{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Statistical tools" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import addutils.toc ; addutils.toc.js(ipy_notebook=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this tutorial we are going to see some of the statistical and computational tools offered by `pandas`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import datetime\n", "import scipy.io\n", "import numpy as np\n", "import pandas as pd\n", "import bokeh.plotting as bk\n", "from IPython.display import display, HTML\n", "from addutils import css_notebook, side_by_side2\n", "css_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Percent change" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given a `pandas.Series` the method `pct_change` returns a new `pandas.Series` object containing percent change over a given number of periods." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = pd.Series(range(10, 18) + np.random.randn(8) / 10)\n", "\n", "pct_ch_1d = s1.pct_change() * 100\n", "pct_ch_3d = s1.pct_change(periods=3) * 100\n", "\n", "HTML(side_by_side2(s1, pct_ch_1d, pct_ch_3d))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 Covariance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given two `pandas.Series` the method `cov` computes covariance between them, excluding missing values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2000-01-03-1.348669
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2000-01-10-1.195086
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" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = pd.util.testing.makeTimeSeries(7)\n", "s2 = s1 + np.random.randn(len(s1)) / 10\n", "HTML(side_by_side2(s1, s2))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.29886732146355777" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1.cov(s2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possibile to compute pairwise covariance of a `pandas.DataFrame` columns using `pandas.DataFrame.cov` method. Here we use the module `pandas.util.testing` in order to generate random data easily:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B C D\n", "2000-01-03 1.873764 0.006606 1.031176 -1.266077\n", "2000-01-04 -0.269824 1.588418 0.675261 -0.437837\n", "2000-01-05 0.223423 0.312011 -0.882502 -0.710833\n", "2000-01-06 -1.163556 1.019891 -0.310532 -1.039061\n", "2000-01-07 0.344085 0.833134 -2.606216 -3.023771\n", " A B C D\n", "A 1.222005 -0.284295 -0.085028 -0.272069\n", "B -0.284295 1.024260 -0.125176 -0.434902\n", "C -0.085028 -0.125176 1.353324 0.492702\n", "D -0.272069 -0.434902 0.492702 1.353826\n" ] } ], "source": [ "d1 = pd.util.testing.makeTimeDataFrame()\n", "print (d1.head())\n", "print (d1.cov())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 Correlation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pandas.Series.corr` allows to compute correlation between two `pandas.Series`. By the `method` paramether it's possible to choose between:\n", "\n", "* Pearson\n", "* Kendall\n", "* Spearman" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9596913637468012" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1.corr(s2, method='pearson')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like we just seen for covariance, it is possibile to call `pandas.DataFrame.corr` to obtain pairwise correlation of columns over a `pandas.DataFrame`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABCD
A1.000000-0.254113-0.066119-0.211525
B-0.2541131.000000-0.106320-0.369322
C-0.066119-0.1063201.0000000.364000
D-0.211525-0.3693220.3640001.000000
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" ], "text/plain": [ " A B C D\n", "A 1.000000 -0.254113 -0.066119 -0.211525\n", "B -0.254113 1.000000 -0.106320 -0.369322\n", "C -0.066119 -0.106320 1.000000 0.364000\n", "D -0.211525 -0.369322 0.364000 1.000000" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 Rolling moments and Binary rolling moments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pandas` provides also a lot of methods for calculating rolling moments." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['rolling_apply',\n", " 'rolling_corr',\n", " 'rolling_count',\n", " 'rolling_cov',\n", " 'rolling_kurt',\n", " 'rolling_max',\n", " 'rolling_mean',\n", " 'rolling_median',\n", " 'rolling_min',\n", " 'rolling_quantile',\n", " 'rolling_skew',\n", " 'rolling_std',\n", " 'rolling_sum',\n", " 'rolling_var',\n", " 'rolling_window']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[n for n in dir(pd) if n.startswith('rolling')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see some examples:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cumsummaxmeanmin
2002-09-2222.55234722.55234712.2817593.117080
2002-09-2322.35043122.55234712.5770963.117080
2002-09-2421.76797922.55234712.8879443.675339
2002-09-2521.62763722.55234713.1871494.936078
2002-09-2620.77324322.55234713.4499344.936078
\n", "
" ], "text/plain": [ " cumsum max mean min\n", "2002-09-22 22.552347 22.552347 12.281759 3.117080\n", "2002-09-23 22.350431 22.552347 12.577096 3.117080\n", "2002-09-24 21.767979 22.552347 12.887944 3.675339\n", "2002-09-25 21.627637 22.552347 13.187149 4.936078\n", "2002-09-26 20.773243 22.552347 13.449934 4.936078" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s3 = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", "s3 = s3.cumsum()\n", "s3_max = s3.rolling(60).max()\n", "s3_mean = s3.rolling(60).mean()\n", "s3_min = s3.rolling(60).min()\n", "data = {'cumsum':s3, 'max':s3_max, 'mean':s3_mean, 'min':s3_min}\n", "df = pd.DataFrame(data)\n", "df.tail()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id !== undefined) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var element_id = msg.content.text.trim();\n", " Bokeh.index[element_id].model.document.clear();\n", " delete Bokeh.index[element_id];\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[0]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"
    \\n\"+\n", " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", " \"
\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"a24f60f0-6dd4-4c89-ba20-ef275a4fe98d\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " }\n", " finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"a24f60f0-6dd4-4c89-ba20-ef275a4fe98d\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a24f60f0-6dd4-4c89-ba20-ef275a4fe98d' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"a24f60f0-6dd4-4c89-ba20-ef275a4fe98d\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"a24f60f0-6dd4-4c89-ba20-ef275a4fe98d\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"a24f60f0-6dd4-4c89-ba20-ef275a4fe98d\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a24f60f0-6dd4-4c89-ba20-ef275a4fe98d' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"a24f60f0-6dd4-4c89-ba20-ef275a4fe98d\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bk.output_notebook()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
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= bk.figure(x_axis_type = \"datetime\", title = 'Rolling Correlation',\n", " plot_width=750, plot_height=400)\n", "fig.line(df2.index, df2['cumsum'], color='cadetblue', legend='Cumulative Sum')\n", "fig.line(df2.index, df2['similar'], color='mediumorchid', legend='Similar')\n", "fig.line(df2.index, df2['rolling correlation'], color='navy', legend='Rolling Corr.')\n", "fig.legend.location = \"bottom_right\"\n", "bk.show(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5 A pratical example: Return indexes and cumulative returns" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date\n", "2012-09-17 699.78\n", "2012-09-18 701.91\n", "2012-09-19 702.10\n", "2012-09-20 698.70\n", "2012-09-21 700.09\n", "Name: Adj Close, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AAPL = pd.read_csv('example_data/p03_AAPL.txt', index_col='Date', parse_dates=True)\n", "price = AAPL['Adj Close']\n", "display(price.tail())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pandas.Series.tail` returns the last n rows of a given `pandas.Series`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "price['2011-10-03'] / price['2011-3-01'] - 1\n", "returns = price.pct_change()\n", "ret_index = (1 + returns).cumprod()\n", "ret_index[0] = 1\n", "monthly_returns = ret_index.resample('BM').last().pct_change()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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