 |        "      <td>NaN</td>\n",
170 |        "      <td>NaN</td>\n",
171 |        "      <td>...</td>\n",
172 |        "      <td>NaN</td>\n",
173 |        "      <td>-0.3</td>\n",
174 |        "      <td>1.1</td>\n",
175 |        "      <td>-0.9</td>\n",
176 |        "      <td>NaN</td>\n",
177 |        "      <td>NaN</td>\n",
178 |        "      <td>3.3</td>\n",
179 |        "      <td>NaN</td>\n",
180 |        "      <td>NaN</td>\n",
181 |        "      <td>2.9</td>\n",
182 |        "    </tr>\n",
183 |        "    <tr>\n",
184 |        "      <td>2005-07-01</td>\n",
185 |        "      <td>NaN</td>\n",
186 |        "      <td>73.78</td>\n",
187 |        "      <td>81.55</td>\n",
188 |        "      <td>98.79</td>\n",
189 |        "      <td>NaN</td>\n",
190 |        "      <td>84.4</td>\n",
191 |        "      <td>85.56</td>\n",
192 |        "      <td>60.25</td>\n",
193 |        "      <td>NaN</td>\n",
194 |        "      <td>NaN</td>\n",
195 |        "      <td>...</td>\n",
196 |        "      <td>NaN</td>\n",
197 |        "      <td>8.3</td>\n",
198 |        "      <td>1.3</td>\n",
199 |        "      <td>0.0</td>\n",
200 |        "      <td>NaN</td>\n",
201 |        "      <td>NaN</td>\n",
202 |        "      <td>3.9</td>\n",
203 |        "      <td>NaN</td>\n",
204 |        "      <td>NaN</td>\n",
205 |        "      <td>2.4</td>\n",
206 |        "    </tr>\n",
207 |        "    <tr>\n",
208 |        "      <td>2005-10-01</td>\n",
209 |        "      <td>NaN</td>\n",
210 |        "      <td>75.24</td>\n",
211 |        "      <td>83.52</td>\n",
212 |        "      <td>100.64</td>\n",
213 |        "      <td>NaN</td>\n",
214 |        "      <td>82.0</td>\n",
215 |        "      <td>91.19</td>\n",
216 |        "      <td>68.14</td>\n",
217 |        "      <td>NaN</td>\n",
218 |        "      <td>123.7</td>\n",
219 |        "      <td>...</td>\n",
220 |        "      <td>NaN</td>\n",
221 |        "      <td>-2.8</td>\n",
222 |        "      <td>0.5</td>\n",
223 |        "      <td>5.9</td>\n",
224 |        "      <td>NaN</td>\n",
225 |        "      <td>NaN</td>\n",
226 |        "      <td>2.6</td>\n",
227 |        "      <td>NaN</td>\n",
228 |        "      <td>NaN</td>\n",
229 |        "      <td>0.2</td>\n",
230 |        "    </tr>\n",
231 |        "    <tr>\n",
232 |        "      <td>2006-01-01</td>\n",
233 |        "      <td>NaN</td>\n",
234 |        "      <td>76.70</td>\n",
235 |        "      <td>87.47</td>\n",
236 |        "      <td>103.04</td>\n",
237 |        "      <td>NaN</td>\n",
238 |        "      <td>83.2</td>\n",
239 |        "      <td>97.68</td>\n",
240 |        "      <td>77.03</td>\n",
241 |        "      <td>NaN</td>\n",
242 |        "      <td>128.3</td>\n",
243 |        "      <td>...</td>\n",
244 |        "      <td>NaN</td>\n",
245 |        "      <td>4.9</td>\n",
246 |        "      <td>1.1</td>\n",
247 |        "      <td>4.3</td>\n",
248 |        "      <td>NaN</td>\n",
249 |        "      <td>NaN</td>\n",
250 |        "      <td>3.5</td>\n",
251 |        "      <td>NaN</td>\n",
252 |        "      <td>NaN</td>\n",
253 |        "      <td>0.6</td>\n",
254 |        "    </tr>\n",
255 |        "  </tbody>\n",
256 |        "</table>\n",
257 |        "<p>5 rows × 84 columns</p>\n",
258 |        "</div>"
259 |       ],
260 |       "text/plain": [
261 |        "UNIT        Quarterly index                                          \\\n",
262 |        "GEO                 Austria   Belgium  Bulgaria    Cyprus   Czechia   \n",
263 |        "FREQ              Quarterly Quarterly Quarterly Quarterly Quarterly   \n",
264 |        "TIME_PERIOD                                                           \n",
265 |        "2005-01-01              NaN     69.68     76.01     93.41       NaN   \n",
266 |        "2005-04-01              NaN     71.24     79.26     95.38       NaN   \n",
267 |        "2005-07-01              NaN     73.78     81.55     98.79       NaN   \n",
268 |        "2005-10-01              NaN     75.24     83.52    100.64       NaN   \n",
269 |        "2006-01-01              NaN     76.70     87.47    103.04       NaN   \n",
270 |        "\n",
271 |        "UNIT                                                                    \\\n",
272 |        "GEO         Germany (until 1990 former territory of the FRG)   Denmark   \n",
273 |        "FREQ                                               Quarterly Quarterly   \n",
274 |        "TIME_PERIOD                                                              \n",
275 |        "2005-01-01                                              84.2     75.49   \n",
276 |        "2005-04-01                                              82.7     79.46   \n",
277 |        "2005-07-01                                              84.4     85.56   \n",
278 |        "2005-10-01                                              82.0     91.19   \n",
279 |        "2006-01-01                                              83.2     97.68   \n",
280 |        "\n",
281 |        "UNIT                                       ... Quarterly rate of change  \\\n",
282 |        "GEO           Estonia    Greece     Spain  ...                   Latvia   \n",
283 |        "FREQ        Quarterly Quarterly Quarterly  ...                Quarterly   \n",
284 |        "TIME_PERIOD                                ...                            \n",
285 |        "2005-01-01      50.59       NaN       NaN  ...                      NaN   \n",
286 |        "2005-04-01      57.07       NaN       NaN  ...                      NaN   \n",
287 |        "2005-07-01      60.25       NaN       NaN  ...                      NaN   \n",
288 |        "2005-10-01      68.14       NaN     123.7  ...                      NaN   \n",
289 |        "2006-01-01      77.03       NaN     128.3  ...                      NaN   \n",
290 |        "\n",
291 |        "UNIT                                                                       \\\n",
292 |        "GEO             Malta Netherlands    Poland  Portugal   Romania    Sweden   \n",
293 |        "FREQ        Quarterly   Quarterly Quarterly Quarterly Quarterly Quarterly   \n",
294 |        "TIME_PERIOD                                                                 \n",
295 |        "2005-01-01        NaN         NaN       NaN       NaN       NaN       NaN   \n",
296 |        "2005-04-01       -0.3         1.1      -0.9       NaN       NaN       3.3   \n",
297 |        "2005-07-01        8.3         1.3       0.0       NaN       NaN       3.9   \n",
298 |        "2005-10-01       -2.8         0.5       5.9       NaN       NaN       2.6   \n",
299 |        "2006-01-01        4.9         1.1       4.3       NaN       NaN       3.5   \n",
300 |        "\n",
301 |        "UNIT                                            \n",
302 |        "GEO          Slovenia  Slovakia United Kingdom  \n",
303 |        "FREQ        Quarterly Quarterly      Quarterly  \n",
304 |        "TIME_PERIOD                                     \n",
305 |        "2005-01-01        NaN       NaN            NaN  \n",
306 |        "2005-04-01        NaN       NaN            2.9  \n",
307 |        "2005-07-01        NaN       NaN            2.4  \n",
308 |        "2005-10-01        NaN       NaN            0.2  \n",
309 |        "2006-01-01        NaN       NaN            0.6  \n",
310 |        "\n",
311 |        "[5 rows x 84 columns]"
312 |       ]
313 |      },
314 |      "execution_count": 17,
315 |      "metadata": {},
316 |      "output_type": "execute_result"
317 |     }
318 |    ],
319 |    "source": [
320 |     "start = dt.datetime(2005, 1, 1)\n",
321 |     "end = dt.datetime(2020, 5, 28)\n",
322 |     "df = pdr.data.DataReader('tipsho40', 'eurostat', start, end)\n",
323 |     "df.head()"
324 |    ]
325 |   },
326 |   {
327 |    "cell_type": "code",
328 |    "execution_count": 18,
329 |    "metadata": {},
330 |    "outputs": [
331 |     {
332 |      "data": {
333 |       "text/plain": [
334 |        "[('Quarterly index', 'Austria', 'Quarterly'),\n",
335 |        " ('Quarterly index', 'Belgium', 'Quarterly'),\n",
336 |        " ('Quarterly index', 'Bulgaria', 'Quarterly'),\n",
337 |        " ('Quarterly index', 'Cyprus', 'Quarterly'),\n",
338 |        " ('Quarterly index', 'Czechia', 'Quarterly'),\n",
339 |        " ('Quarterly index',\n",
340 |        "  'Germany (until 1990 former territory of the FRG)',\n",
341 |        "  'Quarterly'),\n",
342 |        " ('Quarterly index', 'Denmark', 'Quarterly'),\n",
343 |        " ('Quarterly index', 'Estonia', 'Quarterly'),\n",
344 |        " ('Quarterly index', 'Greece', 'Quarterly'),\n",
345 |        " ('Quarterly index', 'Spain', 'Quarterly'),\n",
346 |        " ('Quarterly index', 'Finland', 'Quarterly'),\n",
347 |        " ('Quarterly index', 'France', 'Quarterly'),\n",
348 |        " ('Quarterly index', 'Croatia', 'Quarterly'),\n",
349 |        " ('Quarterly index', 'Hungary', 'Quarterly'),\n",
350 |        " ('Quarterly index', 'Ireland', 'Quarterly'),\n",
351 |        " ('Quarterly index', 'Italy', 'Quarterly'),\n",
352 |        " ('Quarterly index', 'Lithuania', 'Quarterly'),\n",
353 |        " ('Quarterly index', 'Luxembourg', 'Quarterly'),\n",
354 |        " ('Quarterly index', 'Latvia', 'Quarterly'),\n",
355 |        " ('Quarterly index', 'Malta', 'Quarterly'),\n",
356 |        " ('Quarterly index', 'Netherlands', 'Quarterly'),\n",
357 |        " ('Quarterly index', 'Poland', 'Quarterly'),\n",
358 |        " ('Quarterly index', 'Portugal', 'Quarterly'),\n",
359 |        " ('Quarterly index', 'Romania', 'Quarterly'),\n",
360 |        " ('Quarterly index', 'Sweden', 'Quarterly'),\n",
361 |        " ('Quarterly index', 'Slovenia', 'Quarterly'),\n",
362 |        " ('Quarterly index', 'Slovakia', 'Quarterly'),\n",
363 |        " ('Quarterly index', 'United Kingdom', 'Quarterly'),\n",
364 |        " ('Annual rate of change', 'Austria', 'Quarterly'),\n",
365 |        " ('Annual rate of change', 'Belgium', 'Quarterly'),\n",
366 |        " ('Annual rate of change', 'Bulgaria', 'Quarterly'),\n",
367 |        " ('Annual rate of change', 'Cyprus', 'Quarterly'),\n",
368 |        " ('Annual rate of change', 'Czechia', 'Quarterly'),\n",
369 |        " ('Annual rate of change',\n",
370 |        "  'Germany (until 1990 former territory of the FRG)',\n",
371 |        "  'Quarterly'),\n",
372 |        " ('Annual rate of change', 'Denmark', 'Quarterly'),\n",
