{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "UszPR4dNY8sW",
        "J2FPr4LV86OO",
        "aUDER1Q4Htyy",
        "F31svxgPNP_t",
        "zyTGovhuH1_p",
        "AyEv5zepGLGE",
        "3TIfVm6TmBVP",
        "ESibLGhhRp8n",
        "hv0lpsGFldF_",
        "tYzchKpM7DlA"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **MATE Floats! Coding Notebook** - Day 2\n",
        "\n",
        "Created by Ethan C. Campbell for NCAT/MATE/GO-BGC Marine Technology Summer Program\n",
        "\n",
        "Tuesday, August 22, 2023"
      ],
      "metadata": {
        "id": "OxvLAQ1SWpeR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 1: Python and notebooks"
      ],
      "metadata": {
        "id": "UszPR4dNY8sW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Computer code** allows us to work with data, create visualizations, and create repeatable scientific workflows. It is an integral part of the modern scientific method!\n",
        "\n",
        "Every programming language has a specific **syntax**. In English as well as programming languages, syntax describes valid combinations of symbols and words:\n",
        "* Syntactically invalid: \"boy dog cat\"\n",
        "* Syntactically valid: \"boy hugs cat\"\n",
        "* Syntactically valid (but **semantically** invalid): \"cat hugs boy\"\n",
        "\n",
        "**Semantics** refer to whether a phrase has meaning. It's up to us to write computer code that has scientific meaning and is useful. The computer will allow us to write code that is syntactically valid but semantically – or scientifically – incorrect!\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "stif3BkqXGuD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Programming 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)"
      ],
      "metadata": {
        "id": "Djp9pEel9qA6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "(*Image source: [stackoverflow.blog](https://stackoverflow.blog/2017/09/06/incredible-growth-python/)*)\n",
        "\n",
        "No programming language is perfect. As the inventor of C++ once said, *“There are only two kinds of programming languages: the ones people complain about and the ones nobody uses.”*\n",
        "\n",
        "However, there are many reasons that we use Python instead of other programming languages, like MATLAB, Java, or C:\n",
        "- It's free!\n",
        "- It's old, so it's very stable (Python was created in 1991)\n",
        "- It can do almost anything\n",
        "- It's incredibly popular inside and outside of science (so it could help you land a job)\n",
        "- It's open source, which means anyone can help to improve it\n",
        "- It reads a bit like written English, so it's easier to write and understand\n",
        "\n",
        "***Question: How many of you have heard of Python before this course? Who has written code in Python (or a different language) before?***"
      ],
      "metadata": {
        "id": "E752-6589-dV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "This web page is called a **notebook**. It lets us write and run computer code, and the results get displayed and saved alongside the code. If you download this notebook in the File menu, the file extension will be `.ipynb`.\n",
        "\n",
        "Sometimes it makes more sense to create a **script** instead of a notebook. Scripts are code files that run from top to bottom, and they don't save the output.\n",
        "\n",
        "***Question: When we run Python code in this notebook, where is the code actually being run?***"
      ],
      "metadata": {
        "id": "YkJewcBFh3eR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "First, we always have to load **packages** into the notebook using the `import` command! Packages give us additional **functions** that allow us to get more stuff done.\n",
        "\n",
        "To run a coding cell, you can click the \"play\" button or type `Shift`-`Enter` (PC) or `Shift`-`Return` (Mac) on your keyboard. ***Try this with the cell below:***"
      ],
      "metadata": {
        "id": "db2A18q6WXtQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9O6SthNqtT8"
      },
      "outputs": [],
      "source": [
        "import numpy as np                         # NumPy is an array and math library\n",
        "import matplotlib.pyplot as plt            # Matplotlib is a visualization (plotting) library\n",
        "import pandas as pd                        # Pandas lets us work with spreadsheet (.csv) data\n",
        "from datetime import datetime, timedelta   # Datetime helps us work with dates and times"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we write `import numpy as np`, we are saying: \"import the package NumPy and we will access it using the abbreviation `np` from here onwards.\" You could technically write any abbreviation, but `np` is standard for NumPy."
      ],
      "metadata": {
        "id": "vmaVXRWMAxMO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Often we'd like to add notes to our code. You can do this using **comments**, notated above using a \\# (hash) symbol. Everything after the \\# is ignored and not treated like code."
      ],
      "metadata": {
        "id": "_b8DR4MoAlCW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 2: Variables and math"
      ],
      "metadata": {
        "id": "J2FPr4LV86OO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use Python as a calculator. Run the cell below:"
      ],
      "metadata": {
        "id": "ESXwBOoGW8qS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "3 + 9"
      ],
      "metadata": {
        "id": "5LUJIIQ6XWi0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that parentheses can be used to change the order of operations:"
      ],
      "metadata": {
        "id": "SOh05mH6EpLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "1 + 2 * 3 + 4"
      ],
      "metadata": {
        "id": "P06zR16eEi3O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(1 + 2) * (3 + 4)"
      ],
      "metadata": {
        "id": "tLOCMyTBEvck"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If Python doesn't recognize the code, it will give an **error**.\n",
        "\n",
        "***What helpful information does the following error message include?***"
      ],
      "metadata": {
        "id": "s5D32op-iCGK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "3 + hello"
      ],
      "metadata": {
        "id": "uCPfRriciBXp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Try doing some math yourself below. ***Question: Can you figure out how to multiply and divide numbers?***"
      ],
      "metadata": {
        "id": "P3n99f5yXZPs"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PmQitlaNXfKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usually, Python needs to be told when to \"print\" something to the screen. For this, we use the **`print()`** function:"
      ],
      "metadata": {
        "id": "B3kVB6JVXksP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Hello world!')"
