{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Plotting with seaborn\n", "=====================\n", "\n", "``````{admonition} Overview\n", ":class: overview\n", "\n", "Questions:\n", "\n", "* How can I create statistical plots in seaborn?\n", "\n", "Objectives:\n", "\n", "* Visualize linear relationships with seaborn.\n", "\n", "* Visualize correlation coefficients with seaborn.\n", "\n", "``````\n", "\n", "In the last session we created plots using Matplotlib and learned how to make subplots and customize plots. \n", "In this session, we will discuss another Python visualization library called [seaborn](https://seaborn.pydata.org/). \n", "Seaborn is a visualization which is built on top of Matplotlib, but has plots created specifically for **statistical** visualization.\n", "\n", "Beacause seaborn builds on Matplotlib, having a good understanding of Matplotlib will help you create better seaborn plots. \n", "This will be an introduction to what is possible with seaborn. We encourage you to check it out further by viewing the documentation or the [example gallery](https://seaborn.pydata.org/examples/index.html)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Reading in the data\n", "\n", "To begin, we read in the data that we cleanred in the previous lesson." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"data/potts_table1_clean.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Compoundlog PpiHdHaMVR_2log K_octlog K_hexlog K_hep
0water-6.850.450.820.3510.60.00-1.38NaNNaN
1methanol-6.680.440.430.4721.70.28-0.73-2.42-2.80
2methanoicacid-7.080.600.750.3822.30.30-0.54-3.93-3.63
3ethanol-6.660.420.370.4831.90.25-0.32-2.24-2.10
4ethanoicacid-7.010.650.610.4533.40.27-0.31-3.28-2.90
\n", "
" ], "text/plain": [ " Compound log P pi Hd Ha MV R_2 log K_oct log K_hex \\\n", "0 water -6.85 0.45 0.82 0.35 10.6 0.00 -1.38 NaN \n", "1 methanol -6.68 0.44 0.43 0.47 21.7 0.28 -0.73 -2.42 \n", "2 methanoicacid -7.08 0.60 0.75 0.38 22.3 0.30 -0.54 -3.93 \n", "3 ethanol -6.66 0.42 0.37 0.48 31.9 0.25 -0.32 -2.24 \n", "4 ethanoicacid -7.01 0.65 0.61 0.45 33.4 0.27 -0.31 -3.28 \n", "\n", " log K_hep \n", "0 NaN \n", "1 -2.80 \n", "2 -3.63 \n", "3 -2.10 \n", "4 -2.90 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 37 entries, 0 to 36\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Compound 37 non-null object \n", " 1 log P 33 non-null float64\n", " 2 pi 37 non-null float64\n", " 3 Hd 37 non-null float64\n", " 4 Ha 37 non-null float64\n", " 5 MV 37 non-null float64\n", " 6 R_2 37 non-null float64\n", " 7 log K_oct 36 non-null float64\n", " 8 log K_hex 30 non-null float64\n", " 9 log K_hep 24 non-null float64\n", "dtypes: float64(9), object(1)\n", "memory usage: 3.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Review - Plotting with matplotlib\n", "\n", "Since our data seems to be read in correctly and is in order, we are ready to plot. In the paper we obtained this data from, the molecular descriptors in the table are used to fit an equation for skin permeability, or log P. In the paper in question, they fit a multi-linear model to the data.\n", "\n", "We can repeat exactly what is in the paper. However, if we didn't know what to expect for our model one thing we might want to do is to visually inspect the relationship of log P with each variable. One way to do this easily would be with a plot. Let's review what we learned about plotting with matplotlib by creating a plot of `log P` vs. `pi`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'pi')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "ax.scatter(\"pi\", \"log P\", data=df)\n", "ax.legend()\n", "ax.set_ylabel(\"log P\")\n", "ax.set_xlabel(\"pi\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might have also chosen to make this plot with the built-in plotting for pandas dataframes." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing linear relationships with seaborn\n", "\n", "To use the seaborn library, we begin by importing it. When seaborn is imported, it is typically shortened to `sns`. \n", "\n", "Seaborn has plots which will do a linear fit for us and draw that line along with our data. The simplest way to do this is for a single column using the `regplot` command." