Interactive_Tutorial_4_Data_Analysis.ipynb

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      "source": [
        "# Interactive Tutorial: Data Analysis in Python\n",
        "\n",
        "Welcome to the interactive tutorial for data analysis! Remember that you can refer to the [Data Analysis in Python](https://aguaclara.github.io/aguaclara_tutorial/python/data-analysis.html) page in the AguaClara tutorial wiki for help.\n",
        "\n",
        "Before you start, you'll need to install the AguaClara Python package.\n"
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      "source": [
        "!pip install aguaclara"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW6sGZrwHnkS"
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      "source": [
        "### Calculations on Arrays\n",
        "\n",
        "1. Make a NumPy array from the list of numbers called `degrees`. Without using a loop, subtract 5 from every number, convert the differences to radians, and print the result. \n",
        "\n",
        "   *Hint: use a NumPy function for the conversion.*"
      ]
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    {
      "cell_type": "code",
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        "id": "gmEv33rHaFVW"
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      "source": [
        "# Import statement here\n",
        "\n",
        "degrees = [35, 50, 65, 95, 140, 185]\n",
        "\n",
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3Q7FrT90f7R"
      },
      "source": [
        "2. Use the AguaClara package to apply units of degrees to the list given in #1. Then convert the list to radians without using the NumPy package and print each element.\n",
        "\n",
        "   *Hint: use the `.to()` function. Also, you may want to print inside a loop.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K5AF54I4Af7"
      },
      "source": [
        "# Import statement here\n",
        "\n",
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6sdv2Bt64JE7"
      },
      "source": [
        "3. Write the following table, minus the headers, as a 2-dimensional NumPy array. *Hint: make each element of the array a row.*\n",
        "    \n",
        "    Then, print the array, the dimensions of the array, the 4th row, and the value in the 4th row and 2nd column.\n",
        "\n",
        "| Mass (kg)| Velocity (m/s) |\n",
        "|:-:|:-:|\n",
        "|16|18|\n",
        "|3|39|\n",
        "|88|74|\n",
        "|98|94|\n",
        "|84|82|"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fz1JH_YXLRZh"
      },
      "source": [
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dp4ZaBP-LwAK"
      },
      "source": [
        "4. Make an array that contains the momentum ($p=mv$) associated with the mass and velocity from each row."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9v7K713yuxJe"
      },
      "source": [
        "momentum = # Your answer here\n",
        "\n",
        "print(momentum)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9kqChdau3o2"
      },
      "source": [
        "### Pandas\n",
        "\n",
        "5. Use the Pandas package to read the spreadsheet at this URL as a Pandas DataFrame: https://raw.githubusercontent.com/AguaClara/Fluoride-Auto/master/Summer%202019/Data%20and%20Graphs/Molecular%20Ratios.csv. Print the column labels in the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W75g-YarwpKi"
      },
      "source": [
        "# Import statement here\n",
        "\n",
        "data = # Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7LAYA3xxYug"
      },
      "source": [
        "6. Write 1 way to print the 1st row and 2 ways to print the \"Influent PACl (atoms/L)\" column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDdDMjkY8Ran"
      },
      "source": [
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIscVgF8DZyr"
      },
      "source": [
        "7. Print the values in the first 3 rows and the first 4 columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LWBXGeJDjj7"
      },
      "source": [
        "# Your answer here"
      ],
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      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SLjyLvP8Tvm"
      },
      "source": [
        "### Matplotlib\n",
        "\n",
        "8. Continuing to use the data from the Pandas section, plot the \"Effluent Fluoride (mg/L)\" column against the \"Influent Fluoride (mg/L)\" column for rows 11-18. Add x- and y-axis labels.\n",
        "\n",
        "   Is the data continous or discrete, and how should that affect the plot?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SayrncwKHKLQ"
      },
      "source": [
        "# Import statement here\n",
        "\n",
        "# Your answer here"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYJyZprmHRBx"
      },
      "source": [
        "9. Notice that rows 11-18 have the same value for \"Influent PACL (mg/L)\". Make another plot for \"Effluent Fluoride (mg/L)\" vs. \"Influent Fluoride (mg/L)\" for at least three rows with 40 mg/L of influent PACl."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ITnV7H4H-JG"
      },
      "source": [
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc31eiV0IEHX"
      },
      "source": [
        "10. Plot the sets of data from #8 and #9 in the same figure, but give the two plots different colors and/or data symbols, as well as a legend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8rRNGXUIKaV"
      },
      "source": [
        "# Your answer here"
      ],
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      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgR6xpQCMZEF"
      },
      "source": [
        "### AguaClara Package\n",
        "\n",
        "11. Use two functions from the AguaClara Python package to get the 1000-4000<sup>th</sup> rows of the \"Turb_settled ()\" column and the time column from the data at https://raw.githubusercontent.com/AguaClara/aguaclara_tutorial/master/data/plantita%208-12-2019.tsv. Plot the \"Turb_settled ()\" column against the time column for these rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5B-iBS27k0kJ"
      },
      "source": [
        "# Import statement here\n",
        "\n",
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4SZirzKk3s1"
      },
      "source": [
        "12. Pick a function from the AguaClara package to plot the entire \"Turb_settled ()\" and \"Turb_filter ()\" columns against the entire time column. Add axis labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJL2cZD4-W4D"
      },
      "source": [
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fb_aNJ9-nOT"
      },
      "source": [
        "13. Use a function from AguaClara package to print out all the experimental notes inserted into the data file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6rEcQYs--Pb"
      },
      "source": [
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zseKZlxO_HoF"
      },
      "source": [
        "### Curve Fitting\n",
        "\n",
        "14. Use the SciPy package to fit a linear model to \"Turb_settled ()\" column for rows 3200-5000. What is are the slope and intercept? Is the model a good fit?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gbr0sL0hGIkM"
      },
      "source": [
        "# Import statement here\n",
        "\n",
        "# Your answer here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHwSLgOoGMLl"
      },
      "source": [
        "15. Use another feature of the SciPy package to find a better fit to the data in the given rows. *Hint: try a quadratic fit.*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOJMwNS52iTa"
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      "source": [
        "# Import statement here\n",
        "\n",
        "# Your answer here"
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