diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f5bee5d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.ipynb_checkpoints/
+.DS_Store
diff --git a/README.md b/README.md
index 7553fa1..b05511d 100644
--- a/README.md
+++ b/README.md
@@ -3,10 +3,18 @@ A notebook to be used as a python demo in the Data Mindset.
This repo populates the Binder here: [](https://mybinder.org/v2/gh/DecodedCo/quandl-python-demo/master)
+Note that there are two versions of this demo.
+
+## v1.2
Intended use is running the code, then running `data.tail()` and then `data.plot()` in the two empty cells provided.
+## v1.3
+Intended use is running the code once through to see the outputs, then run it again for a different Zillow data
+key on Quandl and critiquing the lack of annotation around the methodology used for the FB Prophet model.
+
## Prep before delivery:
1. Run everything in the notebook to make sure it works
2. Restart kernel and clear all output
3. Run the first cell and then delete it
- 4. If running through Binder, and not locally, then make sure the Binder kernel is running and active 2 minutes before the demo.
+ 4. If running through Binder, and not locally, then make sure the Binder kernel is running and active 2 minutes
+ before the demo.
diff --git a/.ipynb_checkpoints/quandl-api-demo-checkpoint.ipynb b/quandl-api-demo-v-1-2.ipynb
similarity index 73%
rename from .ipynb_checkpoints/quandl-api-demo-checkpoint.ipynb
rename to quandl-api-demo-v-1-2.ipynb
index 4c3d9cc..8da0211 100644
--- a/.ipynb_checkpoints/quandl-api-demo-checkpoint.ipynb
+++ b/quandl-api-demo-v-1-2.ipynb
@@ -6,7 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#PRIOR TO DELIVERY, RUN THIS CELL AND THEN DELETE IT (esc, D, D)\n",
+ "# Configure plotting functions - delete cell before demo (ESC + D + D)\n",
"%matplotlib inline"
]
},
@@ -16,9 +16,11 @@
"metadata": {},
"outputs": [],
"source": [
+ "# Configure libraries\n",
"import quandl\n",
- "quandl.ApiConfig.api_key = \"Byjzu4U8rmR1iEhZnp7V\"\n",
- "data = quandl.get(\"LPPM/PALL\")"
+ "\n",
+ "# Configure Quandl access\n",
+ "quandl.ApiConfig.api_key = \"Byjzu4U8rmR1iEhZnp7V\""
]
},
{
@@ -26,14 +28,28 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "# Get Data\n",
+ "data = quandl.get(\"FRED/GDP\") # Chelsea, NYC, real estate inventory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Examine data"
+ ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "# Plot Data"
+ ]
}
],
"metadata": {
@@ -52,7 +68,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.0"
+ "version": "3.8.5"
},
"varInspector": {
"cols": {
diff --git a/quandl-api-demo-v-1-3.ipynb b/quandl-api-demo-v-1-3.ipynb
new file mode 100644
index 0000000..f9ee5e9
--- /dev/null
+++ b/quandl-api-demo-v-1-3.ipynb
@@ -0,0 +1,462 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Configure plotting functions\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Configure libraries\n",
+ "import quandl\n",
+ "import pandas as pd\n",
+ "from fbprophet import Prophet\n",
+ "\n",
+ "# Configure Quandl access\n",
+ "quandl.ApiConfig.api_key = \"Byjzu4U8rmR1iEhZnp7V\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get Data\n",
+ "data = quandl.get(\"BCB/UDJIAD1\") #Dow Jones Industrial Average"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(32601, 1)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Value | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2016-04-11 | \n",
+ " 17556.41 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-12 | \n",
+ " 17721.25 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-13 | \n",
+ " 17908.28 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-14 | \n",
+ " 17926.43 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-15 | \n",
+ " 17897.46 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot Data\n",
+ "data.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ds | \n",
+ " y | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 2010-01-04 | \n",
+ " 10583.96 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 2010-01-05 | \n",
+ " 10572.02 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2010-01-06 | \n",
+ " 10573.68 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2010-01-07 | \n",
+ " 10606.86 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2010-01-08 | \n",
