1_Introduction__Solutions.ipynb

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   "source": [
    "# Python Language Basics\n",
    "\n",
    "This notebook introduces some Python language basics. It is designed for a code-along during the workshop. Some code blocks are deliberately incomplete or have bugs included.\n",
    "\n",
    "To execute code: Place your cursor in the cell, then hit SHIFT + ENTER"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "When writing computer code, there are *rules* and there are *conventions* (adapted from David Dempsey's [Python for Geoscientists](https://github.com/ddempsey/python_for_geoscientists) course)\n",
    "\n",
    "- If you break a __\\*rule\\*__, the code will not work.\n",
    "\n",
    "- If you break a __\\*convention\\*__, someone, somewhere puts a mark against your name in a book. At the end of times, there will be a final accounting.\n",
    "\n",
    "Some Python rules:\n",
    "\n",
    "- __\\*Syntax\\*__ - we have very precise expectations about how you write computer code. If you type an opening a bracket, you must close it again later. Some lines must be terminated by a colon, - if you omit this, the code will not work. __\\*Learning syntax for a new code is very pedantic and a total pain.\\*__ But you have to do it anyway, and at least Python returns readable error messages to help you understand your missteps...\n",
    "- __\\*Indentation\\*__ - Python reads indentation (not all languages do) and your code will not work if it is not correctly indented. \n",
    "\n",
    "Some Python conventions:\n",
    "\n",
    "- __\\*Commenting\\*__ - it is helpful for the poor soul who has to read your poorly written Python (sometimes that is you, weeks or months later) if you have included little 'sign-posts' in the code, articulating what you are doing. These are called comments. They begin with a # symbol, after which you can write whatever you like and it will not be executed as a command.\n",
    "- __\\*Sensible variable names\\*__ - a word or words relating to the thing the variable represents but not too long. For example, if a variable contains the mean temperature, then Tmean is a sensible variable name, where as the_mean_temperature_of_the_profile or a1 are not sensible variable names. \n",
    "- __\\*Layout\\*__ - Although the computer reads Python line-by-line, using a vertical layout improves the human readability and your ability to comment code. Headings and the liberal use of whitespace improves readbility. \n",
    "- __\\*Structure\\*__ - Python files and projects have a standard structure that improves redabality. The structure of files vary, but all files should start with an explanation of what they do and all packages/modules should be imported at the top. For the advanced folks, more on Python project structure can be found [here](https://docs.python-guide.org/writing/structure/).\n",
    "\n",
    "Units are a common source of error. Approches to managing this include:\n",
    "- Include units in the variable names (my preferred) `Tmean_degC = 300`\n",
    "- Include in-line comments on units and conversions used (good, but can be lost information) `Tmean = 300 # mean temp in degC`\n",
    "- Consider tools like [Pint](https://pint.readthedocs.io/en/stable/) to manage units, if it is a large problem (good, but assumes everyone knows how to use pint)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Python Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages \n",
    "\n",
    "''' Good practice: \n",
    "Import all libraries/packages used at the top of the document'''\n",
    "\n",
    "import matplotlib.pyplot as plt # foundation module for plotting\n",
    "import numpy as np # for array based programming and advanced math\n",
    "import math # for basic mathematical methods\n",
    "import pandas as pd # for spreadsheet-like methods + more\n",
    "import matplotlib.pyplot as plt # foundation module for plotting\n",
    "import seaborn as sns # for advanced plotting methods\n",
    "import matplotlib.dates as mdates # for formatting datetime in plot axis\n",
    "\n",
    "''' Good practice: \n",
    "Packages have standard short names. \n",
