python-pandas-intro.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CcHQbiIKlDoa"
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      "source": [
        "# Hello, World!\n",
        "Print statements in Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "5XpDdW_WjmAF",
        "outputId": "16236a0a-31f2-41c9-8007-e9c3bcd11b68"
      },
      "source": [
        "# Many ways of printing the same text.\n",
        "\n",
        "# Version 1: Write the text in quotes within the print() method: \n",
        "print(\"Hello, World!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello, World!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "source": [
        "# Version 2: Save the text as a variable, then use print()\n",
        "text_1 = \"Hello, World!\"\n",
        "print(text_1)\n",
        "\n",
        "# Note: the variable text_1 is a string (str), verify with the type() method\n",
        "type(text_1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello, World!\n"
          ],
          "name": "stdout"
        },
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      "source": [
        "# We can combine two or more strings in the same print statement with +\n",
        "\n",
        "str1 = \"String 1 is followed by: \"\n",
        "str2 = \"String 2.\"\n",
        "print(str1 + str2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "String 1 is followed by: String 2.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Strings and numerical values can be combined into a print statement with a comma.\n",
        "\n",
        "piString = 'The value of Pi is approxinmately:'\n",
        "x = 3.14\n",
        "print(piString, x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The value of Pi is approxinmately: 3.14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN8Fgc5Flziw"
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      "source": [
        "## Activity 1.1\n",
        "\n",
        "### Write the code to print your name along with your favorite number. Your name and favorite number should be stored as variables.\n",
        "\n",
        "Example Output: My name is Alberto Soto and my favorite number is 21."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3CXNLzT-zVjY"
      },
      "source": [
        "# 1.1 \n",
        "# Write your code in this cell block"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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      "source": [
        "name = \"sergio\"\n",
        "n = \"7\"\n",
        "n2 = 7\n",
        "y = name + n\n",
        "\n",
        "print(name, n2)\n",
        "type(n2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sergio 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
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          "metadata": {
            "tags": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1D78rbktyeh"
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      "source": [
        "# Making Lists (of numbers)\n",
        "In this section we'll generate a list of numerical values using two different methods. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFOIAjPZzMuO"
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      "source": [
        "## Method 1: for-loop"
      ]
    },
    {
      "cell_type": "code",
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        },
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      "source": [
        "# Goal: Use a for loop structure to generate a list of numbers from 0 to 20\n",
        "\n",
        "# Initialize an empty list with []\n",
        "list1 = []\n",
        "\n",
        "# Add numbers to list1 using the append() method\n",
        "for number in range(21):   \n",
        "  list1.append(number)\n",
        "\n",
        "# Note: range(N) produces a sequence of integers up to, but not including N\n",
        "\n",
        "# Print the list, here we don't need to call the print() method\n",
        "list1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]"
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            "tags": []
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          "execution_count": 13
        }
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      "cell_type": "markdown",
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      "source": [
        "### Indexing"
      ]
    },
    {
      "cell_type": "code",
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        "id": "K2V2Z77CDgqT",
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      "source": [
        "# Use indexing to show the first 5 elements of the list\n",
        "list1[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
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      },
      "source": [
        "# Use indexing to show every other element of the list\n",
        "list1[0:21:2]"
      ],