+ " 10618.19 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ds | \n",
+ " yhat | \n",
+ " yhat_lower | \n",
+ " yhat_upper | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2672 | \n",
+ " 2019-04-11 | \n",
+ " 14803.937147 | \n",
+ " 7619.511159 | \n",
+ " 22404.650169 | \n",
+ "
\n",
+ " \n",
+ " | 2673 | \n",
+ " 2019-04-12 | \n",
+ " 14797.684532 | \n",
+ " 7491.524867 | \n",
+ " 22338.846308 | \n",
+ "
\n",
+ " \n",
+ " | 2674 | \n",
+ " 2019-04-13 | \n",
+ " 14874.251415 | \n",
+ " 7651.791458 | \n",
+ " 22450.523737 | \n",
+ "
\n",
+ " \n",
+ " | 2675 | \n",
+ " 2019-04-14 | \n",
+ " 14871.895005 | \n",
+ " 7593.364140 | \n",
+ " 22499.416664 | \n",
+ "
\n",
+ " \n",
+ " | 2676 | \n",
+ " 2019-04-15 | \n",
+ " 14784.559473 | \n",
+ " 7590.075801 | \n",
+ " 22458.762873 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot result\n",
+ "fig1 = m.plot(forecast)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ },
+ "varInspector": {
+ "cols": {
+ "lenName": 16,
+ "lenType": 16,
+ "lenVar": 40
+ },
+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
+ ],
+ "window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/quandl-api-demo-v-2-0.ipynb b/quandl-api-demo-v-2-0.ipynb
new file mode 100644
index 0000000..d8b8493
--- /dev/null
+++ b/quandl-api-demo-v-2-0.ipynb
@@ -0,0 +1,458 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## RUN THEN DELETE THIS CELL BEFORE THE DEMO\n",
+ "\n",
+ "# Import libraries\n",
+ "import matplotlib.pyplot as plt\n",
+ "import quandl\n",
+ "import pandas as pd\n",
+ "from fbprophet import Prophet\n",
+ "\n",
+ "# Configure plotting functions\n",
+ "%matplotlib inline\n",
+ "plt.rcParams['figure.figsize'] = [12, 8]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = quandl.get(\"EURONEXT/AD\", authtoken=\"Byjzu4U8rmR1iEhZnp7V\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Open | \n",
+ " High | \n",
+ " Low | \n",
+ " Last | \n",
+ " Volume | \n",
+ " Turnover | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2020-11-25 | \n",
+ " 23.34 | \n",
+ " 23.48 | \n",
+ " 23.15 | \n",
+ " 23.39 | \n",
+ " 3431815.0 | \n",
+ " 80118256.0 | \n",
+ "
\n",
+ " \n",
+ " | 2020-11-26 | \n",
+ " 23.42 | \n",
+ " 23.70 | \n",
+ " 23.40 | \n",
+ " 23.58 | \n",
+ " 3190483.0 | \n",
+ " 75242677.0 | \n",
+ "
\n",
+ " \n",
+ " | 2020-11-27 | \n",
+ " 23.51 | \n",
+ " 23.95 | \n",
+ " 23.50 | \n",
+ " 23.81 | \n",
+ " 3588911.0 | \n",
+ " 85435454.0 | \n",
+ "
\n",
+ " \n",
+ " | 2020-11-30 | \n",
+ " 23.86 | \n",
+ " 24.21 | \n",
+ " 23.69 | \n",
+ " 23.99 | \n",
+ " 6608226.0 | \n",
+ " 158566187.0 | \n",
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\n",
+ " \n",
+ " | 2020-12-01 | \n",
+ " 23.51 | \n",
+ " 23.92 | \n",
+ " 23.48 | \n",
+ " 23.75 | \n",
+ " 4407301.0 | \n",
+ " 104486874.0 | \n",
+ "
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+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data.Last.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Transform data\n",
+ "prophet_data = pd.DataFrame(data.Last)\n",
+ "prophet_data.reset_index(inplace=True)\n",
+ "prophet_data.columns = [\"ds\", \"y\"]\n",
+ "# Fit model\n",
+ "m = Prophet()\n",
+ "m.fit(prophet_data)\n",
+ "# Generate predictions\n",
+ "future = m.make_future_dataframe(periods=3*365)\n",
+ "forecast = m.predict(future)\n",
+ "# Plot prediction\n",
+ "m.plot(forecast);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dow Jones Industrial Average"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Configure Quandl access\n",
+ "quandl.ApiConfig.api_key = \"Byjzu4U8rmR1iEhZnp7V\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get Data\n",
+ "data = quandl.get(\"BCB/UDJIAD1\") #Dow Jones Industrial Average"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(32601, 1)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Value | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2016-04-11 | \n",
+ " 17556.41 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-12 | \n",
+ " 17721.25 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-13 | \n",
+ " 17908.28 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-14 | \n",
+ " 17926.43 | \n",
+ "
\n",
+ " \n",
+ " | 2016-04-15 | \n",
+ " 17897.46 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "