{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cruise data analysis in Python\n", "\n", "## WCOA 2013 data set\n", "\n", "To download the data used in this tutorial, use the following command in the Terminal (Mac) or Git Bash (Windows).\n", "\n", "```\n", "git clone https://github.com/mlmldata2022/wcoa_cruise.git\n", "```\n", "\n", "The data comes from the West Coast Ocean Acidification (WCOA) cruise in 2013. The goal of this NOAA-supported research cruise is to collect data to help understand the effects of coastal upwelling on ocean acidification, and the impacts of ocean acidification on organisms and ecosystems. [This video](https://www.youtube.com/watch?v=Eesi6e03Yx0&t=134s) gives an idea of life aboard the ship and the type of science operations conducted.\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": 39, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "from scipy import stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction to Pandas dataframes" ] }, { "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": {}, "outputs": [], "source": [ "filename = 'data/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": [ "
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DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBNTCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORA
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...2370.222369.027.294225.0NaN9NaN
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...NaN9NaN97.295225.0NaN9NaN
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...2349.622343.727.282225.043.5213NaN
32013-08-05 02:27:01317W20130803WCOA2013121114248.2...2318.722311.927.287225.045.6412NaN
42013-08-05 02:30:53317W20130803WCOA2013121115248.2...2300.022299.727.308225.047.7412NaN
\n", "

5 rows × 42 columns

\n", "
" ], "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": "code", "execution_count": 4, "metadata": {}, "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "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": 5, "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": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timedelta('0 days 00:00:33')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['DATE_TIME'][1]-df['DATE_TIME'][0]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([48.2 , 48.3 , 48.37, 48.44, 48.5 , 48.53, 48.61, 48.66, 48.71,\n", " 48.78, 48.81, 48.84, 47.97, 48.14, 47.96, 47.68, 47.13, 47.11,\n", " 47.12, 47.34, 46.13, 46.17, 46.19, 46.25, 46.24, 46.12, 44.65,\n", " 44.66, 44.2 , 41.99, 41.97, 41.96, 41.94, 41.9 , 40.25, 40.23,\n", " 40.22, 40.21, 40.1 , 37.67, 37.94, 37.91, 37.87, 37.76, 37.75,\n", " 36.8 , 36.78, 36.76, 36.73, 36.71, 36.69, 36.52, 36.7 ])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.unique(df['LATITUDE'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise\n", "\n", "Create a list of unique station ID’s (“STNNBR”) found in the survey data. Call it `stns`. How many unique stations are there in the data? \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A summary of the dataframe is given by the `.describe()` method." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 969.000000\n", "mean 8.993954\n", "std 3.055917\n", "min 1.738200\n", "25% 7.292000\n", "50% 8.328200\n", "75% 10.752200\n", "max 20.747400\n", "Name: CTDTMP, dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These summary statistics can also be accessed individually with similar syntax." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.993954179566563" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].mean()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.7382" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternate method using Numpy functions." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.7382" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.min(df['CTDTMP'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mathematical operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Converting Celcius to Fahrenheit" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df['CTDTMP_F'] = 9/5*df['CTDTMP'] + 32 " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 38.63894\n", "1 38.64236\n", "2 39.85916\n", "3 41.05328\n", "4 41.82404\n", "Name: CTDTMP_F, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP_F'].head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 3.6883\n", "1 3.6902\n", "2 4.3662\n", "3 5.0296\n", "4 5.4578\n", "Name: CTDTMP, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['CTDTMP'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot latitude as a function of time." