paypal_to_quickbooks_csv.ipynb

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   "source": [
    "import pandas as pd\n",
    "# This script takes paypal transaction exports and converts them so they can be quickbooks imported\n",
    "# \n",
    "#todo \n",
    "#- CSVs can't be more than 1k transactions\n",
    "#- \"gross\" needs commas stripped out "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "data = pd.read_csv(\"Downloads/DHR_ALL.csv\") #note, should skip the \"header\" lines, i took them out manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "data['Type'].unique()"
   ]
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   "execution_count": null,
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   "source": [
    "#these are transactions that are \"authorization\" and duplicates for debit card use\n",
    "cleaner_data = data[~((data['Type'] == 'Payment Authorized') | (\n",
    "    data['Type'] ==  'Transfer to Bank Initiated') | (\n",
    "    data['Type'] ==  'PP Debit Card Authorization') | (\n",
    "    data['Type'] ==  'Authorized Funds Held') | (\n",
    "    data['Type'] ==  'Funds Released'))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "name_nulls = cleaner_data['Name'].isnull()\n",
    "\n",
    "cleaner_data.loc[name_nulls, 'Name'] = cleaner_data[name_nulls]['Subject'] # if Name is not present use Subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
   "source": [
    "name_nulls = cleaner_data['Name'].isnull()\n",
    "\n",
    "\n",
    "cleaner_data.loc[name_nulls, 'Name'] = cleaner_data[name_nulls]['Type'] #if Name still not present use type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cleaner_data.to_csv(\"Downloads/DHR_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "source": []
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