      ],
      "metadata": {
        "id": "PQeI0aJbXstQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Try writing code to print a different message:***"
      ],
      "metadata": {
        "id": "gnjpRXeOiZsz"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WGWdyQjEibZE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note how comments are used in two ways below, both to describe a section of code and to annotate a specific line:"
      ],
      "metadata": {
        "id": "qetc0zzL13rG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This is a section comment\n",
        "print('This is not a comment')\n",
        "print('This is also not a comment')  # This is a line comment"
      ],
      "metadata": {
        "id": "CWCYvrgX2IX9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In Python, we use **variables** to store information. Variables can be numbers (**integers** or **floats**), combinations of characters (called **strings**), a **boolean** (which are either True or False), or other things that are generally called \"**objects**\".\n",
        "\n",
        "To save a variable, we use the equal sign (`=`). You can name your variable anything descriptive, as long as it's one word! Note that underscore (`_`) can be used to join words in a variable name."
      ],
      "metadata": {
        "id": "2yuXcWy5XxWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = -5             # This variable is an \"integer\" because is a whole number (a number without a decimal point)\n",
        "almost_ten = 9.9   # This variable is a \"float\" because is a floating point number (a number with a decimal point)\n",
        "scientific = 2e3   # This variable is also a float, and is written in scientific notation: 2.0 x 10^3 = 1000\n",
        "\n",
        "mate = 'FLOATS'    # This variable is a string\n",
        "mate_2 = \"FLOATS\"  # You can also specify strings using double quotation marks\n",
        "\n",
        "boolean = True     # This variable is a boolean"
      ],
      "metadata": {
        "id": "OGHwCzCiYOiv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(a)"
      ],
      "metadata": {
        "id": "p-6FzKFwYNJ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(almost_ten)"
      ],
      "metadata": {
        "id": "47-LhFOaYQ0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(scientific)"
      ],
      "metadata": {
        "id": "XumLM8cKGAiC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(mate)\n",
        "print(mate_2)"
      ],
      "metadata": {
        "id": "qD3PPGarYXdF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(boolean)"
      ],
      "metadata": {
        "id": "Hegia9C2GdUw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can do math at the same time that you create a variable!"
      ],
      "metadata": {
        "id": "0rlqnIOZ9NlL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = 2023 - 1913\n",
        "print(result)"
      ],
      "metadata": {
        "id": "PuWeV09m9VA_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Try the following:***\n",
        "1. ***Search on Google for the formula to convert Fahrenheit to Celsius.***\n",
        "2. ***Save a variable with the current Seattle temperature in Fahrenheit (feel free to guess, or look it up).***\n",
        "3. ***Then create a new variable with that temperature converted into Celsius (using math).***\n",
        "4. ***Print that result!***"
      ],
      "metadata": {
        "id": "OBmYHJ93MZO1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "oV3vRYprMont"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also change a variable using this compact notation:\n",
        "* `a += b` is the same as `a = a + b`\n",
        "* `a -= b` is the same as `a = a - b`\n",
        "* `a *= b` is the same as `a = a * b`"
      ],
      "metadata": {
        "id": "4KD9Yq3lFHH2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result += 50\n",
        "print(result)"
      ],
      "metadata": {
        "id": "zPlOmwLpFcu9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that Python treats booleans (True and False) like the integers 1 and 0, respectively. ***This means you can do math with booleans. What will the code produce below, and why?***"
      ],
      "metadata": {
        "id": "aIjuN0miGoUt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print((False * 5) + (True * 3))"
      ],
      "metadata": {
        "id": "pIt2B0QQG6TX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***What happens when you add two strings together? Try it below.***"
      ],
      "metadata": {
        "id": "1rp4Jnh27cgX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "K1CmVOqj7hYP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 3: Lists, 1-D arrays, indexing, and slicing"
      ],
      "metadata": {
        "id": "aUDER1Q4Htyy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To store multiple numbers, we use **lists** or **NumPy arrays**. Lists and arrays are types of variables, and NumPy is one of the packages that we imported at the top. Here's how we create a list or array:"
      ],
      "metadata": {
        "id": "_u4V8X5zYWnc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_list = [1,2,3,4,5]"
      ],
      "metadata": {
        "id": "DeEk5f6tGt1I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_array = np.array([1,2,3,4,5,6,7,8,9])"
      ],
      "metadata": {
        "id": "stU_2biAYpWF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(my_list)\n",
        "print(my_array)"
      ],
      "metadata": {
        "id": "1ZZAFrtPYqTi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can add elements to the end of a list by **appending**. The syntax is:\n",
        "\n",
        "> **`list_name.append(NEW_ELEMENT)`**"