    "Using the standard name improves readability. \n",
    "Look at the docs if you are unsure what the standard name is.\n",
    "For example, https://matplotlib.org/3.5.3/api/_as_gen/matplotlib.pyplot.html) ''';\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import antigravity\n",
    "\n",
    "# installing a package can launch code\n",
    "# be careful what you install! "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Language Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "# variable \n",
    "# the string 'hello world' has been assigned to the variable x\n",
    "\n",
    "x = 'hello world'\n",
    "\n",
    "print(x)\n",
    "print(len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# Types: string, integer, float (more on slide)\n",
    "# Python is a dynamic typed language, checking type (variable state)\n",
    "\n",
    "print(type(x))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data structures and the many forms of list https://docs.python.org/3/tutorial/datastructures.html#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n",
      "<class 'list'>\n",
      "[1 2 3 4]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Lists and array (numpy)\n",
    "\n",
    "my_list = [1,2,3,4]\n",
    "print(my_list)\n",
    "print(type(my_list))\n",
    "\n",
    "my_array = np.array([1,2,3,4])\n",
    "print(my_array)\n",
    "print(type(my_array))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15 15 15 15]\n"
     ]
    }
   ],
   "source": [
    "array_of_10s = np.array([10,10,10,10])\n",
    "array_of_5s = np.array([5,5,5,5])\n",
    "\n",
    "new_array = array_of_5s + array_of_10s\n",
    "\n",
    "print(new_array)\n",
    "\n",
    "# array-based programming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 5, 5, 5, 10, 10, 10, 10]\n"
     ]
    }
   ],
   "source": [
    "list_of_10s = [10,10,10,10]\n",
    "list_of_5s = [5,5,5,5]\n",
    "\n",
    "new_list = list_of_5s + list_of_10s\n",
    "\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['harry', 1, 9.5, [2, 3, 4]]\n",
      "['harry' 1 9.5 list([2, 3, 4])] (4,)\n",
      "[[4 5 6]\n",
      " [1 2 3]] (2, 3)\n"
     ]
    }
   ],
   "source": [
    "# Basics: Complex lists and arrays \n",
    "\n",
    "complex_list = ['harry', 1, 9.5, [2,3,4]]\n",
    "print(complex_list)\n",
    "\n",
    "complex_array = np.array(['harry', 1, 9.5,[2,3,4]],dtype=object)\n",
    "print(complex_array, complex_array.shape)\n",
    "\n",
    "nD_array = np.array([[4,5,6],[1,2,3]])\n",
    "print(nD_array, nD_array.shape)\n",
    "\n",
    "# shape tells us the number of elements in each dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "# tuple - a fixed collection of objects or variables\n",
    "\n",
    "my_tuple = (3, 5, 7)\n",
    "\n",
    "my_object = [9.1,9.2,9.3]\n",
    "\n",
    "complex_tuple = (3, 5, 7, ['d','wow',3.2], 'fish', my_object)\n",
    "\n",
    "print(type(complex_tuple))\n",
    "\n",
    "# Lists vs tuples\n",
    "# Tuples are more memory efficient than lists\n",
    "# Generally, people expect lists to contain one type while it is more acceptable to mix types in a tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "wow\n",
      "9.3\n",
      "9.3\n"
     ]
    }
   ],
   "source": [
    "# indexing (lists, tuples and arrays)\n",
    "\n",
    "print(complex_tuple[1])\n",
    "print(complex_tuple[3][1])\n",
    "\n",
    "print(complex_tuple[5][-1]) # get 9.3 out of complex_tuple\n",
    "print(complex_tuple[5][2]) # get 9.3 out of complex_tuple\n",
    "\n",
    "# note how python starts counting at zero, so the first element = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'blue': '#36648B', 'brown': '#8B5D36'}\n",
      "<class 'dict'>\n",
      "#36648B\n",
      "another blue\n"
     ]
    }
   ],
   "source": [
    "# dictionary - a value is mapped to a key\n",
    "colors = {\n",
    "    'blue': '#36648B', # nice low-saturation blue\n",
    "    'brown': '#8B5D36', # complementary color to the blue\n",
    "}\n",
    "\n",
    "print(colors)\n",
    "\n",
    "print(type(colors))\n",
    "\n",
    "print(colors['blue'])\n",
    "\n",
    "colors['blue'] = 'another blue'\n",
