Plotly notebook

plotly/plotly-intro.ipynb

@@ -1,507 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction: Plotting with Plotly\n",
-    "\n",
-    "In this notebook, we will take a look at using the plotly library for plotting in Python. This library allows us to quickly make great plots and add interactive elements to our figures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T14:23:51.790476Z",
-     "start_time": "2018-12-09T14:23:50.493881Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Standard data science libraries\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy import stats\n",
-    "import featuretools as ft\n",
-    "\n",
-    "# Visualization\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "plt.style.use('bmh')\n",
-    "\n",
-    "# Options for pandas\n",
-    "pd.options.display.max_columns = 20\n",
-    "\n",
-    "# Display all cell outputs\n",
-    "from IPython.core.interactiveshell import InteractiveShell\n",
-    "InteractiveShell.ast_node_interactivity = 'all'\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T14:26:08.588372Z",
-     "start_time": "2018-12-09T14:26:06.974868Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~wjk68/13.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import plotly.plotly as py\n",
-    "import plotly.graph_objs as go\n",
-    "\n",
-    "trace1 = go.Scatter(x=[1,2,3], y=[10,5,6], marker={'color': 'red', 'symbol': 104, 'size': 10}, \n",
-    "                    mode=\"markers+lines\",  text=[\"one\",\"two\",\"three\"], name='1st Trace')\n",
-    "                                               \n",
-    "data=go.Data([trace1])\n",
-    "layout=go.Layout(title=\"First Plot\", xaxis={'title':'x1'}, yaxis={'title':'x2'})\n",
-    "figure=go.Figure(data=data,layout=layout)\n",
-    "py.iplot(figure, filename='pyguide_1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T16:51:09.084641Z",
-     "start_time": "2018-12-09T16:51:07.771289Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Figure({\n",
-       "    'data': [{'marker': {'color': 'red', 'size': 10, 'symbol': 104},\n",
-       "              'mode': 'markers+lines',\n",
-       "              'name': '1st Trace',\n",
-       "              'text': [one, two, three],\n",
-       "              'type': 'scatter',\n",
-       "              'uid': '5dc16f36-fbbe-11e8-a878-acde48001122',\n",
-       "              'x': [1, 2, 3],\n",
-       "              'y': [10, 5, 6]}],\n",
-       "    'layout': {'title': 'Plot update', 'xaxis': {'title': 'x1'}, 'yaxis': {'title': 'x2'}}\n",
-       "})"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~wjk68/15.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "figure.update(dict(layout=dict(title='Plot update'), data=dict(marker=dict(color='blue'))))\n",
-    "py.iplot(figure, filename='pyguide_2')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T17:09:08.724233Z",
-     "start_time": "2018-12-09T17:09:06.805967Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~wjk68/13.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = pd.date_range(pd.datetime(2018, 3, 1), pd.datetime(2018, 12, 1))\n",
-    "y = np.random.randn(len(x))\n",
-    "\n",
-    "trace1 = go.Scatter(x=x, y=y, marker={'color': 'red', 'symbol': 104, 'size': 10}, \n",
-    "                    mode=\"markers+lines\", name='1st Trace')\n",
-    "                                               \n",
-    "#data=go.Scatter([trace1])\n",
-    "layout=go.Layout(title=\"First Plot\", xaxis={'title':'x1'}, yaxis={'title':'x2'})\n",
-    "figure=go.Figure(data=[trace1],\n",
-    "                 layout=layout)\n",
-    "py.iplot(figure, filename='pyguide_1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T17:13:46.753471Z",
-     "start_time": "2018-12-09T17:13:46.746181Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "line1 = go.Scatter(x=[pd.datetime(2018, 12, 1, 5, 30),\n",
-    "                   pd.datetime(2018, 12, 1, 5, 30)], \n",
-    "                y = [0, 2], mode = 'lines')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T17:13:59.767756Z",
-     "start_time": "2018-12-09T17:13:57.685957Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/site-packages/IPython/core/display.py:689: UserWarning:\n",
-      "\n",
-      "Consider using IPython.display.IFrame instead\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~wjk68/13.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = pd.date_range(pd.datetime(2018, 12, 1, 2, 30), \n",
-    "                  pd.datetime(2018, 12, 1, 14, 30),\n",
-    "                  freq = '15 min')\n",
-    "y = np.random.randn(len(x))\n",
-    "\n",
-    "trace1 = go.Scatter(x=x, y=y, marker={'color': 'red', 'symbol': 104, 'size': 10}, \n",
-    "                    mode=\"markers+lines\", name='1st Trace')\n",
-    "                                               \n",
