slides_version_freeze.ipynb

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    "# An Introduction to Data Analysis with Python\n",
    "\n",
    "```\n",
    "Prepared for Participants of JALT 2023\n",
    "\n",
    "Chris Pirotto\n",
    "Senior Lecturer, Fukui University of Technology\n",
    "chris.pirotto@gmail.com\n",
    "\n",
    "Daniel Cook\n",
    "Assistant Professor of Foreign Language, Fukui University of Technology\n",
    "cook.r.dan@gmail.com\n",
    "```"
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    "![](screenshots/spss.png)"
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    "![](screenshots/r-data.png)"
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    "![](screenshots/r.png)"
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    "![](screenshots/python-data.png)"
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    "![](screenshots/python.png)"
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   "source": [
    "<!-- toc -->"
   ]
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   "source": [
    "# Introduction"
   ]
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   "source": [
    "This Jupyter Notebook has been written and designed to introduce researchers in the field of TESOL to powerful tools that can be used for data analysis.\n",
    "\n",
    "In order to introduce these tools and what they are capable of, we give a walkthrough of a research project that should be familiar to the audience. We hope this provides less friction involved with understanding the tools introduced.\n",
    "\n",
    "The Notebook is meant to be read from top-to-bottom. We will first introduce you to common tools used in data analysis, then walk through a typical workflow of importing data, inspecting it, cleaning it, and finally analyzing it.\n",
    "\n",
    "We understand that you may not have experience with programming - that is okay! Python is quite an intuitive programming language. We have try to provide examples that are fairly simple and also show some intermediate uses here and there. In some cases complex parts are broken down into a series of small steps.\n",
    "\n",
    "Enjoy and happy coding!"
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   "source": [
    "# Tools for Data Analysis\n",
    "\n",
    "The tools that we will use are Python, pandas, and Jupyter notebooks. The following is a very brief introduction to each of these."
   ]
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   "source": [
    "## A *very* short introduction to Python"
   ]
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    "Python is a high-level, versatile, and easy-to-read programming language. It's known for its simplicity and readability, making it a popular choice for beginners and experienced developers alike. \n",
    "\n",
    "There are many programming languages and some have very special use cases. Keep in mind that a programming language is only an interface between you (the programmar) and the computer.\n",
    "\n",
    "Essentially, with any programming language, we are telling the computer what to do.\n",
    "\n",
    "The following is a simple code snipper. It has a comment that is essentially ignored by the computer, and a statement that would be executed. This tells the computer to print \"Hello, World\" to the screen."
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     "name": "stdout",
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     "text": [
      "Hello, World!\n"
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   "source": [
    "# Comments start with a hash\n",
    "print(f\"Hello, World!\")"
   ]
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    "### Why use Python for Data Analysis?\n",
    "\n",
    "There are a great number of software tools for data analysis with a number of similarities between them. We're not advocating one tool over another, but simply want to say that **it's best to use the tool that suites your needs**. That being said, wouldn't it be interesting if you could make your own tools? Utilizing a programming language gives you the opportunity to do just that.\n",
    "\n",
    "Here are some other reasons we think using Python is great:\n",
    "\n",
    "- open source and free\n",
    "- compared to other programming languages it's easier to learn\n",
    "- widely used in the fields of data science (another noteable choice is `R`)\n",
    "- your process can be documented and repeatable from start to finish\n",
    "- great ecosystem of tools and libraries\n",
    "- a skill to add to your resume\n",
    "- code and results are testable (See **Appendix 2**)\n",
    "- code projects are easily scalable\n",
    "- you can apply programming skills to *many*, *many* other things"
   ]
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   "source": [
    "## A *very* short introduction to Pandas"
   ]
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    "Pandas is a popular *Python library* for data manipulation and analysis. It provides easy-to-use data structures and functions to work with structured data, such as tables and spreadsheets. Pandas simplifies tasks like data cleaning, filtering, and statistical analysis, making it a cornerstone of data science and analysis in Python."
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    "If you have experienced using a spreadsheet, you will know that it is a table that holds data. Pandas uses a similar structure to hold data, and it is called a `DataFrame`. Dataframes have a number of attributes and methods that are at the core of using the library."
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    "Pandas is a popular *Python library* for data manipulation and analysis. It provides easy-to-use data structures and functions to work with structured data, such as tables and spreadsheets. Pandas simplifies tasks like data cleaning, filtering, and statistical analysis, making it a cornerstone of data science and analysis in Python."
   ]
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    "If you have experienced using a spreadsheet, you will know that it is a table that holds data. Pandas uses a similar structure to hold data, and it is called a `DataFrame`. Dataframes have a number of attributes and methods that are at the core of using the library."
   ]
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    "![An Example DataFrame](assets/example_dataframe.svg)"
   ]
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    "Another important data structure that Pandas uses is called a `Series`. You could think of it as a single column with an index."
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   "source": [
    "![A Pandas Series](assets/example_series.svg)"
   ]
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   "source": [
    "## A *very* short introduction to Jupyter Notebooks\n",
    "\n",
    "![jupyterlab](assets/jupyterlab.png)\n",
    "\n",
    "*Picture Credit: jupyter.org*"
   ]
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   "source": [
    "Python and Pandas can be used in a number of contexts like scripts, but for the purpose of data analysis, someone created this wonderful tool called Jupyter Notebooks."
   ]
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   "source": [
    "What is a notebook? From the [Jupyter documentation](https://docs.jupyter.org/en/latest/):\n",
    "\n",
    "> A notebook is a shareable document that combines computer code, plain language descriptions, data, rich visualizations like 3D models, charts, graphs and figures, and interactive controls. A notebook, along with an editor (like JupyterLab), provides a fast interactive environment for prototyping and explaining code, exploring and visualizing data, and sharing ideas with others."
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   "source": [
    "With notebooks you break your work into cells. A cell can contain code, Markdown, or raw text. Users can run code cells to see immediate results, making it an ideal tool for experimenting, documenting, and presenting data analysis and research in a collaborative and reproducible way.\n",
    "\n",
    "Each cell takes input, and then depending on the contents, produces output. Since you can see immediate results, it is an ideal tool for experimenting, documenting, and presenting data analysis and research.\n",
    "\n",
    "It also helps the process of your analysis or research to be reproducible from start to finish."
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    "### Some Example Cells:"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello JALT\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello JALT\")"
   ]
  },
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    "Even though cells appear separate, they exist in the same namespace. If you define a variable `a` in one cell, it can be used or overwritten in another. The notebook will evaluate the cell if you have a variable, function, or object on the last line, it will be used for output:"
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     "data": {
      "text/plain": [
       "'I eat pizza'"
      ]
     },
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     "output_type": "execute_result"
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    "a = \"I eat pizza\"\n",
    "a "
   ]
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   "source": [
    "We can reuse/change variables from other cells that have been executed. Let's add something else to the variable that we made above:"
   ]
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   "id": "3bb5c163-c621-4553-8849-cbed9e154648",
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     "data": {
      "text/plain": [
       "'I eat pizza every. single. day. 🍕😜'"
      ]
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   "source": [
    "a = a + \" every. single. day. 🍕😜\"\n",
    "a"
   ]
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  {
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   "source": [
    "Now that you've had a brief introduction to some of these tools, let's put them to use."
   ]
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   "source": [
    "# Our Research Project"
   ]
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   "source": [
    "In order to demonstrate how these tools can be used in a common research workflow, we created a research project. We made something that should be familiar to anyone doing research in the field of TESOL."
   ]
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   "source": [
    "**The Research Question:**\n",
    "\n",
    "```text\n",
    "Does incorporating pizza-related content into language learning materials positively impact English as a Foreign Language (EFL) learners' language acquisition, motivation, and confidence?\n",
    "```"
   ]
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   "source": [
    "To test this theory, we can explore how the number of hours exposed to material correlates with motivation and confidence."
   ]
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   "source": [
    "**Some details about the data:**\n",
    "\n",
    "- We used a Pre/Post Test for language skills with treatment being self-study exposure to pizza-related materials\n",
    "- Confidence and motivation are measured by Likert values (1: \"Very Low\", 2: \"Low\", 3: \"Medium\", 4: \"High\", 5: \"Very High\")\n",
    "- Total test score for both tests was 100\n",
    "- Students were expected to spend between 0 and 50 hours on the material given the timeframe\n",
    "- There were two forms used for our research (pre and post) which resulted in two excel files that we will use."