      ],
      "metadata": {
        "id": "KF7f04zmPAva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Append to the list that you created earlier:\n",
        "my_list.append(6)\n",
        "my_list.append(7)\n",
        "print(my_list)"
      ],
      "metadata": {
        "id": "3l95QDNjPON5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can convert a list to an array by putting it inside **`np.array()`**:"
      ],
      "metadata": {
        "id": "lakCzdpAOyys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.array(my_list))"
      ],
      "metadata": {
        "id": "qVHEFrDVO30V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A list can store a combination of numbers and strings, while an array can store only one variable type (so just numbers OR just strings):"
      ],
      "metadata": {
        "id": "tTFXh0wFH_WO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combo_list = ['element #1', 2, 'element #3', 4]\n",
        "print(combo_list)"
      ],
      "metadata": {
        "id": "cNOev1VOH-rf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Naturally, we can do math with arrays. This is very useful!\n",
        "\n",
        "***Before running the cells below, what do you expect will be the result of each line of code?***"
      ],
      "metadata": {
        "id": "55hydvn0YtqH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_array + 5"
      ],
      "metadata": {
        "id": "eLmXjAhFYs8U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_array * 2"
      ],
      "metadata": {
        "id": "ob3atI21Y1WW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_array + my_array"
      ],
      "metadata": {
        "id": "185UbNiqY3Db"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***What happens when you add two lists together? Try it!***"
      ],
      "metadata": {
        "id": "_V4cxdvBQwdy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "SrKe3oWZQ1bg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we want to retrieve certain elements from a list or array, we need to count the position of the elements, which we call an **index**. More than one index are **indices**. For example:\n",
        "\n",
        "* List: `['A', 'B', 'C', 'D', 'E', 'F', 'G']`\n",
        "\n",
        "* Indices: A = 0, B = 1, C = 2, D = 3, E = 4, F = 5, G = 6\n",
        "\n",
        "To extract the element, we can **index** or **slice** into the list or array using a bracket **[ ]** after the variable name:\n",
        "\n",
        "* Indexing: **`variable_name[INDEX]`**\n",
        "* Slicing: **`variable_name[START (optional) : END (optional)]`** (note: `END` is exclusive, so it is the index after the final element that you want)\n",
        "\n",
        "***Run each cell below and think about why the results make sense:***"
      ],
      "metadata": {
        "id": "oTg8kxr7GB1i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "year = [2,0,2,3]\n",
        "print(year)"
      ],
      "metadata": {
        "id": "VfMxSqQESQxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Examples of indexing:\n",
        "print(year[0])\n",
        "print(year[3])\n",
        "print(year[-1])   # This is pretty cool! Negative indexing counts backwards from the end"
      ],
      "metadata": {
        "id": "31P9AAA63yxZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Examples of slicing:\n",
        "print(year[0:4])\n",
        "print(year[:2])"
      ],
      "metadata": {
        "id": "HF2d3rOc3zD5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Can you find two different ways to extract the last two elements (`['2','3']`) of the variable `year`?***\n",
        "\n",
        "***Try using one of them to save (`['2','3']`) into a new variable.***"
      ],
      "metadata": {
        "id": "UfnTZRSI5Q91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "9AtXnl7A5tL9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similarly, you can use indexing or slicing to assign new values in a list or array:"
      ],
      "metadata": {
        "id": "fzu-AQ4pTbSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "array_to_modify = np.array([10,20,30,40,50])\n",
        "array_to_modify[0] = 0\n",
        "array_to_modify[1:4] = np.array([21,31,41])\n",
        "array_to_modify[4] *= 2"
      ],
      "metadata": {
        "id": "wvH6Lpb4Ti9d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***What will `array_to_modify` be after these modifications? Test your prediction by printing the variable below:***"
      ],
      "metadata": {
        "id": "vlfG--UHT_pY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "ZSBSfaHEUJCQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***What happens when you index or slice into a string? Try it!***"
      ],
      "metadata": {
        "id": "fW9RymUp9st2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "CVt-kKZF90xq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 4: 2-D arrays"
      ],
      "metadata": {
        "id": "F31svxgPNP_t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NumPy arrays can also be **two-dimensional** (or higher dimensions). Whoa!\n",
        "\n",
        "This allows us to represent data on multiple **axes** using nested brackets: **[ [ ], [ ], [ ], etc. ]**. Below, I've created a 2-D NumPy array where each column is average monthly temperature for a city. Each row is a different city. I've found the data for [Pasadena, CA](https://en.climate-data.org/north-america/united-states-of-america/california/pasadena-715014/#climate-table) (top row - index 0) and [Seattle, WA](https://en.climate-data.org/north-america/united-states-of-america/washington/seattle-593/#climate-table) (bottom row - index 1) on [climate-data.org](https://en.climate-data.org/)."