    "print(colors['blue'])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colors in Python:\n",
    "\n",
    "- Hex colors https://www.color-hex.com/\n",
    "- Named colors https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "- Colormaps https://matplotlib.org/stable/tutorials/colors/colormaps.html\n",
    "\n",
    "Beware the impact of color on your analysis! https://hess.copernicus.org/articles/25/4549/2021/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"https://imgs.xkcd.com/comics/painbow_award.png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python as Calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.274333882308138\n"
     ]
    }
   ],
   "source": [
    "\n",
    "radius_m = 3\n",
    "area_m2 = math.pi * radius_m**2\n",
    "\n",
    "print(area_m2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimating well injectivity using field operational data:\n",
    "\n",
    "$$\n",
    "I I=\\frac{Q}{P_{H}+W H P-P_{F}-P_{F Z}}\n",
    "$$\n",
    "\n",
    "where $Q$ Flow rate (t/hr), $P_{H}$ is hydrostatic pressure inside the well (bara), $WHP$ is wellhead pressure (bara), $P_{F}$ is the pressure of frictional losses (bara), and $P_{F Z}$ is the reservoir pressure at the feedzone or pivot point (bara). \n",
    "\n",
    "_(Siega et al. (2014) Quantifying the effect of temperature on well injectivity, New Zealand Geothermal Workshop)_\n",
    "\n",
    "Python uses Latex for formatting mathematical expressions https://www.overleaf.com/learn/latex/Mathematical_expressions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.937219730941706\n"
     ]
    }
   ],
   "source": [
    "# Variable names: \n",
    "# Use common abbreviations or write it out\n",
    "# Do not just use equation terms (Q, Ph, WHP) unless they are universal and defined somewhere in your file\n",
    "\n",
    "flow_rate_tph = 400 \n",
    "hydrostatic_pressure_bara = 115\n",
    "wellhead_pressure_bara = 1\n",
    "friction_loss_bara = 9.7\n",
    "feedzone_pressure_bara = 84\n",
    "\n",
    "ii_tphrpbar = flow_rate_tph / (hydrostatic_pressure_bara + wellhead_pressure_bara - friction_loss_bara - feedzone_pressure_bara)\n",
    "\n",
    "print(ii_tphrpbar)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17.937219730941706, 13.45291479820628, 8.968609865470853, 4.4843049327354265]\n"
     ]
    }
   ],
   "source": [
    "# Using for loops to calculate value for multiple values - iterate over one list\n",
    "\n",
    "flow_rates_tph = [400, 300, 200, 100]\n",
    "\n",
    "varied_rate_ii_tphrpbar = []\n",
    "\n",
    "for rate in flow_rates_tph:\n",
    "    ii = rate / (hydrostatic_pressure_bara + wellhead_pressure_bara - friction_loss_bara - feedzone_pressure_bara)\n",
    "    varied_rate_ii_tphrpbar.append(ii)\n",
    "\n",
    "print(varied_rate_ii_tphrpbar)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithm building: Logical operators and flow control\n",
    "\n",
    "Tests that return True or False\n",
    "\n",
    "### Boolean operators\n",
    "\n",
    "- And\n",
    "- Or\n",
    "- Not\n",
    "\n",
    "### Comparison operators\n",
    "\n",
    "- == Equal to\n",
    "- != Not equal to\n",
    "- <\tLess than\n",
    "- \\> Greater Than\n",
    "- <= Less than or Equal to\n",
    "- \\>= Greater than or Equal to\n",
    "\n",
    "### \"if\" statements\n",
    "\n",
    "- if\n",
    "- elif\n",
    "- else\n",
    "\n",
    "### 'for' loop\n",
    "\n",
    "- pass in a list or array that is iterated over\n",
    "\n",
    "Refer to [this link](https://www.pythoncheatsheet.org/cheatsheet/control-flow) for other options and various examples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "# Basics: \"for loop\" and whitespace\n",
    "for n in [1,2,3,4,5]:\n",
    "    print(n + 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.779552715654953, 10.238907849829353, 7.604562737642587, 4.4843049327354265]\n"
     ]
    }
   ],
   "source": [
    "# Using for loops to calculate value for multiple values - iterate over many lists\n",
    "\n",
    "flow_rates_tph = [400, 300, 200, 100]\n",
    "wellhead_pressures_bara = [10, 8, 5, 1]\n",
    "\n",
    "varied_rate_and_whp_ii_tphrpbar = []\n",
    "\n",
