L1_intro_to_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 1: Introduction to Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Pandas, conventionally imported as pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />\n",
    "\n",
    "During your career in an Image Facility, you will undoubtedly need to handle data, possibly lots of data. The data comes in lots of formats, and you will spend much of your time **wrangling** the data to get it into a usable form.\n",
    "\n",
    "Pandas is the primary tool in the Python ecosystem for handling data. Its primary object, the `DataFrame` is extremely useful in wrangling data. We will explore some of that functionality here, and will put it to use in the next lesson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data set\n",
    "\n",
    "We will explore using Pandas with a real data set. We will use a data set published in [Beattie, et al., Perceptual impairment in face identification with poor sleep, *Royal Society Open Science*, **3**, 160321, 2016](https://doi.org/10.1098/rsos.160321). In this paper, researchers used the [Glasgow Facial Matching Test](https://doi.org/10.3758/BRM.42.1.286) (GMFT) to investigate how sleep deprivation affects a subject's ability to match faces, as well as the confidence the subject has in those matches. Briefly, the test works by having subjects look at a pair of faces. Two such pairs are shown below.\n",
    "\n",
    "<center><img src=\"Dataset/gfmt_faces.jpg\" alt=\"Dataset/gfmt_faces.jpg\" width=\"300\"></center><br/>\n",
    "\n",
    "The top two pictures are the same person, the bottom two pictures are different people. For each pair of faces, the subject gets as much time as he or she needs and then says whether or not they are the same person. The subject then rates his or her confidence in the choice.\n",
    "\n",
    "In this study, subjects also took surveys to determine properties about their sleep. The Sleep Condition Indicator (SCI) is a measure of insomnia disorder over the past month (scores of 16 and below indicate insomnia). The Pittsburgh Sleep Quality Index (PSQI) quantifies how well a subject sleeps in terms of interruptions, latency, etc. A higher score indicates poorer sleep. The Epworth Sleepiness Scale (ESS) assesses daytime drowsiness.\n",
    "\n",
    "The data set is stored in the file `~/Lessons/Dataset/gfmt_sleep.csv`. The contents of this file were adapted from [the Excel file posted on the public Dryad repository](https://doi.org/10.5061/dryad.r620r). \n",
    "\n",
    "This is a **CSV file**, where CSV stands for comma-separated value. This is a text file that is easily read into data structures in many programming languages. You should generally always store your data in such a format, not necessarily CSV, but a format that is open, has a well-defined specification, and is readable in many contexts. Excel files do not meet these criteria. Neither to `.mat` files.\n",
    "\n",
    "Let's take a look at the CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "﻿participant number,gender,age,correct hit percentage,correct reject percentage,percent correct,confidence when correct hit,confidence incorrect hit,confidence correct reject,confidence incorrect reject,confidence when correct,confidence when incorrect,sci,psqi,ess\n",
      "8,f,39,65,80,72.5,91,90,93,83.5,93,90,9,13,2\n",
      "16,m,42,90,90,90,75.5,55.5,70.5,50,75,50,4,11,7\n",
      "18,f,31,90,95,92.5,89.5,90,86,81,89,88,10,9,3\n",
      "22,f,35,100,75,87.5,89.5,*,71,80,88,80,13,8,20\n",
      "27,f,74,60,65,62.5,68.5,49,61,49,65,49,13,9,12\n",
      "28,f,61,80,20,50,71,63,31,72.5,64.5,70.5,15,14,2\n",
      "30,m,32,90,75,82.5,67,56.5,66,65,66,64,16,9,3\n",
      "33,m,62,45,90,67.5,54,37,65,81.5,62,61,14,9,9\n",
      "34,f,33,80,100,90,70.5,76.5,64.5,*,68,76.5,14,12,10\n"
     ]
    }
   ],
   "source": [
    "!head Dataset/gfmt_sleep.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first line contains the **headers** for each column. They are participant number, gender, age, etc. The data follow. There are two important things to note here. First, notice that the `gender` column has string data (`m` or `f`), while the rest of the data are numeric. Note also that there are some **missing data**, denoted by the `*`s in the file.\n",
    "\n",
    "It is possible to parse this file and extract the data you want. Hoever, this might be kind of painful. If the file format is nice and clean, like we more or less have here, we can use pre-built tools. Pandas has a very powerful function, `pd.read_csv()` that can read in a CSV file and store the contents in a convenient data structure called a `DataFrame`. \n",
    "\n",
    "*Pandas is also able to read xls, json, etc. For more information check: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in data\n",
    "\n",
    "Let's first look at the doc string of `pd.read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Read a comma-separated values (csv) file into DataFrame.\n",
       "\n",
       "Also supports optionally iterating or breaking of the file\n",
       "into chunks.\n",
       "\n",
       "Additional help can be found in the online docs for\n",
       "`IO Tools <http://pandas.pydata.org/pandas-docs/stable/io.html>`_.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "filepath_or_buffer : str, path object, or file-like object\n",
       "    Any valid string path is acceptable. The string could be a URL. Valid\n",
       "    URL schemes include http, ftp, s3, and file. For file URLs, a host is\n",
       "    expected. A local file could be: file://localhost/path/to/table.csv.\n",
       "\n",
       "    If you want to pass in a path object, pandas accepts either\n",
       "    ``pathlib.Path`` or ``py._path.local.LocalPath``.\n",
       "\n",
       "    By file-like object, we refer to objects with a ``read()`` method, such as\n",
       "    a file handler (e.g. via builtin ``open`` function) or ``StringIO``.\n",
       "sep : str, default ','\n",
       "    Delimiter to use. If sep is None, the C engine cannot automatically detect\n",
       "    the separator, but the Python parsing engine can, meaning the latter will\n",
       "    be used and automatically detect the separator by Python's builtin sniffer\n",
       "    tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n",
       "    different from ``'\\s+'`` will be interpreted as regular expressions and\n",
       "    will also force the use of the Python parsing engine. Note that regex\n",
       "    delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\n",
       "delimiter : str, default ``None``\n",
       "    Alias for sep.\n",
       "header : int, list of int, default 'infer'\n",
       "    Row number(s) to use as the column names, and the start of the\n",