      ],
      "metadata": {
        "id": "f-vWngOeHAP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = np.array([[53.6,53.9,57.3,60.5,64.8,70.1,75.7,76.4,74.1,67.3,59.8,52.9],   # (Pasadena)\n",
        "                 [40.0,40.6,44.2,48.4,54.9,60.2,66.2,66.7,60.5,52.0,44.5,39.6]])  # (Seattle)\n",
        "\n",
        "print(temp)"
      ],
      "metadata": {
        "id": "UtAc_AUKHFZC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just like `len()` gives the length of a 1-D array, the command **`.shape`** (a property, not a function!) gives the dimensions of a 2-D (or 3-D, 4-D, etc.) array:"
      ],
      "metadata": {
        "id": "3MpPjhtuknQg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp.shape    # returns: (number of rows, number of columns)"
      ],
      "metadata": {
        "id": "jOqkONCIkwpS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Axis 0** goes across rows and **axis 1** goes down columns.\n",
        "\n",
        "We still index and slice into 2-D arrays using brackets, with the index for each dimension separated by a comma: `,`:\n",
        "\n",
        "> **`array_name[ROW_INDEX, COLUMN_INDEX]`**\n",
        "\n",
        "So we'd get the temperature in Pasadena (row index 0) in June (month #6, so column index 5) by writing:"
      ],
      "metadata": {
        "id": "foHcxcjTJFgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(temp[0,5])"
      ],
      "metadata": {
        "id": "8QsmFBS_JFAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Use indexing to retrieve the December average temperature in Seattle. Print your result:***"
      ],
      "metadata": {
        "id": "HvY3DkuCLGaK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code below\n"
      ],
      "metadata": {
        "id": "P7Ki5VVqLMY-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Slicing works the same way. Instead of a single row or column index, use the range of indices:\n",
        "\n",
        "> **`array_name[ROW_START:ROW_END, COLUMN_START:COLUMN_END]`**\n",
        "\n",
        "To get all the elements along a certain axis, just use a single colon, `:`."
      ],
      "metadata": {
        "id": "q08mizMUJ9Mn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Try using slicing to get the temperatures for the first half of the year for Pasadena:***"
      ],
      "metadata": {
        "id": "vFO3sKq0LZtj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code below\n"
      ],
      "metadata": {
        "id": "N_iFNlNELfuN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Next, try using slicing to obtain the average temperatures for both cities in August:***"
      ],
      "metadata": {
        "id": "DRvUqWbrLCeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code below\n"
      ],
      "metadata": {
        "id": "qDGhe5fuLkjj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Finally, using slicing and mathematical operations to calculate the average temperatures for both cities between December to February (three months). You got this!***"
      ],
      "metadata": {
        "id": "QJ6ZQMAbL0a5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code below\n"
      ],
      "metadata": {
        "id": "HB_tMQP_MAFP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 5: Functions"
      ],
      "metadata": {
        "id": "zyTGovhuH1_p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You already know two functions: `print()` and `np.array()`. Functions usually take at least one input \"**argument**\" inside the parentheses, with multiple arguments separated by commas. Then the function \"**returns**\" or \"**outputs**\" something back.\n",
        "\n",
        "Let's learn a few other functions...\n",
        "\n",
        "The function **`len(INPUT)`** returns the length of a list, array, or string. ***Do the following outputs make sense based on the input arguments?***"
      ],
      "metadata": {
        "id": "yyBf1Z4xYlcY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "year = np.array([2,0,2,3])\n",
        "array_digits = len(year)\n",
        "print(array_digits)"
      ],
      "metadata": {
        "id": "zzcRF9jzMLGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "year = '2023'\n",
        "str_digits = len(year)\n",
        "print(str_digits)"
      ],
      "metadata": {
        "id": "TxHtHB09MUcz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The NumPy function **`np.arange(START, END, INTERVAL)`** creates a list of numbers from START to END with a certain INTERVAL between each number.\n",
        "\n",
        "***Can you guess what the result of the code below will be?***"
      ],
      "metadata": {
        "id": "ccT1EH-aLxgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.arange(0,100,5)"
      ],
      "metadata": {
        "id": "gEV7V5IXZXiD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that **`np.arange(END)`** is a shorter way of writing **`np.arange(0,END,1)`**:"
      ],
      "metadata": {
        "id": "MlBxrW2iSEQr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.arange(10))\n",
        "print(np.arange(0,10,1))"
      ],
      "metadata": {
        "id": "Ts_1PauNSMNR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Additionally, the NumPy package has many useful functions for mathematical operations:\n",
        "\n",
        "* `np.mean(INPUT)` calculates the average value of elements in an `INPUT` list or array\n",
        "* `np.sum(INPUT)` calculates the sum of elements in an `INPUT` list or array\n",
        "* `np.max(INPUT)` and `np.min(INPUT)` find the maximum or minimum values in `INPUT`\n",
        "* `np.ones(N)` creates a new array of length `N` filled with the integer `1`\n",
        "* `np.zeros(N)` creates a new array of length `N` filled with the integer `0`\n",
        "\n",
        "For example:"
      ],
      "metadata": {
        "id": "j7r_aHOBKj63"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Do some math on arrays:\n",
        "test = np.array([1,2,3])\n",
        "print(np.mean(test))\n",
        "print(np.sum(test))\n",
        "print(np.max(test))\n",
        "\n",
        "# Create new arrays:\n",
        "print(np.ones(5))\n",
        "print(np.zeros(5))"
      ],
      "metadata": {
        "id": "57G_kWHPLOis"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Many functions can be **called** (applied) to a variable in two different ways. For example:"
      ],
      "metadata": {
        "id": "SDu-P969RI9_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(test)    # Option 1"
      ],
      "metadata": {
        "id": "HQEVUCGIROpg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.mean()      # Option 2 (same result!)"