   ]
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   "source": [
    "The data for this project is stored in two excel files:\n",
    "\n",
    "1. pre_pizza_material.xlsx\n",
    "2. post_pizza_material.xlsx"
   ]
  },
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   "source": [
    "# Getting the Data into Python\n",
    "\n",
    "To analyze the data that we collected, we first need to import `pandas`. It's common to give pandas an alias of `pd`:"
   ]
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   "source": [
    "import pandas as pd"
   ]
  },
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   "source": [
    "## Loading Data"
   ]
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   "source": [
    "You can load data from various sources, such as CSV files, Excel spreadsheets, databases, or web APIs. Pandas reads the data and makes a `dataframe`.\n",
    "\n",
    "Dataframes are kind of like spreadsheets - they have rows and columns. They also contain other important information about the data.\n",
    "\n",
    "This is how we can load an excel file into a dataframe:"
   ]
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   "source": [
    "pre_df = pd.read_excel(\"pre_pizza_material.xlsx\")"
   ]
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   "source": [
    "We can see the top 5 rows by using a method called `head()`:"
   ]
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       "      <th>Last Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>yrs_eflexp</th>\n",
       "      <th>favorite_topping</th>\n",
       "      <th>pineapple</th>\n",
       "      <th>exp_pzmat</th>\n",
       "      <th>score</th>\n",
       "      <th>motivation</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91600124</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Pepperoni</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91600185</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>Sausage</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91600140</td>\n",
       "      <td>James</td>\n",
       "      <td>Brown</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Onions</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91600145</td>\n",
       "      <td>Emma</td>\n",
       "      <td>Williams</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>Olives</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91600173</td>\n",
       "      <td>William</td>\n",
       "      <td>Jones</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>Green Peppers</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Number     Name Last Name  Age     Sex  yrs_eflexp favorite_topping  \\\n",
       "0   91600124     John     Smith   18    Male          13        Pepperoni   \n",
       "1   91600185   Olivia   Johnson   19  Female           9          Sausage   \n",
       "2   91600140    James     Brown   19    Male          14           Onions   \n",
       "3   91600145     Emma  Williams   19  Female          10           Olives   \n",
       "4   91600173  William     Jones   18    Male          11    Green Peppers   \n",
       "\n",
       "  pineapple exp_pzmat  score  motivation  confidence  \n",
       "0         y         n     71           2         2.0  \n",
       "1         y         n     45           2         3.0  \n",
       "2         y         n     33           2         2.0  \n",
       "3         n         n     62           1         2.0  \n",
       "4         n         n     56           1         1.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6343d1ec-642a-4dae-a2f8-42d1d55dd887",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "It's the same process for the post-material data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9ea970df-31c6-4753-b1f7-f9bdae317160",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID Number</th>\n",
       "      <th>hrs_pzmat</th>\n",
       "      <th>score</th>\n",
       "      <th>motivation</th>\n",
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       "      <td>4</td>\n",
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      "text/plain": [
       "   ID Number  hrs_pzmat  score  motivation  confidence favorite_topping\n",
       "0   91600124         25     82           3           2        Pineapple\n",
       "1   91600185         14     76           2           5        Pineapple\n",
       "2   91600140         20     68           2           3        Pineapple\n",
       "3   91600145         11     90           1           4        Pineapple\n",
       "4   91600173         18     78           4           4        Pineapple"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_df = pd.read_excel(\"post_pizza_material.xlsx\")\n",
    "post_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f71513-db89-4330-88ca-1f18ebcb6d7c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "## Merging the two Dataframes\n",
    "\n",
    "We have two dataframes now that we need to merge together. Conceptually, this is what happens:\n",
    "\n",
    "![pd_merge](assets/pd_merge.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a23087-032f-4161-b40d-877b67821759",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We will use Pandas' `merge()` method to merge the two dataframes on the `ID Number` column. Some of the column names are the same, and when that happens pandas will automatically add suffixes. We want specific suffixes so we will instruct the method to use `_pre` and `_post`.\n",
    "\n",
    "We will assign the merged dataframe to a variable **`df`** which will be used throughout the rest of the notebook. The variable name could be anything, but it's common to use `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "feba1ff5-553f-49f0-a015-f258f09f81af",
   "metadata": {
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    {
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       "<div>\n",
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       "      <td>n</td>\n",
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       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   ID Number     Name Last Name  Age     Sex  yrs_eflexp favorite_topping_pre  \\\n",
       "0   91600124     John     Smith   18    Male          13            Pepperoni   \n",
       "1   91600185   Olivia   Johnson   19  Female           9              Sausage   \n",
       "2   91600140    James     Brown   19    Male          14               Onions   \n",
       "3   91600145     Emma  Williams   19  Female          10               Olives   \n",
       "4   91600173  William     Jones   18    Male          11        Green Peppers   \n",
       "\n",
       "  pineapple exp_pzmat  score_pre  motivation_pre  confidence_pre  hrs_pzmat  \\\n",
       "0         y         n         71               2             2.0         25   \n",
       "1         y         n         45               2             3.0         14   \n",
       "2         y         n         33               2             2.0         20   \n",
       "3         n         n         62               1             2.0         11   \n",
       "4         n         n         56               1             1.0         18   \n",
       "\n",
       "   score_post  motivation_post  confidence_post favorite_topping_post  \n",
       "0          82                3                2             Pineapple  \n",
       "1          76                2                5             Pineapple  \n",
       "2          68                2                3             Pineapple  \n",
       "3          90                1                4             Pineapple  \n",
       "4          78                4                4             Pineapple  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(\n",
    "    pre_df, post_df,\n",
    "    on=\"ID Number\",\n",
    "    suffixes=(\"_pre\", \"_post\"),\n",
    "    validate=\"one_to_one\"\n",
    ")\n",
    "\n",
    "# Let's see it!\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1666a6-37ac-4dd4-8a05-4094ecc8a2f2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "# Handling a Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a32be86-d2cc-400a-8257-1d16f88a7928",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We now have a **dataframe** and it is crucial to learn how to view and select different parts of the data. We may also want to change columns and save it. This section gives an overview of some of the methods to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c3417b-4c8d-45fe-a8f2-7e74ff477f5d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Inspecting the Dataframe\n",
    "\n",
    "Apart from `head()`, Pandas has some convenient ways to get an overview of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32afddd4-1beb-4f97-b1e6-13391d974834",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "A dataframe has a `shape` attribute that gives the number of rows and columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "36e828b6-6e28-48b4-a64f-368b30d65580",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 17)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e0c1f7-bd59-43ab-91d4-6fa2a414573f",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "A dataframe's `describe()` method will generate descriptive statistics *all* numeric columns.\n",
    "\n",
    "Usually it will print out long float values, but we can round it to two decimal places using `round()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c918a7c2-7cd6-440f-8ea1-924d8971afdf",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>yrs_eflexp</th>\n",
       "      <th>score_pre</th>\n",
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       "      <th>count</th>\n",
       "      <td>72.00</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.54</td>\n",
       "      <td>9.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.26</td>\n",
       "      <td>17.69</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.26</td>\n",
       "      <td>9.71</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.32</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.71</td>\n",
       "      <td>7.18</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>13.75</td>\n",
       "      <td>0.67</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.25</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.61</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>23.25</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.79</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  yrs_eflexp  score_pre  motivation_pre  confidence_pre  \\\n",
       "count  72.00       72.00      72.00           72.00           72.00   \n",
       "mean   19.54        9.83       0.48            1.94            2.26   \n",
       "std     1.32        2.69       0.17            0.98            0.71   \n",
       "min    18.00        5.00       0.11            1.00            1.00   \n",
       "25%    19.00        7.75       0.34            1.00            2.00   \n",
       "50%    19.00       10.00       0.48            2.00            2.00   \n",
       "75%    20.25       12.00       0.61            3.00            3.00   \n",
       "max    22.00       14.00       0.79            5.00            4.00   \n",
       "\n",
       "       hrs_pzmat  score_post  motivation_post  confidence_post  \\\n",
       "count      72.00       72.00            72.00            72.00   \n",
       "mean       17.69        0.76             3.85             3.26   \n",
       "std         7.18        0.13             1.03             1.29   \n",
       "min         2.00        0.34             1.00             1.00   \n",
       "25%        13.75        0.67             3.00             2.00   \n",
       "50%        16.00        0.76             4.00             3.50   \n",
       "75%        23.25        0.85             5.00             4.00   \n",
       "max        30.00        0.99             5.00             5.00   \n",
       "\n",
       "       start_efl_year  motivation_change  \n",
       "count           72.00              72.00  \n",
       "mean             9.71               1.90  \n",
       "std              2.95               1.37  \n",
       "min              4.00              -1.00  \n",
       "25%              7.75               1.00  \n",
       "50%              9.00               2.00  \n",
       "75%             12.00               3.00  \n",
       "max             16.00               4.00  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.describe(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd91d5c-cf97-45d2-8cb6-9ea0b7dc69cd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "If you spend a few moments looking at the result of `describe()`, you may have some questions:\n",
    "\n",
    "1. Why do all columns except one have a *count* of 72?\n",
    "2. Why is the max of *hrs_pzmat* 603?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3a1bcc-8e65-473a-be32-31aed411678b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Another method, `info()`, is used to list the columns. It will show the column index (**#**), **Column**, the **Non-Null Count**, and **Dtype**.\n",
    "\n",
    "**Dtype** is the data type of the column. With our data, some columns will be *int64* (integers) or *float64* (floats), but most are *object* because they contained strings.\n",
    "\n",
    "At the bottom of the printed table, we can see counts of each data type and how much memory the dataframe is consuming.\n",
    "\n",
    "We can inspect the **Non-Null Count** column to see if we have any missing values.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa6a1501-70a9-4940-aa29-fbbce6591cfa",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 72 entries, 0 to 71\n",