       "    data.  Default behavior is to infer the column names: if no names\n",
       "    are passed the behavior is identical to ``header=0`` and column\n",
       "    names are inferred from the first line of the file, if column\n",
       "    names are passed explicitly then the behavior is identical to\n",
       "    ``header=None``. Explicitly pass ``header=0`` to be able to\n",
       "    replace existing names. The header can be a list of integers that\n",
       "    specify row locations for a multi-index on the columns\n",
       "    e.g. [0,1,3]. Intervening rows that are not specified will be\n",
       "    skipped (e.g. 2 in this example is skipped). Note that this\n",
       "    parameter ignores commented lines and empty lines if\n",
       "    ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n",
       "    data rather than the first line of the file.\n",
       "names : array-like, optional\n",
       "    List of column names to use. If file contains no header row, then you\n",
       "    should explicitly pass ``header=None``. Duplicates in this list will cause\n",
       "    a ``UserWarning`` to be issued.\n",
       "index_col : int, sequence or bool, optional\n",
       "    Column to use as the row labels of the DataFrame. If a sequence is given, a\n",
       "    MultiIndex is used. If you have a malformed file with delimiters at the end\n",
       "    of each line, you might consider ``index_col=False`` to force pandas to\n",
       "    not use the first column as the index (row names).\n",
       "usecols : list-like or callable, optional\n",
       "    Return a subset of the columns. If list-like, all elements must either\n",
       "    be positional (i.e. integer indices into the document columns) or strings\n",
       "    that correspond to column names provided either by the user in `names` or\n",
       "    inferred from the document header row(s). For example, a valid list-like\n",
       "    `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n",
       "    Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n",
       "    To instantiate a DataFrame from ``data`` with element order preserved use\n",
       "    ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n",
       "    in ``['foo', 'bar']`` order or\n",
       "    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n",
       "    for ``['bar', 'foo']`` order.\n",
       "\n",
       "    If callable, the callable function will be evaluated against the column\n",
       "    names, returning names where the callable function evaluates to True. An\n",
       "    example of a valid callable argument would be ``lambda x: x.upper() in\n",
       "    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n",
       "    parsing time and lower memory usage.\n",
       "squeeze : bool, default False\n",
       "    If the parsed data only contains one column then return a Series.\n",
       "prefix : str, optional\n",
       "    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
       "mangle_dupe_cols : bool, default True\n",
       "    Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
       "    'X'...'X'. Passing in False will cause data to be overwritten if there\n",
       "    are duplicate names in the columns.\n",
       "dtype : Type name or dict of column -> type, optional\n",
       "    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n",
       "    'c': 'Int64'}\n",
       "    Use `str` or `object` together with suitable `na_values` settings\n",
       "    to preserve and not interpret dtype.\n",
       "    If converters are specified, they will be applied INSTEAD\n",
       "    of dtype conversion.\n",
       "engine : {'c', 'python'}, optional\n",
       "    Parser engine to use. The C engine is faster while the python engine is\n",
       "    currently more feature-complete.\n",
       "converters : dict, optional\n",
       "    Dict of functions for converting values in certain columns. Keys can either\n",
       "    be integers or column labels.\n",
       "true_values : list, optional\n",
       "    Values to consider as True.\n",
       "false_values : list, optional\n",
       "    Values to consider as False.\n",
       "skipinitialspace : bool, default False\n",
       "    Skip spaces after delimiter.\n",
       "skiprows : list-like, int or callable, optional\n",
       "    Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
       "    at the start of the file.\n",
       "\n",
       "    If callable, the callable function will be evaluated against the row\n",
       "    indices, returning True if the row should be skipped and False otherwise.\n",
       "    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n",
       "skipfooter : int, default 0\n",
       "    Number of lines at bottom of file to skip (Unsupported with engine='c').\n",
       "nrows : int, optional\n",
       "    Number of rows of file to read. Useful for reading pieces of large files.\n",
       "na_values : scalar, str, list-like, or dict, optional\n",
       "    Additional strings to recognize as NA/NaN. If dict passed, specific\n",
       "    per-column NA values.  By default the following values are interpreted as\n",
       "    NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
       "    '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan',\n",
       "    'null'.\n",
       "keep_default_na : bool, default True\n",
       "    Whether or not to include the default NaN values when parsing the data.\n",
       "    Depending on whether `na_values` is passed in, the behavior is as follows:\n",
       "\n",
       "    * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
       "      is appended to the default NaN values used for parsing.\n",
       "    * If `keep_default_na` is True, and `na_values` are not specified, only\n",
       "      the default NaN values are used for parsing.\n",
       "    * If `keep_default_na` is False, and `na_values` are specified, only\n",
       "      the NaN values specified `na_values` are used for parsing.\n",
       "    * If `keep_default_na` is False, and `na_values` are not specified, no\n",
       "      strings will be parsed as NaN.\n",
       "\n",
       "    Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
       "    `na_values` parameters will be ignored.\n",
       "na_filter : bool, default True\n",
       "    Detect missing value markers (empty strings and the value of na_values). In\n",
       "    data without any NAs, passing na_filter=False can improve the performance\n",
       "    of reading a large file.\n",
       "verbose : bool, default False\n",
       "    Indicate number of NA values placed in non-numeric columns.\n",
