week04b-cruise-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WCOA 2013 cruise data analysis\n",
    "\n",
    "In this part of the tutorial, we will go over the basics of working with dates in Pandas and Numpy, make some exploratory plots and start a regression analysis. The data exploration will be largely guided by student interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use Pandas to import the csv data file. \n",
    "\n",
    "Here, there is an optional `parse_dates` argument. The numbers in double brackets `[[8,9]]` indicate which columns to interpret as dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'data/week03_wcoa_cruise/WCOA2013_hy1.csv'\n",
    "df = pd.read_csv(filename,header=31,na_values=-999,\n",
    "                 parse_dates=[[8,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>EXPOCODE</th>\n",
       "      <th>SECT_ID</th>\n",
       "      <th>LEG</th>\n",
       "      <th>LINE</th>\n",
       "      <th>STNNBR</th>\n",
       "      <th>CASTNO</th>\n",
       "      <th>BTLNBR</th>\n",
       "      <th>BTLNBR_FLAG_W</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>...</th>\n",
       "      <th>TCARBN</th>\n",
       "      <th>TCARBN_FLAG_W</th>\n",
       "      <th>ALKALI</th>\n",
       "      <th>ALKALI_FLAG_W</th>\n",
       "      <th>PH_TOT</th>\n",
       "      <th>PH_TOT_FLAG_W</th>\n",
       "      <th>PH_TMP</th>\n",
       "      <th>CO32</th>\n",
       "      <th>CO32__FLAG_W</th>\n",
       "      <th>CHLORA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-05 02:12:20</td>\n",
       "      <td>317W20130803</td>\n",
       "      <td>WCOA2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2370.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.294</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-05 02:12:53</td>\n",
       "      <td>317W20130803</td>\n",
       "      <td>WCOA2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>7.295</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-05 02:19:58</td>\n",
       "      <td>317W20130803</td>\n",
       "      <td>WCOA2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2349.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2343.7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.282</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>43.521</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-05 02:27:01</td>\n",
       "      <td>317W20130803</td>\n",
       "      <td>WCOA2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2318.7</td>\n",
       "      <td>2</td>\n",
       "      <td>2311.9</td>\n",
       "      <td>2</td>\n",
       "      <td>7.287</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>45.641</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-05 02:30:53</td>\n",
       "      <td>317W20130803</td>\n",
       "      <td>WCOA2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2299.7</td>\n",
       "      <td>2</td>\n",
       "      <td>7.308</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>47.741</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE_TIME      EXPOCODE   SECT_ID  LEG LINE  STNNBR  CASTNO  \\\n",
       "0 2013-08-05 02:12:20  317W20130803  WCOA2013    1    2      11       1   \n",
       "1 2013-08-05 02:12:53  317W20130803  WCOA2013    1    2      11       1   \n",
       "2 2013-08-05 02:19:58  317W20130803  WCOA2013    1    2      11       1   \n",
       "3 2013-08-05 02:27:01  317W20130803  WCOA2013    1    2      11       1   \n",
       "4 2013-08-05 02:30:53  317W20130803  WCOA2013    1    2      11       1   \n",
       "\n",
       "   BTLNBR  BTLNBR_FLAG_W  LATITUDE   ...    TCARBN  TCARBN_FLAG_W  ALKALI  \\\n",
       "0       1              2      48.2   ...    2370.2              2  2369.0   \n",
       "1       2              2      48.2   ...       NaN              9     NaN   \n",
       "2       3              2      48.2   ...    2349.6              2  2343.7   \n",
       "3       4              2      48.2   ...    2318.7              2  2311.9   \n",
       "4       5              2      48.2   ...    2300.0              2  2299.7   \n",
       "\n",
       "   ALKALI_FLAG_W  PH_TOT  PH_TOT_FLAG_W  PH_TMP    CO32  CO32__FLAG_W  CHLORA  \n",
       "0              2   7.294              2    25.0     NaN             9     NaN  \n",
       "1              9   7.295              2    25.0     NaN             9     NaN  \n",
       "2              2   7.282              2    25.0  43.521             3     NaN  \n",
       "3              2   7.287              2    25.0  45.641             2     NaN  \n",
       "4              2   7.308              2    25.0  47.741             2     NaN  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of strings, the dates are now in a special `datetime64` format. This means that, instead of treating the dates in the same way as any other collection of characters, pandas and NumPy can understand how this variable represents time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2013-08-05 02:12:20\n",