373 |        " ('Annual rate of change', 'Estonia', 'Quarterly'),\n",
374 |        " ('Annual rate of change', 'Greece', 'Quarterly'),\n",
375 |        " ('Annual rate of change', 'Spain', 'Quarterly'),\n",
376 |        " ('Annual rate of change', 'Finland', 'Quarterly'),\n",
377 |        " ('Annual rate of change', 'France', 'Quarterly'),\n",
378 |        " ('Annual rate of change', 'Croatia', 'Quarterly'),\n",
379 |        " ('Annual rate of change', 'Hungary', 'Quarterly'),\n",
380 |        " ('Annual rate of change', 'Ireland', 'Quarterly'),\n",
381 |        " ('Annual rate of change', 'Italy', 'Quarterly'),\n",
382 |        " ('Annual rate of change', 'Lithuania', 'Quarterly'),\n",
383 |        " ('Annual rate of change', 'Luxembourg', 'Quarterly'),\n",
384 |        " ('Annual rate of change', 'Latvia', 'Quarterly'),\n",
385 |        " ('Annual rate of change', 'Malta', 'Quarterly'),\n",
386 |        " ('Annual rate of change', 'Netherlands', 'Quarterly'),\n",
387 |        " ('Annual rate of change', 'Poland', 'Quarterly'),\n",
388 |        " ('Annual rate of change', 'Portugal', 'Quarterly'),\n",
389 |        " ('Annual rate of change', 'Romania', 'Quarterly'),\n",
390 |        " ('Annual rate of change', 'Sweden', 'Quarterly'),\n",
391 |        " ('Annual rate of change', 'Slovenia', 'Quarterly'),\n",
392 |        " ('Annual rate of change', 'Slovakia', 'Quarterly'),\n",
393 |        " ('Annual rate of change', 'United Kingdom', 'Quarterly'),\n",
394 |        " ('Quarterly rate of change', 'Austria', 'Quarterly'),\n",
395 |        " ('Quarterly rate of change', 'Belgium', 'Quarterly'),\n",
396 |        " ('Quarterly rate of change', 'Bulgaria', 'Quarterly'),\n",
397 |        " ('Quarterly rate of change', 'Cyprus', 'Quarterly'),\n",
398 |        " ('Quarterly rate of change', 'Czechia', 'Quarterly'),\n",
399 |        " ('Quarterly rate of change',\n",
400 |        "  'Germany (until 1990 former territory of the FRG)',\n",
401 |        "  'Quarterly'),\n",
402 |        " ('Quarterly rate of change', 'Denmark', 'Quarterly'),\n",
403 |        " ('Quarterly rate of change', 'Estonia', 'Quarterly'),\n",
404 |        " ('Quarterly rate of change', 'Greece', 'Quarterly'),\n",
405 |        " ('Quarterly rate of change', 'Spain', 'Quarterly'),\n",
406 |        " ('Quarterly rate of change', 'Finland', 'Quarterly'),\n",
407 |        " ('Quarterly rate of change', 'France', 'Quarterly'),\n",
408 |        " ('Quarterly rate of change', 'Croatia', 'Quarterly'),\n",
409 |        " ('Quarterly rate of change', 'Hungary', 'Quarterly'),\n",
410 |        " ('Quarterly rate of change', 'Ireland', 'Quarterly'),\n",
411 |        " ('Quarterly rate of change', 'Italy', 'Quarterly'),\n",
412 |        " ('Quarterly rate of change', 'Lithuania', 'Quarterly'),\n",
413 |        " ('Quarterly rate of change', 'Luxembourg', 'Quarterly'),\n",
414 |        " ('Quarterly rate of change', 'Latvia', 'Quarterly'),\n",
415 |        " ('Quarterly rate of change', 'Malta', 'Quarterly'),\n",
416 |        " ('Quarterly rate of change', 'Netherlands', 'Quarterly'),\n",
417 |        " ('Quarterly rate of change', 'Poland', 'Quarterly'),\n",
418 |        " ('Quarterly rate of change', 'Portugal', 'Quarterly'),\n",
419 |        " ('Quarterly rate of change', 'Romania', 'Quarterly'),\n",
420 |        " ('Quarterly rate of change', 'Sweden', 'Quarterly'),\n",
421 |        " ('Quarterly rate of change', 'Slovenia', 'Quarterly'),\n",
422 |        " ('Quarterly rate of change', 'Slovakia', 'Quarterly'),\n",
423 |        " ('Quarterly rate of change', 'United Kingdom', 'Quarterly')]"
424 |       ]
425 |      },
426 |      "execution_count": 18,
427 |      "metadata": {},
428 |      "output_type": "execute_result"
429 |     }
430 |    ],
431 |    "source": [
432 |     "list(df)"
433 |    ]
434 |   },
435 |   {
436 |    "cell_type": "code",
437 |    "execution_count": 19,
438 |    "metadata": {},
439 |    "outputs": [
440 |     {
441 |      "data": {
442 |       "text/plain": [
443 |        "MultiIndex([(         'Quarterly index', ...),\n",
444 |        "            (         'Quarterly index', ...),\n",
445 |        "            (         'Quarterly index', ...),\n",
446 |        "            (         'Quarterly index', ...),\n",
447 |        "            (         'Quarterly index', ...),\n",
448 |        "            (         'Quarterly index', ...),\n",
449 |        "            (         'Quarterly index', ...),\n",
450 |        "            (         'Quarterly index', ...),\n",
451 |        "            (         'Quarterly index', ...),\n",
452 |        "            (         'Quarterly index', ...),\n",
453 |        "            (         'Quarterly index', ...),\n",
454 |        "            (         'Quarterly index', ...),\n",
455 |        "            (         'Quarterly index', ...),\n",
456 |        "            (         'Quarterly index', ...),\n",
457 |        "            (         'Quarterly index', ...),\n",
458 |        "            (         'Quarterly index', ...),\n",
459 |        "            (         'Quarterly index', ...),\n",
460 |        "            (         'Quarterly index', ...),\n",
461 |        "            (         'Quarterly index', ...),\n",
462 |        "            (         'Quarterly index', ...),\n",
463 |        "            (         'Quarterly index', ...),\n",
464 |        "            (         'Quarterly index', ...),\n",
465 |        "            (         'Quarterly index', ...),\n",
466 |        "            (         'Quarterly index', ...),\n",
467 |        "            (         'Quarterly index', ...),\n",
468 |        "            (         'Quarterly index', ...),\n",
469 |        "            (         'Quarterly index', ...),\n",
470 |        "            (         'Quarterly index', ...),\n",
471 |        "            (   'Annual rate of change', ...),\n",
472 |        "            (   'Annual rate of change', ...),\n",
473 |        "            (   'Annual rate of change', ...),\n",
474 |        "            (   'Annual rate of change', ...),\n",
475 |        "            (   'Annual rate of change', ...),\n",
476 |        "            (   'Annual rate of change', ...),\n",
477 |        "            (   'Annual rate of change', ...),\n",
478 |        "            (   'Annual rate of change', ...),\n",
479 |        "            (   'Annual rate of change', ...),\n",
480 |        "            (   'Annual rate of change', ...),\n",
481 |        "            (   'Annual rate of change', ...),\n",
482 |        "            (   'Annual rate of change', ...),\n",
483 |        "            (   'Annual rate of change', ...),\n",
484 |        "            (   'Annual rate of change', ...),\n",
485 |        "            (   'Annual rate of change', ...),\n",
486 |        "            (   'Annual rate of change', ...),\n",
487 |        "            (   'Annual rate of change', ...),\n",
488 |        "            (   'Annual rate of change', ...),\n",
489 |        "            (   'Annual rate of change', ...),\n",
490 |        "            (   'Annual rate of change', ...),\n",
491 |        "            (   'Annual rate of change', ...),\n",
492 |        "            (   'Annual rate of change', ...),\n",
493 |        "            (   'Annual rate of change', ...),\n",
494 |        "            (   'Annual rate of change', ...),\n",
495 |        "            (   'Annual rate of change', ...),\n",
496 |        "            (   'Annual rate of change', ...),\n",
497 |        "            (   'Annual rate of change', ...),\n",
498 |        "            (   'Annual rate of change', ...),\n",
499 |        "            ('Quarterly rate of change', ...),\n",
500 |        "            ('Quarterly rate of change', ...),\n",
501 |        "            ('Quarterly rate of change', ...),\n",
502 |        "            ('Quarterly rate of change', ...),\n",
503 |        "            ('Quarterly rate of change', ...),\n",
504 |        "            ('Quarterly rate of change', ...),\n",
505 |        "            ('Quarterly rate of change', ...),\n",
506 |        "            ('Quarterly rate of change', ...),\n",
507 |        "            ('Quarterly rate of change', ...),\n",
508 |        "            ('Quarterly rate of change', ...),\n",
509 |        "            ('Quarterly rate of change', ...),\n",
510 |        "            ('Quarterly rate of change', ...),\n",
511 |        "            ('Quarterly rate of change', ...),\n",
512 |        "            ('Quarterly rate of change', ...),\n",
513 |        "            ('Quarterly rate of change', ...),\n",
514 |        "            ('Quarterly rate of change', ...),\n",
515 |        "            ('Quarterly rate of change', ...),\n",
516 |        "            ('Quarterly rate of change', ...),\n",
517 |        "            ('Quarterly rate of change', ...),\n",
518 |        "            ('Quarterly rate of change', ...),\n",
519 |        "            ('Quarterly rate of change', ...),\n",
520 |        "            ('Quarterly rate of change', ...),\n",
521 |        "            ('Quarterly rate of change', ...),\n",
522 |        "            ('Quarterly rate of change', ...),\n",
523 |        "            ('Quarterly rate of change', ...),\n",
524 |        "            ('Quarterly rate of change', ...),\n",
525 |        "            ('Quarterly rate of change', ...),\n",
526 |        "            ('Quarterly rate of change', ...)],\n",
527 |        "           names=['UNIT', 'GEO', 'FREQ'])"
528 |       ]
529 |      },
530 |      "execution_count": 19,
531 |      "metadata": {},
532 |      "output_type": "execute_result"
533 |     }
534 |    ],
535 |    "source": [
536 |     "df.columns"
537 |    ]
538 |   },
539 |   {
540 |    "cell_type": "code",
541 |    "execution_count": 20,
542 |    "metadata": {},
543 |    "outputs": [
544 |     {
545 |      "data": {
546 |       "text/plain": [
547 |        "MultiIndex([(1,  'red'),\n",
548 |        "            (1, 'blue'),\n",
549 |        "            (2,  'red'),\n",
550 |        "            (2, 'blue')],\n",
551 |        "           names=['number', 'color'])"
552 |       ]
553 |      },
554 |      "execution_count": 20,
555 |      "metadata": {},
556 |      "output_type": "execute_result"
557 |     }
558 |    ],
559 |    "source": [
560 |     "arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]\n",