       "skip_blank_lines : bool, default True\n",
       "    If True, skip over blank lines rather than interpreting as NaN values.\n",
       "parse_dates : bool or list of int or names or list of lists or dict, default False\n",
       "    The behavior is as follows:\n",
       "\n",
       "    * boolean. If True -> try parsing the index.\n",
       "    * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
       "      each as a separate date column.\n",
       "    * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
       "      a single date column.\n",
       "    * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n",
       "      result 'foo'\n",
       "\n",
       "    If a column or index cannot be represented as an array of datetimes,\n",
       "    say because of an unparseable value or a mixture of timezones, the column\n",
       "    or index will be returned unaltered as an object data type. For\n",
       "    non-standard datetime parsing, use ``pd.to_datetime`` after\n",
       "    ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n",
       "    specify ``date_parser`` to be a partially-applied\n",
       "    :func:`pandas.to_datetime` with ``utc=True``. See\n",
       "    :ref:`io.csv.mixed_timezones` for more.\n",
       "\n",
       "    Note: A fast-path exists for iso8601-formatted dates.\n",
       "infer_datetime_format : bool, default False\n",
       "    If True and `parse_dates` is enabled, pandas will attempt to infer the\n",
       "    format of the datetime strings in the columns, and if it can be inferred,\n",
       "    switch to a faster method of parsing them. In some cases this can increase\n",
       "    the parsing speed by 5-10x.\n",
       "keep_date_col : bool, default False\n",
       "    If True and `parse_dates` specifies combining multiple columns then\n",
       "    keep the original columns.\n",
       "date_parser : function, optional\n",
       "    Function to use for converting a sequence of string columns to an array of\n",
       "    datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
       "    conversion. Pandas will try to call `date_parser` in three different ways,\n",
       "    advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
       "    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
       "    string values from the columns defined by `parse_dates` into a single array\n",
       "    and pass that; and 3) call `date_parser` once for each row using one or\n",
       "    more strings (corresponding to the columns defined by `parse_dates`) as\n",
       "    arguments.\n",
       "dayfirst : bool, default False\n",
       "    DD/MM format dates, international and European format.\n",
       "iterator : bool, default False\n",
       "    Return TextFileReader object for iteration or getting chunks with\n",
       "    ``get_chunk()``.\n",
       "chunksize : int, optional\n",
       "    Return TextFileReader object for iteration.\n",
       "    See the `IO Tools docs\n",
       "    <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_\n",
       "    for more information on ``iterator`` and ``chunksize``.\n",
       "compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n",
       "    For on-the-fly decompression of on-disk data. If 'infer' and\n",
       "    `filepath_or_buffer` is path-like, then detect compression from the\n",
       "    following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n",
       "    decompression). If using 'zip', the ZIP file must contain only one data\n",
       "    file to be read in. Set to None for no decompression.\n",
       "\n",
       "    .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.\n",
       "\n",
       "thousands : str, optional\n",
       "    Thousands separator.\n",
       "decimal : str, default '.'\n",
       "    Character to recognize as decimal point (e.g. use ',' for European data).\n",
       "lineterminator : str (length 1), optional\n",
       "    Character to break file into lines. Only valid with C parser.\n",
       "quotechar : str (length 1), optional\n",
       "    The character used to denote the start and end of a quoted item. Quoted\n",
       "    items can include the delimiter and it will be ignored.\n",
       "quoting : int or csv.QUOTE_* instance, default 0\n",
       "    Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
       "    QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
       "doublequote : bool, default ``True``\n",
       "   When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n",
       "   whether or not to interpret two consecutive quotechar elements INSIDE a\n",
       "   field as a single ``quotechar`` element.\n",
       "escapechar : str (length 1), optional\n",
       "    One-character string used to escape other characters.\n",
       "comment : str, optional\n",
       "    Indicates remainder of line should not be parsed. If found at the beginning\n",
       "    of a line, the line will be ignored altogether. This parameter must be a\n",
       "    single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
       "    fully commented lines are ignored by the parameter `header` but not by\n",
       "    `skiprows`. For example, if ``comment='#'``, parsing\n",
       "    ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n",
       "    treated as the header.\n",
       "encoding : str, optional\n",
       "    Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
       "    standard encodings\n",
       "    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .\n",
       "dialect : str or csv.Dialect, optional\n",
       "    If provided, this parameter will override values (default or not) for the\n",
       "    following parameters: `delimiter`, `doublequote`, `escapechar`,\n",
       "    `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n",
       "    override values, a ParserWarning will be issued. See csv.Dialect\n",
       "    documentation for more details.\n",
       "tupleize_cols : bool, default False\n",
       "    Leave a list of tuples on columns as is (default is to convert to\n",
       "    a MultiIndex on the columns).\n",
       "\n",
       "    .. deprecated:: 0.21.0\n",
       "       This argument will be removed and will always convert to MultiIndex\n",
       "\n",
       "error_bad_lines : bool, default True\n",
       "    Lines with too many fields (e.g. a csv line with too many commas) will by\n",
       "    default cause an exception to be raised, and no DataFrame will be returned.\n",
       "    If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
       "    returned.\n",
       "warn_bad_lines : bool, default True\n",
       "    If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