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regplot" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "g = sns.regplot(x=\"pi\", y=\"log P\", data=df)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ " Notice that we must create a figure before we execute the code which creates the plot. As we mentioned earlier, seaborn in built on top of matplotlib. It uses the procedural interface to matplotlib, meaning that the seaborn figure will be drawn on top of the most recent figure axis.\n", " \n", "We can combine regplot with what we learned about subplots in the previous session to create subplots showing the linear relationships." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get columns which are numbers\n", "df2 = df.select_dtypes(include=\"float\")\n", "\n", "# Create a figure with multiple subplots (4 rows, 2 columns)\n", "fig, ax = plt.subplots(4, 2, figsize=(8,10))\n", "\n", "# This flattens the array to one dimension so we can loop through it\n", "# with a single count.\n", "ax = ax.reshape(-1)\n", "\n", "count = 0\n", "for x_col in df2.columns:\n", " if x_col !=\"log P\":\n", " axis = ax[count]\n", " sns.regplot(data=df2, x=x_col, y=\"log P\", ax=axis)\n", " count += 1\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### lmplot\n", "\n", "A very similar effect will be achieved with an alternate seaborn plotting function called `lmplot`. On the surface, this plot does not appear much different." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
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i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.lmplot(x=\"pi\", y=\"log P\", data=df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Underneath the surface `lmplot` using something in seaborn called `facetgrid` which uses subplots in matplotlib. `Lmplot` allows us to quickly plot the relationship between different data sets.\n", "\n", "Unfortunately, it's not quite as easy as it sounds. Use of `lmplot` requires that our data be what is called \"tidy\" or “long-form”, meaning that we should have dataframe where each column is a variable and each row is an observation." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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log PpiHdHaMVR_2log K_octlog K_hexlog K_hep
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" ], "text/plain": [ " log P pi Hd Ha MV R_2 log K_oct log K_hex log K_hep\n", "0 -6.85 0.45 0.82 0.35 10.6 0.00 -1.38 NaN NaN\n", "1 -6.68 0.44 0.43 0.47 21.7 0.28 -0.73 -2.42 -2.80\n", "2 -7.08 0.60 0.75 0.38 22.3 0.30 -0.54 -3.93 -3.63\n", "3 -6.66 0.42 0.37 0.48 31.9 0.25 -0.32 -2.24 -2.10\n", "4 -7.01 0.65 0.61 0.45 33.4 0.27 -0.31 -3.28 -2.90" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.head()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We can acheive this by \"melting\" the dataframe. When we use the `melt` function, we specify the variable of interest (`log P` for us). This will keep the log P column, while melting the others." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df2_melt = df2.melt(id_vars=\"log P\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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log Pvariablevalue
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1-6.68pi0.44
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" ], "text/plain": [ " log P variable value\n", "0 -6.85 pi 0.45\n", "1 -6.68 pi 0.44\n", "2 -7.08 pi 0.60\n", "3 -6.66 pi 0.42\n", "4 -7.01 pi 0.65" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2_melt.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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log Pvariablevalue
291-5.28log K_hep1.16
292-4.84log K_hep1.65
293-5.21log K_hep1.95
294-4.77log K_hep2.28
295-4.66log K_hep2.91
\n", "