561 |     "pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))"
562 |    ]
563 |   },
564 |   {
565 |    "cell_type": "code",
566 |    "execution_count": null,
567 |    "metadata": {},
568 |    "outputs": [],
569 |    "source": []
570 |   }
571 |  ],
572 |  "metadata": {
573 |   "kernelspec": {
574 |    "display_name": "Python 3",
575 |    "language": "python",
576 |    "name": "python3"
577 |   },
578 |   "language_info": {
579 |    "codemirror_mode": {
580 |     "name": "ipython",
581 |     "version": 3
582 |    },
583 |    "file_extension": ".py",
584 |    "mimetype": "text/x-python",
585 |    "name": "python",
586 |    "nbconvert_exporter": "python",
587 |    "pygments_lexer": "ipython3",
588 |    "version": "3.7.4"
589 |   },
590 |   "toc": {
591 |    "base_numbering": 1,
592 |    "nav_menu": {},
593 |    "number_sections": true,
594 |    "sideBar": true,
595 |    "skip_h1_title": false,
596 |    "title_cell": "Table of Contents",
597 |    "title_sidebar": "Contents",
598 |    "toc_cell": true,
599 |    "toc_position": {},
600 |    "toc_section_display": true,
601 |    "toc_window_display": false
602 |   }
603 |  },
604 |  "nbformat": 4,
605 |  "nbformat_minor": 2
606 | }
607 | 


--------------------------------------------------------------------------------
/Corporate Profit Cycle.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "toc": true
  7 |    },
  8 |    "source": [
  9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
 10 |     "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "from pandas_datareader import data, wb\n",
 20 |     "import pandas_datareader as pdr\n",
 21 |     "import matplotlib.pyplot as plt\n",
 22 |     "import datetime as dt\n",
 23 |     "import pandas as pd\n",
 24 |     "from pandas.plotting import register_matplotlib_converters\n",
 25 |     "register_matplotlib_converters() # Allow matplotlib have access to timestamp \n",
 26 |     "import matplotlib.ticker as mplticker"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 3,
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "# Retrieve data from FRED, check my notebook for pandareader's user guide\n",
 36 |     "start = dt.datetime(2000, 1, 1)\n",
 37 |     "end = dt.datetime.today()\n",
 38 |     "Cprofit = pdr.data.DataReader('CP', 'fred', start, end) #  Corporate Profits After Tax (without IVA and CCAdj)\n",
 39 |     "GDP_growth = pdr.data.DataReader('A191RL1Q225SBEA', 'fred', start, end)"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 8,
 45 |    "metadata": {},
 46 |    "outputs": [
 47 |     {
 48 |      "data": {
 49 |       "image/png": 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\n",
 50 |       "text/plain": [
 51 |        "<Figure size 936x576 with 1 Axes>"
 52 |       ]
 53 |      },
 54 |      "metadata": {
 55 |       "needs_background": "light"
 56 |      },
 57 |      "output_type": "display_data"
 58 |     }
 59 |    ],
 60 |    "source": [
 61 |     "Cprofit = Cprofit.pct_change()\n",
 62 |     "fig, ax = plt.subplots(figsize = (13, 8))\n",
 63 |     "ax.plot(Cprofit, label = 'Corporate Profits After Tax ')\n",
 64 |     "#ax.plot(GDP_growth, label = 'GDP Growth')\n",
 65 |     "ax.legend()\n",
 66 |     "alp = .3\n",
 67 |     "ax.axvspan('2007-12-1','2009-6-1',color = 'gray', alpha = alp, zorder = -1)\n",
 68 |     "ax.axvspan('2020-3-1','2020-6-1',color = 'gray', alpha = alp, zorder = -1)\n",
 69 |     "ax.yaxis.grid(True) # only horizontal grid\n",
 70 |     "plt.show()"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": null,
 76 |    "metadata": {},
 77 |    "outputs": [],
 78 |    "source": []
 79 |   }
 80 |  ],
 81 |  "metadata": {
 82 |   "kernelspec": {
 83 |    "display_name": "Python 3",
 84 |    "language": "python",
 85 |    "name": "python3"
 86 |   },
 87 |   "language_info": {
 88 |    "codemirror_mode": {
 89 |     "name": "ipython",
 90 |     "version": 3
 91 |    },
 92 |    "file_extension": ".py",
 93 |    "mimetype": "text/x-python",
 94 |    "name": "python",
 95 |    "nbconvert_exporter": "python",
 96 |    "pygments_lexer": "ipython3",
 97 |    "version": "3.7.4"
 98 |   },
 99 |   "toc": {
100 |    "base_numbering": 1,
101 |    "nav_menu": {},
102 |    "number_sections": true,
103 |    "sideBar": true,
104 |    "skip_h1_title": false,
105 |    "title_cell": "Table of Contents",
106 |    "title_sidebar": "Contents",
107 |    "toc_cell": true,
108 |    "toc_position": {},
109 |    "toc_section_display": true,
110 |    "toc_window_display": false
111 |   }
112 |  },
113 |  "nbformat": 4,
114 |  "nbformat_minor": 2
115 | }
116 | 


--------------------------------------------------------------------------------
/US Recession Shades.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "toc": true
  7 |    },
  8 |    "source": [
  9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
 10 |     "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 43,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "import pandas_datareader as pdr\n",
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "import datetime as dt\n",
 22 |     "import pandas as pd\n",
 23 |     "from pandas.plotting import register_matplotlib_converters\n",
 24 |     "register_matplotlib_converters() # Allow matplotlib have access to timestamp \n",
 25 |     "import matplotlib.ticker as mplticker"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 44,
 31 |    "metadata": {},
 32 |    "outputs": [],
 33 |    "source": [
 34 |     "USRec = pd.read_excel('recDates.xlsx')"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 45,
 40 |    "metadata": {},
 41 |    "outputs": [
 42 |     {
 43 |      "data": {
 44 |       "text/html": [
 45 |        "<div>\n",
 46 |        "<style scoped>\n",
 47 |        "    .dataframe tbody tr th:only-of-type {\n",
 48 |        "        vertical-align: middle;\n",
 49 |        "    }\n",
 50 |        "\n",
 51 |        "    .dataframe tbody tr th {\n",
 52 |        "        vertical-align: top;\n",
 53 |        "    }\n",
 54 |        "\n",
 55 |        "    .dataframe thead th {\n",
 56 |        "        text-align: right;\n",
 57 |        "    }\n",
 58 |        "</style>\n",
 59 |        "<table border=\"1\" class=\"dataframe\">\n",
 60 |        "  <thead>\n",
 61 |        "    <tr style=\"text-align: right;\">\n",
 62 |        "      <th></th>\n",
 63 |        "      <th>Peak</th>\n",
 64 |        "      <th>Trough</th>\n",
 65 |        "    </tr>\n",
 66 |        "  </thead>\n",
 67 |        "  <tbody>\n",
 68 |        "    <tr>\n",
 69 |        "      <td>23</td>\n",
 70 |        "      <td>1953-07-01 00:00:00</td>\n",
 71 |        "      <td>1954-05-01 00:00:00</td>\n",
 72 |        "    </tr>\n",
 73 |        "    <tr>\n",
 74 |        "      <td>24</td>\n",
 75 |        "      <td>1957-08-01 00:00:00</td>\n",
 76 |        "      <td>1958-04-01 00:00:00</td>\n",
 77 |        "    </tr>\n",
 78 |        "    <tr>\n",
 79 |        "      <td>25</td>\n",
 80 |        "      <td>1960-04-01 00:00:00</td>\n",
 81 |        "      <td>1961-02-01 00:00:00</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <td>26</td>\n",
 85 |        "      <td>1969-12-01 00:00:00</td>\n",
 86 |        "      <td>1970-11-01 00:00:00</td>\n",
 87 |        "    </tr>\n",
 88 |        "    <tr>\n",
 89 |        "      <td>27</td>\n",
 90 |        "      <td>1973-11-01 00:00:00</td>\n",
 91 |        "      <td>1975-03-01 00:00:00</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <td>28</td>\n",
 95 |        "      <td>1980-01-01 00:00:00</td>\n",
 96 |        "      <td>1980-07-01 00:00:00</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <td>29</td>\n",
100 |        "      <td>1981-07-01 00:00:00</td>\n",
101 |        "      <td>1982-11-01 00:00:00</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <td>30</td>\n",
105 |        "      <td>1990-07-01 00:00:00</td>\n",
106 |        "      <td>1991-03-01 00:00:00</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <td>31</td>\n",
110 |        "      <td>2001-03-01 00:00:00</td>\n",
111 |        "      <td>2001-11-01 00:00:00</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <td>32</td>\n",
115 |        "      <td>2007-12-01 00:00:00</td>\n",
116 |        "      <td>2009-06-01 00:00:00</td>\n",
117 |        "    </tr>\n",
118 |        "  </tbody>\n",
119 |        "</table>\n",
120 |        "</div>"
121 |       ],
122 |       "text/plain": [
123 |        "                   Peak               Trough\n",
124 |        "23  1953-07-01 00:00:00  1954-05-01 00:00:00\n",
125 |        "24  1957-08-01 00:00:00  1958-04-01 00:00:00\n",
126 |        "25  1960-04-01 00:00:00  1961-02-01 00:00:00\n",
127 |        "26  1969-12-01 00:00:00  1970-11-01 00:00:00\n",
128 |        "27  1973-11-01 00:00:00  1975-03-01 00:00:00\n",
129 |        "28  1980-01-01 00:00:00  1980-07-01 00:00:00\n",
130 |        "29  1981-07-01 00:00:00  1982-11-01 00:00:00\n",
131 |        "30  1990-07-01 00:00:00  1991-03-01 00:00:00\n",
132 |        "31  2001-03-01 00:00:00  2001-11-01 00:00:00\n",
133 |        "32  2007-12-01 00:00:00  2009-06-01 00:00:00"
134 |       ]
135 |      },
136 |      "execution_count": 45,
137 |      "metadata": {},
138 |      "output_type": "execute_result"
139 |     }
140 |    ],
141 |    "source": [
142 |     "USRec = USRec.iloc[23:]; USRec"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": 46,
148 |    "metadata": {},
149 |    "outputs": [],
150 |    "source": [
151 |     "for col in USRec.columns:\n",
152 |     "    USRec[col] = pd.to_datetime(USRec[col]) "
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 49,
158 |    "metadata": {},
159 |    "outputs": [],
160 |    "source": [
161 |     "start = dt.datetime(1950, 1, 1)\n",
162 |     "end = dt.datetime.today()\n",
163 |     "FFR = pdr.data.DataReader('FEDFUNDS', 'fred', start, end)\n",
164 |     "#TrWt_DollarIndex = pdr.data.DataReader('FEDFDTWEXBGSUNDS', 'fred', start, end)"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 53,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "data": {
174 |       "text/plain": [
175 |        "FEDFUNDS    19.1\n",
176 |        "dtype: float64"
177 |       ]
178 |      },
179 |      "execution_count": 53,
180 |      "metadata": {},
181 |      "output_type": "execute_result"
182 |     }
183 |    ],
184 |    "source": [
185 |     "FFR.max()+1"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 70,
191 |    "metadata": {