       "1   2013-08-05 02:12:53\n",
       "2   2013-08-05 02:19:58\n",
       "3   2013-08-05 02:27:01\n",
       "4   2013-08-05 02:30:53\n",
       "Name: DATE_TIME, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DATE_TIME'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, subtracting `datetime64` objects with pandas gives a `Timedelta` object, which is specifically used to represent differences between times. The first two samples in the cruise data are separated by 33 seconds (the time between firing of bottles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:00:33')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DATE_TIME'][1]-df['DATE_TIME'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, pyplot automatically understands `datetime64` objects so it is easy to see how the ship moved between stations from north to south as weeks passed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 735084.,  735087.,  735090.,  735093.,  735096.,  735099.,\n",
       "         735102.,  735105.,  735108.]), <a list of 9 Text xticklabel objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['DATE_TIME'],df['LATITUDE'],'-o')\n",
    "plt.xticks(rotation=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11eb78710>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df['LONGITUDE'], df['LATITUDE'], 'ro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `scatter()` function allows points to be colored according to the value of a variable. In the case of dates, later dates are shown as warmer colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11ebb5278>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(df['LONGITUDE'],df['LATITUDE'],c=df['DATE_TIME'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the vertical coordinate is pressure (not depth, which indicates the bottom depth rather than the depth of the sample). To plot dissolved oxygen with depth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'pressure[dbar]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df['OXYGEN'],df['CTDPRS'],'.')\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.xlabel('$O_2 [\\mu M]$')\n",
    "plt.ylabel('pressure[dbar]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, suppose we want to compute the regression of nitrate on phosphate using all of the available data. In theory the slope is given by the \"Redfield ratio\". \n",
    "\n",
    "We can use `stats.linregress`, but this gives us back `NaN` values. This is because there are `NaN` values in the inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=nan, intercept=nan, rvalue=nan, pvalue=nan, stderr=nan)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['PHSPHT']\n",
    "y = df['NITRAT']\n",
    "\n",
    "results = stats.linregress(x,y)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `np.finite()` function and create an array of boolean (`True` and `False`) values to select only finite numbers (which does not include `NaN`s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=15.447256509328257, intercept=-6.1269014162479678, rvalue=0.98732121974431175, pvalue=0.0, stderr=0.082372778989147466)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ii=(np.isfinite(x) & np.isfinite(y))\n",
    "results=stats.linregress(x[ii],y[ii])\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f35dcc0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df['PHSPHT'][ii],df['NITRAT'][ii],'o')\n",
    "plt.plot(np.array([0,3.5]),results.slope*np.array([0,3.5])+results.intercept,'r-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Execrises\n",
    "\n",
    "* What scientific questions can be addressed with this data set?\n",
    "* What relationships might occur between different variables?\n",
    "* What differences might occur within the same variables, but at different locations or times?\n",
    "* Create exploratory plots\n",
    "* Do a correlation and regression analysis. Obtain r and p values. Draw both the regression of $y$ on $x$, and the regression of $x$ on $y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DATE_TIME', 'EXPOCODE', 'SECT_ID', 'LEG', 'LINE', 'STNNBR', 'CASTNO',\n",
       "       'BTLNBR', 'BTLNBR_FLAG_W', 'LATITUDE', 'LONGITUDE', 'DEPTH', 'CTDPRS',\n",
       "       'CTDTMP', 'CTDSAL', 'CTDSAL_FLAG_W', 'CTDOXY', 'CTDOXY_FLAG_W',\n",
       "       'SALNTY', 'SALNTY_FLAG_W', 'OXYGEN', 'OXYGEN_FLAG_W', 'SILCAT',\n",
       "       'SILCAT_FLAG_W', 'NITRAT', 'NITRAT_FLAG_W', 'NITRIT', 'NITRIT_FLAG_W',\n",
       "       'PHSPHT', 'PHSPHT_FLAG_W', 'AMMONI', 'AMMONI_FLAG_W', 'TCARBN',\n",
       "       'TCARBN_FLAG_W', 'ALKALI', 'ALKALI_FLAG_W', 'PH_TOT', 'PH_TOT_FLAG_W',\n",
       "       'PH_TMP', 'CO32', 'CO32__FLAG_W', 'CHLORA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}