    "for rate, whp in zip(flow_rates_tph, wellhead_pressures_bara):\n",
    "    ii = rate / (hydrostatic_pressure_bara + whp - friction_loss_bara - feedzone_pressure_bara)\n",
    "    varied_rate_and_whp_ii_tphrpbar.append(ii)\n",
    "\n",
    "print(varied_rate_and_whp_ii_tphrpbar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Great well!\n",
      "Great well!\n",
      ":-(\n",
      ":-(\n"
     ]
    }
   ],
   "source": [
    "# Using if statements to dictate behavior\n",
    "\n",
    "flow_rates_tph = [400, 300, 200, 100]\n",
    "wellhead_pressures_bara = [10, 8, 5, 1]\n",
    "\n",
    "for rate, whp in zip(flow_rates_tph, wellhead_pressures_bara):\n",
    "    ii = rate / (hydrostatic_pressure_bara + whp - friction_loss_bara - feedzone_pressure_bara)\n",
    "    if ii > 10:\n",
    "        print('Great well!')\n",
    "    else:\n",
    "        print(':-(')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d7ea4d5760>]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple\n",
    "\n",
    "plt.plot(flow_rates_tph, varied_rate_ii_tphrpbar)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# More flexibility: the axs method\n",
    "# when googling, use \"matplotlib axs\" to get the correct documentation \n",
    "\n",
    "fig, ax = plt.subplots(1,1) \n",
    "\n",
    "# the method plt.subplots() returns two things\n",
    "# one we have called figure (f or fig) and this is analogous to a page in your book\n",
    "# the other is the axis (one or many ax) which is analogous to the plot you draw on the that page\n",
    "\n",
    "ax.plot(\n",
    "    flow_rates_tph,\n",
    "    varied_rate_ii_tphrpbar,\n",
    "    color = colors['brown'],\n",
    "    marker = 'o',\n",
    "    label = 'Stable WHP'\n",
    "    )\n",
    "\n",
    "ax.plot(\n",
    "    flow_rates_tph,\n",
    "    varied_rate_and_whp_ii_tphrpbar,\n",
    "    color = 'k',\n",
    "    marker = 'o',\n",
    "    label = 'Increased WHP with increasing Q'\n",
    "    )\n",
    "\n",
    "ax.set_ylabel('II [T/hr/bar]')\n",
    "ax.set_xlabel('Flow rate [T/hr]')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "\n",
    "# https://github.com/ICWallis/tutorial-publication-ready-figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.53981633974483\n"
     ]
    }
   ],
   "source": [
    "# Functions = re-useable tools\n",
    "\n",
    "def circle_area(radius):\n",
    "    '''Calculate the area of a circle from the radius'''\n",
    "\n",
    "    area = math.pi * radius**2\n",
    "\n",
    "    return area\n",
    "\n",
    "a = circle_area(5)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.94\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Function\n",
    "#\n",
    "\n",
    "def ii_from_operational_conditions(Q,Ph,WHP,Pf,Pfz):\n",
    "    '''Calculate injectivity using field operational data\n",
    "    \n",
    "    For usage and case study, refer to Siega et al. (2014) \n",
    "    Quantifying the effect of temperature on well injectivity, \n",
    "    New Zealand Geothermal Workshop\n",
    "\n",
    "        Args:\n",
    "            Q (float): flow rate - t/hr\n",
    "            Ph (float): hydrostatic pressure inside the well - bara\n",
    "            WHP (float): wellhead pressure - bara\n",
    "            Pf (float): pressure due to friction - bara\n",
    "            Pfz (float): reservoir pressure at the feedzone or pivot point - bara\n",
    "\n",
    "        Returns:\n",
    "            injectivity - T/hr/bar\n",
    "    '''\n",
    "\n",
    "    ii = Q / (Ph + WHP - Pf - Pfz)\n",
    "\n",
    "    return ii\n",
    "\n",
    "#\n",
    "# Usage\n",
    "#\n",
    "\n",
    "# Input parameters for well WJ-13\n",
    "Q_tph = 400 # average for June\n",
    "Ph_bara = 115 # calculated using depth to feedzone and average injection temperature\n",
    "WHP_bara = 1 # average for June\n",
    "Pf_bara = 9.7 # calculated by finding the root of the Colebrook equation\n",
    "Pfz_bara = 84 # from the reservoir pressure correlation\n",
    "\n",
    "WJ13_June_ii_tphrpbar = ii_from_operational_conditions(Q_tph, Ph_bara, WHP_bara, Pf_bara, Pfz_bara)\n",
    "\n",
    "print(round(WJ13_June_ii_tphrpbar,2))\n",
    "\n",
    "\n",