192 |     "scrolled": false
193 |    },
194 |    "outputs": [
195 |     {
196 |      "data": {
197 |       "image/png": 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\n",
198 |       "text/plain": [
199 |        "<Figure size 864x504 with 1 Axes>"
200 |       ]
201 |      },
202 |      "metadata": {
203 |       "needs_background": "light"
204 |      },
205 |      "output_type": "display_data"
206 |     }
207 |    ],
208 |    "source": [
209 |     "fig, ax = plt.subplots(figsize = (12, 7))\n",
210 |     "for ix, row in USRec.iterrows():\n",
211 |     "    ax.fill_between(row,FFR.max()+1, color='gray', alpha=.3)\n",
212 |     "    ax.plot(FFR, color = 'CornflowerBlue')\n",
213 |     "ax.yaxis.grid(True)\n",
214 |     "ax.set_title('Federal Funds Rate')\n",
215 |     "ax.set_ylim((FFR.min()-.2).iloc[0], (FFR.max()+1).iloc[0])\n",
216 |     "plt.show()"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "code",
221 |    "execution_count": 72,
222 |    "metadata": {},
223 |    "outputs": [
224 |     {
225 |      "data": {
226 |       "text/html": [
227 |        "<div>\n",
228 |        "<style scoped>\n",
229 |        "    .dataframe tbody tr th:only-of-type {\n",
230 |        "        vertical-align: middle;\n",
231 |        "    }\n",
232 |        "\n",
233 |        "    .dataframe tbody tr th {\n",
234 |        "        vertical-align: top;\n",
235 |        "    }\n",
236 |        "\n",
237 |        "    .dataframe thead th {\n",
238 |        "        text-align: right;\n",
239 |        "    }\n",
240 |        "</style>\n",
241 |        "<table border=\"1\" class=\"dataframe\">\n",
242 |        "  <thead>\n",
243 |        "    <tr style=\"text-align: right;\">\n",
244 |        "      <th></th>\n",
245 |        "      <th>Peak</th>\n",
246 |        "      <th>Trough</th>\n",
247 |        "    </tr>\n",
248 |        "  </thead>\n",
249 |        "  <tbody>\n",
250 |        "    <tr>\n",
251 |        "      <td>23</td>\n",
252 |        "      <td>1953-07-01</td>\n",
253 |        "      <td>1954-05-01</td>\n",
254 |        "    </tr>\n",
255 |        "    <tr>\n",
256 |        "      <td>24</td>\n",
257 |        "      <td>1957-08-01</td>\n",
258 |        "      <td>1958-04-01</td>\n",
259 |        "    </tr>\n",
260 |        "    <tr>\n",
261 |        "      <td>25</td>\n",
262 |        "      <td>1960-04-01</td>\n",
263 |        "      <td>1961-02-01</td>\n",
264 |        "    </tr>\n",
265 |        "    <tr>\n",
266 |        "      <td>26</td>\n",
267 |        "      <td>1969-12-01</td>\n",
268 |        "      <td>1970-11-01</td>\n",
269 |        "    </tr>\n",
270 |        "    <tr>\n",
271 |        "      <td>27</td>\n",
272 |        "      <td>1973-11-01</td>\n",
273 |        "      <td>1975-03-01</td>\n",
274 |        "    </tr>\n",
275 |        "    <tr>\n",
276 |        "      <td>28</td>\n",
277 |        "      <td>1980-01-01</td>\n",
278 |        "      <td>1980-07-01</td>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <td>29</td>\n",
282 |        "      <td>1981-07-01</td>\n",
283 |        "      <td>1982-11-01</td>\n",
284 |        "    </tr>\n",
285 |        "    <tr>\n",
286 |        "      <td>30</td>\n",
287 |        "      <td>1990-07-01</td>\n",
288 |        "      <td>1991-03-01</td>\n",
289 |        "    </tr>\n",
290 |        "    <tr>\n",
291 |        "      <td>31</td>\n",
292 |        "      <td>2001-03-01</td>\n",
293 |        "      <td>2001-11-01</td>\n",
294 |        "    </tr>\n",
295 |        "    <tr>\n",
296 |        "      <td>32</td>\n",
297 |        "      <td>2007-12-01</td>\n",
298 |        "      <td>2009-06-01</td>\n",
299 |        "    </tr>\n",
300 |        "  </tbody>\n",
301 |        "</table>\n",
302 |        "</div>"
303 |       ],
304 |       "text/plain": [
305 |        "         Peak     Trough\n",
306 |        "23 1953-07-01 1954-05-01\n",
307 |        "24 1957-08-01 1958-04-01\n",
308 |        "25 1960-04-01 1961-02-01\n",
309 |        "26 1969-12-01 1970-11-01\n",
310 |        "27 1973-11-01 1975-03-01\n",
311 |        "28 1980-01-01 1980-07-01\n",
312 |        "29 1981-07-01 1982-11-01\n",
313 |        "30 1990-07-01 1991-03-01\n",
314 |        "31 2001-03-01 2001-11-01\n",
315 |        "32 2007-12-01 2009-06-01"
316 |       ]
317 |      },
318 |      "execution_count": 72,
319 |      "metadata": {},
320 |      "output_type": "execute_result"
321 |     }
322 |    ],
323 |    "source": [
324 |     "USRec"
325 |    ]
326 |   },
327 |   {
328 |    "cell_type": "code",
329 |    "execution_count": 73,
330 |    "metadata": {},
331 |    "outputs": [],
332 |    "source": [
333 |     "USRec.iterrows?"
334 |    ]
335 |   },
336 |   {
337 |    "cell_type": "code",
338 |    "execution_count": null,
339 |    "metadata": {},
340 |    "outputs": [],
341 |    "source": []
342 |   }
343 |  ],
344 |  "metadata": {
345 |   "kernelspec": {
346 |    "display_name": "Python 3",
347 |    "language": "python",
348 |    "name": "python3"
349 |   },
350 |   "language_info": {
351 |    "codemirror_mode": {
352 |     "name": "ipython",
353 |     "version": 3
354 |    },
355 |    "file_extension": ".py",
356 |    "mimetype": "text/x-python",
357 |    "name": "python",
358 |    "nbconvert_exporter": "python",
359 |    "pygments_lexer": "ipython3",
360 |    "version": "3.7.4"
361 |   },
362 |   "toc": {
363 |    "base_numbering": 1,
364 |    "nav_menu": {},
365 |    "number_sections": true,
366 |    "sideBar": true,
367 |    "skip_h1_title": false,
368 |    "title_cell": "Table of Contents",
369 |    "title_sidebar": "Contents",
370 |    "toc_cell": true,
371 |    "toc_position": {},
372 |    "toc_section_display": true,
373 |    "toc_window_display": false
374 |   }
375 |  },
376 |  "nbformat": 4,
377 |  "nbformat_minor": 2
378 | }
379 | 


--------------------------------------------------------------------------------
/Log Returns.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "toc": true
  7 |    },
  8 |    "source": [
  9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
 10 |     "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#-Adjustification-of-Log-Returns-\" data-toc-modified-id=\"-Adjustification-of-Log-Returns--1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span><font face=\"gotham\" color=\"purple\"> Adjustification of Log Returns </font></a></span></li><li><span><a href=\"#-Relations-of-Log-Return-and-Simple-Return\" data-toc-modified-id=\"-Relations-of-Log-Return-and-Simple-Return-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span><font face=\"gotham\" color=\"purple\"> Relations of Log Return and Simple Return</font></a></span></li></ul></div>"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 22,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "import pandas as pd\n",
 20 |     "import numpy as np\n",
 21 |     "import scipy as sp\n",
 22 |     "import scipy.stats\n",
 23 |     "import matplotlib.pyplot as plt"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 28,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "import warnings\n",
 33 |     "warnings.filterwarnings('ignore')"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "markdown",
 38 |    "metadata": {},
 39 |    "source": [
 40 |     "# <font face=\"gotham\" color=\"purple\"> Adjustification of Log Returns </font>"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     "The adjustification of using log return/growth is to avoid positive bias of arithmic return. An example will show the difference."
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "markdown",
 52 |    "metadata": {},
 53 |    "source": [
 54 |     "Suppose you are hold a stock share for four periods."
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 6,
 60 |    "metadata": {},
 61 |    "outputs": [
 62 |     {
 63 |      "data": {
 64 |       "text/html": [
 65 |        "<div>\n",
 66 |        "<style scoped>\n",
 67 |        "    .dataframe tbody tr th:only-of-type {\n",
 68 |        "        vertical-align: middle;\n",
 69 |        "    }\n",
 70 |        "\n",
 71 |        "    .dataframe tbody tr th {\n",
 72 |        "        vertical-align: top;\n",
 73 |        "    }\n",
 74 |        "\n",
 75 |        "    .dataframe thead th {\n",
 76 |        "        text-align: right;\n",
 77 |        "    }\n",
 78 |        "</style>\n",
 79 |        "<table border=\"1\" class=\"dataframe\">\n",
 80 |        "  <thead>\n",
 81 |        "    <tr style=\"text-align: right;\">\n",
 82 |        "      <th></th>\n",
 83 |        "      <th>price</th>\n",
 84 |        "    </tr>\n",
 85 |        "  </thead>\n",
 86 |        "  <tbody>\n",
 87 |        "    <tr>\n",
 88 |        "      <td>0</td>\n",
 89 |        "      <td>100</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <td>1</td>\n",
 93 |        "      <td>200</td>\n",
 94 |        "    </tr>\n",
 95 |        "    <tr>\n",
 96 |        "      <td>2</td>\n",
 97 |        "      <td>150</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <td>3</td>\n",
101 |        "      <td>90</td>\n",
102 |        "    </tr>\n",
103 |        "  </tbody>\n",
104 |        "</table>\n",
105 |        "</div>"
106 |       ],
107 |       "text/plain": [
108 |        "   price\n",
109 |        "0    100\n",
110 |        "1    200\n",
111 |        "2    150\n",
112 |        "3     90"
113 |       ]
114 |      },
115 |      "execution_count": 6,
116 |      "metadata": {},
117 |      "output_type": "execute_result"
118 |     }
119 |    ],
120 |    "source": [
121 |     "data = pd.DataFrame([100, 200, 150, 90], columns = ['price']);data"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "markdown",
126 |    "metadata": {},
127 |    "source": [
128 |     "To calculate the simple arithmetic return:"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 13,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "data": {
138 |       "text/plain": [
139 |        "price    0.116667\n",
140 |        "dtype: float64"
141 |       ]
142 |      },
143 |      "execution_count": 13,
144 |      "metadata": {},
145 |      "output_type": "execute_result"
146 |     }
147 |    ],
148 |    "source": [
149 |     "data.pct_change().mean()"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "markdown",
154 |    "metadata": {},
155 |    "source": [
156 |     "We found a ludicrous outcome, that even if you lost money comparing to your initial investment, you have an average of $11.67\\%$ annual return."