      "Data columns (total 17 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   ID Number              72 non-null     int64  \n",
      " 1   Name                   72 non-null     object \n",
      " 2   Last Name              72 non-null     object \n",
      " 3   Age                    72 non-null     int64  \n",
      " 4   Sex                    72 non-null     object \n",
      " 5   yrs_eflexp             72 non-null     int64  \n",
      " 6   favorite_topping_pre   72 non-null     object \n",
      " 7   pineapple              72 non-null     object \n",
      " 8   exp_pzmat              72 non-null     object \n",
      " 9   score_pre              72 non-null     int64  \n",
      " 10  motivation_pre         72 non-null     int64  \n",
      " 11  confidence_pre         71 non-null     float64\n",
      " 12  hrs_pzmat              72 non-null     int64  \n",
      " 13  score_post             72 non-null     int64  \n",
      " 14  motivation_post        72 non-null     int64  \n",
      " 15  confidence_post        72 non-null     int64  \n",
      " 16  favorite_topping_post  72 non-null     object \n",
      "dtypes: float64(1), int64(9), object(7)\n",
      "memory usage: 10.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b76d3c9-40b7-43d9-a0b8-b56f28d1b043",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Selecting Parts of a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9428f17d-df1b-4a53-8b2e-59ca005d0a4f",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "A **dataframe** is certainy similar to a spreadsheet. We can see parts of it with methods like `head()`, but how can we select and view different parts of it? The following will illustrate some methods to do this.\n",
    "\n",
    "These and other selection and viewing skills are essential building blocks for manipulating and analyzing the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3256073-777c-48e1-90bb-e77bcf8823c2",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Selecting a Column\n",
    "\n",
    "The syntax is like this: `dataframe[column_name]`. Note that when you select a column, what is returned is a `Series`.\n",
    "\n",
    "![selecting a column](assets/select_column.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "229536d1-b399-4512-96e1-77ee4d3a028f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     18\n",
       "1     19\n",
       "2     19\n",
       "3     19\n",
       "4     18\n",
       "      ..\n",
       "67    22\n",
       "68    19\n",
       "69    22\n",
       "70    18\n",
       "71    20\n",
       "Name: Age, Length: 72, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's select the Age column in our dataframe\n",
    "df[\"Age\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3170d104-5283-47c7-89cc-8398828fb393",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Selecting Multiple Columns\n",
    "\n",
    "The above used one column name. If we have more columns we can provide them as comma-separated list. Now, instead of a `Series` what is returned is a `DataFrame`.\n",
    "\n",
    "![selecting multiple columns](assets/select_multiple_columns.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4cd0abd5-8a1f-48a7-b153-f8df886b0495",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex\n",
       "0    18    Male\n",
       "1    19  Female\n",
       "2    19    Male\n",
       "3    19  Female\n",
       "4    18    Male\n",
       "..  ...     ...\n",
       "67   22  Female\n",
       "68   19    Male\n",
       "69   22  Female\n",
       "70   18    Male\n",
       "71   20  Female\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Age\", \"Sex\"]]   # The list is [\"Age\", \"Sex\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1324dd6c-aa05-43ba-9652-42775c45821f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Selecting Rows\n",
    "\n",
    "We can select rows (one or many). To select rows by the index label we can use `loc`. \n",
    "\n",
    "![select rows with loc](assets/select_rows.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb5d409-e123-4442-98b6-1937af1b6c7b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Let's try it. We select rows where the index label starts at 4 to where the label is and includes 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "70e574d8-c846-4fd1-93de-37d82d57722e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>yrs_eflexp</th>\n",
       "      <th>favorite_topping_pre</th>\n",
       "      <th>pineapple</th>\n",
       "      <th>exp_pzmat</th>\n",
       "      <th>score_pre</th>\n",
       "      <th>motivation_pre</th>\n",
       "      <th>confidence_pre</th>\n",
       "      <th>hrs_pzmat</th>\n",
       "      <th>score_post</th>\n",
       "      <th>motivation_post</th>\n",
       "      <th>confidence_post</th>\n",
       "      <th>favorite_topping_post</th>\n",
       "      <th>start_efl_year</th>\n",
       "      <th>motivation_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>MALE</td>\n",
       "      <td>11</td>\n",
       "      <td>GREEN PEPPERS</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.78</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>13</td>\n",
       "      <td>CHICKEN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.66</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>MALE</td>\n",
       "      <td>14</td>\n",
       "      <td>BACON</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>9</td>\n",
       "      <td>SAUSAGE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>MALE</td>\n",
       "      <td>12</td>\n",
       "      <td>ONIONS</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>9</td>\n",
       "      <td>OLIVES</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.72</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>MALE</td>\n",
       "      <td>9</td>\n",
       "      <td>PEPPERONI</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.86</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex  yrs_eflexp favorite_topping_pre  pineapple  exp_pzmat  \\\n",
       "4    18    MALE          11        GREEN PEPPERS      False      False   \n",
       "5    19  FEMALE          13              CHICKEN       True      False   \n",
       "6    19    MALE          14                BACON       True      False   \n",
       "7    19  FEMALE           9              SAUSAGE      False      False   \n",
       "8    19    MALE          12               ONIONS      False      False   \n",
       "9    19  FEMALE           9               OLIVES      False      False   \n",
       "10   18    MALE           9            PEPPERONI       True      False   \n",
       "\n",
       "    score_pre  motivation_pre  confidence_pre  hrs_pzmat  score_post  \\\n",
       "4        0.56               1             1.0         18        0.78   \n",
       "5        0.42               2             2.0         16        0.66   \n",
       "6        0.79               2             3.0         22        0.89   \n",
       "7        0.38               2             3.0         10        0.60   \n",
       "8        0.50               3             2.0         27        0.80   \n",
       "9        0.53               2             1.0         16        0.72   \n",
       "10       0.69               2             2.0         12        0.86   \n",
       "\n",
       "    motivation_post  confidence_post favorite_topping_post  start_efl_year  \\\n",
       "4                 4                4             PINEAPPLE               7   \n",
       "5                 5                5             PINEAPPLE               6   \n",
       "6                 3                2             PINEAPPLE               5   \n",
       "7                 5                3             PINEAPPLE              10   \n",
       "8                 4                1             PINEAPPLE               7   \n",
       "9                 4                4             PINEAPPLE              10   \n",
       "10                4                2             PINEAPPLE               9   \n",
       "\n",
       "    motivation_change  \n",
       "4                   3  \n",
       "5                   3  \n",
       "6                   1  \n",
       "7                   3  \n",
       "8                   1  \n",
       "9                   2  \n",
       "10                  2  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[4:10] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf2d798-6c64-4e65-a33f-c10418ff47dd",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Selecting a Slice\n",
    "We can select a slice of the table by indicating the index labels (like above) and a column name.\n",
    "\n",
    "![select rows by column](assets/select_rows_and_cols.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "289bdea5-9568-45f3-96b2-fd1c9c27baa7",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Name'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[90], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mName\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m \n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# note that you could provide more columns as a list.\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexing.py:1067\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1065\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m   1066\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m-> 1067\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1069\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m   1070\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexing.py:1247\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1245\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[1;32m   1246\u001b[0m     tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_ellipsis(tup)\n\u001b[0;32m-> 1247\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_lowerdim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1249\u001b[0m \u001b[38;5;66;03m# no multi-index, so validate all of the indexers\u001b[39;00m\n\u001b[1;32m   1250\u001b[0m tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tuple_indexer(tup)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexing.py:967\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    963\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tup):\n\u001b[1;32m    964\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_label_like(key):\n\u001b[1;32m    965\u001b[0m         \u001b[38;5;66;03m# We don't need to check for tuples here because those are\u001b[39;00m\n\u001b[1;32m    966\u001b[0m         \u001b[38;5;66;03m#  caught by the _is_nested_tuple_indexer check above.\u001b[39;00m\n\u001b[0;32m--> 967\u001b[0m         section \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    969\u001b[0m         \u001b[38;5;66;03m# We should never have a scalar section here, because\u001b[39;00m\n\u001b[1;32m    970\u001b[0m         \u001b[38;5;66;03m#  _getitem_lowerdim is only called after a check for\u001b[39;00m\n\u001b[1;32m    971\u001b[0m         \u001b[38;5;66;03m#  is_scalar_access, which that would be.\u001b[39;00m\n\u001b[1;32m    972\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m section\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim:\n\u001b[1;32m    973\u001b[0m             \u001b[38;5;66;03m# we're in the middle of slicing through a MultiIndex\u001b[39;00m\n\u001b[1;32m    974\u001b[0m             \u001b[38;5;66;03m# revise the key wrt to `section` by inserting an _NS\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexing.py:1312\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1310\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[1;32m   1311\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_label\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexing.py:1260\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m   1259\u001b[0m     \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[0;32m-> 1260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/generic.py:4041\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   4039\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4040\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m drop_level:\n\u001b[0;32m-> 4041\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   4042\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m   4043\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Name'"
     ]
    }
   ],
   "source": [
    "df.loc[4:10, \"Name\"] \n",
    "# note that you could provide more columns as a list."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12faa29f-5663-4121-a5d2-a8450b792e15",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "## Applying a Mask (Filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a291ddb-313e-448c-ba4c-e1fdcc753545",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "What if we want to select rows from the dataframe that match a certain criteria? This is a filtering process, or what might be referred to as **applying a mask**. In the pandas documentation this is referred to as *boolean indexing*.\n",
    "\n",
    "A mask is actually **Series** that contains only boolean (True/False) values. Here's a diagram showing how a mask is made:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c73868c-7746-4d57-acc4-863c5e80073f",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "![making a mask](assets/example_mask.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1614d04e-a2e2-4851-8e62-29b00bb91909",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "It's possible to combine multiple criteria together in order to make a mask, but let's start with something simple. The code below is the basic method for making a mask. We're not assigning it to a variable so that we can see what it does. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b46ea689-037a-453f-b61b-749eb156f111",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "      ...  \n",