       "    \"bad line\" will be output.\n",
       "delim_whitespace : bool, default False\n",
       "    Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be\n",
       "    used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n",
       "    is set to True, nothing should be passed in for the ``delimiter``\n",
       "    parameter.\n",
       "\n",
       "    .. versionadded:: 0.18.1 support for the Python parser.\n",
       "\n",
       "low_memory : bool, default True\n",
       "    Internally process the file in chunks, resulting in lower memory use\n",
       "    while parsing, but possibly mixed type inference.  To ensure no mixed\n",
       "    types either set False, or specify the type with the `dtype` parameter.\n",
       "    Note that the entire file is read into a single DataFrame regardless,\n",
       "    use the `chunksize` or `iterator` parameter to return the data in chunks.\n",
       "    (Only valid with C parser).\n",
       "memory_map : bool, default False\n",
       "    If a filepath is provided for `filepath_or_buffer`, map the file object\n",
       "    directly onto memory and access the data directly from there. Using this\n",
       "    option can improve performance because there is no longer any I/O overhead.\n",
       "float_precision : str, optional\n",
       "    Specifies which converter the C engine should use for floating-point\n",
       "    values. The options are `None` for the ordinary converter,\n",
       "    `high` for the high-precision converter, and `round_trip` for the\n",
       "    round-trip converter.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame or TextParser\n",
       "    A comma-separated values (csv) file is returned as two-dimensional\n",
       "    data structure with labeled axes.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "to_csv : Write DataFrame to a comma-separated values (csv) file.\n",
       "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
       "read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> pd.read_csv('data.csv')  # doctest: +SKIP\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Holy cow! There are so many options we can specify for reading in a CSV file. You will likely find reasons to use many of these throughout your work. For this particular data set, we really only need the `na_values` kwarg. This specifies what characters signify that a data point is missing. The resulting `DataFrame` is populated with a **NaN**, or not-a-number, wherever this character is present in the file. In this case, we want `na_values='*'`. So, let's load in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Dataset/gfmt_sleep.csv', na_values='*')\n",
    "\n",
    "# Check the type\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the data stored in a `DataFrame`. We can look at it in the Jupyter notebook, since Jupyter will display it in a well-organized, pretty way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant number</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>correct hit percentage</th>\n",
       "      <th>correct reject percentage</th>\n",
       "      <th>percent correct</th>\n",
       "      <th>confidence when correct hit</th>\n",
       "      <th>confidence incorrect hit</th>\n",
       "      <th>confidence correct reject</th>\n",
       "      <th>confidence incorrect reject</th>\n",
       "      <th>confidence when correct</th>\n",
       "      <th>confidence when incorrect</th>\n",
       "      <th>sci</th>\n",
       "      <th>psqi</th>\n",
       "      <th>ess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>39</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>72.5</td>\n",
       "      <td>91.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>m</td>\n",
       "      <td>42</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>f</td>\n",
       "      <td>31</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>92.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>f</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>87.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>f</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "      <td>68.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>50.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>64.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>m</td>\n",
       "      <td>32</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>82.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>66.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33</td>\n",
       "      <td>m</td>\n",
       "      <td>62</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>67.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34</td>\n",
       "      <td>f</td>\n",
       "      <td>33</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>76.5</td>\n",
       "      <td>64.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35</td>\n",
       "      <td>f</td>\n",
       "      <td>53</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>75.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>38</td>\n",
       "      <td>f</td>\n",
       "      <td>41</td>\n",
       "      <td>70</td>\n",
       "      <td>55</td>\n",
       "      <td>62.5</td>\n",
       "      <td>82.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>41</td>\n",
       "      <td>f</td>\n",
       "      <td>36</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>75.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46</td>\n",
       "      <td>f</td>\n",
       "      <td>40</td>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>79.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>f</td>\n",
       "      <td>24</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>80.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>55</td>\n",
       "      <td>f</td>\n",
       "      <td>32</td>\n",
       "      <td>75</td>\n",
       "      <td>55</td>\n",
       "      <td>65.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>71</td>\n",
       "      <td>f</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "      <td>70.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>76</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>70.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>77</td>\n",
       "      <td>f</td>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>80.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>90.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>88.5</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>78</td>\n",
       "      <td>m</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>85.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>80</td>\n",
       "      <td>m</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>89</td>\n",