    "# Functions can be placed in a file that is imported at the start\n",
    "# This reduces the code you are looking at\n",
    "# and enables you to focus on the science, rather than the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well</th>\n",
       "      <th>Date</th>\n",
       "      <th>Q_tph</th>\n",
       "      <th>Ph_bara</th>\n",
       "      <th>WHP_bara</th>\n",
       "      <th>Pf_bara</th>\n",
       "      <th>Pfz_bara</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Well       Date  Q_tph  Ph_bara  WHP_bara  Pf_bara  Pfz_bara\n",
       "0  W-1 2022-01-01    400      115       1.0     9.70        84\n",
       "1  W-1 2022-02-01    400      115       1.5     9.70        84\n",
       "2  W-1 2022-03-01    400      115       2.0     9.70        84\n",
       "3  W-1 2022-04-01    400      115       2.8     9.70        84\n",
       "4  W-2 2022-01-01    200      100       8.0     9.68        70\n",
       "5  W-2 2022-02-01    200      100       5.0     9.68        70\n",
       "6  W-2 2022-03-01    200      100       2.0     9.68        70\n",
       "7  W-2 2022-04-01    200      100       1.0     9.68        70"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'1_Introduction__Case_study_data.xlsx', sheet_name='Sheet1')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   Well      8 non-null      object        \n",
      " 1   Date      8 non-null      datetime64[ns]\n",
      " 2   Q_tph     8 non-null      int64         \n",
      " 3   Ph_bara   8 non-null      int64         \n",
      " 4   WHP_bara  8 non-null      float64       \n",
      " 5   Pf_bara   8 non-null      float64       \n",
      " 6   Pfz_bara  8 non-null      int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(1)\n",
      "memory usage: 576.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well</th>\n",
       "      <th>Date</th>\n",
       "      <th>Q_tph</th>\n",
       "      <th>Ph_bara</th>\n",
       "      <th>WHP_bara</th>\n",
       "      <th>Pf_bara</th>\n",
       "      <th>Pfz_bara</th>\n",
       "      <th>ii_tphrpbar</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.70</td>\n",
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       "      <td>2022-03-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "      <td>17.167382</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "      <td>16.597510</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2022-01-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>7.062147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>8.960573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>9.380863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Well       Date  Q_tph  Ph_bara  WHP_bara  Pf_bara  Pfz_bara  ii_tphrpbar\n",
       "0  W-1 2022-01-01    400      115       1.0     9.70        84    17.937220\n",
       "1  W-1 2022-02-01    400      115       1.5     9.70        84    17.543860\n",
       "2  W-1 2022-03-01    400      115       2.0     9.70        84    17.167382\n",
       "3  W-1 2022-04-01    400      115       2.8     9.70        84    16.597510\n",
       "4  W-2 2022-01-01    200      100       8.0     9.68        70     7.062147\n",
       "5  W-2 2022-02-01    200      100       5.0     9.68        70     7.898894\n",
       "6  W-2 2022-03-01    200      100       2.0     9.68        70     8.960573\n",
       "7  W-2 2022-04-01    200      100       1.0     9.68        70     9.380863"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ii_tphrpbar'] = ii_from_operational_conditions(df.Q_tph, df.Ph_bara, df.WHP_bara, df.Pf_bara, df.Pfz_bara)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date', ylabel='ii_tphrpbar'>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Advanced plot for data analysis\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,6))\n",
    "\n",
    "# https://seaborn.pydata.org/generated/seaborn.scatterplot.html\n",
    "\n",
    "sns.scatterplot(\n",
    "    x='Date', # Pandas column name for x data\n",
    "    y='ii_tphrpbar', # Pandas column name for y data\n",
    "    data=df, # Pandas dataframe name\n",
    "    ax=ax, # name of the axis that the seaborn plot goes in\n",
    "    s=140, # marker size\n",