       "67    False\n",
       "68    False\n",
       "69    False\n",
       "70    False\n",
       "71    False\n",
       "Name: favorite_topping_pre, Length: 72, dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Show us whether or not the values in the 'favorite_topping_pre' column are equal to 'Pineapple'\"\n",
    "df[\"favorite_topping_pre\"] == \"Pineapple\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007dcd73-5f73-4d52-ad91-ccf60df51d42",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We get a **Series** of `True` / `False` values.  If the value is `True` that means the corresponding row had a participant that indicated they really like pineapple in the pre-material survey.\n",
    "\n",
    "We use these boolean Series *a lot* in pandas. When we use them as a mask, pandas checks if the row meets the condition (`True` is a match). Let's see how that works in a diagram:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9a2974-69af-4516-b290-1df7f11a2b97",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "![applying a mask](assets/example_applying_mask.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37879ada-59d2-4589-a015-0bd860c0c105",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Let's try it out. In the example below, we first make a mask and then apply it to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "85ee535d-860d-4622-b23a-cf92869d6c5a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>yrs_eflexp</th>\n",
       "      <th>favorite_topping_pre</th>\n",
       "      <th>pineapple</th>\n",
       "      <th>exp_pzmat</th>\n",
       "      <th>score_pre</th>\n",
       "      <th>motivation_pre</th>\n",
       "      <th>confidence_pre</th>\n",
       "      <th>hrs_pzmat</th>\n",
       "      <th>score_post</th>\n",
       "      <th>motivation_post</th>\n",
       "      <th>confidence_post</th>\n",
       "      <th>favorite_topping_post</th>\n",
       "      <th>start_efl_year</th>\n",
       "      <th>motivation_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Age, Sex, yrs_eflexp, favorite_topping_pre, pineapple, exp_pzmat, score_pre, motivation_pre, confidence_pre, hrs_pzmat, score_post, motivation_post, confidence_post, favorite_topping_post, start_efl_year, motivation_change]\n",
       "Index: []"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a mask\n",
    "mask = df[\"favorite_topping_pre\"] == \"Pineapple\"\n",
    "\n",
    "# Apply the mask (only shows where the mask is 'True')\n",
    "df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded260c9-1eaa-4fcf-820b-5abf8c7b987d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Making or Overwriting Columns\n",
    "\n",
    "Making or overwriting columns uses the same approach and if the column doesn't exist a new one will be made.\n",
    "\n",
    "Let's try making a new column called `start_efl_year`. This will be the difference of `Age` and `yrs_eflexp`. If you're familiar with a spreadsheet program, the process to do this might be to type equal (`=`), select a cell from one column, type minus (`-`), select a cell from another column, then do a fill down. \n",
    "\n",
    "In pandas, this kind of process is greatly simplified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "380b936e-c7bc-4c50-96d6-e1f76dfbe0c6",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Make a new column 'start_efl_year'\n",
    "# In some sense, you can imagine that this is going through row by row and doing the substraction\n",
    "# and the new value is being placed in the new column.\n",
    "df[\"start_efl_year\"] = df[\"Age\"] - df[\"yrs_eflexp\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db2d704-82e5-4a39-b12f-9fd4f809909f",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Removing Columns\n",
    "\n",
    "There are a few columns that we do not need for our analysis. We use the `drop()` method to delete columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "084ad1c0-7278-4bb9-9865-55d619790db4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['ID Number', 'Name', 'Last Name'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[93], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m columns_to_drop \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mID Number\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mName\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLast Name\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;66;03m# a list of column names\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns_to_drop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# axis 1 means \"column\"\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/frame.py:5399\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5251\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m   5252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m   5253\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5260\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   5261\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5262\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   5263\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   5264\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5397\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   5398\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5399\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5400\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5401\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5402\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5403\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5404\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5405\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5406\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5407\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/generic.py:4505\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   4504\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4505\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m   4508\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/generic.py:4546\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4545\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4546\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4547\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4549\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4550\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pandas/core/indexes/base.py:6934\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m   6933\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 6934\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6935\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m   6936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['ID Number', 'Name', 'Last Name'] not found in axis\""
     ]
    }
   ],
   "source": [
    "columns_to_drop = [\"ID Number\", \"Name\", \"Last Name\"] # a list of column names\n",
    "df = df.drop(columns_to_drop, axis=1) # axis 1 means \"column\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14c86ea-0f3a-4055-9213-acc8471b99f6",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Exporting a Dataframe\n",
    "\n",
    "If you need to save your dataframe to a file, you can do that anytime. There are a number of conversion formats (18, actually) including CSV, TSV, and Excel. Here's how to save it as a CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a2560de1-52e8-4b4c-b5d6-e513d38bab55",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"pzmat_data_merged.csv\", index=False) # we don't want the index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97bb0085-4fdc-4470-9403-d8392607f655",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7438b392-768b-4664-a665-7a7cb86a0591",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Now that we have imported the data, we can move on to cleaning it.\n",
    "\n",
    "Some issues and tasks have been prepared with the data beforehand in order to demonstrate some common data cleaning tasks:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbece4ab-d713-4fb9-bba7-ac9f98be24bb",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "1. A missing value\n",
    "2. An entry error (spelling mistake)\n",
    "3. An entry error (too many hours)\n",
    "4. An invalid entry for a \"y\" or \"n\" question\n",
    "5. Convert text input columns to upper case\n",
    "6. Convert Yes/No Columns to Boolean\n",
    "7. Convert the Test Scores to Percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24049d48-ffb0-4f5d-8a8d-3ea92b3d2acf",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #1: A Missing Value\n",
    "\n",
    "When looking at the output from `df.info()` we saw a null value in the 'confidence_pre' column.\n",
    "\n",
    "We can see which row this is by applying a **mask**. Basically we're telling the computer, *\"show me the rows where the value in the column 'confidence_pre' is null.\"*\n",
    "\n",
    "Let's break down how that would be done:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09718d2f-82f1-4cab-a81b-f39e3786b21c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "**STEP 1:** We want the `confidence_pre` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e6fdcdb1-74b9-4a87-a7f1-da1559375e22",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "target_column = df[\"confidence_pre\"]\n",
    "# target_column will be a Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "691ac53a-0e9c-4c77-974c-fa3d743d99a0",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "**STEP 2:** We want to know which of the values is null\n",
    "\n",
    "*With pandas, empty cells are refered to as NaN (Not a Number). This is something from the engine that pandas uses under the hood.*\n",
    "\n",
    "We will make a mask checking for empty values using pandas `isna()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e962d518-f76c-48cc-98be-b133e5d3b939",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "confidence_is_null_mask = pd.isna(target_column)\n",
    "# result is a boolean Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3b403c-bc0d-4809-90ae-b37764e5772d",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "**STEP 3:** We apply that mask to the dataframe.\n",
    "\n",
    "Any row of the dataframe that has `True` in the mask will print out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "52b7e78c-fbe8-48c0-a466-64e05aad20ac",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>yrs_eflexp</th>\n",
       "      <th>favorite_topping_pre</th>\n",
       "      <th>pineapple</th>\n",
       "      <th>exp_pzmat</th>\n",
       "      <th>score_pre</th>\n",
       "      <th>motivation_pre</th>\n",
       "      <th>confidence_pre</th>\n",
       "      <th>hrs_pzmat</th>\n",
       "      <th>score_post</th>\n",
       "      <th>motivation_post</th>\n",
       "      <th>confidence_post</th>\n",
       "      <th>favorite_topping_post</th>\n",
       "      <th>start_efl_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Pineapple</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex  yrs_eflexp favorite_topping_pre pineapple exp_pzmat  \\\n",
       "71   20  Female           7              Chicken         n         n   \n",
       "\n",
       "    score_pre  motivation_pre  confidence_pre  hrs_pzmat  score_post  \\\n",
       "71         21               3             NaN         14          34   \n",
       "\n",
       "    motivation_post  confidence_post favorite_topping_post  start_efl_year  \n",
       "71                5                4             Pineapple              13  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[confidence_is_null_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e4e957-f387-45da-b0a7-a29551fa2fb5",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "**STEP 4:** We decide what to do about this Missing Data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b3cc49-6f77-47b2-9a50-d7493d941a47",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Pandas has a number of built in methods for filling in missing data.\n",
    "\n",
    "We will fill this with the most frequent value which requires a bit more work.\n",
    "\n",
    "So, we're going to tell the computer to *\"fill any empty cells with the most frequent value from the column 'confidence_pre'.\"*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a21e600-d484-42d7-af06-18315bb63a0b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "**STEP 5:** Get the most frequent value from `confidence_pre`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e523414-4a7a-4123-abaa-69337805f916",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Dataframes and Series have a `value_counts()` method that counts the frequency of each value. By default the result is sorted in descending order. Using `value_counts()` gives us a **Series** - something like this:\n",
    "\n",
    "| index | count |\n",
    "| ----- | ----- |\n",
    "| 2.0   |  45   |\n",
    "| 3.0   |  15   |\n",
    "| 1.0   |   6   |\n",
    "| 4.0   |   5   |\n",
    "\n",
    "We want the index value (not the count result) of whatever ends up in the first row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "216d4235-8d75-4fbf-ab1a-1d91e1e8b828",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "confidence_value_counts = df[\"confidence_pre\"].value_counts()\n",