       "      <td>f</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>90</td>\n",
       "      <td>m</td>\n",
       "      <td>45</td>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "      <td>97.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>93</td>\n",
       "      <td>f</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>87.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>100</td>\n",
       "      <td>f</td>\n",
       "      <td>44</td>\n",
       "      <td>65</td>\n",
       "      <td>25</td>\n",
       "      <td>45.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>73.5</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>101</td>\n",
       "      <td>f</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>70.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>42</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>72.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>85.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>67.5</td>\n",
       "      <td>63.5</td>\n",
       "      <td>64.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>64</td>\n",
       "      <td>f</td>\n",
       "      <td>69</td>\n",
       "      <td>95</td>\n",
       "      <td>80</td>\n",
       "      <td>87.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>65</td>\n",
       "      <td>f</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "      <td>97.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>66</td>\n",
       "      <td>f</td>\n",
       "      <td>44</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>92.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>67</td>\n",
       "      <td>f</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>68</td>\n",
       "      <td>f</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>61.5</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>69</td>\n",
       "      <td>f</td>\n",
       "      <td>47</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>70</td>\n",
       "      <td>f</td>\n",
       "      <td>33</td>\n",
       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "      <td>77.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>72</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>77.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>66.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>73</td>\n",
       "      <td>f</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>82.5</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>74</td>\n",
       "      <td>m</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40.0</td>\n",
       "      <td>90.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>75</td>\n",
       "      <td>f</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>95</td>\n",
       "      <td>87.5</td>\n",
       "      <td>74.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>79</td>\n",
       "      <td>f</td>\n",
       "      <td>37</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>81</td>\n",
       "      <td>m</td>\n",
       "      <td>41</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>87.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>82</td>\n",
       "      <td>f</td>\n",
       "      <td>41</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>77.5</td>\n",
       "      <td>94.5</td>\n",
       "      <td>61.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>83</td>\n",
       "      <td>f</td>\n",
       "      <td>34</td>\n",
       "      <td>90</td>\n",
       "      <td>35</td>\n",
       "      <td>62.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>84</td>\n",
       "      <td>f</td>\n",
       "      <td>39</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>72.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>85</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>91.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>86</td>\n",
       "      <td>f</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>92.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>87</td>\n",
       "      <td>m</td>\n",
       "      <td>26</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>88</td>\n",
       "      <td>m</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "      <td>72.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>66.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>91</td>\n",
       "      <td>m</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>90.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.5</td>\n",
       "      <td>82.0</td>\n",
       "      <td>79.5</td>\n",
       "      <td>82.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>92</td>\n",
       "      <td>m</td>\n",
       "      <td>22</td>\n",
       "      <td>85</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>f</td>\n",
       "      <td>41</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>m</td>\n",
       "      <td>46</td>\n",
       "      <td>95</td>\n",
       "      <td>80</td>\n",
       "      <td>87.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>f</td>\n",
       "      <td>56</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>65.5</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>f</td>\n",
       "      <td>23</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>77.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>66.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>f</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>87.5</td>\n",
       "      <td>65.5</td>\n",
       "      <td>85.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>f</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>75.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>102</td>\n",
       "      <td>f</td>\n",
       "      <td>40</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>70.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>103</td>\n",
       "      <td>f</td>\n",
       "      <td>33</td>\n",
       "      <td>85</td>\n",
       "      <td>40</td>\n",
       "      <td>62.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     participant number gender  age  correct hit percentage  \\\n",
       "0                     8      f   39                      65   \n",
       "1                    16      m   42                      90   \n",
       "2                    18      f   31                      90   \n",
       "3                    22      f   35                     100   \n",
       "4                    27      f   74                      60   \n",
       "5                    28      f   61                      80   \n",
       "6                    30      m   32                      90   \n",
       "7                    33      m   62                      45   \n",