157 |    ]
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": 19,
162 |    "metadata": {},
163 |    "outputs": [
164 |     {
165 |      "data": {
166 |       "text/html": [
167 |        "<div>\n",
168 |        "<style scoped>\n",
169 |        "    .dataframe tbody tr th:only-of-type {\n",
170 |        "        vertical-align: middle;\n",
171 |        "    }\n",
172 |        "\n",
173 |        "    .dataframe tbody tr th {\n",
174 |        "        vertical-align: top;\n",
175 |        "    }\n",
176 |        "\n",
177 |        "    .dataframe thead th {\n",
178 |        "        text-align: right;\n",
179 |        "    }\n",
180 |        "</style>\n",
181 |        "<table border=\"1\" class=\"dataframe\">\n",
182 |        "  <thead>\n",
183 |        "    <tr style=\"text-align: right;\">\n",
184 |        "      <th></th>\n",
185 |        "      <th>price</th>\n",
186 |        "    </tr>\n",
187 |        "  </thead>\n",
188 |        "  <tbody>\n",
189 |        "    <tr>\n",
190 |        "      <td>0</td>\n",
191 |        "      <td>NaN</td>\n",
192 |        "    </tr>\n",
193 |        "    <tr>\n",
194 |        "      <td>1</td>\n",
195 |        "      <td>0.693147</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <td>2</td>\n",
199 |        "      <td>-0.287682</td>\n",
200 |        "    </tr>\n",
201 |        "    <tr>\n",
202 |        "      <td>3</td>\n",
203 |        "      <td>-0.510826</td>\n",
204 |        "    </tr>\n",
205 |        "  </tbody>\n",
206 |        "</table>\n",
207 |        "</div>"
208 |       ],
209 |       "text/plain": [
210 |        "      price\n",
211 |        "0       NaN\n",
212 |        "1  0.693147\n",
213 |        "2 -0.287682\n",
214 |        "3 -0.510826"
215 |       ]
216 |      },
217 |      "execution_count": 19,
218 |      "metadata": {},
219 |      "output_type": "execute_result"
220 |     }
221 |    ],
222 |    "source": [
223 |     "logReturn = np.log(data) - np.log(data.shift(1)); logReturn"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "markdown",
228 |    "metadata": {},
229 |    "source": [
230 |     "We can that annual log return gives us a fairly precise measurement of the performance, annual growth rate of $-3.51\\%$"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 20,
236 |    "metadata": {},
237 |    "outputs": [
238 |     {
239 |      "data": {
240 |       "text/plain": [
241 |        "price   -0.03512\n",
242 |        "dtype: float64"
243 |       ]
244 |      },
245 |      "execution_count": 20,
246 |      "metadata": {},
247 |      "output_type": "execute_result"
248 |     }
249 |    ],
250 |    "source": [
251 |     "logReturn.mean()"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "markdown",
256 |    "metadata": {},
257 |    "source": [
258 |     "# <font face=\"gotham\" color=\"purple\"> Relations of Log Return and Simple Return</font>"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "markdown",
263 |    "metadata": {},
264 |    "source": [
265 |     "The log return is calculated by\n",
266 |     "\n",
267 |     "$$\n",
268 |     "r_t = \\ln \\frac{P_t}{P_{t-1}} = p_t-p_{t-1}\n",
269 |     "$$\n",
270 |     "\n",
271 |     "where $p_t$ is $\\ln P_t$."
272 |    ]
273 |   },
274 |   {
275 |    "cell_type": "markdown",
276 |    "metadata": {},
277 |    "source": [
278 |     "We take the exponential to recover single return $R_t$\n",
279 |     "\n",
280 |     "$$\n",
281 |     "e^{r_t} = \\frac{P_t}{P_{t-1}} = 1+R_{t} \\quad \\Longrightarrow \\quad R_t = e^{r_t}-1\n",
282 |     "$$"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 29,
288 |    "metadata": {},
289 |    "outputs": [],
290 |    "source": [
291 |     "R = np.linspace(-1,1,100)\n",
292 |     "r = np.log(R+1)\n"
293 |    ]
294 |   },
295 |   {
296 |    "cell_type": "code",
297 |    "execution_count": 47,
298 |    "metadata": {},
299 |    "outputs": [
300 |     {
301 |      "data": {
302 |       "image/png": 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\n",
303 |       "text/plain": [
304 |        "<Figure size 720x720 with 1 Axes>"
305 |       ]
306 |      },
307 |      "metadata": {
308 |       "needs_background": "light"
309 |      },
310 |      "output_type": "display_data"
311 |     }
312 |    ],
313 |    "source": [
314 |     "fig, ax = plt.subplots(figsize = (10, 10))\n",
315 |     "ax.plot(R, r, label = '$r_t = \\ln(R_t+1)$')\n",
316 |     "ax.plot(R, R, label = '$y =x$', ls = '--')\n",
317 |     "ax.set_xlabel('$R_t$', size = 18)\n",
318 |     "ax.set_ylabel('$r_t$', size = 18)\n",
319 |     "ax.scatter(0, 0)\n",
320 |     "ax.set_xlim([-.7, .7])\n",
321 |     "ax.set_ylim([-1.3, .7])\n",
322 |     "ax.legend(fontsize = 14)\n",
323 |     "ax.grid()"
324 |    ]
325 |   },
326 |   {
327 |    "cell_type": "markdown",
328 |    "metadata": {},
329 |    "source": [
330 |     "As we can see if $R_t$ is small enough, it can be approximated by $r_t$."
331 |    ]
332 |   }
333 |  ],
334 |  "metadata": {
335 |   "kernelspec": {
336 |    "display_name": "Python 3",
337 |    "language": "python",
338 |    "name": "python3"
339 |   },
340 |   "language_info": {
341 |    "codemirror_mode": {
342 |     "name": "ipython",
343 |     "version": 3
344 |    },
345 |    "file_extension": ".py",
346 |    "mimetype": "text/x-python",
347 |    "name": "python",
348 |    "nbconvert_exporter": "python",
349 |    "pygments_lexer": "ipython3",
350 |    "version": "3.7.4"
351 |   },
352 |   "toc": {
353 |    "base_numbering": 1,
354 |    "nav_menu": {},
355 |    "number_sections": true,
356 |    "sideBar": true,
357 |    "skip_h1_title": false,
358 |    "title_cell": "Table of Contents",
359 |    "title_sidebar": "Contents",
360 |    "toc_cell": true,
361 |    "toc_position": {},
362 |    "toc_section_display": true,
363 |    "toc_window_display": false
364 |   }
365 |  },
366 |  "nbformat": 4,
367 |  "nbformat_minor": 2
368 | }
369 | 


--------------------------------------------------------------------------------
/Bureau of Economic Analysis (BEA).ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "toc": true
  7 |    },
  8 |    "source": [
  9 |     "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
 10 |     "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 4,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "name": "stdout",
 20 |      "output_type": "stream",
 21 |      "text": [
 22 |       "Collecting pybea\n",
 23 |       "  Using cached https://files.pythonhosted.org/packages/89/74/f4639713b2ec002435847498bc21bf011e99c75f499157369cc310252790/pybea-0.7.0a0-py2.py3-none-any.whl\n",
 24 |       "Installing collected packages: pybea\n",
 25 |       "Successfully installed pybea-0.7.0a0\n",
 26 |       "Note: you may need to restart the kernel to use updated packages.\n"
 27 |      ]
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "pip install pybea"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 1,
 37 |    "metadata": {},
 38 |    "outputs": [],
 39 |    "source": [
 40 |     "import pybea"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 2,
 46 |    "metadata": {},
 47 |    "outputs": [],
 48 |    "source": [
 49 |     "pybea.get_data_set_list?"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 3,
 55 |    "metadata": {},
 56 |    "outputs": [],
 57 |    "source": [
 58 |     "# replace this with your BEA data API key!\n",
 59 |     "USER_ID = '8D6E4F9E-A6D9-4B12-B240-6704A2A4B454'"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "code",
 64 |    "execution_count": 4,
 65 |    "metadata": {},
 66 |    "outputs": [
 67 |     {
 68 |      "data": {
 69 |       "text/html": [
 70 |        "<div>\n",
 71 |        "<style scoped>\n",
 72 |        "    .dataframe tbody tr th:only-of-type {\n",
 73 |        "        vertical-align: middle;\n",
 74 |        "    }\n",
 75 |        "\n",
 76 |        "    .dataframe tbody tr th {\n",
 77 |        "        vertical-align: top;\n",
 78 |        "    }\n",
 79 |        "\n",
 80 |        "    .dataframe thead th {\n",
 81 |        "        text-align: right;\n",
 82 |        "    }\n",
 83 |        "</style>\n",
 84 |        "<table border=\"1\" class=\"dataframe\">\n",
 85 |        "  <thead>\n",
 86 |        "    <tr style=\"text-align: right;\">\n",
 87 |        "      <th></th>\n",
 88 |        "      <th>DatasetName</th>\n",
 89 |        "      <th>DatasetDescription</th>\n",
 90 |        "    </tr>\n",
 91 |        "  </thead>\n",
 92 |        "  <tbody>\n",
 93 |        "    <tr>\n",
 94 |        "      <td>0</td>\n",
 95 |        "      <td>NIPA</td>\n",
 96 |        "      <td>Standard NIPA tables</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <td>1</td>\n",
100 |        "      <td>NIUnderlyingDetail</td>\n",
101 |        "      <td>Standard NI underlying detail tables</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <td>2</td>\n",
105 |        "      <td>MNE</td>\n",
106 |        "      <td>Multinational Enterprises</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <td>3</td>\n",
110 |        "      <td>FixedAssets</td>\n",
111 |        "      <td>Standard Fixed Assets tables</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <td>4</td>\n",
115 |        "      <td>ITA</td>\n",
116 |        "      <td>International Transactions Accounts</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <td>5</td>\n",
120 |        "      <td>IIP</td>\n",
121 |        "      <td>International Investment Position</td>\n",
122 |        "    </tr>\n",
123 |        "    <tr>\n",
124 |        "      <td>6</td>\n",
125 |        "      <td>GDPbyIndustry</td>\n",
126 |        "      <td>GDP by Industry</td>\n",
127 |        "    </tr>\n",
128 |        "    <tr>\n",
129 |        "      <td>7</td>\n",
130 |        "      <td>InputOutput</td>\n",
131 |        "      <td>Input-Output Data</td>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <td>8</td>\n",
135 |        "      <td>UnderlyingGDPbyIndustry</td>\n",
136 |        "      <td>Underlying GDP by Industry</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <td>9</td>\n",