    "# result is a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd5dc9c6-1894-48bb-80ad-0228c8451f3c",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the index value of the most frequent\n",
    "# index 0 is the first one\n",
    "most_frequent_value = confidence_value_counts.index[0]\n",
    "\n",
    "\n",
    "# Let's see what it is\n",
    "most_frequent_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0294085c-37b3-4131-9bab-318904ded22c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "**STEP 6:** Fill in the missing value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c6d9e2-0802-4741-b67f-d4592fa6034c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "After we get the value, we'll use the `fillna()` method on the `confidence_pre` column to fill in that value. Note: this would fill in *any* missing values in that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fe79ac2-6e32-4395-9949-cc849a23cdf0",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace the missing value using fillna()\n",
    "df[\"confidence_pre\"] = df[\"confidence_pre\"].fillna(most_frequent_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742349be-8375-4a4c-a439-d4e0f30e8a94",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #2: A Spelling Mistake\n",
    "\n",
    "There is a spelling mistake in `favorite_topping_pre`.\n",
    "\n",
    "We can see all of the values in the column with the `unique()` method. There aren't many different values so we should be able to spotthe spelling mistake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0817e3b3-0858-419a-a138-fa4b6a67b1fd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pepperoni', 'Sausage', 'Onions', 'Olives', 'Green Peppers',\n",
       "       'Chicken', 'Bacon', 'Mushrooms', 'Pineapple', 'Pinapple'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"favorite_topping_pre\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5abc57-a861-41c7-b327-c3edf4ebcc0f",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "This doesn't tell us how many times *Pinapple* occurs. We can use the `replace()` method to change this. You'll notice that we're feeding a python dictionary into the method. The dictionary key is the mistaken spelling and the value is the correct spelling. This will replace any of those mistakes for the entire column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b568244-b71b-4669-bc48-5afb92b0df5e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Let's change \"Pinapple\" to \"Pineapple\"\n",
    "df[\"favorite_topping_pre\"] = df[\"favorite_topping_pre\"].replace({\"Pinapple\": \"Pineapple\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fe611c-57e9-4372-a05c-e62c066cb669",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #3: A Number Mistake\n",
    "\n",
    "Students should have a maximum of **50 hours** with the material. We can expect that any value less than 0 or greater than 50 will be wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cdc2cd2c-ef68-4adc-a552-31ae2ee85155",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We will make a mask that is combined with two conditions.\n",
    "# Notice the two conditions are separated by a pipe character which means OR \n",
    "# So we are looking for anything less than 0 OR greater than 50\n",
    "\n",
    "out_of_range_mask = (df[\"hrs_pzmat\"] < 0) | (df[\"hrs_pzmat\"] > 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "58be7991-4b34-44b4-a492-1ec0e1eb6c99",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>yrs_eflexp</th>\n",
       "      <th>favorite_topping_pre</th>\n",
       "      <th>pineapple</th>\n",
       "      <th>exp_pzmat</th>\n",
       "      <th>score_pre</th>\n",
       "      <th>motivation_pre</th>\n",
       "      <th>confidence_pre</th>\n",
       "      <th>hrs_pzmat</th>\n",
       "      <th>score_post</th>\n",
       "      <th>motivation_post</th>\n",
       "      <th>confidence_post</th>\n",
       "      <th>favorite_topping_post</th>\n",
       "      <th>start_efl_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>603</td>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Pineapple</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age   Sex  yrs_eflexp favorite_topping_pre pineapple exp_pzmat  score_pre  \\\n",
       "28   19  Male          12              Chicken         y         n         37   \n",
       "\n",
       "    motivation_pre  confidence_pre  hrs_pzmat  score_post  motivation_post  \\\n",
       "28               3             2.0        603          63                4   \n",
       "\n",
       "    confidence_post favorite_topping_post  start_efl_year  \n",
       "28                3             Pineapple               7  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see who is out of range\n",
    "df[out_of_range_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839f73a9-67d4-4e9c-957c-a5147387bd46",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We could fix this by telling the computer, \"Give this one student the average hours of male students his age.\"\n",
    "\n",
    "After getting the value, we will apply it using `loc` which is what we use to access a group of rows. `loc` is also one of the few ways that we can assign a new value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4c677e3-8690-41a5-a6f7-7cde64ee88bf",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we need a mask for those participants that are in range\n",
    "# This happens to be the inverse of the out_of_range_mask we made above\n",
    "in_range_mask = ~out_of_range_mask   # invert a mask with a prefixed tilde ~\n",
    "\n",
    "# We need another mask for Male participants that are 19\n",
    "# These are separated by an ampersand (&) meaning a conditional AND\n",
    "male_19_mask = (df[\"Age\"] == 19) & (df[\"Sex\"] == \"Male\")\n",
    "\n",
    "# We can not apply the mask to the dataframe\n",
    "# Notice that we are combining with AND:\n",
    "#   in_range_mask & male_19_mask\n",
    "# Then we get the mean for the column, and finally round it\n",
    "value = df[in_range_mask & male_19_mask][\"hrs_pzmat\"].mean()\n",
    "average_hours_19_male = round(value)\n",
    "\n",
    "# Let's see what it is\n",
    "average_hours_19_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6da507dd-4713-4332-b4e3-8ce494856859",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Now we will apply the value\n",
    "# We already have the masks that we need in order to select the row we want\n",
    "# We also need to specify the column we are assigning the new value to\n",
    "\n",
    "df.loc[out_of_range_mask & male_19_mask, \"hrs_pzmat\"] = average_hours_19_male"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695a0968-6c87-4413-a3d3-1efa1799796c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Admittedly, this approach for this one row is slightly overkill but it is meant to show how flexible and powerful masks can be. We had one 19 year old male with a problem value. What if we had another participant that was female and wanted to assign a different value? We could easily make a different mask so that we aren't applying wrong values to the wrong group. Now try to think about how helpful these masks would be if you are dealing with many thousands of rows and you have over 500 such errors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4484749b-bdac-4483-a0a0-97f3a67cbac2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #4: An Input Error\n",
    "Students also had to fill in **y** or **n** for some parts of the form. There is a wrong value in the `pineapple` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "936b72d4-fe03-4e48-af25-1f0142f05d3d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50    NO!\n",
       "Name: pineapple, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a mask that would show anything that is not y or not n\n",
    "yes_no_mask = (df[\"pineapple\"] != \"y\") & (df[\"pineapple\"] != \"n\")\n",
    "\n",
    "# Apply the mask and look at the pineapple column\n",
    "df[yes_no_mask][\"pineapple\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eef958e-8d3c-4cd5-8906-65830e0fe398",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "It looks like there is an emphatic **\"NO!\"** for one student. The value should be \"n\" so it's an easy fix with the `replace()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "88bd6edb-da0d-4844-b447-1acd3137f80b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"pineapple\"] = df[\"pineapple\"].replace({\"NO!\": \"n\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb75bdf-56cc-4486-bcb9-c6f13e71890b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #5: Convert Text Input Columns to Upper Case\n",
    "\n",
    "Examine these strings:\n",
    "\n",
    "1. pineapple\n",
    "2. Pineapple\n",
    "3. PINEAPPLE\n",
    "\n",
    "Are they the same? They certainly are some level, however, in python code these are not equal since they have a mixture of upper and lower case. If we tried to make a group out of these, it would not work.\n",
    "\n",
    "One simple approach to solve this is to make strings have the same case.\n",
    "\n",
    "In our dataframe, we have several columns that have such strings. They would have been text inputs on the forms that participants filled out. \n",
    "\n",
    "We want to change all of these columns to the same case. This could be a lot of code to write depending on the number of columns. But we're using a programming language! We can make a list of columns, and then iterate through them in a `for` loop. The same code will be used within the `for` loop to change the string to uppercase for the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "24b879b2-c2d1-43b4-8d5d-65cb45a298df",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>favorite_topping_pre</th>\n",
       "      <th>favorite_topping_post</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PEPPERONI</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAUSAGE</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ONIONS</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OLIVES</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GREEN PEPPERS</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>OLIVES</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ONIONS</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>GREEN PEPPERS</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>PEPPERONI</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>CHICKEN</td>\n",
       "      <td>PINEAPPLE</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   favorite_topping_pre favorite_topping_post     Sex\n",
       "0             PEPPERONI             PINEAPPLE    MALE\n",
       "1               SAUSAGE             PINEAPPLE  FEMALE\n",
       "2                ONIONS             PINEAPPLE    MALE\n",
       "3                OLIVES             PINEAPPLE  FEMALE\n",
       "4         GREEN PEPPERS             PINEAPPLE    MALE\n",
       "..                  ...                   ...     ...\n",
       "67               OLIVES             PINEAPPLE  FEMALE\n",
       "68               ONIONS             PINEAPPLE    MALE\n",
       "69        GREEN PEPPERS             PINEAPPLE  FEMALE\n",
       "70            PEPPERONI             PINEAPPLE    MALE\n",
       "71              CHICKEN             PINEAPPLE  FEMALE\n",
       "\n",
       "[72 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a list of columns we will convert to uppercase\n",
    "text_input_columns = [\"favorite_topping_pre\", \"favorite_topping_post\", \"Sex\"]\n",
    "\n",
    "# Make a 'for' loop\n",
    "for column in text_input_columns:\n",
    "    # for every column in the list of columns, change the string to uppercase\n",
    "    df[column] = df[column].str.upper()\n",
    "\n",
    "# Let's see the result by selecting those columns\n",
    "df[text_input_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b326a811-624f-4871-86fe-bffeef686f62",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #6: Convert Yes/No Columns to Boolean\n",
    "\n",
    "In python we have a `bool` type which is important to use for comparisons. We have a few columns that are using binary values (yes or no) but leaving them as is would create more work later. Some built-in operations in python work with bools, but wouldn't work well with strings of \"y\" or \"n\". The same is true for Pandas.\n",
    "\n",
    "To fix this, we will loop through the columns that have yes/no values and replace the value with `True` or `False`.\n",
    "\n",
    "The *better* approach to this would be to use `replace()` like we have earlier. However, it's a simple example to show another very powerful method: `apply()`. The `apply()` method allows you to use functions to manipulate values. In the example below we will define a python function, and then tell pandas to use that function with the `apply()` method.\n",
    "\n",
    "Note that after we change all the values in these columns to bools, pandas will automatically convert the `dtype` for those columns to `bool`. This can be checked with `df.info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "51d1ed5a-bf5f-46fc-84dc-0353b6e5f097",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pineapple</th>\n",
       "      <th>exp_pzmat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    pineapple  exp_pzmat\n",
       "0        True      False\n",
       "1        True      False\n",
       "2        True      False\n",
       "3       False      False\n",
       "4       False      False\n",
