donor.reports.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_FOLDER = 'data/Canonical/'\n",
    "REPORT_FOLDER = 'data/Report/'\n",
    "\n",
    "YEAR_NUM = 2016\n",
    "\n",
    "ngo = 'FoodLink'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def available_csvs():\n",
    "    return os.listdir(base_path())\n",
    "\n",
    "def base_path():\n",
    "    return ROOT_FOLDER + ngo + '/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[fn for fn in available_csvs() if \".\".join([str(YEAR_NUM), 'collection']) in fn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[fn for fn in available_csvs() if 'donor' in fn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "donors = pd.concat([pd.read_csv(ROOT_FOLDER + ngo + '/' + fx) for fx in\n",
    "                [fn for fn in available_csvs() if 'donor' in fn]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "donors.head()\n",
    "cols = ['id','efficiency', 'name_en']\n",
    "donors = donors[cols]\n",
    "donors.columns = ['donor','efficiency', 'name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([pd.read_csv(ROOT_FOLDER + ngo + '/' + fx) for fx in [fn for fn in available_csvs() if \".\".join([str(YEAR_NUM), 'collection']) in fn]]).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.merge(donors, on='donor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.datetime = pd.to_datetime(df.datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wide = df.groupby('donor').resample('W-MON', label='left').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[df.donor == 'FISHungHom']['efficiency'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = df.groupby('donor').min()['efficiency']\n",
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = df.groupby('donor').min()['efficiency'] == df.groupby('donor').max()['efficiency']\n",
    "subset[subset==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "efficiencies = df.groupby('donor').min()['efficiency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('efficiency',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx = df.groupby('donor').resample('W-MON', label='left').sum().sum(axis=1).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx = dfx.set_index('donor').join(efficiencies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx['value'] =  dfx[0] * dfx['efficiency'] / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for donor in dfx.index.unique():\n",
    "    print dfx.ix[donor,:].value.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx.join(efficiencies,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv(ROOT_FOLDER + )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from scripts.report.foodlink.donors import *\n",
    "\n",
    "opts = {\n",
    "        \"ngo\" : 'FoodLink',\n",
    "}\n",
    "\n",
    "report = FoodLinkDonorReport(**opts)\n",
    "# report.data_to_pdf()\n",
    "report.prepare_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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