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
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      "cell_type": "markdown",
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        "id": "2BDBLs_XEAzW"
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      "source": [
        "The format for indexing is: \n",
        "```list_name[start:end:increment]```"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "jx5guEXwEOaw",
        "outputId": "8b340906-0aee-4bd1-a42d-4a528277b9e1"
      },
      "source": [
        "# We can also go backwards by setting the increment to -1, while leaving start/end blank\n",
        "list1[::-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
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      "cell_type": "markdown",
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        "id": "ga6_BgFE5ii3"
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      "source": [
        "### Activity 2.1\n",
        "\n",
        "### Use a for loop to generate a list of the first ten perfect squares\n",
        "\n",
        "Hint: use ```*``` to multiple two numerical values as in: \n",
        "      ``` 3 * 7 = 21```\n",
        "\n"
      ]
    },
    {
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      "source": [
        "# 2.1 \n",
        "# Write code in this cell\n",
        "\n",
        "squares1 = []\n",
        "\n",
        "# Add values to list using the append() method\n",
        "for number in range(10): \n",
        "  # value = number * number\n",
        "  squares1.append(number * number)\n",
        "print(squares1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfU2D-SMzPbf"
      },
      "source": [
        "## Method 2: list comprehension \n",
        "\n",
        "**for loops** are fine for this task, but Python also has a powerful and elegant method to generate lists called **list comprehensions**"
      ]
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "# A single line of code does the same thing as Method 1 using list comprehension\n",
        "list2 = [number for number in range(1, 21)]\n",
        "\n",
        "# I used range(0, 21) instead of range(21), they do exactly the same thing\n",
        "\n",
        "# Print the list\n",
        "list2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPbBNkfw39aN"
      },
      "source": [
        "### Activity 2.2\n",
        "\n",
        "### Use list comprehensions to generate a list of the first ten perfect squares"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4asu9p06Plm",
        "outputId": "058e1a46-7484-4583-85b4-9a9fe5267aef"
      },
      "source": [
        "# 2.2\n",
        "# Write code in this cell\n",
        "\n",
        "squares2 = [number*number for number in range(0, 10)]\n",
        "\n",
        "squares2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gn4DUVZzvoZF"
      },
      "source": [
        "#Conditional Statements\n",
        "In this section we'll use conditional (if/else) statements.\n",
        "\n",
        "A conditional statement has the form\n",
        "\n",
        "\n",
        "```\n",
        "if conditional:\n",
        "  code block 1\n",
        "else:\n",
        "  code block 2\n",
        "```\n",
        "where ```conditional``` is a test that returns a boolean value, either ```True``` or ```False```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rqa3GAxOFcBR"
      },
      "source": [
        "## if Statement Example"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "I3ODljQBEgFb",
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      },
      "source": [
        "a = 1\n",
        "b = 2\n",
        "\n",
        "if a < b:\n",
        "  print(a, \"is bigger than\", b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 is bigger than 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DaN4Yl29J1Bl"
      },
      "source": [
        "## if/else Statement Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1why6KYGWFN",
        "outputId": "7ba8283d-27dc-4b4f-c1d4-e9dfcaa6c64b"
      },
      "source": [
        "x = 3**2\n",
        "y = 2**3\n",
        "\n",
        "if x < y:\n",
        "  print(\"What does ** do?\")\n",
        "else:\n",
        "  print(x, 'is greater than', y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9 is greater than 8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1D8D_TAxLbZk"
      },
      "source": [
        "## if/else inside a for loop Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbWpKbGgHblr",
        "outputId": "fdb405ea-ad80-4ebb-dde1-e0d39ac6964d"
      },
      "source": [
        "# Create a list of integers from -10 to 10\n",
        "intList = [x for x in range(-10, 11)]\n",
        "\n",
        "# Use a for loop to go through each element in the list\n",
        "# and print only the even numbers \n",
        "\n",
        "for elem in intList:\n",