       "..        ...        ...\n",
       "67      False      False\n",
       "68      False       True\n",
       "69      False      False\n",
       "70       True      False\n",
       "71      False      False\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function that will return True for a \"y\" value or False for a \"n\" value\n",
    "def convert_yes_no(value):\n",
    "    if value == \"y\":\n",
    "        return True\n",
    "    elif value == \"n\":\n",
    "        return False\n",
    "    return value # if there wasn't a y / n, return the original value\n",
    "\n",
    "# Specify the columns in a list\n",
    "yes_no_columns = [\"pineapple\", \"exp_pzmat\"]\n",
    "\n",
    "# Iterate over the list of columns and use apply to convert the values\n",
    "for column in yes_no_columns:\n",
    "    # each value in the column will be passed to the convert_yes_no function\n",
    "    # the original value will be replaced by the returned value\n",
    "    df[column] = df[column].apply(convert_yes_no)\n",
    "\n",
    "# Let's see the result by selecting those columns\n",
    "df[yes_no_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4437be-242c-409d-982a-343c0cd107f8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning Task #7: Convert the Test Scores to Percentages\n",
    "\n",
    "The test scores were recorded as is but we will convert them to a percentage (as a float). The tests had a total score of 100. To calculate the percentage we would use the formula `score / total`. \n",
    "\n",
    "We can easily apply such a formula to an entire column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "68d7cbb0-ad3a-4b7b-bbbd-fca9c135cca8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score_pre</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.82</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.33</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.62</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    score_pre  score_post\n",
       "0        0.71        0.82\n",
       "1        0.45        0.76\n",
       "2        0.33        0.68\n",
       "3        0.62        0.90\n",
       "4        0.56        0.78\n",
       "..        ...         ...\n",
       "67       0.11        0.63\n",
       "68       0.54        0.87\n",
       "69       0.34        0.67\n",
       "70       0.52        0.67\n",
       "71       0.21        0.34\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_score = 100\n",
    "df[\"score_pre\"] = df[\"score_pre\"] / total_score\n",
    "df[\"score_post\"] = df[\"score_post\"] / total_score\n",
    "\n",
    "# Let's see the effect\n",
    "df[[\"score_pre\", \"score_post\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc9282e-7c89-4002-b7e0-e1973d95c368",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## An Important Note\n",
    "\n",
    "There are a number of methods that you can use to replace or fill in data. We've demonstrated several approaches above, but keep in mind that the approach must be suitable for your particular dataset.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>NOTE:</b> You should also take into consideration that missing data in pandas will generally <b><i>not</i></b> be included in calculations.\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a99d133-0b0d-44af-a6dd-5bddaea66ab1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Data Analysis / Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a50edc5-e5c3-4c17-9b38-204ba398d8d7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Now that the data is cleaned, we can start analyzing it.\n",
    "\n",
    "Pandas has a number of methods that give quick access to common statistics (standard deviation, variance, etc). For this section we're going to focus on some visual representations of our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af1e485-ee76-4852-beea-efb330cfb36b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Next, let's create some visualizations to understand our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcd7aba-77d2-43d9-a43a-75e8af041d17",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Analysis 1: Overview of the Participants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c9dc99-4087-41a1-ac3f-176118bf4993",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "### Ratio of Male and Female Participants (Pie Chart)\n",
    "\n",
    "First we want to understand the ratio of male and female participants in the form of a pie chart. This can be done in one line of code , but we'll break this down into some smaller steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4675a9eb-ee0d-4e21-9239-79a5af7f5236",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "**STEP 1:** Make a group.\n",
    "\n",
    "The `groupby()` method is used to make a group. It can also take a list of columns to group by multiple features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "edb5e037-4e4a-41e6-a265-35bb583e86bf",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "male_female_df = df.groupby(\"Sex\")\n",
    "\n",
    "# The above gives us the entire dataframe grouped by Sex\n",
    "# But we only need the \"Sex\" column\n",
    "\n",
    "male_female_series = male_female_df[\"Sex\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38f503b-e6fa-4842-88fe-efaa035b72a4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "**STEP 2:** Count each one in each group.\n",
    "\n",
    "Usually when using `groupby()` in pandas, we do some kind of aggregation on the group. What we want to do is count how many are in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "41a88bec-c7a3-4cc6-879f-65e95c39ed57",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "FEMALE    36\n",
       "MALE      36\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_female_counts = male_female_series.count()  # will be a groupby series\n",
    "male_female_counts "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28aa4eb-02dc-41f6-9b26-69262009d16e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "**STEP 3:** Make a Pie Chart\n",
    "\n",
    "The method `plot()` is fairly straightforward to use. Here we want to plot a pie chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "542e94e5-13c1-47b7-986b-573d87dc8b2c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Breakdown of Male/Female Participants'}, ylabel='Sex'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_female_counts.plot.pie(title=\"Breakdown of Male/Female Participants\", autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e2e6cf38-4fa9-4d02-b1ac-5aaf3a6ce70d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# The above steps were broken down but could be done in one line:\n",
    "# df.groupby(\"Sex\")[\"Sex\"].count().plot.pie(title=\"Breakdown of Male/Female Participants\", autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442f9aac-a170-482c-8675-e0d28b5d01f7",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Distribution of Age (Bar Chart / Histogram)\n",
    "\n",
    "We can see the maximum, minimum, and mean age for participants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "77a06d82-f66e-4dc1-a597-c2f352918c1c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum age of participants was 18,the maximum was 22 and the mean was 19.54.\n"
     ]
    }
   ],
   "source": [
    "min_age = df[\"Age\"].min()\n",
    "max_age = df[\"Age\"].max()\n",
    "mean_age = round(df[\"Age\"].mean(), 2)  # rounded to 2 decimal places\n",
    "\n",
    "print(f\"\"\"The minimum age of participants was {min_age},\\\n",
    "the maximum was {max_age} and the mean was {mean_age}.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98efc39-cde1-403f-bafa-4b8eeea3387b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Let's plot the Age distribution. Pandas does have a `hist()` method that would plot a histogram, but getting the expected visual it would be slightly complicated in this case. Instead, we can use a simple bar graph. We can get the value counts (not sorted by frequency) and then plot them in a bar graph. We'll also use the mean from above in the title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "62df320b-6bf5-4d5c-bd2c-614b487201ec",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Age\"].value_counts(sort=False).plot.bar(\n",
    "    title=f\"Distribution of Age (Mean = {mean_age})\",\n",
    "    xlabel=\"Age\",        # label the x-axis\n",
    "    ylabel=\"Frequency\",  # label the y-axis\n",
    "    rot=0,               # rotate the x-axis labels\n",
    "    figsize=(5,3),\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0817213-c4d6-41d8-ad43-c073da7f0362",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Male/Female Breakdown by Age (Stacked Bar Chart)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fc32e6-54bc-4114-90b8-8fa7b68c509a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Next, let's breakdown the age of participants by Sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "09c37ac8-191b-4d6e-a32c-7a607fd5da90",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Age\n",
       "FEMALE  18      2\n",
       "        19     15\n",
       "        20      6\n",
       "        21      4\n",
       "        22      9\n",
       "MALE    18     13\n",
       "        19     16\n",
       "        20      2\n",
       "        21      4\n",
       "        22      1\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a group using two columns\n",
    "# select the Age column\n",
    "# then and do a count (aggregate)\n",
    "sex_age_series = df.groupby([\"Sex\", \"Age\"])[\"Age\"].count()\n",
    "sex_age_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "43b29a39-bcd9-42b6-9fe4-fa8c2b0c73f1",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Age</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FEMALE</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALE</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Age     18  19  20  21  22\n",
       "Sex                       \n",
       "FEMALE   2  15   6   4   9\n",
       "MALE    13  16   2   4   1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The result from above is a Series\n",
    "# with a special index called a MultiIndex\n",
    "# We need to unstack the series from above\n",
    "# The unstack() method reshapes the Series\n",
    "# The result is something like doing a pivot\n",
    "# and will be a DataFrame\n",
    "sex_age_df = sex_age_series.unstack()\n",
    "sex_age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3593a6e9-001b-4bf1-ac8d-15b1dc49e355",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph, indicating that it should be stacked\n",
    "sex_age_df.plot.bar(\n",
    "    stacked=True,\n",
    "    title=\"Stacked Bar Chart of Age by Sex\",\n",
    "    xlabel=\"Sex\",\n",
    "    legend=\"reverse\",\n",
    "    rot=0,\n",
    "    colormap=\"tab20\",\n",
    "    figsize=(5,3),\n",
    "    );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a1dd7a0-c612-40ca-8095-58e1007cd7b0",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Favorite Pizza Topping (as a Word Cloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01df1ab2-59ed-4b7f-8de7-c3708784c0bd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We will do one special kind of visualization (a wordcloud) with another library called `wordcloud`. \n",
    "\n",
    "If you don't have it installed, you can install it with the command `pip install wordcloud`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f7fef15d-460b-4c15-ad0b-1e1407345892",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12fadc0-5cc9-4a0f-bf9a-ae41f511ffe5",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We also need to use the `matplotlib` library to show the image. Pandas actually uses this library under the hood for all of the plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3526fb00-6553-4646-8c99-9af831d83580",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6b8dd596-c6bf-41b3-a7ec-0d071898ed67",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We need a python dictionary of the value_counts\n",
    "words = df[\"favorite_topping_pre\"].value_counts().to_dict()\n",
    "\n",
    "# Make the wordcloud\n",
    "wordcloud = WordCloud(\n",
    "    background_color=\"white\"\n",
    "    ).generate_from_frequencies(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cec4805b-349e-4cdb-b49f-357c6ed51cda",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the matplotlib library to show the image\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a19cc92-253e-4239-b877-26ba8433d1da",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "## Analysis 2: Pre vs. Post Data\n",
    "\n",
    "Let's see the difference between pre and post variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe214f7f-47a0-43ef-b328-d150b2817686",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Comparing Pre-Post Scores (Box Plot)\n",
    "\n",
    "Let's see if how the participants faired with the test by using a box plot to visually compare the test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "390a7791-b7f9-4dfd-bc41-1ccfd3102824",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# First we'll select and multiply the score columns\n",
    "# (They are in float / percent format)\n",
    "scores = df[[\"score_pre\", \"score_post\"]] * 100\n",
    "\n",
    "# We'll also rename the columns\n",
    "scores.columns = [\"Pre-Test Scores\", \"Post-Test Scores\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f0ed5449-ccb1-4b69-a6a3-3c8b48778cd3",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a box plot\n",
    "scores.plot.box(\n",