" ], "text/plain": [ " log P variable value\n", "291 -5.28 log K_hep 1.16\n", "292 -4.84 log K_hep 1.65\n", "293 -5.21 log K_hep 1.95\n", "294 -4.77 log K_hep 2.28\n", "295 -4.66 log K_hep 2.91" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2_melt.tail()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use `lmplot` to create the previous plot in one line.\n", "This type of plot is good for seeing the relationship between multiple variables and a variable of interest." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.lmplot(data=df2_melt, y=\"log P\", x=\"value\", col=\"variable\", col_wrap=2, facet_kws={\"sharex\": False})" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "By default, the x and y range on these plots will be the same. However by using the `facet_kws={\"sharex\": False})` option, these will be put on different scales." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation Plots\n", "\n", "Pandas dataframes have a `.corr` method which shows the correlation between each column." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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log PpiHdHaMVR_2log K_octlog K_hexlog K_hep
log P1.0000000.023011-0.533354-0.5208660.6680040.3314240.8650840.9075580.885660
pi0.0230111.0000000.432698-0.4430450.1454300.8099930.231781-0.232893-0.199002
Hd-0.5333540.4326981.0000000.102839-0.0684680.091703-0.209763-0.504429-0.401620
Ha-0.520866-0.4430450.1028391.0000000.026161-0.602861-0.383236-0.1566960.200349
MV0.6680040.145430-0.0684680.0261611.0000000.1803320.9105970.6772460.927141
R_20.3314240.8099930.091703-0.6028610.1803321.0000000.4200270.048637-0.095938
log K_oct0.8650840.231781-0.209763-0.3832360.9105970.4200271.0000000.7626370.870178
log K_hex0.907558-0.232893-0.504429-0.1566960.6772460.0486370.7626371.0000000.889605
log K_hep0.885660-0.199002-0.4016200.2003490.927141-0.0959380.8701780.8896051.000000
\n", "
" ], "text/plain": [ " log P pi Hd Ha MV R_2 \\\n", "log P 1.000000 0.023011 -0.533354 -0.520866 0.668004 0.331424 \n", "pi 0.023011 1.000000 0.432698 -0.443045 0.145430 0.809993 \n", "Hd -0.533354 0.432698 1.000000 0.102839 -0.068468 0.091703 \n", "Ha -0.520866 -0.443045 0.102839 1.000000 0.026161 -0.602861 \n", "MV 0.668004 0.145430 -0.068468 0.026161 1.000000 0.180332 \n", "R_2 0.331424 0.809993 0.091703 -0.602861 0.180332 1.000000 \n", "log K_oct 0.865084 0.231781 -0.209763 -0.383236 0.910597 0.420027 \n", "log K_hex 0.907558 -0.232893 -0.504429 -0.156696 0.677246 0.048637 \n", "log K_hep 0.885660 -0.199002 -0.401620 0.200349 0.927141 -0.095938 \n", "\n", " log K_oct log K_hex log K_hep \n", "log P 0.865084 0.907558 0.885660 \n", "pi 0.231781 -0.232893 -0.199002 \n", "Hd -0.209763 -0.504429 -0.401620 \n", "Ha -0.383236 -0.156696 0.200349 \n", "MV 0.910597 0.677246 0.927141 \n", "R_2 0.420027 0.048637 -0.095938 \n", "log K_oct 1.000000 0.762637 0.870178 \n", "log K_hex 0.762637 1.000000 0.889605 \n", "log K_hep 0.870178 0.889605 1.000000 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.corr()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize this correlation using a heat map in seaborn." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. 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Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig=plt.figure(figsize=(12,8))\n", "sns.heatmap(df2.corr(), cmap=\"rocket_r\", annot=True)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "``````{admonition} Key Points\n", ":class: key\n", "\n", "* Seaborn is built on top of matplotlib and can be used to easily create statistical visualizations.\n", "\n", "* `lmplot` can create plots showing the relationship between a column of data and other columns, but the data must be \"long form\" for this function.\n", "\n", "``````" ] } ], "metadata": { "kernelspec": { "display_name": "molssi-training", "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": "3.11.0 | packaged by conda-forge | (main, Oct 25 2022, 06:24:40) [GCC 10.4.0]" }, "vscode": { "interpreter": { "hash": "16d4a7bb199d969b1271ebe46f77414b0d9cd01b3c3983c2b2742fc6cd4503d3" } } }, "nbformat": 4, "nbformat_minor": 4 }