        "  if elem % 2 == 0:         # checks if the element is even\n",
        "    print(elem, 'is even')\n",
        "  else:\n",
        "    print()                 # prints an empty line"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-10 is even\n",
            "\n",
            "-8 is even\n",
            "\n",
            "-6 is even\n",
            "\n",
            "-4 is even\n",
            "\n",
            "-2 is even\n",
            "\n",
            "0 is even\n",
            "\n",
            "2 is even\n",
            "\n",
            "4 is even\n",
            "\n",
            "6 is even\n",
            "\n",
            "8 is even\n",
            "\n",
            "10 is even\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G64AtszGQOWj"
      },
      "source": [
        "### Activity 3.1\n",
        "\n",
        "### Use an if/else for-loop combo to print **positive** numbers from a list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnKOsnUmMDhj"
      },
      "source": [
        "# Here's the list, same as above\n",
        "intList "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-7B0hfwRYL1",
        "outputId": "845e0a33-fc1c-408f-967e-86fbb4a31d2a"
      },
      "source": [
        "# 3.1\n",
        "# Write code in this cell\n",
        "\n",
        "# Hint: For what values of x does the conditional statement x < 0 return True? \n",
        "\n",
        "for elem in intList:\n",
        "  if elem > 0:         # checks if the element is positive\n",
        "    print(elem, 'is positive')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 is positive\n",
            "2 is positive\n",
            "3 is positive\n",
            "4 is positive\n",
            "5 is positive\n",
            "6 is positive\n",
            "7 is positive\n",
            "8 is positive\n",
            "9 is positive\n",
            "10 is positive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OF4z5vdGThRb"
      },
      "source": [
        "### Activity 3.2\n",
        "\n",
        "### Use an if/else for loop combo to append the even numbers to an empty list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3C2cnfjTuUZ",
        "outputId": "1d6455a5-9fe4-44b3-e1b0-95dbc50dd2ac"
      },
      "source": [
        "# 3.2\n",
        "# Write code in this cell\n",
        "\n",
        "# Initialize an empty list\n",
        "intList_evens = []\n",
        "\n",
        "for elem in intList:\n",
        "  if elem % 2 == 0:         # checks if the element is even\n",
        "    intList_evens.append(elem)\n",
        "\n",
        "print(intList_evens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iJnZGCMSSgS"
      },
      "source": [
        "### Here's the list comprehension version of extracting even numbers from a list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7IAgmrYaSQgl",
        "outputId": "d5ad1be0-9345-461b-a20b-082e7cca6b17"
      },
      "source": [
        "# List comprehension with a conditional statement \n",
        "intList_evens_lc = [elem for elem in intList if elem % 2 == 0]\n",
        "\n",
        "intList_evens_lc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drGGOUNFTREM"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJHsdNDg4jtP"
      },
      "source": [
        "# Pandas library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "posCFIET5F93"
      },
      "source": [
        "# The pandas library is a great tool for working with tabular data in Python\n",
        "\n",
        "# import the pandas library under the shorthand name \"pd\"\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60UU9LGp5DM6"
      },
      "source": [
        "## White Shark Data Exploration\n",
        "\n",
        "### In this section we'll:\n",
        "\n",
        "1.   import a CSV data file\n",
        "2.   explore some functionality of the pandas DataFrame\n",
        "3.   plot data in several ways\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fc7s063n7ESa"
      },
      "source": [
        "### import data to DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMlacExem0E9"
      },
      "source": [
        "# set the url\n",
        "url = 'https://github.com/adsoto/Shark-Analysis/raw/main/jws-sample-data.csv'\n",
        "\n",
        "# read the the csv file\n",
        "jws = pd.read_csv(url) \n",
        "\n",
        "# jws is the variable that holds the imported data as a DataFrame"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvGwgk548NMT"
      },
      "source": [
        "### Activity 4.1\n",
        "\n",
        "### Check that ```jws``` is the correct type\n",
        "\n",
        "Hint: use a single command"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cCSpy3V8vnq",
        "outputId": "7a967ad2-1db7-4d26-9231-2f4f20eda6b1"
      },
      "source": [
        "# 4.1\n",
        "# Write the code here \n",