    "    title=\"Pre/Post Score Box Plot Comparison\",\n",
    "    ylabel=\"Score (Percent)\",\n",
    "    figsize=(5,3)\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5e0d6d-ddbf-4b1d-8a86-e2d86b0e268e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "###  Motivation and Confidence Pre/Post Comparison (Box Plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7f26e7-67a8-4420-a322-6564a259216a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Next, let's see if there was an affect on the motivation and confidence of participants after exposure to the pizza material. The process is the same as the above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "57e7dfe7-960c-4e67-b23c-25b8feae0404",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Select the columns we want to use\n",
    "mot_conf_df = df[[\n",
    "     \"motivation_pre\",\n",
    "     \"motivation_post\",\n",
    "     \"confidence_pre\",\n",
    "     \"confidence_post\"\n",
    "    ]]\n",
    "\n",
    "# Rename the columns\n",
    "mot_conf_df.columns = [\n",
    "    \"Motivation (Pre)\", \n",
    "    \"Motivation (Post)\", \n",
    "    \"Confidence (Pre)\", \n",
    "    \"Confidence (Post)\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "51e5dca3-5391-460b-bd8b-c72844bc2505",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot it with a title and specify the y ticks\n",
    "mot_conf_df.plot.box(\n",
    "    title=\"Motivation and Confidence Pre/Post Comparison\",\n",
    "    yticks=[1,2,3,4,5],\n",
    "    figsize=(7,3)\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1531b2c-a73a-4e2c-a5b7-73e1811b441e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "## Analysis 3: Number of Hours Correlated with Motivation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e05b35-569c-4004-b4e0-7598abbde2a0",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We could see that there was some affect that the pizza material had on the participants. Let's visualize the correlation between the hours spent on the pizza material and the impact on motivation. \n",
    "\n",
    "First we'll make a new column indicating the change of motivation change.\n",
    "\n",
    "Then we'll make a scatterplot where `x` and `y` are the dataframe columns to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "45c23ea4-47f7-487e-8d89-b1fa1cb31001",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"motivation_change\"] = df[\"motivation_post\"] - df[\"motivation_pre\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3b2a49ce-fb33-4932-ab0d-b8b1d9bd46bd",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(\n",
    "    x=\"hrs_pzmat\",\n",
    "    y=\"motivation_change\",\n",
    "    title=\"Hours Spent on Material Correlated to Change in Motivation\",\n",
    "    xlabel=\"Hours Spent on Material\",\n",
    "    ylabel=\"Change in Motivation\",\n",
    "    figsize=(5,3),\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2996ea7-f614-485a-8c6e-a176e17f52af",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "## Analysis 4: Post-Favorite Pizza Topping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8689c984-7a28-48ec-8f36-c856b994c6c7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "The best indication that the pizza material was successful is found by analyzing the participants' favorite pizza topping after they had exposure to the material. We can generate a wordcloud again. This is the best way, very scientific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "70167a9d-43a9-41db-aafb-3ff8849fae46",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We need a python dictionary of the value_counts\n",
    "words = df[\"favorite_topping_post\"].value_counts().to_dict()\n",
    "\n",
    "# Make the wordcloud\n",
    "wordcloud = WordCloud(\n",
    "    background_color=\"white\"\n",
    ").generate_from_frequencies(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "945e4ac5-8650-4bf2-bd17-2565c5d07eb6",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the matplotlib library to show the image\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f990ef9b-d260-4781-9ded-47afb9f13f52",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "## Research Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51d8567-8408-42f2-94bf-53a6580cf378",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "100% of participants indicated that Pineapple was their favorite topping post-material exposure. Therefore treatment of pizza material was 100% effective."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9dea23-3401-46b5-bfb5-018db0ea9d65",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "slideshow": {
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    },
    "tags": []
   },
   "source": [
    "# Resources\n",
    "\n",
    "There are a large number of resources here and a massive number of resources online. Most of the resources below are free to access. \n",
    "\n",
    "Remember this: one of the greatest resources is the **documentation**.\n",
    "\n",
    "## Try it out!\n",
    "\n",
    "If you would like to try out Jupyter in the browser without the need to install anything on your computer, you can get started with these websites:\n",
    "\n",
    "- [JupyterLite](https://jupyterlite.github.io/demo/lab/index.html) - Try a lite version of Jupyter Notebook in your browser.\n",
    "- [Google Colab](https://colab.research.google.com/) - Colab, or \"Colaboratory\", allows you to write and execute Python in your browser, with zero configuration required, access to GPUs free of charge, easy sharing.\n",
    "\n",
    "\n",
    "## Installation\n",
    "\n",
    "It's not possible to write down the steps to installation here for your specific machine. Please see documentation and/or check online for installation steps if you get stuck. Here are some general links:\n",
    "\n",
    "- [Installing Python](https://www.python.org/downloads/)\n",
    "- [Installing Pandas](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)\n",
    "- [Installing Jupyter Lab (Notebook)](https://jupyter.org/install)\n",
    "- Rather than trying to manage installing many tools separately, you could use [Anaconda](https://www.anaconda.com/download)\n",
    "\n",
    "\n",
    "\n",
    "## Learning\n",
    "\n",
    "There are a lot of amazing resources to help you learn python and pandas and some related tools. Here are some things to get you started, in no particular order:\n",
    "\n",
    "- [100 Days of Code: The Complete Python Pro Bootcamp for 2023 (Udemy - Dr. Angela Yu)](https://www.udemy.com/course/100-days-of-code/) - 100 Days, 1 hour per day, learn to build 1 project per day. At 60+ hours of content, this Python course is an extremely comprehensive Python course. Even if you have zero programming experience, this course will help you be a proficient Python programmer. You will build projects like a Pong Game, Auto Swipe on Tinder, Analyze Lego Datasets, and more.\n",
    "- [Python for Everybody (Dr. Charles Severance)](https://www.py4e.com/) - A course based on the book, \"Python for Everybody\", by University of Michigan's Dr. Charles Severance. The book has been turned into a free course on various platforms like Coursera, EdX, and FreeCodeCamp with videos made by Dr. Severance himself. It covers the fundamentals of Python programming, web development, data visualization, and basic data analysis with Python. \n",
    "- [Python Beginners Guide (for those with experience programming)](https://wiki.python.org/moin/BeginnersGuide/Programmers) - Big list of resources, tutorials, and courses.\n",
    "- [Python Beginners Guide (no programming experience)](https://wiki.python.org/moin/BeginnersGuide/NonProgrammers) - Big list of resources, tutorials, books, and etc.\n",
    "- The official [Python Tutorial](https://docs.python.org/3/tutorial/index.html) - A must.\n",
    "- [Pandas Getting Started Tutorials](https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/index.html)\n",
    "- [Scientific Computing with Python Certification](https://www.freecodecamp.org/learn/scientific-computing-with-python/) - Free Certificate course from FreeCodeCamp\n",
    "- [Data Analysis with Python Certification](https://www.freecodecamp.org/learn/data-analysis-with-python/) - Free certificate course from FreeCodeCamp\n",
    "- [10 Minutes to Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html)\n",
    "- [Visual Guide to Pandas](https://betterprogramming.pub/pandas-illustrated-the-definitive-visual-guide-to-pandas-c31fa921a43)\n",
    "- [Pandas CheatSheet](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf)\n",
    "- [Pandas Exercises](https://github.com/guipsamora/pandas_exercises) - A repository with jupyter notebooks to practice data analysis skills using pandas.\n",
    "- [PythonData](https://github.com/yunuserbas/PythonData) - This is the Data Analysis Repository using Python to practice data coding, cleaning, manipulation, and visualization, including python libraries such as Numpy, Pandas, Matplotlib, and Seaborn.\n",
    "- [Matplotlib Tutorials](https://matplotlib.org/stable/tutorials/index.html) - Some tutorials and guides for matplotlib.\n",
    "- [Think Stats 2nd Edition](https://greenteapress.com/wp/think-stats-2e/) - an introduction to Probability and Statistics for Python programmers. (Has a free PDF)\n",
    "- [Data Analysis with Python 3rd Edition](https://wesmckinney.com/book/) - Great resource you can read online for free.\n",
    "- [Learn Python with Jypyter](https://learnpythonwithjupyter.com/assets/book/learn_python_with_jupyter.pdf) (Free PDF Book)\n",
    "- [datawars.io](https://www.datawars.io) - Practice Data Science with Real Life Projects\n",
    "- [Markdown Guide](https://www.markdownguide.org) - Learn how to write in markdown (this simple syntax is used in Jupyter Notebook 'Markdown' cells). It's not a must, but incredibly useful!\n",
    "- [The Ultimate Markdown Guide for Jupyter Notebook](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) - Jupyter Notebooks have a lot more than standard markdown. This shows you what is possible.\n",
    "\n",
    "## More Resources\n",
    "\n",
    "Some people work hard at curating awesome resources - here are some of those collections:\n",
    "\n",
    "- [Awesome Python](https://github.com/vinta/awesome-python) - A curated list of awesome Python frameworks, libraries, software and resources\n",
    "- [Awesome Pandas](https://github.com/tommyod/awesome-pandas) - A collection of resources for pandas and related subjects\n",
    "- [Awesome Jupyter](https://github.com/markusschanta/awesome-jupyter) - A curated list of resources related to Jupyter Notebooks\n",
    "- [Python Graph Gallery](https://python-graph-gallery.com) - **GREAT** resource to see code example for all kinds of graphs\n",
    "- [Python Cheatsheet](https://gto76.github.io/python-cheatsheet/)\n",
    "\n",
    "\n",
    "## Related Libraries for Data Analysis and Visualization\n",
    "\n",
    "If you want to take your data analysis game to the next level, be sure to check out some of these:\n",
    "\n",
    "- [seaborn](https://seaborn.pydata.org) - a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n",
    "- [polars](https://www.pola.rs) - Lightning-fast DataFrame library for Rust and Python (alternative to Pandas)\n",
    "- [matplotlib](https://matplotlib.org/stable/) - Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations.\n",
    "- [plotly](https://plotly.com/python/) - make interactive, publication-quality graphs\n",
    "- [bokeh](https://bokeh.org) - a Python library for creating interactive visualizations for modern web browsers\n",
    "\n",
    "\n",
    "## Publishing Jupyter Notebooks\n",
    "\n",
    "- [quarto](https://quarto.org/) - An open-source scientific and technical publishing system. Publish reproducible, production quality articles, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more. \n",
    "- [Jupyter Book](https://jupyterbook.org/en/stable/intro.html) - Build beautiful, publication-quality books and documents from computational content. (This is part of the Executable Books project found at https://executablebooks.org/en/latest/)\n",
    "\n",
    "\n",
    "## Create Dashboards or Webapps\n",
    "\n",
    "- [Voilà](https://voila.readthedocs.io/en/stable/) - Voilà turns Jupyter notebooks into standalone web applications.\n",
    "- [Mercury](https://runmercury.com) - Turn your Python Notebook into a Web App with the open-source Mercury framework. Share your results with non-technical users.\n",
    "- [streamlit](https://streamlit.io) - Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.\n",
    "- [pygwalker](https://docs.kanaries.net/pygwalker) - PyGWalker (opens in a new tab) can simplify your Jupyter Notebook data analysis and data visualization workflow, by turning your pandas dataframe (and polars dataframe) into a Tableau-style User Interface for visual exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb625a9-b2c4-40ae-9127-e946e792a58b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "# Appendix 1: A Pie Chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb87001-0701-4248-9089-ca8491681f5d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "We can make more customized charts and graphs by using matplotlib. Here's an example of the age distribution of participants as a donut chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e41c3b88-c27e-4cb4-83f4-136b5ef91150",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This requires using matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# First, we'll get the value counts for the \"Age\" column\n",
    "age_groups = df[\"Age\"].value_counts()\n",