       "8                    34      f   33                      80   \n",
       "9                    35      f   53                     100   \n",
       "10                   38      f   41                      70   \n",
       "11                   41      f   36                      90   \n",
       "12                   46      f   40                      95   \n",
       "13                   49      f   24                      85   \n",
       "14                   55      f   32                      75   \n",
       "15                   71      f   40                      40   \n",
       "16                   76      f   61                     100   \n",
       "17                   77      f   42                      70   \n",
       "18                   78      m   31                     100   \n",
       "19                   80      m   28                     100   \n",
       "20                   89      f   26                      60   \n",
       "21                   90      m   45                     100   \n",
       "22                   93      f   28                     100   \n",
       "23                  100      f   44                      65   \n",
       "24                  101      f   28                     100   \n",
       "25                    1      f   42                      80   \n",
       "26                    2      f   45                      80   \n",
       "27                    3      f   16                      70   \n",
       "28                    4      f   21                      70   \n",
       "29                    5      f   18                      90   \n",
       "..                  ...    ...  ...                     ...   \n",
       "72                   64      f   69                      95   \n",
       "73                   65      f   31                     100   \n",
       "74                   66      f   44                      90   \n",
       "75                   67      f   25                     100   \n",
       "76                   68      f   45                      70   \n",
       "77                   69      f   47                      90   \n",
       "78                   70      f   33                      85   \n",
       "79                   72      f   18                      80   \n",
       "80                   73      f   74                      85   \n",
       "81                   74      m   21                      40   \n",
       "82                   75      f   45                      80   \n",
       "83                   79      f   37                      90   \n",
       "84                   81      m   41                      90   \n",
       "85                   82      f   41                      80   \n",
       "86                   83      f   34                      90   \n",
       "87                   84      f   39                      75   \n",
       "88                   85      f   18                      85   \n",
       "89                   86      f   31                     100   \n",
       "90                   87      m   26                      95   \n",
       "91                   88      m   66                      60   \n",
       "92                   91      m   62                     100   \n",
       "93                   92      m   22                      85   \n",
       "94                   94      f   41                      35   \n",
       "95                   95      m   46                      95   \n",
       "96                   96      f   56                      70   \n",
       "97                   97      f   23                      70   \n",
       "98                   98      f   70                      90   \n",
       "99                   99      f   24                      70   \n",
       "100                 102      f   40                      75   \n",
       "101                 103      f   33                      85   \n",
       "\n",
       "     correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "0                           80             72.5                         91.0   \n",
       "1                           90             90.0                         75.5   \n",
       "2                           95             92.5                         89.5   \n",
       "3                           75             87.5                         89.5   \n",
       "4                           65             62.5                         68.5   \n",
       "5                           20             50.0                         71.0   \n",
       "6                           75             82.5                         67.0   \n",
       "7                           90             67.5                         54.0   \n",
       "8                          100             90.0                         70.5   \n",
       "9                           50             75.0                         74.5   \n",
       "10                          55             62.5                         82.0   \n",
       "11                         100             95.0                         76.5   \n",
       "12                          65             80.0                         80.0   \n",
       "13                          75             80.0                         58.0   \n",
       "14                          55             65.0                         85.0   \n",
       "15                         100             70.0                         69.0   \n",
       "16                          40             70.0                         69.5   \n",
       "17                          90             80.0                         87.0   \n",
       "18                          70             85.0                         92.0   \n",
       "19                          50             75.0                        100.0   \n",
       "20                          80             70.0                         70.0   \n",
       "21                          95             97.5                        100.0   \n",
       "22                          75             87.5                         89.5   \n",
       "23                          25             45.0                         62.0   \n",
       "24                          40             70.0                         87.0   \n",
       "25                          65             72.5                         51.5   \n",
       "26                          90             85.0                         75.0   \n",
       "27                          80             75.0                         70.0   \n",
       "28                          65             67.5                         63.5   \n",
       "29                         100             95.0                         76.5   \n",