        "type(jws)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q13fQ-LN_5Nf"
      },
      "source": [
        "### View a selection of the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "QzPsI4JmAHhc",
        "outputId": "8955cdc2-7e81-42c8-e323-3ebfc8a885c0"
      },
      "source": [
        "# View a small portion of the entire DataFrame with head()\n",
        "jws.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TRANSMITTER</th>\n",
              "      <th>DETECTEDID</th>\n",
              "      <th>DATETIME</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>D</th>\n",
              "      <th>LAT</th>\n",
              "      <th>LON</th>\n",
              "      <th>n</th>\n",
              "      <th>HPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 0:46</td>\n",
              "      <td>5374.69</td>\n",
              "      <td>5292.34</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.401724</td>\n",
              "      <td>-119.545133</td>\n",
              "      <td>2</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 0:57</td>\n",
              "      <td>5122.30</td>\n",
              "      <td>5560.71</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.404144</td>\n",
              "      <td>-119.547878</td>\n",
              "      <td>2</td>\n",
              "      <td>7.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 1:15</td>\n",
              "      <td>4637.29</td>\n",
              "      <td>5997.78</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.408084</td>\n",
              "      <td>-119.553153</td>\n",
              "      <td>3</td>\n",
              "      <td>6.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 4:31</td>\n",
              "      <td>5010.76</td>\n",
              "      <td>5676.12</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.405184</td>\n",
              "      <td>-119.549091</td>\n",
              "      <td>1</td>\n",
              "      <td>11.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 4:36</td>\n",
              "      <td>4854.59</td>\n",
              "      <td>5796.55</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.406270</td>\n",
              "      <td>-119.550790</td>\n",
              "      <td>1</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  TRANSMITTER     DETECTEDID        DATETIME  ...         LON  n   HPE\n",
              "0     2020-21  A69-9006-7849  6/20/2020 0:46  ... -119.545133  2   4.7\n",
              "1     2020-21  A69-9006-7849  6/20/2020 0:57  ... -119.547878  2   7.1\n",
              "2     2020-21  A69-9006-7849  6/20/2020 1:15  ... -119.553153  3   6.8\n",
              "3     2020-21  A69-9006-7849  6/20/2020 4:31  ... -119.549091  1  11.6\n",
              "4     2020-21  A69-9006-7849  6/20/2020 4:36  ... -119.550790  1   4.6\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6v3I--Z-oRc"
      },
      "source": [
        "Notice the format of the left-most column. \n",
        "This is how the the data is indexed, each row is assigned an integer, starting at $0$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "d0hbDUiiAjx1",
        "outputId": "4c695195-9367-419b-92f6-be9b7a53e3a5"
      },
      "source": [
        "# Use indexing to print the first 5 rows (expect the same output as above)\n",
        "jws[2:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TRANSMITTER</th>\n",
              "      <th>DETECTEDID</th>\n",
              "      <th>DATETIME</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>D</th>\n",
              "      <th>LAT</th>\n",
              "      <th>LON</th>\n",
              "      <th>n</th>\n",
              "      <th>HPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 1:15</td>\n",
              "      <td>4637.29</td>\n",
              "      <td>5997.78</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.408084</td>\n",
              "      <td>-119.553153</td>\n",
              "      <td>3</td>\n",
              "      <td>6.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 4:31</td>\n",
              "      <td>5010.76</td>\n",
              "      <td>5676.12</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.405184</td>\n",
              "      <td>-119.549091</td>\n",
              "      <td>1</td>\n",
              "      <td>11.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-21</td>\n",
              "      <td>A69-9006-7849</td>\n",
              "      <td>6/20/2020 4:36</td>\n",
              "      <td>4854.59</td>\n",
              "      <td>5796.55</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.406270</td>\n",
              "      <td>-119.550790</td>\n",
              "      <td>1</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  TRANSMITTER     DETECTEDID        DATETIME  ...         LON  n   HPE\n",
              "2     2020-21  A69-9006-7849  6/20/2020 1:15  ... -119.553153  3   6.8\n",
