lab_status_bar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab Status Bar\n",
    "### Using plotting techniques to visualize progress in the lab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Status Bar###\n",
    "I use spreadsheets to keep up with my data; this is a nifty way to visualize my progress. Lab work progresses in this order: 'extract', 'pcr', 'plate', 'done'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>master_label</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CR17-0052</td>\n",
       "      <td>Mound 12</td>\n",
       "      <td>999</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CR17-0053</td>\n",
       "      <td>Mound 12</td>\n",
       "      <td>999</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CR17-0054</td>\n",
       "      <td>Mound 12</td>\n",
       "      <td>999</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CR17-0055</td>\n",
       "      <td>Mound 12</td>\n",
       "      <td>999</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CR17-0056</td>\n",
       "      <td>Mound 12</td>\n",
       "      <td>999</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  master_label  Locality  Depth Status\n",
       "0    CR17-0052  Mound 12    999   done\n",
       "1    CR17-0053  Mound 12    999   done\n",
       "2    CR17-0054  Mound 12    999   done\n",
       "3    CR17-0055  Mound 12    999   done\n",
       "4    CR17-0056  Mound 12    999   done"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "df = pd.read_csv('status_test.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot \"Status\" of samples, grouped by locality** (easy way to check how much is done or left to go)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nsme_0123 = {'extract': 0, 'pcr': 1, 'plate': 2, 'done': 3}\n",
    "df['Status_no'] = df['Status'].map(nsme_0123)\n",
    "df2 = df.sort_values('Status_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dfa7e59c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df2, x=\"Locality\", hue=\"Status\", orient=\"v\", palette=\"Blues\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Progress by Sampling Locality');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen above, as processing of samples is completed in the lab, they get shifted over to the darker blue status bars. This rendering groups samples by locality so it's easy to see which localities require further processing and which localities are under-sampled in general."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot \"Status\" of samples, grouped by depth**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Rel_depth'] = pd.cut(df2['Depth'], [900, 1500, 1800, 3000], labels=['Shallow', 'Mid', 'Deep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dfa801ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df2, x=\"Rel_depth\", hue=\"Status\", orient=\"v\", palette=\"Blues\")\n",
    "plt.legend(loc='upper center')\n",
    "plt.title('Progress by Depth');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same color rendering as locality plot. This rendering groups samples by depth so it's easy to see which depths require further processing and which depths are under-sampled in general."
   ]
  }
 ],
 "metadata": {
  "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",
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