      ],
      "metadata": {
        "id": "PSI8XXGTRRho"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To learn more about a function, you can always consult its online **documentation**! A package's documentation website usually has a page for each function describing its arguments, outputs, and examples of how to use it.\n",
        "\n",
        "***Google \"numpy mean\" to find the documentation page for that function. How is the webpage structured, and what information does it tell us about the arguments needed to apply `np.mean()` to 2-D arrays?***\n",
        "\n",
        "Now that you've discovered named arguments... ***use `np.mean()` to calculate and print the average annual (yearly) temperature in Seattle using the variable `temp` from earlier:***"
      ],
      "metadata": {
        "id": "Sds1U_tEE1vr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "A1sFRubGNqvb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 6*: Logical operations"
      ],
      "metadata": {
        "id": "AyEv5zepGLGE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Often, we will want to compare two numbers or variables. We do this using the following **logical operations**:\n",
        "\n",
        "* `==` : equal\n",
        "* `!=` : not equal\n",
        "* `>` : greater than\n",
        "* `>=` : greater than or equal to\n",
        "* `<` : less than\n",
        "* `<=` : less than or equal to\n",
        "* `and` or `&` : are both booleans true?\n",
        "* `or` or `|` : is either boolean true?\n",
        "* `not` or `~` : reverse the boolean (True -> False, False -> True)\n",
        "* `in` : is a member\n",
        "* `not in` : is not a member\n",
        "\n",
        "Each logical operation **evaluates to** (returns) a boolean — True or False. Consider the following examples:"
      ],
      "metadata": {
        "id": "daEFmmF-WWwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "3 == 3"
      ],
      "metadata": {
        "id": "vicDzeODXYQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "3 == 3.0    # integers can be compared to floating-point numbers"
      ],
      "metadata": {
        "id": "AYo-JMq9XiI6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "not 3 == 3"
      ],
      "metadata": {
        "id": "rLtyg4YuYRGj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "3 == 5"
      ],
      "metadata": {
        "id": "Vf-bE8BdXcsv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "3 != 5"
      ],
      "metadata": {
        "id": "XkQYFoOsXeO5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "3 > 5"
      ],
      "metadata": {
        "id": "BVTJ7DsuXfcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "5 <= 5"
      ],
      "metadata": {
        "id": "A01RZl1yXrz4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(11 == 12) or (12 == 12)"
      ],
      "metadata": {
        "id": "65J31-aKdojg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(11 == 12) and (12 == 12)"
      ],
      "metadata": {
        "id": "ZIf4G-zQduE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying a logical comparison to a NumPy array gives a **boolean array**!"
      ],
      "metadata": {
        "id": "H5CEY27qXJKx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([1,2,3,4,5,6])\n",
        "\n",
        "print(x < 4)\n",
        "print(x <= 4)"
      ],
      "metadata": {
        "id": "HM9BJ_oGXwIv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Note: \"not\" can't be applied to an entire boolean array. Instead, we have to use \"~\":\n",
        "print(~np.array([True,False,True]))"
      ],
      "metadata": {
        "id": "MHyuNtiTZOKj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that membership tests work on lists, arrays, and strings:"
      ],
      "metadata": {
        "id": "4WtW0WZYZthB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(3 in x)      # this is asking: \"is 3 in x?\""
      ],
      "metadata": {
        "id": "4jK2SArSZMIl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(7 in x)"
      ],
      "metadata": {
        "id": "0P0q7toGZMvZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(3 not in x)  # this is asking: \"is 3 not in x?\""
      ],
      "metadata": {
        "id": "W6ntHithZNYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('hello' in 'hello world')"
      ],
      "metadata": {
        "id": "cMOnhhwpZbe3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('o w' in 'hello world')"
      ],
      "metadata": {
        "id": "5BQOtilZZlQG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('World' in 'hello world')   # note that string membership is case-sensitive"
      ],
      "metadata": {
        "id": "Myd1SxUcZbmb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heads up: this next skill is super powerful. We saw above that applying a logical comparison to an array of numbers gives us a boolean array.\n",
        "\n",
        "We can use boolean arrays as \"**masks**\" to select certain elements of an array. This is called **boolean indexing**."