-    "#data=go.Scatter([trace1])\n",
-    "layout=go.Layout(title=\"First Plot with vertical line\", xaxis={'title':'x1'}, yaxis={'title':'x2'})\n",
-    "figure=go.Figure(data=[trace1, line1],\n",
-    "                 layout=layout)\n",
-    "py.iplot(figure, filename='pyguide_1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T17:21:40.308277Z",
-     "start_time": "2018-12-09T17:21:40.211468Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>10726</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>34810.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>5016.113806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1132.176968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>441.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>4121.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>4710.400000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5740.800000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>9420.800000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              10726\n",
-       "count  34810.000000\n",
-       "mean    5016.113806\n",
-       "std     1132.176968\n",
-       "min      441.600000\n",
-       "25%     4121.600000\n",
-       "50%     4710.400000\n",
-       "75%     5740.800000\n",
-       "max     9420.800000"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv('ESB_power.csv', parse_dates = ['measured_at'])\n",
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T17:23:54.272668Z",
-     "start_time": "2018-12-09T17:23:49.753718Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The draw time for this plot will be slow for clients without much RAM.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~wjk68/13.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = df['measured_at']\n",
-    "y = df['10726']\n",
-    "\n",
-    "trace1 = go.Scatter(x=x, y=y, marker={'color': 'red', 'symbol': 104, 'size': 10}, \n",
-    "                    mode=\"markers+lines\", name='1st Trace')\n",
-    "                                               \n",
-    "#data=go.Scatter([trace1])\n",
-    "layout=go.Layout(title=\"First Plot with vertical line\", xaxis={'title':'x1'}, yaxis={'title':'x2'})\n",
-    "figure=go.Figure(data=[trace1],\n",
-    "                 layout=layout)\n",
-    "py.iplot(figure, filename='pyguide_1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T17:59:22.589569Z",
-     "start_time": "2018-12-09T17:59:22.585532Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.date(2017, 12, 6)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.datetime(2017, 12, 6).date()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-09T18:00:28.342586Z",
-     "start_time": "2018-12-09T18:00:26.951855Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~wjk68/13.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = list(df.loc[df['measured_at'].dt.date == pd.datetime(2018, 3, 18).date(),\n",
-    "           'measured_at'])\n",
-    "y = list(df.loc[df['measured_at'].dt.date == pd.datetime(2018, 3, 18).date(),\n",
-    "       '10726'])\n",
-    "\n",
-    "trace1 = go.Scatter(x=x, y=y, marker={'color': 'red', 'symbol': 104, 'size': 10}, \n",
-    "                    mode=\"markers+lines\", name='1st Trace')\n",
-    "                                               \n",
-    "#data=go.Scatter([trace1])\n",
-    "layout=go.Layout(title=\"First Plot with vertical line\", xaxis={'title':'x1'}, yaxis={'title':'x2'})\n",
-    "figure=go.Figure(data=[trace1],\n",
-    "                 layout=layout)\n",
-    "py.iplot(figure, filename='pyguide_1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

slack_interaction/example_magic/Untitled.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-08T22:23:52.726784Z",
+     "start_time": "2018-12-08T22:23:51.937346Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Standard data science libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "import featuretools as ft\n",
+    "\n",
+    "# Visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.style.use('bmh')\n",
+    "\n",
+    "# Options for pandas\n",
+    "pd.options.display.max_columns = 20\n",
+    "\n",
+    "# Display all cell outputs\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = 'all'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-12-08T22:24:15.663307Z",
+     "start_time": "2018-12-08T22:24:15.654447Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'Abracadabra'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-fbf105797f87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mabracadabra\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAbracadabra\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Abracadabra'"
+     ]
+    }
+   ],
+   "source": [
+    "from abracadabra import Abracadabra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}