summary_stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pd.read_csv('sorghum_subset_data-multi-value-traits.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "genotype = []\n",
    "ids = dataframe['plantbarcode'].tolist()\n",
    "\n",
    "for i in ids:\n",
    "    if str(i)[3:5] == \"22\":\n",
    "        genotype.append(\"Atlas\")\n",
    "    elif str(i)[3:5] == \"16\":\n",
    "        genotype.append(\"PI_195754\")\n",
    "dataframe[\"genotype\"] = genotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle_df = dataframe.loc[dataframe['trait'] == 'segment_insertion_angle']\n",
    "relevant_data = [pd.to_numeric(angle_df[\"value\"]), angle_df[\"genotype\"]]\n",
    "headers = [\"value\", \"genotype\"]\n",
    "df = pd.concat(relevant_data, axis=1, keys=headers)\n",
    "\n",
    "grouped = df.groupby('genotype')['value'].mean().plot(kind='barh', title='Mean Leaf Angle in Degrees')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle_df = dataframe.loc[dataframe['trait'] == 'segment_path_length']\n",
    "relevant_data = [pd.to_numeric(angle_df[\"value\"]), angle_df[\"genotype\"]]\n",
    "headers = [\"value\", \"genotype\"]\n",
    "df = pd.concat(relevant_data, axis=1, keys=headers)\n",
    "\n",
    "grouped = df.groupby('genotype')['value'].mean().plot(kind='barh',title=\"Average Leaf Length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yBvBMum+wfW1VLe5nOzpPkrTSLM832C6hu58kSdLIDHuPSZKkVWLoKyZN7cJrb2Ph4f827jIkaZW68v37jGzbXjFJkppiMEmSmmIwSZKaYjBJkppiMEmSmmIwSZKaYjBJkppiMEmSmmIwSZKaYjBJkppiMEmSmmIwSZKaYjBJkppiMEmSmmIwSZKaYjBJkppiMEmSmmIwSZKaYjBJkppiMEmSmmIwSZKaYjBJkppiMEmSmrLaBFOSFyepJDv30wuTvGJg/h5Jvj6+CiVJw1htggk4EPgRcEA/vRB4xbRLS5KatFoEU5INgGcAh/CHYHo/8Kwk5yd586Tld01ydpKf9j936tsfn+S8fp0LkuywSg9EksSCcRewkuwHnFlVlya5OclTgcOBt1bVC6HryhtY/hfA7lX1QJK9gP8JvBR4LfCxqvpckrWBNafaWZJDgUMB1txoi1EdkyTNS6tLMB0IfLR//vl++t9mWH5j4IT+iqiAtfr2HwNHJtkGOLWqfjnVylV1HHAcwDpb7lArXr4kacKcD6YkmwF7Ak9IUnRXOQWcMcNq7wW+V1UvTrIQ+D5AVZ2c5FxgH+CbSV5TVWeNsHxJ0iSrwz2m/YETq2q7qlpYVdsCVwAPAhtOs87GwLX984MnGpM8GvhVVf0jcDrwpJFVLUma0uoQTAcCp01q+wrdIIgHkvxs8uAH4GjgH5L8Ow+9j/Ry4KIk5wM7AyeOqGZJ0jRS5S2SFbHOljvUlgd9dNkLStJq5Mr377NC6ydZUlWLppq3OlwxSZJWIwaTJKkpBpMkqSkGkySpKQaTJKkpBpMkqSkGkySpKQaTJKkpBpMkqSkGkySpKQaTJKkpBpMkqSkGkySpKQaTJKkpBpMkqSkGkySpKQaTJKkpBpMkqSkGkySpKQaTJKkpBpMkqSkGkySpKQvGXcBc98StN2bx+/cZdxmStNrwikmS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUFINJktQUg0mS1BSDSZLUlFTVuGuY05LcAVwy7jpWwObA0nEXsYLm+jFY//jN9WOYi/VvV1VbTDVjwaquZDV0SVUtGncRs5Vk8VyuH+b+MVj/+M31Y5jr9U9mV54kqSkGkySpKQbTijtu3AWsoLleP8z9Y7D+8ZvrxzDX638IBz9IkpriFZMkqSkGkySpKQbTCkjy/CSXJLksyeHjrmdZkmyb5HtJLk7yH0ne2Lc/PMm3k/yy/7npuGudSZI1k/w0ydf76e2TnNvX/4Uka4+7xpkk2STJl5P8oj8Xu82lc5Dkzf2/n4uSnJLkYS2fgySfTXJjkosG2qZ8vdP5x/53+oIkTx1f5X8wzTF8sP83dEGS05JsMjDviP4YLknyvPFUPXsG0ywlWRP4BLA38MfAgUn+eLxVLdMDwFuq6nHA04E39DUfDny3qnYAvttPt+yNwMUD0x8AjunrvwU4ZCxVDe9jwJlVtTPwZLpjmRPnIMnWwN8Ci6rqCcCawAG0fQ6OB54/qW2613tvYIf+cShw7CqqcVmO5z8fw7eBJ1TVk4BLgSMA+t/pA4DH9+t8sn+/mjMMptnbFbisqn5VVfcBnwf2HXNNM6qq66rqJ/3zO+jeELemq/uEfrETgP3GU+GyJdkG2Af45346wJ7Al/tFWq9/I2B34DMAVXVfVd3KHDoHdH+Yv26SBcB6wHU0fA6q6gfAzZOap3u99wVOrM45wCZJtlw1lU5vqmOoqm9V1QP95DnANv3zfYHPV9W9VXUFcBnd+9WcYTDN3tbA1QPT1/Rtc0KShcBTgHOBR1bVddCFF/CI8VW2TB8FDgMe7Kc3A24d+AVt/Tw8Gvgt8C99d+Q/J1mfOXIOqupa4EPAr+kC6TZgCXPrHMD0r/dc/b3+a+Ab/fO5egy/ZzDNXqZomxNj75NsAHwFeFNV3T7ueoaV5IXAjVW1ZLB5ikVbPg8LgKcCx1bVU4C7aLTbbir9vZh9ge2BrYD16bq/Jmv5HMxkrv17IsmRdN30n5tommKxpo9hMoNp9q4Bth2Y3gb4zZhqGVqStehC6XNVdWrffMNEd0X/88Zx1bcMzwBelORKuq7TPemuoDbpu5Wg/fNwDXBNVZ3bT3+ZLqjmyjnYC7iiqn5bVfcDpwL/lbl1DmD613tO/V4nOQh4IfDK+sMfpc6pY5iKwTR7/xfYoR+NtDbdzcbTx1zTjPr7MZ8BLq6qjwzMOh04qH9+EPC1VV3bMKrqiKrapqoW0r3eZ1XVK4HvAfv3izVbP0BVXQ9cnWSnvuk5wM+ZI+eArgvv6UnW6/89TdQ/Z85Bb7rX+3TgVf3ovKcDt010+bUmyfOBtwMvqqq7B2adDhyQZJ0k29MN5DhvHDXOWlX5mOUDeAHdaJjLgSPHXc8Q9T6T7pL+AuD8/vECuvs03wV+2f98+LhrHeJY9gC+3j9/NN0v3mXAl4B1xl3fMmrfBVjcn4evApvOpXMAvBv4BXARcBKwTsvnADiF7n7Y/XRXE4dM93rTdYN9ov+dvpBu9GGrx3AZ3b2kid/lTw0sf2R/DJcAe4+7/uV9+JFEkqSm2JUnSWqKwSRJaorBJElqisEkSWqKwSRJaorBJElqisEkSWrK/wcKgh5u83EK3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle_df = dataframe.loc[dataframe['trait'] == 'segment_tangent_angle']\n",
    "relevant_data = [pd.to_numeric(angle_df[\"value\"]), angle_df[\"genotype\"]]\n",
    "headers = [\"value\", \"genotype\"]\n",
    "df = pd.concat(relevant_data, axis=1, keys=headers)\n",
    "\n",
    "grouped = df.groupby('genotype')['value'].mean().plot(kind='barh', title='Mean Leaf Curvature Angle')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (PlantCV)",
   "language": "python",
   "name": "plantcv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}