140 |        "      <td>IntlServTrade</td>\n",
141 |        "      <td>International Services Trade</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <td>10</td>\n",
145 |        "      <td>Regional</td>\n",
146 |        "      <td>Regional data sets</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <td>11</td>\n",
150 |        "      <td>APIDatasetMetaData</td>\n",
151 |        "      <td>Metadata about other API datasets</td>\n",
152 |        "    </tr>\n",
153 |        "  </tbody>\n",
154 |        "</table>\n",
155 |        "</div>"
156 |       ],
157 |       "text/plain": [
158 |        "                DatasetName                    DatasetDescription\n",
159 |        "0                      NIPA                  Standard NIPA tables\n",
160 |        "1        NIUnderlyingDetail  Standard NI underlying detail tables\n",
161 |        "2                       MNE             Multinational Enterprises\n",
162 |        "3               FixedAssets          Standard Fixed Assets tables\n",
163 |        "4                       ITA   International Transactions Accounts\n",
164 |        "5                       IIP     International Investment Position\n",
165 |        "6             GDPbyIndustry                       GDP by Industry\n",
166 |        "7               InputOutput                     Input-Output Data\n",
167 |        "8   UnderlyingGDPbyIndustry            Underlying GDP by Industry\n",
168 |        "9             IntlServTrade          International Services Trade\n",
169 |        "10                 Regional                    Regional data sets\n",
170 |        "11       APIDatasetMetaData     Metadata about other API datasets"
171 |       ]
172 |      },
173 |      "execution_count": 4,
174 |      "metadata": {},
175 |      "output_type": "execute_result"
176 |     }
177 |    ],
178 |    "source": [
179 |     "# access the BEA data API...\n",
180 |     "available_datasets = pybea.get_data_set_list(USER_ID)\n",
181 |     "available_datasets"
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": 5,
187 |    "metadata": {},
188 |    "outputs": [
189 |     {
190 |      "data": {
191 |       "text/html": [
192 |        "<div>\n",
193 |        "<style scoped>\n",
194 |        "    .dataframe tbody tr th:only-of-type {\n",
195 |        "        vertical-align: middle;\n",
196 |        "    }\n",
197 |        "\n",
198 |        "    .dataframe tbody tr th {\n",
199 |        "        vertical-align: top;\n",
200 |        "    }\n",
201 |        "\n",
202 |        "    .dataframe thead th {\n",
203 |        "        text-align: right;\n",
204 |        "    }\n",
205 |        "</style>\n",
206 |        "<table border=\"1\" class=\"dataframe\">\n",
207 |        "  <thead>\n",
208 |        "    <tr style=\"text-align: right;\">\n",
209 |        "      <th></th>\n",
210 |        "      <th>DatasetName</th>\n",
211 |        "      <th>DatasetDescription</th>\n",
212 |        "    </tr>\n",
213 |        "  </thead>\n",
214 |        "  <tbody>\n",
215 |        "    <tr>\n",
216 |        "      <td>0</td>\n",
217 |        "      <td>NIPA</td>\n",
218 |        "      <td>Standard NIPA tables</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <td>1</td>\n",
222 |        "      <td>NIUnderlyingDetail</td>\n",
223 |        "      <td>Standard NI underlying detail tables</td>\n",
224 |        "    </tr>\n",
225 |        "    <tr>\n",
226 |        "      <td>2</td>\n",
227 |        "      <td>MNE</td>\n",
228 |        "      <td>Multinational Enterprises</td>\n",
229 |        "    </tr>\n",
230 |        "    <tr>\n",
231 |        "      <td>3</td>\n",
232 |        "      <td>FixedAssets</td>\n",
233 |        "      <td>Standard Fixed Assets tables</td>\n",
234 |        "    </tr>\n",
235 |        "    <tr>\n",
236 |        "      <td>4</td>\n",
237 |        "      <td>ITA</td>\n",
238 |        "      <td>International Transactions Accounts</td>\n",
239 |        "    </tr>\n",
240 |        "    <tr>\n",
241 |        "      <td>5</td>\n",
242 |        "      <td>IIP</td>\n",
243 |        "      <td>International Investment Position</td>\n",
244 |        "    </tr>\n",
245 |        "    <tr>\n",
246 |        "      <td>6</td>\n",
247 |        "      <td>GDPbyIndustry</td>\n",
248 |        "      <td>GDP by Industry</td>\n",
249 |        "    </tr>\n",
250 |        "    <tr>\n",
251 |        "      <td>7</td>\n",
252 |        "      <td>InputOutput</td>\n",
253 |        "      <td>Input-Output Data</td>\n",
254 |        "    </tr>\n",
255 |        "    <tr>\n",
256 |        "      <td>8</td>\n",
257 |        "      <td>UnderlyingGDPbyIndustry</td>\n",
258 |        "      <td>Underlying GDP by Industry</td>\n",
259 |        "    </tr>\n",
260 |        "    <tr>\n",
261 |        "      <td>9</td>\n",
262 |        "      <td>IntlServTrade</td>\n",
263 |        "      <td>International Services Trade</td>\n",
264 |        "    </tr>\n",
265 |        "    <tr>\n",
266 |        "      <td>10</td>\n",
267 |        "      <td>Regional</td>\n",
268 |        "      <td>Regional data sets</td>\n",
269 |        "    </tr>\n",
270 |        "    <tr>\n",
271 |        "      <td>11</td>\n",
272 |        "      <td>APIDatasetMetaData</td>\n",
273 |        "      <td>Metadata about other API datasets</td>\n",
274 |        "    </tr>\n",
275 |        "  </tbody>\n",
276 |        "</table>\n",
277 |        "</div>"
278 |       ],
279 |       "text/plain": [
280 |        "                DatasetName                    DatasetDescription\n",
281 |        "0                      NIPA                  Standard NIPA tables\n",
282 |        "1        NIUnderlyingDetail  Standard NI underlying detail tables\n",
283 |        "2                       MNE             Multinational Enterprises\n",
284 |        "3               FixedAssets          Standard Fixed Assets tables\n",
285 |        "4                       ITA   International Transactions Accounts\n",
286 |        "5                       IIP     International Investment Position\n",
287 |        "6             GDPbyIndustry                       GDP by Industry\n",
288 |        "7               InputOutput                     Input-Output Data\n",
289 |        "8   UnderlyingGDPbyIndustry            Underlying GDP by Industry\n",
290 |        "9             IntlServTrade          International Services Trade\n",
291 |        "10                 Regional                    Regional data sets\n",
292 |        "11       APIDatasetMetaData     Metadata about other API datasets"
293 |       ]
294 |      },
295 |      "execution_count": 5,
296 |      "metadata": {},
297 |      "output_type": "execute_result"
298 |     }
299 |    ],
300 |    "source": [
301 |     "request = pybea.api.DataSetListRequest(USER_ID,\n",
302 |     "                                       ResultFormat=\"JSON\")\n",
303 |     "request.data_set_list"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": 9,
309 |    "metadata": {},
310 |    "outputs": [
311 |     {
312 |      "data": {
313 |       "text/html": [
314 |        "<div>\n",
315 |        "<style scoped>\n",
316 |        "    .dataframe tbody tr th:only-of-type {\n",
317 |        "        vertical-align: middle;\n",
318 |        "    }\n",
319 |        "\n",
320 |        "    .dataframe tbody tr th {\n",
321 |        "        vertical-align: top;\n",
322 |        "    }\n",
323 |        "\n",
324 |        "    .dataframe thead th {\n",
325 |        "        text-align: right;\n",
326 |        "    }\n",
327 |        "</style>\n",
328 |        "<table border=\"1\" class=\"dataframe\">\n",
329 |        "  <thead>\n",
330 |        "    <tr style=\"text-align: right;\">\n",
331 |        "      <th></th>\n",
332 |        "      <th>MultipleAcceptedFlag</th>\n",
333 |        "      <th>ParameterDataType</th>\n",
334 |        "      <th>ParameterDefaultValue</th>\n",
335 |        "      <th>ParameterDescription</th>\n",
336 |        "      <th>ParameterIsRequiredFlag</th>\n",
337 |        "      <th>ParameterName</th>\n",
338 |        "    </tr>\n",
339 |        "  </thead>\n",
340 |        "  <tbody>\n",
341 |        "    <tr>\n",
342 |        "      <td>0</td>\n",
343 |        "      <td>1</td>\n",
344 |        "      <td>string</td>\n",
345 |        "      <td></td>\n",
346 |        "      <td>A - Annual, Q-Quarterly, M-Monthly</td>\n",
347 |        "      <td>1</td>\n",
348 |        "      <td>Frequency</td>\n",
349 |        "    </tr>\n",
350 |        "    <tr>\n",
351 |        "      <td>1</td>\n",
352 |        "      <td>0</td>\n",
353 |        "      <td>string</td>\n",
354 |        "      <td>N</td>\n",
355 |        "      <td>A flag indicating that million-dollar data sho...</td>\n",
356 |        "      <td>0</td>\n",
357 |        "      <td>ShowMillions</td>\n",
358 |        "    </tr>\n",
359 |        "    <tr>\n",
360 |        "      <td>2</td>\n",
361 |        "      <td>0</td>\n",
362 |        "      <td>integer</td>\n",
363 |        "      <td>None</td>\n",
364 |        "      <td>The standard NIPA table identifier</td>\n",
365 |        "      <td>0</td>\n",
366 |        "      <td>TableID</td>\n",
367 |        "    </tr>\n",
368 |        "    <tr>\n",
369 |        "      <td>3</td>\n",
370 |        "      <td>0</td>\n",
371 |        "      <td>string</td>\n",
372 |        "      <td>None</td>\n",
373 |        "      <td>The new NIPA table identifier</td>\n",
374 |        "      <td>0</td>\n",
375 |        "      <td>TableName</td>\n",
376 |        "    </tr>\n",
377 |        "    <tr>\n",
378 |        "      <td>4</td>\n",
379 |        "      <td>1</td>\n",
380 |        "      <td>integer</td>\n",
381 |        "      <td></td>\n",
382 |        "      <td>List of year(s) of data to retrieve (X for All)</td>\n",
383 |        "      <td>1</td>\n",
384 |        "      <td>Year</td>\n",
385 |        "    </tr>\n",
386 |        "  </tbody>\n",
387 |        "</table>\n",
388 |        "</div>"
389 |       ],