    "    hue='WHP_bara', # marker colour\n",
    "    style='Well', # marker style\n",
    ")\n",
    "\n",
    "#plt.savefig('Case_study_plot1.png',facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Irene\\miniconda3\\envs\\workshop_env\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\nplt.savefig(\\n    'Case_study_plot2.png',\\n    dpi=400,\\n    facecolor='w',\\n    bbox_inches='tight'\\n    )\""
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building on the above code to make a nice version for a report/publication\n",
    "\n",
    "# Make empty plot\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,4))\n",
    "\n",
    "# Add data to plot\n",
    "sns.scatterplot(\n",
    "    x='Date', # Pandas column name for x data\n",
    "    y='ii_tphrpbar', # Pandas column name for y data\n",
    "    data=df, # Pandas dataframe name\n",
    "    ax=ax, # name of the axis that the seaborn plot goes in\n",
    "    s=140, # marker size\n",
    "    style='Well', # marker style\n",
    "    hue='WHP_bara', # marker colour\n",
    "    #palette='cividis',\n",
    "    #size='Q_tph',\n",
    "    #sizes=(140, 200)\n",
    "    zorder=10 # places markers in front of grid\n",
    ")\n",
    "\n",
    "# Seaborn color pallets https://seaborn.pydata.org/tutorial/color_palettes.html\n",
    "\n",
    "# Format axis\n",
    "ax.set_ylim(5,20)\n",
    "\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%d %b\"))\n",
    "\n",
    "ax.set_xlabel('') # Replaces auto date label with nothing\n",
    "\n",
    "ax.set_ylabel('II [T/hr/bar]')\n",
    "\n",
    "# Place legend outside the plot area\n",
    "ax.legend(\n",
    "    loc='center left', \n",
    "    bbox_to_anchor=(1.05, 0.45), \n",
    "    ncol=1,\n",
    ")\n",
    "\n",
    "# Add title\n",
    "plt.title('Injection Well Performance Q1 2022')\n",
    "\n",
    "# Add grid\n",
    "ax.grid(linestyle=':', alpha=0.8)\n",
    "\n",
    "# Export figure\n",
    "'''\n",
    "plt.savefig(\n",
    "    'Case_study_plot2.png',\n",
    "    dpi=400,\n",
    "    facecolor='w',\n",
    "    bbox_inches='tight'\n",
    "    )'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sub-select data using comparison operator  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Well', 'Date', 'Q_tph', 'Ph_bara', 'WHP_bara', 'Pf_bara', 'Pfz_bara',\n",
       "       'ii_tphrpbar'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well</th>\n",
       "      <th>Date</th>\n",
       "      <th>Q_tph</th>\n",
       "      <th>Ph_bara</th>\n",
       "      <th>WHP_bara</th>\n",
       "      <th>Pf_bara</th>\n",
       "      <th>Pfz_bara</th>\n",
       "      <th>ii_tphrpbar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "      <td>17.937220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "      <td>17.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "      <td>17.167382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9.70</td>\n",
       "      <td>84</td>\n",
       "      <td>16.597510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>7.062147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>7.898894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>8.960573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>W-2</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.68</td>\n",
       "      <td>70</td>\n",
       "      <td>9.380863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Well       Date  Q_tph  Ph_bara  WHP_bara  Pf_bara  Pfz_bara  ii_tphrpbar\n",
       "0  W-1 2022-01-01    400      115       1.0     9.70        84    17.937220\n",
       "1  W-1 2022-02-01    400      115       1.5     9.70        84    17.543860\n",
       "2  W-1 2022-03-01    400      115       2.0     9.70        84    17.167382\n",
       "3  W-1 2022-04-01    400      115       2.8     9.70        84    16.597510\n",
       "4  W-2 2022-01-01    200      100       8.0     9.68        70     7.062147\n",
       "5  W-2 2022-02-01    200      100       5.0     9.68        70     7.898894\n",
       "6  W-2 2022-03-01    200      100       2.0     9.68        70     8.960573\n",