      ],
      "metadata": {
        "id": "dh9dJWyaaCdU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's revisit the Seattle temperatures from earlier:\n",
        "seattle_temps = np.array([40.0,40.6,44.2,48.4,54.9,60.2,66.2,66.7,60.5,52.0,44.5,39.6])\n",
        "\n",
        "# Applying a logical comparison creates a boolean array, or \"mask\":\n",
        "print(seattle_temps > 60)"
      ],
      "metadata": {
        "id": "MCeGAg1KazgD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now let's use the mask to retrieve only the elements where the mask is True:\n",
        "seattle_temps[seattle_temps > 60]\n",
        "\n",
        "# Note: this only works when the mask is the same length as the array!"
      ],
      "metadata": {
        "id": "ukVZKFk4bSDO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# The boolean indexing gives the same result as specifying the actual array indices:\n",
        "seattle_temps[[5,6,7,8]]"
      ],
      "metadata": {
        "id": "2PQ1TvPVcSsY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***How many months of the year is Seattle 40°F or colder? Try using boolean indexing and a function that you've learned to calculate and print the answer:***"
      ],
      "metadata": {
        "id": "DAtgHv27be7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "WkQW0l6dbn5Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 7*: `if` statements and `for` loops"
      ],
      "metadata": {
        "id": "3TIfVm6TmBVP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use logical operations to create **conditional actions** using the **`if-else` statement**.\n",
        "\n",
        "If the first condition evaluates to `True`, then the lines inside the first block are executed.\n",
        "\n",
        "If the first condition evaluates to `False`, then Python tests the second\n",
        "`elif` statement.\n",
        "\n",
        "If all of the `if` and `elif` statements are `False`, then Python will finally run the `else` statement.\n",
        "\n",
        "```\n",
        "if <CONDITION #1>:\n",
        "  <ACTION>\n",
        "  <ACTION>\n",
        "  etc.\n",
        "elif <CONDITION #2>:  (optional)\n",
        "  <ACTION>\n",
        "elif <CONDITION #3>:  (optional)\n",
        "  <ACTION>\n",
        "else <CONDITION #4>:  (optional)\n",
        "  <ACTION>\n",
        "```\n",
        "IMPORTANT: note the colons (**`:`**) and how the lines below each condition are indented using a `Tab` or two spaces on your keyboard.\n"
      ],
      "metadata": {
        "id": "JRQXELtbcs-6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Try changing the value of `rain_chance` below and running the code. Do you understand the control flow?***\n",
        "\n",
        "***What range of values for `rain_chance` will trigger the `else` statement?***"
      ],
      "metadata": {
        "id": "15duR-xzeoeE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rain_chance = 5    # i.e., a 5% chance of rain\n",
        "\n",
        "if rain_chance >= 50:\n",
        "  print('Ugh... I better bring an umbrella.')\n",
        "elif rain_chance == 0:\n",
        "  print('I definitely will not need an umbrella.')\n",
        "elif rain_chance <= 20:\n",
        "  print('I should be okay without an umbrella.')\n",
        "else:\n",
        "  print('I am not sure what to do.')"
      ],
      "metadata": {
        "id": "BrwnP44Rfmry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sometimes, we might want to perform an action again and again. Coding makes this possible using **loops**!\n",
        "\n",
        "A **`for` loop** has the following syntax:\n",
        "\n",
        "```\n",
        "for <ELEMENT> in <ITERABLE>:\n",
        "  <ACTION>\n",
        "  <ACTION>\n",
        "  etc.\n",
        "```\n",
        "Here, `<ITERABLE>` can be a list, array, string, or other collection of elements. You can give `<ELEMENT>` any variable name, and that variable can *only* be used inside the loop.\n",
        "\n",
        "***Run and consider the following examples:***"
      ],
      "metadata": {
        "id": "hOg7bceSWEd-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countdown = [4,3,2,1]\n",
        "\n",
        "for item in countdown:\n",
        "  print(item)"
      ],
      "metadata": {
        "id": "KlpsHwSMhV7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for character in 'floats':\n",
        "  print(character)"
      ],
      "metadata": {
        "id": "IyvdevH0hpQ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for even_number in np.arange(0,7,2):\n",
        "  print(even_number)"
      ],
      "metadata": {
        "id": "LQetubWdhtf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***You already learned how to calculate the sum of an array of numbers using `np.sum()`.***\n",
        "\n",
        "***Now, try to calculate the average Seattle annual temperaturre by writing a `for` loop below. There are at least two different ways to do this!***"
      ],
      "metadata": {
        "id": "ZQObEHLXh-xs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seattle_temps = np.array([40.0,40.6,44.2,48.4,54.9,60.2,66.2,66.7,60.5,52.0,44.5,39.6])\n",
        "\n",
        "# Write your code below:\n",
        "\n",
        "\n",
        "# Finally, print the average temperature by adding it to the print() statement:\n",
        "print('The average temperature is:',)"
      ],
      "metadata": {
        "id": "1qSyaJBoiK0P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 8*: Missing data"
      ],
      "metadata": {
        "id": "ESibLGhhRp8n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the real world, you'll frequently encounter missing data in an array.\n",
        "\n",
        "Missing data is represented by the float **`np.nan`** or **`np.NaN`** (the two are the same). NaN stands for \"Not a Number\"."
      ],
      "metadata": {
        "id": "ArvW0A6qmKQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pH_measurements = np.array([7.84, 7.91, 8.05, np.nan, 7.96, 8.03])\n",
        "\n",
        "print(pH_measurements)"
      ],
      "metadata": {
        "id": "SZn9Cq_7mbcz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can test for missing values using the function **`np.isnan()`**, which returns a boolean (or a boolean array when applied to an array):"
      ],
      "metadata": {
        "id": "sqSHEDzWnNmH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.isnan(5)"
      ],
      "metadata": {
        "id": "sfF17YZ3nTcF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.isnan(np.nan)"
      ],
      "metadata": {
        "id": "h-bbsucSnVDv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.isnan(pH_measurements)"
      ],
      "metadata": {
        "id": "WzRxDMMWnMSQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Do you remember boolean indexing? We can use it to extract only the valid data from an array:"
      ],
      "metadata": {
        "id": "uCsjn0klnYYO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pH_measurements[~np.isnan(pH_measurements)]"
      ],
      "metadata": {
        "id": "SwEClP51nh0o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's good to be aware that missing data can cause functions like `np.mean()` to fail:"
      ],
      "metadata": {
        "id": "ljnxDSL-nyup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(pH_measurements)"
      ],
      "metadata": {
        "id": "JF6HxqP3n50y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Many functions have a \"NaN-safe\" version that ignores missing values and still calculates the result, such as **`np.nanmean()`**:"
      ],
      "metadata": {
        "id": "9AiNIclLoANX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.nanmean(pH_measurements)"
      ],
      "metadata": {
        "id": "DElGwXISoIQd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 9: Line and scatter plots"
      ],
      "metadata": {
        "id": "hv0lpsGFldF_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wow! It's time to start creating visualizations of data, called **plots**.\n",
        "\n",
        "Earlier, we imported the package Matplotlib using:\n",
        "\n",
        "> `import matplotlib.pyplot as plt`\n",
        "\n",
        "Creating a **line plot** is simple. Use the Matplotlib function **`plt.plot()`**. The basic form of the function is:\n",
        "\n",
        "> **`plt.plot(X, Y, <FORMAT_ARGUMENTS>...)`**\n",
        "\n",
        "where `X` and `Y` are 1-D arrays of data, and the `<FORMAT_ARGUMENTS>` can be found on Matplotlib's [documentation webpage](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html)."
      ],
      "metadata": {
        "id": "cUQUaS4voeSG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([0,1,2,3,4])\n",
        "y = np.array([0,4,2,6,4])\n",
        "\n",
        "plt.plot(x,y)"
      ],
      "metadata": {
        "id": "pSJL5_gRobxX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some formatting arguments include:\n",
        "* `c` or `color`: line color (options: `'k'` or `'black'` for black, `'red'` for red, etc. – see [this page](https://matplotlib.org/stable/gallery/color/named_colors.html) for color options)\n",
        "* `lw` or `linewidth`: line width (a number; the default is 1.5)\n",
        "* `ls` or `linestyle`: line style (options: `'-', '--', '-.', ':'`)\n",
        "* `marker`: optional marker style (options: `'.', 'o', 'v', '^', '<', '>', 's', '*',` etc.)\n",
        "\n",
        "***Try plotting x versus y again, except this time use a \"goldenrod\"-colored dashed line of width 2.5 with star-shaped markers:***"
      ],
      "metadata": {
        "id": "zO_MeypJp4nE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "D1XzxR0MqmG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some other options include changing the figure size by starting with a call to:\n",
        "\n",
        "> **`plt.figure(figsize=(WIDTH,HEIGHT))`**\n",
        "\n",
        "Adding x-axis and y-axis labels and a title at the top:\n",
        "\n",
        "> **``plt.xlabel(STRING)``**\n",
        "\n",
        "> **``plt.ylabel(STRING)``**\n",
        "\n",
        "> **``plt.title(STRING)``**\n",
        "\n",
        "Or adding grid lines using:\n",
        "\n",
        "> **`plt.grid()`**\n",
        "\n",
        "Or adding multiple lines by specifying the **`label`** argument in `plt.plot()` and adding a key using:\n",
        "\n",
        "> **`plt.legend()`**\n",
        "\n",
        "Check out these additional formatting options below:"
      ],
      "metadata": {
        "id": "wN74Irogq33z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,3))\n",
        "plt.plot(x,y,label='Original data')\n",
        "plt.plot(x,2*y,label='2 * y')   # y-values are multiplied by 2 here\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.xlabel('x-values')\n",
        "plt.ylabel('y-values')\n",
        "plt.title('This is a title');"
      ],
      "metadata": {
        "id": "58RRTpadrTRJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also create a **scatter plot** with just the points (no line). The function is similar to ``plt.plot()``:\n",
        "\n",
        "> **``plt.scatter(X, Y, s=SIZE, c=COLOR, marker=MARKER_STYLE, etc.)``**"
      ],
      "metadata": {
        "id": "xbe69iiiuh-g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,3))\n",
        "plt.scatter(x,y,s=100,c='dodgerblue',marker='^');"
      ],
      "metadata": {
        "id": "CeRzx1aXu63M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Let's bring it all together! Below, try plotting the monthly temperatures in Pasadena, CA and Seattle, WA. Use line plots with circle-shaped markers (or add scatter points separately). Include a legend and label the plot appropriately.***"
      ],
      "metadata": {
        "id": "q-Q6iROxso70"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = np.array([[53.6,53.9,57.3,60.5,64.8,70.1,75.7,76.4,74.1,67.3,59.8,52.9],   # (Pasadena)\n",
        "                 [40.0,40.6,44.2,48.4,54.9,60.2,66.2,66.7,60.5,52.0,44.5,39.6]])  # (Seattle)\n",
        "\n",
        "# Write your code below:\n"
      ],
      "metadata": {
        "id": "pNENXVMUtB3o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 10: Loading and plotting spreadsheet data (example with R/V Rachel Carson CTD casts)"
      ],
      "metadata": {
        "id": "tYzchKpM7DlA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Up until now, we've been using data that we've typed directly into Python. However, most real-world data is stored in files that we'd like to open using Python.\n",
        "\n",
        "The most common type of data file is a **spreadsheet**, which has rows and columns. Generally, the columns will have column labels.\n",
        "\n",
        "Spreadsheets are often stored in **comma-separated value (CSV)** format, with the file extension being `.csv`. Data files in this format can be opened using Microsoft Excel or Google Sheets, as well as Python.\n",
        "\n",
        "In Python, we use the `pandas` package to work with spreadsheet data. We imported the package earlier using:\n",
        "\n",
        "> `import pandas as pd`\n",
        "\n",
        "Just like NumPy has arrays, Pandas has two types of objects: `Series` and `DataFrame`. This is what they look like:\n",
        "![Pandas example.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "1BPxKNjg7SZq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For now, we'll just be applying simple operations to read spreadsheet data using `pandas`. But if you would like to learn more, check out these [lesson slides](https://ethan-campbell.github.io/OCEAN_215/materials/lessons/lesson_9.pdf)."
      ],
      "metadata": {
        "id": "HQr4nB64_8p0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's download two `.csv` data files from Google Drive here: https://drive.google.com/drive/folders/1Am6XdlB-APQ3ccOvLeGK8DFPQ2OnPeJD?usp=share_link. Each file is a CTD cast that was collected from the R/V Rachel Carson off of Carkeek Park near Seattle. ***Save these two files to your computer.***\n",
        "\n",
        "Next, we can upload the files to this Google Colab notebook. ***Click the sidebar folder icon on the left, then use the page-with-arrow icon at the top to select the files and upload them.*** NOTE: uploaded files will be deleted from Google Colab when you refresh this notebook!\n",
        "\n",
        "We will specify each **filepath** using string variables:"
      ],
      "metadata": {
        "id": "czGyp7MTAc5T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filepath_0 = '/content/2023051001001_Carkeek.csv'\n",
        "filepath_1 = '/content/2023051101001_Carkeek.csv'"
      ],
      "metadata": {
        "id": "gnrD640dB5ds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can load the files using `pandas`:\n",
        "\n",
        "> **`pd.read_csv(FILEPATH, ARGUMENTS...)`**\n",
        "\n",
        "This function is very customizable using the many optional `ARGUMENTS`, which allow it to handle almost any file. You can find documentation about the arguments [at this link](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html).\n",
        "\n",
        "***Let's first take a look at the data file using a simple text editor. Notice the long header. What argument can we use to exclude the header from being loaded?***\n",
        "\n",
        "Below, we'll load each data file using ``pd.read_csv()`` and store each file into a new variable.\n",
        "\n",
        "We can look at the data using **`display()`** (which is a fancy version of `print()` for DataFrames):"
      ],
      "metadata": {
        "id": "XaUCH7ikB6Sy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_0 = pd.read_csv(filepath_0,comment='#')\n",
        "data_1 = pd.read_csv(filepath_1,comment='#')\n",
        "\n",
        "display(data_0)"
      ],
      "metadata": {
        "id": "4boQwvSg7R5J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data in a `pandas` DataFrame is similar to a NumPy 2-D array, except we use **column labels** to refer to columns and **index** values to refer to rows.\n",
        "\n",
        "To retrieve a specific column, we use bracket notation: **`data_frame[COLUMN_LABEL]`**."
      ],
      "metadata": {
        "id": "HYem5ZznDUfk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For example:\n",
        "data_0['density00']"
      ],
      "metadata": {
        "id": "-k030Au_Dyd_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***With these tools, can you make a line plot of temperature vs. depth that includes both CTD casts? (Alternatively, you could try plotting salinity, oxygen, or fluorescence vs. depth.)***\n",
        "\n",
        "You may need the following line of code to flip the y-axis so the surface is at the top: `plt.gca().invert_yaxis()`."
      ],
      "metadata": {
        "id": "TDLpAjCnELuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n"
      ],
      "metadata": {
        "id": "dh6QZ2Np9gXs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}