seaborn - tsplop

seaborn - tsplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.tsplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 12.78221857],\n",
       "       [ -5.00007582],\n",
       "       [  8.38759436],\n",
       "       [-16.87175354],\n",
       "       [ 11.58252492],\n",
       "       [-10.45305129],\n",
       "       [-11.65842279],\n",
       "       [  0.51397186],\n",
       "       [ -2.52706473],\n",
       "       [  7.12696817]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(10, 1) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 15, 31)\n",
    "data_1 = np.sin(x) + np.random.rand(10, 31) + np.random.randn(10, 1)\n",
    "data_2 = np.sin(x) + np.random.rand(10, 31) + np.random.randn(10, 1) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch_ref</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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      ],
      "text/plain": [
       "   batch_ref  timestamp     value\n",
       "0          0          0  0.877193\n",
       "1          0          1  2.165036\n",
       "2          0          2  1.840036\n",
       "3          0          3  2.330214\n",
       "4          0          4  2.655499"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe_1 = pd.DataFrame(data_1).stack(0).reset_index()\n",
    "dframe_1.rename(columns={'level_0': 'batch_ref', 'level_1': 'timestamp', 0:'value'}, inplace=True)\n",
    "dframe_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.521986</td>\n",
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       "      <td>0.128566</td>\n",
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       "      <td>0.186196</td>\n",
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      ],
      "text/plain": [
       "   batch_ref  timestamp     value\n",
       "0          0          0 -0.672354\n",
       "1          0          1 -0.683557\n",
       "2          0          2 -0.521986\n",
       "3          0          3  0.128566\n",
       "4          0          4  0.186196"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe_2 = pd.DataFrame(data_2).stack(0).reset_index()\n",
    "dframe_2.rename(columns={'level_0': 'batch_ref', 'level_1': 'timestamp', 0:'value'}, inplace=True)\n",
    "dframe_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dframe = pd.concat({'engineer_1':dframe_1, 'engineer_2':dframe_2})\n",
    "dframe.reset_index(inplace=True)\n",
    "dframe.rename(columns={'level_0':'engineer'}, inplace = True)\n",
    "dframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/karl/anaconda2/envs/py36-test/lib/python3.6/site-packages/seaborn/timeseries.py:183: UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
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B2NuDt46YdLj8H8Jr2/Q6/O9xVP6aoetxQmO5f44VyljLAitw5nXD6nki7jrmdhh+Xft/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f252f9ffcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sns.tsplot(data=dframe, time='timestamp', value='value', unit='batch_ref', condition='engineer', ci=30)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py36-test",
   "language": "python",
   "name": "py36-test"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}