    "\n",
    "# This line initializes a new figure (fig) and an associated axes object (ax) for creating a plot in Matplotlib.\n",
    "# The figure represents the entire graphical window or canvas\n",
    "# The axes object represents a specific subplot.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Make a pie chart on the axis\n",
    "ax.pie(age_groups, autopct='%1.1f%%', startangle=90, pctdistance=0.75, labels=age_groups.index, wedgeprops={'width':0.5})\n",
    "\n",
    "# Equal aspect ratio ensures that pie chart is drawn as a circle.\n",
    "ax.axis('equal')\n",
    "\n",
    "# Add title\n",
    "plt.title('Age Distribution of Participants')\n",
    "\n",
    "# Show it\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b8b3fb-76e5-4102-baf3-c1bf3e2a103d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "That's okay, but maybe we want a more customized version. Matplotlib can do a huge number of things. Here's a more difficult example of a donut chart like the one above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b2776ad7-8038-449f-aaf3-40dac62aa484",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make a figure and axes\n",
    "fig, ax = plt.subplots(figsize=(10, 5), subplot_kw=dict(aspect=\"equal\"))\n",
    "\n",
    "# Arrange the data - value counts for the age\n",
    "age_value_counts = df[\"Age\"].value_counts()\n",
    "age_tuples = [i for i in age_value_counts.items()]\n",
    "data = age_value_counts.values\n",
    "total_values = len(df)\n",
    "\n",
    "# Do the initial donut (there will be no labels)\n",
    "wedges, texts = ax.pie(\n",
    "    data,\n",
    "    wedgeprops=dict(width=0.4),\n",
    "    startangle=-40,\n",
    "    colors=['#FF5733', '#3498DB', '#2ECC71', '#9B59B6', '#F1C40F'],\n",
    "    )\n",
    "\n",
    "# Set up the styling for the labels\n",
    "bbox_props = dict(boxstyle=\"square, pad=0.5\", fc=\"w\", ec=\"k\", lw=0.5)\n",
    "kw = dict(arrowprops=dict(arrowstyle=\"-\"),\n",
    "          bbox=bbox_props, zorder=0, va=\"center\")\n",
    "\n",
    "def make_label(idx):\n",
    "    # returns a string label for the index provided\n",
    "    age, count = age_tuples[idx]\n",
    "    percent = (count / total_values) * 100\n",
    "    return f\"{age} ({percent:.2f}%)\"\n",
    "\n",
    "# Iterate through the wedges of the donut\n",
    "# Do some calculations to set a line from the donut to the label\n",
    "# then annotate with a label\n",
    "for i, p in enumerate(wedges):\n",
    "    ang = (p.theta2 - p.theta1)/2. + p.theta1\n",
    "    y = np.sin(np.deg2rad(ang))\n",
    "    x = np.cos(np.deg2rad(ang))\n",
    "    horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
    "    connectionstyle = f\"angle,angleA=0,angleB={ang}\"\n",
    "    kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n",
    "    ax.annotate(make_label(i), xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),\n",
    "                horizontalalignment=horizontalalignment, **kw)\n",
    "\n",
    "# Set a title\n",
    "ax.set_title(\"Age Distribution of Participants\")\n",
    "\n",
    "# Show the result\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118a50db-4fe7-4877-a57d-984aeea6d7e8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "# Appendix 2: Testing\n",
    "\n",
    "One of the benefits of using a programming language to wrangle and analyze your dataset is that you can create tests in order to ensure that your data has the expected values.\n",
    "\n",
    "Imagine some of the following scenarios:\n",
    "    \n",
    "1. You collaborate with several other Professors and you need a way to combine all of the data and then verify that it is both uniform and within the parameters.\n",
    "2. You set up your data cleaning code *before* you receive datasets from collaboraters. Testing that your cleaning code matches certain parameters will ensure the quality of your overall dataset.\n",
    "2. You have provided participants with a form that requires them to write something. Manual input can result in a great number of inconsistencies and errors.\n",
    "3. You have a huge amount of data. Browsing through it to ensure that values are within certain parameters is prone to error.\n",
    "\n",
    "Python has an `assert` keyword that checks if something is **True** or **False**. If it is **True** the check passes. **False** will actually result in the program giving an error. Within a normal script the script would exit with an error. Within a Jupyter notebook you will see error output.\n",
    "\n",
    "We can use `assert` to ensure that our datasets have values that fit within certain parameters.\n",
    "\n",
    "Let's see an example of how to apply a test to a DataFrame column.\n",
    "\n",
    "First we will make a mask. The hours spent on pizza material should be between 0 and 50, so the mask will be the inversion of that. We want to see anything in the DataFrame that has less than 0 OR more than 50 hours. Note that the pipe character `|` means OR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5facfe5e-0d90-4887-868f-c88dc33a590a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = (df[\"hrs_pzmat\"] < 0) | (df[\"hrs_pzmat\"] > 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7410a81-f586-4d39-b397-31b1ebf31857",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "If we apply this mask to our DataFrame we can check the length using `len()` function. The length of the DataFrame is how many rows it has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a5adbd84-5fde-4060-97c8-61cbae62d168",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "out_of_range = len(df[mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77ae9a8-94e3-43e1-87aa-0ddf3856bdbd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Now we assert that there are no rows out of range. Notice that we can provide a custom message that would be printed out if the assertion doesn't pass. If the assertion is `True`, nothing will actually happen and that is desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b55474b6-cb95-4c75-b50f-0e938325d5ae",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "assert out_of_range == 0, \"There are values in 'hrs_pzmat' that are out of range\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cf20c37e-6bb4-4ec7-a7fe-7e054773f1f6",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#This could be done with one line of code:\n",
    "    \n",
    "assert len(df[(df[\"hrs_pzmat\"] < 0) | (df[\"hrs_pzmat\"] > 50)]) == 0, \"There are values in 'hrs_pzmat' that are out of range\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2b3690-8693-481a-aebf-be8b2488d2ed",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "An interesting practical application of this kind of testing would be to write a function that tests for certain parameters. If the function was defined at the beginning of your notebook you could then use it throughout the notebook to test your progress as you wrangle data.\n",
    "\n",
    "Here's an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1db76b1e-3165-46b0-89cf-6616701515d2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_tests(df):\n",
    "    assert len(df[(df[\"hrs_pzmat\"] < 0) | (df[\"hrs_pzmat\"] > 50)]) == 0, \"There are values in 'hrs_pzmat' that are out of range\"\n",
    "    # other tests\n",
    "    \n",
    "run_tests(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "997fd8de-5ce5-40ee-a396-81f5549c40c5",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "# Appendix 3: Saving a Figure (plot) to File\n",
    "\n",
    "To save a figure to file, there are few steps needed. One thing that is required is matplotlib's `pyplot` module. Usually people use an alias of `plt` for it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8e93ebd5-42fb-458b-84ad-e3c4f1654aa3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed8aeb2-3b04-4e31-b0c5-9be627dac659",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <b>Note:</b> If you are making a notebook, it's best to include <i>all</i> imports at the start of the notebook.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8cc3931-95cc-4ffa-8856-59b9b792e5ec",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Next, we will need to make a `figure` and `axes` and assign them to variables. This will result in an empty figure. \n",
    "\n",
    "The behavior of `plt` is slightly different than what you might expect. After we make the figure and axes and then call `plt.show()` we can't use this same figure in another cell. So it's best to do this in one cell.\n",
    "\n",
    "When we use the `plot()` method(s) in pandas, we can specify the axes to plot it to.\n",
    "\n",
    "At the end we will save the figure with the `savefig()` method which requires a file name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "20cb352d-6658-486d-ab41-24e0d170b8db",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# plot the scores\n",
    "df[\"score_pre\"].plot.hist(\n",
    "    ax=ax, \n",
    "    title=\"Pre-Material Score Distribution (as Percent)\", \n",
    "    )\n",
    "\n",
    "# Save the figure\n",
    "# This will also show it in the notebook\n",
    "# The filename we will use is figure01.png\n",
    "# This will be saved in the current directory\n",
    "plt.savefig(\"figure01.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b69dcf-8e7a-4ecc-9dba-0f3dca9a0317",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "The default format for `savefig()` is **png** regardless of what filename you use. See the [savefig documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html) for more options."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82563a08-6c5b-4a99-bc76-2e7a36e5e1bb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "# Appendix 4: Customizing a Figure\n",
    "\n",
    "You may want to customize a figure in some ways. Let's first make a simple bar graph that compares the mean hours spent on pizza material between males and females:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d99c3a84-67cd-4ea0-92ad-e318a5175517",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Sex'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Sex\")[\"hrs_pzmat\"].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "627728da-857c-4b34-9092-d511ac0f69d7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Great! But, it's somewhat difficult to make out what the actual numbers are. It would be great to have some labels. It may also be useful to have a line indicating the overall average of hours spent on material. If we want to be really picky, the labels on the x-axis should be horizontal, and we really should use some different colors for the bars.\n",
    "\n",
    "Some of the customizations we want to apply can be done using only pandas, but going further requires using **matplotlib**, and specifically it's **pyplot** module. This is the library that pandas uses under the hood to generate figures.\n",
    "\n",
    "It may be helpful to imagine the above figure as a 2D canvas with a horizontal (x) and vertical (y) axis. To add something to the canvas, we would need to specify *where* it should go. A canvas with a size of 2x2 would have a center spot of x=1 and y=1.\n",
    "\n",
    "Now also consider that a figure has objects on it such as x and y axis labels, lines, and in the case of the above example, two bars. \n",
    "\n",
    "Why does that matter? **We are able to access the properties of all of the objects of the figure programatically.** For example, we can get the dimensions of the MALE bar in the above figure. We would need these if we want to add a label in the middle of that bar.\n",
    "\n",
    "The below code utilizes these kind of properties to customize the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e7940d20-c629-4dbe-8889-e0edce6d926b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We need to use matplotlib.pyplot for this\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Use some different colors for male/female\n",
    "colors = [\"#a855f7\", \"#3b82f6\"]\n",
    "\n",
    "# plot the bar graph and assign the axes to use\n",
    "title = \"Average Hours Spent on Pizza Material, Split by Sex\"\n",
    "df.groupby(\"Sex\")[\"hrs_pzmat\"].mean().plot.bar(\n",
    "    ax=ax,\n",
    "    title=title,\n",
    "    ylabel=\"Average Hours\",\n",
    "    color=colors,\n",
    "    rot=0,\n",
    "    )\n",
    "\n",
    "# Iterate through each bar (patch) and annotate it\n",
    "for patch in ax.patches:\n",
    "    \n",
    "    # Get the actual value for the bar\n",
    "    value = round(patch.get_height(), 2)\n",
    "    \n",
    "    # Get the x and y coordinates for the annotation\n",
    "    # based on the geometry of the bar\n",
    "    # we'll make them vertically aligned at 10\n",
    "    x_coord = patch.get_x() + patch.get_width() / 2\n",
    "    y_coord = 10\n",
    "    \n",
    "    # Apply an annotation\n",
    "    ax.annotate(\n",
    "        str(value),\n",
    "        (x_coord, y_coord),\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        color=\"white\"\n",
    "    )\n",
    "\n",
    "# Add a horizontal line to the axes\n",
    "hrs_mean = df[\"hrs_pzmat\"].mean()\n",
    "ax.axhline(y=hrs_mean, color=\"#94a3b8\", linestyle=\"-\")\n",
    "\n",
    "# Add some text just above the hrs_mean line\n",
    "ax.text(\n",
    "    -0.45, hrs_mean + 0.3,\n",
    "    f\"Overall Average ({round(hrs_mean, 2)})\",\n",
    "    color=\"#334155\"\n",
    "    )\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "toc-autonumbering": false,
  "toc-showmarkdowntxt": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}