              "3     2020-21  A69-9006-7849  6/20/2020 4:31  ... -119.549091  1  11.6\n",
              "4     2020-21  A69-9006-7849  6/20/2020 4:36  ... -119.550790  1   4.6\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBd4tEkUFaZA"
      },
      "source": [
        "### Activity 4.2\n",
        "\n",
        "### Use indexing to show the 21st row\n",
        "\n",
        "Hint: Use the format ```df[row-1:row]```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Ugn5yWdnFrF9",
        "outputId": "f393165e-ea30-4b03-d47e-5a271dc2e400"
      },
      "source": [
        "# 4.2\n",
        "# Write code in this cell\n",
        "\n",
        "jws[20:21]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TRANSMITTER</th>\n",
              "      <th>DETECTEDID</th>\n",
              "      <th>DATETIME</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>D</th>\n",
              "      <th>LAT</th>\n",
              "      <th>LON</th>\n",
              "      <th>n</th>\n",
              "      <th>HPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-20</td>\n",
              "      <td>A69-9006-7853</td>\n",
              "      <td>6/20/2020 7:22</td>\n",
              "      <td>4770.51</td>\n",
              "      <td>5549.47</td>\n",
              "      <td>-0.607</td>\n",
              "      <td>34.404042</td>\n",
              "      <td>-119.551704</td>\n",
              "      <td>4</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   TRANSMITTER     DETECTEDID        DATETIME  ...         LON  n  HPE\n",
              "20     2020-20  A69-9006-7853  6/20/2020 7:22  ... -119.551704  4  2.6\n",
              "\n",
              "[1 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_9Zw6XFG4Ny"
      },
      "source": [
        "### Operations on the DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZGn0O63HK1N"
      },
      "source": [
        "### value_counts()\n",
        "Use the value_counts() method on the \"TRANSMITTER\" column to see how many times each shark was observed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjG_Br4_DDge",
        "outputId": "78c37993-f464-4617-bae9-fa5148ea4b1a"
      },
      "source": [
        "# Count number of times each shark was observed   \n",
        "tag_counts = jws[\"TRANSMITTER\"].value_counts()\n",
        "\n",
        "tag_counts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-20    1078\n",
              "2020-21     963\n",
              "2020-19     424\n",
              "2020-32       3\n",
              "2020-31       1\n",
              "Name: TRANSMITTER, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCk67k30H2Zp"
      },
      "source": [
        "### Plot a Histogram \n",
        "\n",
        "DataFrames (and a few other related object types) have built-in plotting methods. \n",
        "\n",
        "We can make a bar plot of the count data with a call to plot.bar()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "kPncatauHdP4",
        "outputId": "9e3ce32d-653e-46bd-fd5f-bde6683f2acf"
      },
      "source": [
        "# Call plot.bar() and set it to a variable ax_1\n",
        "ax_1 = tag_counts.plot.bar()\n",
        "\n",
        "ax_1.set_xlabel(\"Shark ID\", labelpad=20, weight='bold', size=12)\n",
        "ax_1.set_ylabel(\"Observations\", labelpad=20, weight='bold', size=12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Observations')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVauy-C7MenY"
      },
      "source": [
        "## Use the describe() method to get summary statistics\n",
        "\n",
        "The describe() method will operate on numerical columns of our shark dataframe. \n",
        "\n",
        "The output contains things like count, mean, max, etc..\n",
        "\n",
        "This will be more useful once we do our own calculations (e.g., speed) using the data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QqceRxrcMiJp",
        "outputId": "3c34ad8a-3abb-42c2-a8c4-457816684a6f"
      },
      "source": [
        "jws.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>D</th>\n",
              "      <th>LAT</th>\n",
              "      <th>LON</th>\n",
              "      <th>n</th>\n",
              "      <th>HPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2469.000000</td>\n",
              "      <td>2469.000000</td>\n",
              "      <td>2.469000e+03</td>\n",
              "      <td>2469.000000</td>\n",
              "      <td>2469.000000</td>\n",
              "      <td>2469.000000</td>\n",
              "      <td>2469.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4836.241790</td>\n",
              "      <td>5336.075144</td>\n",
              "      <td>-6.070000e-01</td>\n",
              "      <td>34.402119</td>\n",
              "      <td>-119.550989</td>\n",
              "      <td>2.612799</td>\n",