390 |       "text/plain": [
391 |        "   MultipleAcceptedFlag ParameterDataType ParameterDefaultValue  \\\n",
392 |        "0                     1            string                         \n",
393 |        "1                     0            string                     N   \n",
394 |        "2                     0           integer                  None   \n",
395 |        "3                     0            string                  None   \n",
396 |        "4                     1           integer                         \n",
397 |        "\n",
398 |        "                                ParameterDescription  ParameterIsRequiredFlag  \\\n",
399 |        "0                 A - Annual, Q-Quarterly, M-Monthly                        1   \n",
400 |        "1  A flag indicating that million-dollar data sho...                        0   \n",
401 |        "2                 The standard NIPA table identifier                        0   \n",
402 |        "3                      The new NIPA table identifier                        0   \n",
403 |        "4    List of year(s) of data to retrieve (X for All)                        1   \n",
404 |        "\n",
405 |        "  ParameterName  \n",
406 |        "0     Frequency  \n",
407 |        "1  ShowMillions  \n",
408 |        "2       TableID  \n",
409 |        "3     TableName  \n",
410 |        "4          Year  "
411 |       ]
412 |      },
413 |      "execution_count": 9,
414 |      "metadata": {},
415 |      "output_type": "execute_result"
416 |     }
417 |    ],
418 |    "source": [
419 |     "NIPA_params = pybea.get_parameter_list(USER_ID, DataSetName='NIPA', ResultFormat='XML')\n",
420 |     "NIPA_params"
421 |    ]
422 |   },
423 |   {
424 |    "cell_type": "code",
425 |    "execution_count": 10,
426 |    "metadata": {},
427 |    "outputs": [
428 |     {
429 |      "data": {
430 |       "text/html": [
431 |        "<div>\n",
432 |        "<style scoped>\n",
433 |        "    .dataframe tbody tr th:only-of-type {\n",
434 |        "        vertical-align: middle;\n",
435 |        "    }\n",
436 |        "\n",
437 |        "    .dataframe tbody tr th {\n",
438 |        "        vertical-align: top;\n",
439 |        "    }\n",
440 |        "\n",
441 |        "    .dataframe thead th {\n",
442 |        "        text-align: right;\n",
443 |        "    }\n",
444 |        "</style>\n",
445 |        "<table border=\"1\" class=\"dataframe\">\n",
446 |        "  <thead>\n",
447 |        "    <tr style=\"text-align: right;\">\n",
448 |        "      <th></th>\n",
449 |        "      <th>ParameterName</th>\n",
450 |        "      <th>ParameterDataType</th>\n",
451 |        "      <th>ParameterDescription</th>\n",
452 |        "      <th>ParameterIsRequiredFlag</th>\n",
453 |        "      <th>ParameterDefaultValue</th>\n",
454 |        "      <th>MultipleAcceptedFlag</th>\n",
455 |        "      <th>AllValue</th>\n",
456 |        "    </tr>\n",
457 |        "  </thead>\n",
458 |        "  <tbody>\n",
459 |        "    <tr>\n",
460 |        "      <td>0</td>\n",
461 |        "      <td>Frequency</td>\n",
462 |        "      <td>string</td>\n",
463 |        "      <td>A - Annual, Q-Quarterly, M-Monthly</td>\n",
464 |        "      <td>1</td>\n",
465 |        "      <td></td>\n",
466 |        "      <td>1</td>\n",
467 |        "      <td></td>\n",
468 |        "    </tr>\n",
469 |        "    <tr>\n",
470 |        "      <td>1</td>\n",
471 |        "      <td>ShowMillions</td>\n",
472 |        "      <td>string</td>\n",
473 |        "      <td>A flag indicating that million-dollar data sho...</td>\n",
474 |        "      <td>0</td>\n",
475 |        "      <td>N</td>\n",
476 |        "      <td>0</td>\n",
477 |        "      <td></td>\n",
478 |        "    </tr>\n",
479 |        "    <tr>\n",
480 |        "      <td>2</td>\n",
481 |        "      <td>TableID</td>\n",
482 |        "      <td>integer</td>\n",
483 |        "      <td>The standard NIPA table identifier</td>\n",
484 |        "      <td>0</td>\n",
485 |        "      <td>NaN</td>\n",
486 |        "      <td>0</td>\n",
487 |        "      <td></td>\n",
488 |        "    </tr>\n",
489 |        "    <tr>\n",
490 |        "      <td>3</td>\n",
491 |        "      <td>TableName</td>\n",
492 |        "      <td>string</td>\n",
493 |        "      <td>The new NIPA table identifier</td>\n",
494 |        "      <td>0</td>\n",
495 |        "      <td>NaN</td>\n",
496 |        "      <td>0</td>\n",
497 |        "      <td></td>\n",
498 |        "    </tr>\n",
499 |        "    <tr>\n",
500 |        "      <td>4</td>\n",
501 |        "      <td>Year</td>\n",
502 |        "      <td>integer</td>\n",
503 |        "      <td>List of year(s) of data to retrieve (X for All)</td>\n",
504 |        "      <td>1</td>\n",
505 |        "      <td></td>\n",
506 |        "      <td>1</td>\n",
507 |        "      <td>X</td>\n",
508 |        "    </tr>\n",
509 |        "  </tbody>\n",
510 |        "</table>\n",
511 |        "</div>"
512 |       ],
513 |       "text/plain": [
514 |        "  ParameterName ParameterDataType  \\\n",
515 |        "0     Frequency            string   \n",
516 |        "1  ShowMillions            string   \n",
517 |        "2       TableID           integer   \n",
518 |        "3     TableName            string   \n",
519 |        "4          Year           integer   \n",
520 |        "\n",
521 |        "                                ParameterDescription  ParameterIsRequiredFlag  \\\n",
522 |        "0                 A - Annual, Q-Quarterly, M-Monthly                        1   \n",
523 |        "1  A flag indicating that million-dollar data sho...                        0   \n",
524 |        "2                 The standard NIPA table identifier                        0   \n",
525 |        "3                      The new NIPA table identifier                        0   \n",
526 |        "4    List of year(s) of data to retrieve (X for All)                        1   \n",
527 |        "\n",
528 |        "  ParameterDefaultValue  MultipleAcceptedFlag AllValue  \n",
529 |        "0                                           1           \n",
530 |        "1                     N                     0           \n",
531 |        "2                   NaN                     0           \n",
532 |        "3                   NaN                     0           \n",
533 |        "4                                           1        X  "
534 |       ]
535 |      },
536 |      "execution_count": 10,
537 |      "metadata": {},
538 |      "output_type": "execute_result"
539 |     }
540 |    ],
541 |    "source": [
542 |     "request = pybea.api.ParameterListRequest(USER_ID,\n",
543 |     "                                         DataSetName='NIPA',\n",
544 |     "                                         ResultFormat='JSON')\n",
545 |     "request.parameter_list"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "code",
550 |    "execution_count": 14,
551 |    "metadata": {},
552 |    "outputs": [],
553 |    "source": [
554 |     "data = pybea.get_data(USER_ID,\n",
555 |     "                      DataSetName='NIPA',\n",
556 |     "                      TableName='T10101',\n",
557 |     "                      Frequency=['A', 'Q'],\n",
558 |     "                      Year='ALL',\n",
559 |     "                      ResultFormat=\"XML\"\n",
560 |     "                      )"
561 |    ]
562 |   },
563 |   {
564 |    "cell_type": "code",
565 |    "execution_count": 15,
566 |    "metadata": {},
567 |    "outputs": [
568 |     {
569 |      "data": {
570 |       "text/html": [
571 |        "<div>\n",
572 |        "<style scoped>\n",
573 |        "    .dataframe tbody tr th:only-of-type {\n",
574 |        "        vertical-align: middle;\n",
575 |        "    }\n",
576 |        "\n",
577 |        "    .dataframe tbody tr th {\n",
578 |        "        vertical-align: top;\n",
579 |        "    }\n",
580 |        "\n",
581 |        "    .dataframe thead th {\n",
582 |        "        text-align: right;\n",
583 |        "    }\n",
584 |        "</style>\n",
585 |        "<table border=\"1\" class=\"dataframe\">\n",
586 |        "  <thead>\n",
587 |        "    <tr style=\"text-align: right;\">\n",
588 |        "      <th></th>\n",
589 |        "      <th>TableName</th>\n",
590 |        "      <th>SeriesCode</th>\n",
591 |        "      <th>LineNumber</th>\n",
592 |        "      <th>LineDescription</th>\n",
593 |        "      <th>TimePeriod</th>\n",
594 |        "      <th>CL_UNIT</th>\n",
595 |        "      <th>UNIT_MULT</th>\n",
596 |        "      <th>METRIC_NAME</th>\n",
597 |        "      <th>DataValue</th>\n",
598 |        "    </tr>\n",
599 |        "  </thead>\n",
600 |        "  <tbody>\n",
601 |        "    <tr>\n",
602 |        "      <td>0</td>\n",
603 |        "      <td>T10101</td>\n",
604 |        "      <td>A191RL</td>\n",
605 |        "      <td>1</td>\n",
606 |        "      <td>Gross domestic product</td>\n",
607 |        "      <td>1947Q2</td>\n",
608 |        "      <td>Percent change, annual rate</td>\n",
609 |        "      <td>0</td>\n",
610 |        "      <td>Fisher Quantity Index</td>\n",
611 |        "      <td>-1.0</td>\n",
612 |        "    </tr>\n",
613 |        "    <tr>\n",
614 |        "      <td>1</td>\n",
615 |        "      <td>T10101</td>\n",
616 |        "      <td>A191RL</td>\n",
617 |        "      <td>1</td>\n",
618 |        "      <td>Gross domestic product</td>\n",
619 |        "      <td>1947Q3</td>\n",
620 |        "      <td>Percent change, annual rate</td>\n",
621 |        "      <td>0</td>\n",
622 |        "      <td>Fisher Quantity Index</td>\n",
623 |        "      <td>-0.8</td>\n",
624 |        "    </tr>\n",
625 |        "    <tr>\n",
626 |        "      <td>2</td>\n",
627 |        "      <td>T10101</td>\n",
628 |        "      <td>A191RL</td>\n",
629 |        "      <td>1</td>\n",
630 |        "      <td>Gross domestic product</td>\n",
631 |        "      <td>1947Q4</td>\n",
