example_wrangling_crossing_data.ipynb

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     "source": [
      "# Data Wrangling DHS Border Crossing Data\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cur_dir = os.path.dirname(os.path.realpath('__file__'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a list of elements counting from 1995 to 2013\n",
      "years = list(range(1995, 2014, 1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for year in years:\n",
      "    data = pd.read_csv(cur_dir + '/data/cross_raw_data/bc_' + str(year) + '.csv', header=1, skipfooter=8)\n",
      "    df = pd.concat([df, data])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.ix[:, 0:-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
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     "input": [
      "# Create two lists for the loop results to be placed\n",
      "city = []\n",
      "state = []\n",
      "\n",
      "# For each row in a varible,\n",
      "for row in df['Port Name']:\n",
      "    # Try to,\n",
      "    try:\n",
      "        # Split the row by comma and append\n",
      "        # everything before the comma to lat\n",
      "        city.append(row.split(': ')[1])\n",
      "        # Split the row by comma and append\n",
      "        # everything after the comma to lon\n",
      "        state.append(row.split(': ')[0])\n",
      "    # But if you get an error\n",
      "    except:\n",
      "        # append a missing value to lat\n",
      "        city.append(np.NaN)\n",
      "        # append a missing value to lon\n",
      "        state.append(np.NaN)\n",
      "\n",
      "# Create two new columns from lat and lon\n",
      "df['City'] = city\n",
      "df['State'] = state"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.reset_index()\n",
      "df = df.drop('index', axis=1)\n",
      "df = df.rename(columns=lambda x: x.strip())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv(cur_dir + '/data/cross_raw_data/bc_' + 'full_crossing_data.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    }
   ],
   "metadata": {}
  }
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