              "      <td>4.453625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>448.454473</td>\n",
              "      <td>449.845658</td>\n",
              "      <td>1.443582e-14</td>\n",
              "      <td>0.004055</td>\n",
              "      <td>0.004878</td>\n",
              "      <td>2.465469</td>\n",
              "      <td>27.105129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3282.690000</td>\n",
              "      <td>4317.910000</td>\n",
              "      <td>-6.070000e-01</td>\n",
              "      <td>34.392939</td>\n",
              "      <td>-119.567887</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4591.840000</td>\n",
              "      <td>4982.390000</td>\n",
              "      <td>-6.070000e-01</td>\n",
              "      <td>34.398930</td>\n",
              "      <td>-119.553647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4930.240000</td>\n",
              "      <td>5240.970000</td>\n",
              "      <td>-6.070000e-01</td>\n",
              "      <td>34.401261</td>\n",
              "      <td>-119.549967</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5124.050000</td>\n",
              "      <td>5610.660000</td>\n",
              "      <td>-6.070000e-01</td>\n",
              "      <td>34.404594</td>\n",
              "      <td>-119.547859</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6572.250000</td>\n",
              "      <td>6670.660000</td>\n",
              "      <td>-6.070000e-01</td>\n",
              "      <td>34.414149</td>\n",
              "      <td>-119.532110</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>1331.700000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 X            Y  ...            n          HPE\n",
              "count  2469.000000  2469.000000  ...  2469.000000  2469.000000\n",
              "mean   4836.241790  5336.075144  ...     2.612799     4.453625\n",
              "std     448.454473   449.845658  ...     2.465469    27.105129\n",
              "min    3282.690000  4317.910000  ...     1.000000     2.400000\n",
              "25%    4591.840000  4982.390000  ...     1.000000     2.800000\n",
              "50%    4930.240000  5240.970000  ...     1.000000     3.100000\n",
              "75%    5124.050000  5610.660000  ...     4.000000     3.900000\n",
              "max    6572.250000  6670.660000  ...    17.000000  1331.700000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjN0Af00Kges"
      },
      "source": [
        "### Time Series Plot\n",
        "\n",
        "Here you'll manipulate the data frame to extract all the data \n",
        "for an individual shark and plot it using the pyplot (plt) library. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6gmXYb5Kr1Z"
      },
      "source": [
        "# First, import pyplot\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "mvusDARrOfpQ",
        "outputId": "fcf4718d-9cbc-458f-9a1a-56bf9a6c220e"
      },
      "source": [
        "x  = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
        "y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]\n",
        "y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]\n",
        "\n",
        "plt.plot(x, y1, label=\"line L\")\n",
        "plt.plot(x, y2, label=\"line H\")\n",
        "plt.plot()\n",
        "\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"y axis\")\n",
        "plt.title(\"Line Graph Example\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "6Wo8HD0VKaKF",
        "outputId": "b0b2a773-fdd3-4483-9f34-b7d54521db01"
      },
      "source": [
        "# Use logical indexing to extract all data for a specific animal (coded by TRANSMITTER field)\n",
        "shark20 = jws[jws.TRANSMITTER == '2020-20']\n",
        "\n",
        "shark20.head()\n",
        "\n",
        "# Plot the x-y position (its trajectory) as a blue line\n",
        "plt.plot(shark20.X, shark20.Y, 'b-')\n",
        "\n",
        "plt.xlabel('X-position (m)')\n",
        "plt.ylabel('Y-position (m)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMWAXxmaLmGL"
      },
      "source": [
        "### Activity 4.3\n",
        "\n",
        "### Create a plot of the x-y position for shark 19"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "BAM4R28DLI97",
        "outputId": "a1cfe10f-6fae-48cf-ad77-982e58e60620"
      },
      "source": [
        "# 4.3\n",
        "# Write code in this cell\n",
        "\n",
        "# Use logical indexing to extract all data for a specific animal (coded by TRANSMITTER field)\n",
        "shark19 = jws[jws.TRANSMITTER == '2020-19']\n",
        "\n",
        "# Plot the x-y position (its trajectory) as a blue line\n",
        "plt.plot(shark19.X, shark19.Y, 'r-')\n",
        "\n",
        "plt.xlabel('X-position (m)')\n",
        "plt.ylabel('Y-position (m)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32J-KA8yhtT2"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}