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([15922., 15926., 15930., 15934., 15938., 15942., 15946.]),\n", " [Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, ''),\n", " Text(0, 0, '')])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df['DATE_TIME'],df['LATITUDE'],'-o')\n", "plt.xticks(rotation=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `pyplot` library 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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'pressure[dbar]')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "df['CTDTMP'].hist()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "df['CTDTMP'].hist(bins=50)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "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', 'CTDTMP_F'],\n", " dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercises\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 (one PDF, one scatter plot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Slicing and subsetting data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "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", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI ALKALI_FLAG_W \\\n", "0 1 2 48.2 ... 2 2369.0 2 \n", "1 2 2 48.2 ... 9 NaN 9 \n", "2 3 2 48.2 ... 2 2343.7 2 \n", "3 4 2 48.2 ... 2 2311.9 2 \n", "\n", " PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W CHLORA CTDTMP_F \n", "0 7.294 2 25.0 NaN 9 NaN 38.63894 \n", "1 7.295 2 25.0 NaN 9 NaN 38.64236 \n", "2 7.282 2 25.0 43.521 3 NaN 39.85916 \n", "3 7.287 2 25.0 45.641 2 NaN 41.05328 \n", "\n", "[4 rows x 43 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[0:3]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CTDTMPCTDPRS
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" ], "text/plain": [ " CTDTMP CTDPRS\n", "0 3.6883 999.5\n", "1 3.6902 1000.8\n", "2 4.3662 749.0\n", "3 5.0296 503.9" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[0:3,['CTDTMP','CTDPRS']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise\n", "\n", "What do you expect to happen when you execute:\n", "```\n", "df[0:1]\n", "df[:4]\n", "df[:-1]\n", "```\n", "\n", "What do you expect to happen when you call:\n", "```\n", "df.iloc[0:4, 1:4]\n", "df.loc[0:4, 1:4]\n", "```\n", "\n", "How are the two commands different?\n", "\n", "Adapted from: https://datacarpentry.org/python-ecology-lesson/03-index-slice-subset/index.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsetting Data using Criteria" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
02013-08-05 02:12:20317W20130803WCOA2013121111248.2...22369.027.294225.0NaN9NaN38.63894
12013-08-05 02:12:53317W20130803WCOA2013121112248.2...9NaN97.295225.0NaN9NaN38.64236
22013-08-05 02:19:58317W20130803WCOA2013121113248.2...22343.727.282225.043.5213NaN39.85916
32013-08-05 02:27:01317W20130803WCOA2013121114248.2...22311.927.287225.045.6412NaN41.05328
42013-08-05 02:30:53317W20130803WCOA2013121115248.2...22299.727.308225.047.7412NaN41.82404
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5 rows × 43 columns

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DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
7702013-08-25 21:23:3532P020130821WCOA201321013311237.67...62430.467.493225.073.3462NaN35.12876
7712013-08-25 21:32:1332P020130821WCOA201321013312237.67...22427.627.467225.069.8122NaN35.30336
7722013-08-25 21:40:4432P020130821WCOA201321013313237.67...22424.327.442225.065.5022NaN35.55914
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7742013-08-25 21:57:5532P020130821WCOA201321013315237.67...22401.427.374225.057.7392NaN36.92894
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" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "770 2013-08-25 21:23:35 32P020130821 WCOA2013 2 10 133 1 \n", "771 2013-08-25 21:32:13 32P020130821 WCOA2013 2 10 133 1 \n", "772 2013-08-25 21:40:44 32P020130821 WCOA2013 2 10 133 1 \n", "773 2013-08-25 21:49:45 32P020130821 WCOA2013 2 10 133 1 \n", "774 2013-08-25 21:57:55 32P020130821 WCOA2013 2 10 133 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI \\\n", "770 1 2 37.67 ... 6 2430.4 \n", "771 2 2 37.67 ... 2 2427.6 \n", "772 3 2 37.67 ... 2 2424.3 \n", "773 4 2 37.67 ... 2 2413.7 \n", "774 5 2 37.67 ... 