632 |        "      <td>Percent change, annual rate</td>\n",
633 |        "      <td>0</td>\n",
634 |        "      <td>Fisher Quantity Index</td>\n",
635 |        "      <td>6.4</td>\n",
636 |        "    </tr>\n",
637 |        "    <tr>\n",
638 |        "      <td>3</td>\n",
639 |        "      <td>T10101</td>\n",
640 |        "      <td>A191RL</td>\n",
641 |        "      <td>1</td>\n",
642 |        "      <td>Gross domestic product</td>\n",
643 |        "      <td>1948Q1</td>\n",
644 |        "      <td>Percent change, annual rate</td>\n",
645 |        "      <td>0</td>\n",
646 |        "      <td>Fisher Quantity Index</td>\n",
647 |        "      <td>6.2</td>\n",
648 |        "    </tr>\n",
649 |        "    <tr>\n",
650 |        "      <td>4</td>\n",
651 |        "      <td>T10101</td>\n",
652 |        "      <td>A191RL</td>\n",
653 |        "      <td>1</td>\n",
654 |        "      <td>Gross domestic product</td>\n",
655 |        "      <td>1948Q2</td>\n",
656 |        "      <td>Percent change, annual rate</td>\n",
657 |        "      <td>0</td>\n",
658 |        "      <td>Fisher Quantity Index</td>\n",
659 |        "      <td>6.8</td>\n",
660 |        "    </tr>\n",
661 |        "    <tr>\n",
662 |        "      <td>...</td>\n",
663 |        "      <td>...</td>\n",
664 |        "      <td>...</td>\n",
665 |        "      <td>...</td>\n",
666 |        "      <td>...</td>\n",
667 |        "      <td>...</td>\n",
668 |        "      <td>...</td>\n",
669 |        "      <td>...</td>\n",
670 |        "      <td>...</td>\n",
671 |        "      <td>...</td>\n",
672 |        "    </tr>\n",
673 |        "    <tr>\n",
674 |        "      <td>7295</td>\n",
675 |        "      <td>T10101</td>\n",
676 |        "      <td>A191RP</td>\n",
677 |        "      <td>27</td>\n",
678 |        "      <td>Gross domestic product, current dollars</td>\n",
679 |        "      <td>2019Q1</td>\n",
680 |        "      <td>Percent change, annual rate</td>\n",
681 |        "      <td>0</td>\n",
682 |        "      <td>Current Dollars</td>\n",
683 |        "      <td>3.9</td>\n",
684 |        "    </tr>\n",
685 |        "    <tr>\n",
686 |        "      <td>7296</td>\n",
687 |        "      <td>T10101</td>\n",
688 |        "      <td>A191RP</td>\n",
689 |        "      <td>27</td>\n",
690 |        "      <td>Gross domestic product, current dollars</td>\n",
691 |        "      <td>2019Q2</td>\n",
692 |        "      <td>Percent change, annual rate</td>\n",
693 |        "      <td>0</td>\n",
694 |        "      <td>Current Dollars</td>\n",
695 |        "      <td>4.7</td>\n",
696 |        "    </tr>\n",
697 |        "    <tr>\n",
698 |        "      <td>7297</td>\n",
699 |        "      <td>T10101</td>\n",
700 |        "      <td>A191RP</td>\n",
701 |        "      <td>27</td>\n",
702 |        "      <td>Gross domestic product, current dollars</td>\n",
703 |        "      <td>2019Q3</td>\n",
704 |        "      <td>Percent change, annual rate</td>\n",
705 |        "      <td>0</td>\n",
706 |        "      <td>Current Dollars</td>\n",
707 |        "      <td>3.8</td>\n",
708 |        "    </tr>\n",
709 |        "    <tr>\n",
710 |        "      <td>7298</td>\n",
711 |        "      <td>T10101</td>\n",
712 |        "      <td>A191RP</td>\n",
713 |        "      <td>27</td>\n",
714 |        "      <td>Gross domestic product, current dollars</td>\n",
715 |        "      <td>2019Q4</td>\n",
716 |        "      <td>Percent change, annual rate</td>\n",
717 |        "      <td>0</td>\n",
718 |        "      <td>Current Dollars</td>\n",
719 |        "      <td>3.5</td>\n",
720 |        "    </tr>\n",
721 |        "    <tr>\n",
722 |        "      <td>7299</td>\n",
723 |        "      <td>T10101</td>\n",
724 |        "      <td>A191RP</td>\n",
725 |        "      <td>27</td>\n",
726 |        "      <td>Gross domestic product, current dollars</td>\n",
727 |        "      <td>2020Q1</td>\n",
728 |        "      <td>Percent change, annual rate</td>\n",
729 |        "      <td>0</td>\n",
730 |        "      <td>Current Dollars</td>\n",
731 |        "      <td>-3.4</td>\n",
732 |        "    </tr>\n",
733 |        "  </tbody>\n",
734 |        "</table>\n",
735 |        "<p>7300 rows × 9 columns</p>\n",
736 |        "</div>"
737 |       ],
738 |       "text/plain": [
739 |        "     TableName SeriesCode  LineNumber  \\\n",
740 |        "0       T10101     A191RL           1   \n",
741 |        "1       T10101     A191RL           1   \n",
742 |        "2       T10101     A191RL           1   \n",
743 |        "3       T10101     A191RL           1   \n",
744 |        "4       T10101     A191RL           1   \n",
745 |        "...        ...        ...         ...   \n",
746 |        "7295    T10101     A191RP          27   \n",
747 |        "7296    T10101     A191RP          27   \n",
748 |        "7297    T10101     A191RP          27   \n",
749 |        "7298    T10101     A191RP          27   \n",
750 |        "7299    T10101     A191RP          27   \n",
751 |        "\n",
752 |        "                              LineDescription TimePeriod  \\\n",
753 |        "0                      Gross domestic product     1947Q2   \n",
754 |        "1                      Gross domestic product     1947Q3   \n",
755 |        "2                      Gross domestic product     1947Q4   \n",
756 |        "3                      Gross domestic product     1948Q1   \n",
757 |        "4                      Gross domestic product     1948Q2   \n",
758 |        "...                                       ...        ...   \n",
759 |        "7295  Gross domestic product, current dollars     2019Q1   \n",
760 |        "7296  Gross domestic product, current dollars     2019Q2   \n",
761 |        "7297  Gross domestic product, current dollars     2019Q3   \n",
762 |        "7298  Gross domestic product, current dollars     2019Q4   \n",
763 |        "7299  Gross domestic product, current dollars     2020Q1   \n",
764 |        "\n",
765 |        "                          CL_UNIT  UNIT_MULT            METRIC_NAME  DataValue  \n",
766 |        "0     Percent change, annual rate          0  Fisher Quantity Index       -1.0  \n",
767 |        "1     Percent change, annual rate          0  Fisher Quantity Index       -0.8  \n",
768 |        "2     Percent change, annual rate          0  Fisher Quantity Index        6.4  \n",
769 |        "3     Percent change, annual rate          0  Fisher Quantity Index        6.2  \n",
770 |        "4     Percent change, annual rate          0  Fisher Quantity Index        6.8  \n",
771 |        "...                           ...        ...                    ...        ...  \n",
772 |        "7295  Percent change, annual rate          0        Current Dollars        3.9  \n",
773 |        "7296  Percent change, annual rate          0        Current Dollars        4.7  \n",
774 |        "7297  Percent change, annual rate          0        Current Dollars        3.8  \n",
775 |        "7298  Percent change, annual rate          0        Current Dollars        3.5  \n",
776 |        "7299  Percent change, annual rate          0        Current Dollars       -3.4  \n",
777 |        "\n",
778 |        "[7300 rows x 9 columns]"
779 |       ]
780 |      },
781 |      "execution_count": 15,
782 |      "metadata": {},
783 |      "output_type": "execute_result"
784 |     }
785 |    ],
786 |    "source": [
787 |     "data"
788 |    ]
789 |   },
790 |   {
791 |    "cell_type": "code",
792 |    "execution_count": 20,
793 |    "metadata": {},
794 |    "outputs": [
795 |     {
796 |      "data": {
797 |       "text/plain": [
798 |        "<matplotlib.axes._subplots.AxesSubplot at 0xacd7448>"
799 |       ]
800 |      },
801 |      "execution_count": 20,
802 |      "metadata": {},
803 |      "output_type": "execute_result"
804 |     },
805 |     {
806 |      "data": {
807 |       "image/png": 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\n",
808 |       "text/plain": [
809 |        "<Figure size 432x288 with 1 Axes>"
810 |       ]
811 |      },
812 |      "metadata": {
813 |       "needs_background": "light"
814 |      },
815 |      "output_type": "display_data"
816 |     }
817 |    ],
818 |    "source": [
819 |     "data['DataValue'].plot()"
820 |    ]
821 |   },
822 |   {
823 |    "cell_type": "code",
824 |    "execution_count": 26,
825 |    "metadata": {},
826 |    "outputs": [
827 |     {
828 |      "data": {
829 |       "text/plain": [
830 |        "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000000000AD68C88>"
831 |       ]
832 |      },
833 |      "execution_count": 26,
834 |      "metadata": {},
835 |      "output_type": "execute_result"
836 |     }
837 |    ],
838 |    "source": [
839 |     "data.groupby('METRIC_NAME')"
840 |    ]
841 |   },
842 |   {
843 |    "cell_type": "code",
844 |    "execution_count": null,
845 |    "metadata": {},
846 |    "outputs": [],
847 |    "source": []
848 |   }
849 |  ],
850 |  "metadata": {
851 |   "kernelspec": {
852 |    "display_name": "Python 3",
853 |    "language": "python",
854 |    "name": "python3"
855 |   },
856 |   "language_info": {
857 |    "codemirror_mode": {
858 |     "name": "ipython",
859 |     "version": 3
860 |    },
861 |    "file_extension": ".py",
862 |    "mimetype": "text/x-python",
863 |    "name": "python",
864 |    "nbconvert_exporter": "python",
865 |    "pygments_lexer": "ipython3",
866 |    "version": "3.7.4"
867 |   },
868 |   "toc": {
869 |    "base_numbering": 1,
870 |    "nav_menu": {},
871 |    "number_sections": true,
872 |    "sideBar": true,
873 |    "skip_h1_title": false,
874 |    "title_cell": "Table of Contents",
875 |    "title_sidebar": "Contents",
876 |    "toc_cell": true,
877 |    "toc_position": {},
878 |    "toc_section_display": true,
879 |    "toc_window_display": false
880 |   }
881 |  },
882 |  "nbformat": 4,
883 |  "nbformat_minor": 2
884 | }
885 | 


--------------------------------------------------------------------------------