       "7  W-2 2022-04-01    200      100       1.0     9.68        70     9.380863"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using reassignment - take care of notebook order!\n",
    "df = df[df.Well == 'W-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well</th>\n",
       "      <th>Date</th>\n",
       "      <th>Q_tph</th>\n",
       "      <th>Ph_bara</th>\n",
       "      <th>WHP_bara</th>\n",
       "      <th>Pf_bara</th>\n",
       "      <th>Pfz_bara</th>\n",
       "      <th>ii_tphrpbar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>84</td>\n",
       "      <td>17.937220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.7</td>\n",
       "      <td>84</td>\n",
       "      <td>17.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>84</td>\n",
       "      <td>17.167382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W-1</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>400</td>\n",
       "      <td>115</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>84</td>\n",
       "      <td>16.597510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Well       Date  Q_tph  Ph_bara  WHP_bara  Pf_bara  Pfz_bara  ii_tphrpbar\n",
       "0  W-1 2022-01-01    400      115       1.0      9.7        84    17.937220\n",
       "1  W-1 2022-02-01    400      115       1.5      9.7        84    17.543860\n",
       "2  W-1 2022-03-01    400      115       2.0      9.7        84    17.167382\n",
       "3  W-1 2022-04-01    400      115       2.8      9.7        84    16.597510"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Where to from here?\n",
    "\n",
    "__\\*The best way forward is to learn by doing!\\*__\n",
    "\n",
    "I most commonly use python to...\n",
    "\n",
    "Document methods and analysis in Jupyter notebooks:\n",
    "- Methods are displayed in sequence\n",
    "- Can combine notes, equations, views on the data and plots in an easily read format\n",
    "- Can be exported to html so non-coding folks can read them\n",
    "\n",
    "Automate tasks: \n",
    "- Especially useful for repetitive data processing tasks\n",
    "- [Automate the Boring Stuff](https://automatetheboringstuff.com/) is a great book for getting started with Python and automation\n",
    "\n",
    "Handle large datasets, do complex calculations, make interactive graphics, and more! \n",
    "\n",
    "__Learn more Python skills__ using on-line resources:\n",
    "- A [list of useful resources](https://mharty3.github.io/resources/) for geoscientists learning python has been put together by Michael Harty\n",
    "- [Practical Python](https://dabeaz-course.github.io/practical-python/Notes/Contents.html) great course if you can survive stock tickers. Self driven and comprehensive. \n",
    "- David Dempsey's [Python for Geoscientists](https://github.com/ddempsey/python_for_geoscientists) specific to geosciences. Designed to be classroom taught, but chock-full of useful examples. \n",
    "- [Plotting with Python](https://github.com/ICWallis/tutorial-publication-ready-figures) for basic to advanced plotting with matplotlib. Designed to be classroom taught, but see solutions and recipes folders for code examples.\n",
    "- [Software carpentry](https://software-carpentry.org/lessons/) a wide range of learning resources\n",
    "- [EdX course](https://www.edx.org/course/introduction-computer-science-harvardx-cs50x)\n",
    "\n",
    "\n",
    "__Find your community__ and join [Software Underground](https://softwareunderground.org/) to connect with people who do computers + subsurface\n",
    "- Active slack community where people help each other (note that they are currently moving to a new platform because of $$)\n",
    "- Annual virtual conference with lots of free training opportunities\n",
    "- [SWUG YouTube channel](https://www.youtube.com/c/softwareunderground), with loads of subsurface-relevant tutorials \n",
    "\n",
    "Don't be discouraged. It takes time to learn any language, including Python. But even if it takes years to become fluent, just a few Python basics can open a whole new world up for you. Have a read of this post by [Peter Norvig](https://norvig.com/21-days.html) for some sage advice on how to learn programming.\n"
   ]
  }
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