2 2401.4 \n", "\n", " ALKALI_FLAG_W PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W \\\n", "770 6 7.493 2 25.0 73.346 2 \n", "771 2 7.467 2 25.0 69.812 2 \n", "772 2 7.442 2 25.0 65.502 2 \n", "773 2 7.400 2 25.0 60.170 2 \n", "774 2 7.374 2 25.0 57.739 2 \n", "\n", " CHLORA CTDTMP_F \n", "770 NaN 35.12876 \n", "771 NaN 35.30336 \n", "772 NaN 35.55914 \n", "773 NaN 36.10526 \n", "774 NaN 36.92894 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.LATITUDE <= 40].head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "dfsub = df[(df.CTDPRS <= 10) & (df.LATITUDE > 40)]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DATE_TIMEEXPOCODESECT_IDLEGLINESTNNBRCASTNOBTLNBRBTLNBR_FLAG_WLATITUDE...TCARBN_FLAG_WALKALIALKALI_FLAG_WPH_TOTPH_TOT_FLAG_WPH_TMPCO32CO32__FLAG_WCHLORACTDTMP_F
182013-08-05 03:00:52317W20130803WCOA20131211119448.20...9NaN9NaN9NaNNaN9NaN56.54660
192013-08-05 03:01:10317W20130803WCOA20131211120248.20...22189.167.983325.0155.0432NaN56.54570
382013-08-05 06:37:22317W20130803WCOA20131212119248.30...92180.067.980325.0152.8682NaN57.72218
392013-08-05 06:37:42317W20130803WCOA20131212120248.30...2NaN97.981225.0NaN5NaN57.72290
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" ], "text/plain": [ " DATE_TIME EXPOCODE SECT_ID LEG LINE STNNBR CASTNO \\\n", "18 2013-08-05 03:00:52 317W20130803 WCOA2013 1 2 11 1 \n", "19 2013-08-05 03:01:10 317W20130803 WCOA2013 1 2 11 1 \n", "38 2013-08-05 06:37:22 317W20130803 WCOA2013 1 2 12 1 \n", "39 2013-08-05 06:37:42 317W20130803 WCOA2013 1 2 12 1 \n", "58 2013-08-05 10:41:19 317W20130803 WCOA2013 1 2 13 1 \n", "\n", " BTLNBR BTLNBR_FLAG_W LATITUDE ... TCARBN_FLAG_W ALKALI \\\n", "18 19 4 48.20 ... 9 NaN \n", "19 20 2 48.20 ... 2 2189.1 \n", "38 19 2 48.30 ... 9 2180.0 \n", "39 20 2 48.30 ... 2 NaN \n", "58 19 2 48.37 ... 2 2178.7 \n", "\n", " ALKALI_FLAG_W PH_TOT PH_TOT_FLAG_W PH_TMP CO32 CO32__FLAG_W \\\n", "18 9 NaN 9 NaN NaN 9 \n", "19 6 7.983 3 25.0 155.043 2 \n", "38 6 7.980 3 25.0 152.868 2 \n", "39 9 7.981 2 25.0 NaN 5 \n", "58 2 7.931 2 25.0 142.629 2 \n", "\n", " CHLORA CTDTMP_F \n", "18 NaN 56.54660 \n", "19 NaN 56.54570 \n", "38 NaN 57.72218 \n", "39 NaN 57.72290 \n", "58 NaN 55.69844 \n", "\n", "[5 rows x 43 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfsub.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "* Select a subset of rows in the `df` DataFrame that contains data from a pressure range between 500 and 1000 dbar. How many rows did you end up with? What did your neighbor get?\n", "\n", "* You can use the isin command in Python to query a DataFrame based upon a list of values as follows:\n", "```\n", "df[df['STNNBR'].isin([listGoesHere])]\n", "```\n", "Use the `isin` function to find all samples from station numbers 11 and 12." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "pH values are contained in the `df['PH_TOT']` DataArray\n", "\n", "Quality control flags for pH are contained in the `df['PH_TOT_FLAG_W']` DataArray.\n", "\n", "World Ocean Circulation Experiment (WOCE) quality control flags are used: \n", "* 2 = good value\n", "* 3 = questionable value\n", "* 4 = bad value \n", "* 5 = value not reported\n", "* 6 = mean of replicate measurements\n", "* 9 = sample not drawn.\n", "\n", "\n", "1. Create a new DataFrame called `dfsub1` that excludes all bad, questionable and missing pH values. Plot the probability density function for pH." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "dfsub = df[(df['CTDPRS'] > 10) & (df['LATITUDE'] > 40)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Create a new DataFrame called `dfsub2` that excludes all bad, questionable and missing pH and CTD oxygen values. Plot oxygen vs. pH." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "dfsub2 = df[(df['CTDOXY_FLAG_W'] == 2) & (df['PH_TOT_FLAG_W'] == 2)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit a linear model in Python" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'pH$_{Tot}$')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(df['CTDOXY'],df['PH_TOT'],'.')\n", "plt.xlabel('dissolved oxygen [$\\mu$mol/kg]')\n", "plt.ylabel('pH$_{Tot}$')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "result = stats.linregress(dfsub2['CTDOXY'],dfsub2['PH_TOT'])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinregressResult(slope=0.0023050765461032387, intercept=7.202036145940104, rvalue=0.951063101996966, pvalue=0.0, stderr=2.7737424376777215e-05, intercept_stderr=0.00497780264159739)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Plot the linear model with the data." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }