PlotPandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current size: [12, 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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sGDRt2jTOPPNMWlpaxBgJ4RtTVRVFUXqsGuq8VjpkksSRRx7Jscce21fhDThZ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\nmiQghBBCaJKAEEIIoelflf3OKpsq/7IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196de190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns #seaborn is a Python library that builds on Matplotlib functionality\n",
    "import pandas as pd #Pandas is for handling spreadsheet data\n",
    "import matplotlib.pyplot as plt #matplotlib is for plotting\n",
    "\n",
    "%matplotlib inline \n",
    "#magic function that enables plot display in notebook\n",
    "\n",
    "data = pd.read_excel('DiffData1.xlsx') #read in the data\n",
    "\n",
    "df=pd.DataFrame(data) #make a dataframe for analysis\n",
    "\n",
    "sns.set_style(\"white\") #gives white style\n",
    "sns.set_style(\"ticks\") #adds tick marks everywhere\n",
    "\n",
    "\n",
    "ax = sns.boxplot(data=data) #create box plot with data\n",
    "ax = sns.swarmplot(data=data, color=\".25\") #show the data points with jitter\n",
    "\n",
    "\n",
    "ax.set_ylabel('tau (ms)', fontsize=14) #add y-axis label and make it 14pt\n",
    "#ax.set_title('Nucleus', fontsize=14) #add title and make it 14pt\n",
    "ax=sns.despine() #take away top and right axes\n",
    "\n",
    "# Get current size\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    " \n",
    "# Prints: [8.0, 6.0]\n",
    " \n",
    "# Set figure width to 12 and height to 9\n",
    "fig_size[0] = 12\n",
    "fig_size[1] = 3\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "\n",
    "print \"Current size:\", fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "left = pd.read_csv('/Users/joshtitlow/Google Drive File Stream/Davis_Lab/smFISH_Screen/Datasets/Bayes_2012_pOne.csv')\n",
    "right = pd.read_csv('/Users/joshtitlow/Desktop/DavidGUI/data/raw-metadata.csv')\n",
    "merge = pd.merge(left, right, how='left', on=['OMERO_Figure_ID'])\n",
    "\n",
    "merge.to_csv('/Users/joshtitlow/Desktop/DavidGUI/merge.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "left = pd.read_csv('/Users/joshtitlow/Google Drive File Stream/My Drive/Davis_Lab/smFISH_Screen/Datasets/Bayes_2012_pOne.csv')\n",
    "right = pd.read_csv('/Users/joshtitlow/Google Drive File Stream/My Drive/Davis_Lab/smFISH_Screen/Datasets/Ensembl_mouse_to_fly.csv')\n",
    "merge = pd.merge(left, right, how='left', on=['Ensembl_mouse'])\n",
    "\n",
    "merge.to_csv('/Users/joshtitlow/Google Drive File Stream/My Drive/Davis_Lab/smFISH_Screen/Datasets/PSD_proteome.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CentRgRHOipqx6dfu3cyx33svSx2DjpBaSJc3BXYRmZyxdvZqb+fovb2dOfI9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e26d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "a = np.random.normal(0,2,1000)\n",
    "b = np.random.normal(-2,7,1000)\n",
    "data = [a,b]\n",
    "\n",
    "\n",
    "plt.boxplot(data) # Or you can use the boxplot from Pandas\n",
    "\n",
    "for i in [1,2]:\n",
    "    y = data[i-1]\n",
    "    x = np.random.normal(i, 0.02, len(y))\n",
    "    plt.plot(x, y, 'r.', alpha=0.2)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joshtitlow/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'int' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-7c9506d1f689>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.02\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "data = pd.read_excel('DiffData.xlsx')\n",
    "\n",
    "df=pd.DataFrame(data)\n",
    "\n",
    "data = [0,1]\n",
    "\n",
    "\n",
    "df.boxplot(data) # Or you can use the boxplot from Pandas\n",
    "\n",
    "for i in [1,2]:\n",
    "    y = data[i-1]\n",
    "    x = (i, 0.02, len(y))\n",
    "    plt.plot(x, y, 'r.', alpha=0.2)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118ee9ed0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z5rW7IskfdV271rYk/5XkP4fX1z2LfK/X5grwTnzGhpOnvge8FfgJc88c3lFVu6d8/27g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\nSWqYIS5JDTPEJalh/w8XyLupf5F+FAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119dedf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "data = pd.read_excel('DiffData.xlsx')\n",
    "\n",
    "df=pd.DataFrame(data)\n",
    "\n",
    "#for i in [1,2]:\n",
    "#    y = data[i-1]\n",
    "#    x = np.random.normal(i, 0.02, len(y))\n",
    "#    plt.plot(x, y, 'r.', alpha=0.2)\n",
    "\n",
    "color = dict(boxes='DarkGreen', whiskers='DarkOrange', medians='DarkBlue', caps='Gray')\n",
    "df.plot.box(color=color, sym='r+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}