       "..                         ...              ...                          ...   \n",
       "72                          80             87.5                         80.0   \n",
       "73                          95             97.5                         98.0   \n",
       "74                          95             92.5                         87.0   \n",
       "75                         100            100.0                         61.5   \n",
       "76                          50             60.0                         80.5   \n",
       "77                         100             95.0                        100.0   \n",
       "78                          70             77.5                         70.0   \n",
       "79                          75             77.5                         67.5   \n",
       "80                          80             82.5                         66.0   \n",
       "81                          40             40.0                         90.5   \n",
       "82                          95             87.5                         74.0   \n",
       "83                          80             85.0                         95.5   \n",
       "84                          85             87.5                         80.0   \n",
       "85                          75             77.5                         94.5   \n",
       "86                          35             62.5                         81.0   \n",
       "87                          70             72.5                         57.0   \n",
       "88                          85             85.0                         93.0   \n",
       "89                          85             92.5                        100.0   \n",
       "90                          75             85.0                         85.0   \n",
       "91                          85             72.5                         67.5   \n",
       "92                          80             90.0                         81.0   \n",
       "93                          95             90.0                         66.0   \n",
       "94                          75             55.0                         55.0   \n",
       "95                          80             87.5                         90.0   \n",
       "96                          50             60.0                         63.0   \n",
       "97                          85             77.5                         77.0   \n",
       "98                          85             87.5                         65.5   \n",
       "99                          80             75.0                         61.5   \n",
       "100                         65             70.0                         53.0   \n",
       "101                         40             62.5                         80.0   \n",
       "\n",
       "     confidence incorrect hit  confidence correct reject  \\\n",
       "0                        90.0                       93.0   \n",
       "1                        55.5                       70.5   \n",
       "2                        90.0                       86.0   \n",
       "3                         NaN                       71.0   \n",
       "4                        49.0                       61.0   \n",
       "5                        63.0                       31.0   \n",
       "6                        56.5                       66.0   \n",
       "7                        37.0                       65.0   \n",
       "8                        76.5                       64.5   \n",
       "9                         NaN                       60.5   \n",
       "10                       61.5                       73.0   \n",
       "11                       75.5                       75.0   \n",
       "12                       89.0                       79.0   \n",
       "13                       50.0                       49.0   \n",
       "14                       81.0                       85.0   \n",
       "15                       56.0                       70.0   \n",
       "16                        NaN                       44.5   \n",
       "17                       72.0                       90.5   \n",
       "18                        NaN                       81.0   \n",
       "19                        NaN                      100.0   \n",
       "20                       77.0                       82.0   \n",
       "21                        NaN                      100.0   \n",
       "22                        NaN                       67.0   \n",
       "23                       72.0                       87.0   \n",
       "24                        NaN                       68.0   \n",
       "25                       44.5                       43.0   \n",
       "26                       55.5                       80.0   \n",
       "27                       57.0                       54.0   \n",
       "28                       64.0                       50.0   \n",
       "29                       83.0                       80.0   \n",
       "..                        ...                        ...   \n",
       "72                       65.0                       78.5   \n",
       "73                        NaN                       90.0   \n",
       "74                       47.5                       69.0   \n",
       "75                        NaN                       58.5   \n",
       "76                       51.5                       63.0   \n",
       "77                        NaN                       71.5   \n",
       "78                       38.0                       58.5   \n",
       "79                       51.5                       66.0   \n",
       "80                       55.0                       63.0   \n",
       "81                       80.0                       74.5   \n",
       "82                       67.0                       76.0   \n",
       "83                       68.0                       83.5   \n",
       "84                       59.5                       70.0   \n",
       "85                       61.5                       86.0   \n",
       "86                       52.0                       71.0   \n",
       "87                       57.0                       59.5   \n",
       "88                       92.0                       91.0   \n",
       "89                        NaN                      100.0   \n",
       "90                       88.0                       82.0   \n",
       "91                       66.0                       74.0   \n",
       "92                        NaN                       74.5   \n",
       "93                       56.0                       72.0   \n",
       "94                       61.0                       80.0   \n",
       "95                       75.0                       80.0   \n",
       "96                       52.5                       67.5   \n",
       "97                       66.5                       77.0   \n",
       "98                       85.5                       87.0   \n",
       "99                       81.0                       70.0   \n",
       "100                      37.0                       84.0   \n",
       "101                      27.0                       31.0   \n",
       "\n",
       "     confidence incorrect reject  confidence when correct  \\\n",
       "0                           83.5                     93.0   \n",
       "1                           50.0                     75.0   \n",
       "2                           81.0                     89.0   \n",
       "3                           80.0                     88.0   \n",
       "4                           49.0                     65.0   \n",
       "5                           72.5                     64.5   \n",
       "6                           65.0                     66.0   \n",
       "7                           81.5                     62.0   \n",
       "8                            NaN                     68.0   \n",
       "9                           65.0                     71.0   \n",
       "10                          69.0                     82.0   \n",
       "11                           NaN                     76.0   \n",
       "12                          58.5                     79.5   \n",
       "13                          68.0                     55.0   \n",
       "14                          86.0                     85.0   \n",
       "15                           NaN                     70.0   \n",
       "16                          73.0                     54.5   \n",
       "17                          43.5                     88.5   \n",
       "18                          60.0                     87.5   \n",
       "19                         100.0                    100.0   \n",
       "20                          67.5                     77.0   \n",
       "21                         100.0                    100.0   \n",
       "22                          60.0                     80.0   \n",
       "23                          77.0                     69.5   \n",
       "24                          54.0                     81.0   \n",
       "25                          49.0                     51.0   \n",
       "26                          75.0                     78.5   \n",
       "27                          53.0                     57.0   \n",
       "28                          50.0                     60.0   \n",
       "29                           NaN                     80.0   \n",
       "..                           ...                      ...   \n",
       "72                          70.5                     80.0   \n",
       "73                          40.0                     92.0   \n",
       "74                          87.0                     83.0   \n",
       "75                           NaN                     60.5   \n",
       "76                          69.0                     72.5   \n",
       "77                          83.0                     97.5   \n",
       "78                          65.0                     68.0   \n",
       "79                          57.0                     67.0   \n",
       "80                          50.5                     65.0   \n",
       "81                          83.0                     82.0   \n",
       "82                          17.0                     75.0   \n",
       "83                          83.0                     94.0   \n",
       "84                          41.0                     77.0   \n",
       "85                          74.0                     92.0   \n",
       "86                          58.0                     81.0   \n",
       "87                          50.0                     58.0   \n",
       "88                          89.0                     91.5   \n",
       "89                          50.0                    100.0   \n",
       "90                          82.0                     85.0   \n",
       "91                          57.0                     74.0   \n",
       "92                          82.0                     79.5   \n",
       "93                          63.0                     70.5   \n",
       "94                          57.0                     72.0   \n",
       "95                          80.0                     85.0   \n",
       "96                          65.5                     64.0   \n",
       "97                          77.5                     77.0   \n",
       "98                          80.0                     74.0   \n",
       "99                          61.0                     65.0   \n",
       "100                         52.0                     81.0   \n",
       "101                         82.5                     81.0   \n",
       "\n",
       "     confidence when incorrect  sci  psqi  ess  \n",
       "0                         90.0    9    13    2  \n",
       "1                         50.0    4    11    7  \n",
       "2                         88.0   10     9    3  \n",
       "3                         80.0   13     8   20  \n",
       "4                         49.0   13     9   12  \n",
       "5                         70.5   15    14    2  \n",
       "6                         64.0   16     9    3  \n",
       "7                         61.0   14     9    9  \n",
       "8                         76.5   14    12   10  \n",
       "9                         65.0   14     8    7  \n",
       "10                        64.0   14     5   19  \n",
       "11                        75.5   15     7    0  \n",
       "12                        63.0   10    12    8  \n",
       "13                        59.0   14    13    4  \n",
       "14                        83.5    5    13    7  \n",
       "15                        56.0    0    11   14  \n",
       "16                        73.0   16     4   12  \n",
       "17                        64.0   11    10   10  \n",
       "18                        60.0   14     6   11  \n",
       "19                       100.0   12     7   12  \n",
       "20                        70.5   14     8    1  \n",
       "21                       100.0   14     9    6  \n",
       "22                        60.0   16     7    4  \n",
       "23                        73.5    1    15    6  \n",
       "24                        54.0   14     7    2  \n",
       "25                        49.0   29     1    5  \n",
       "26                        67.0   19     5    1  \n",
       "27                        54.5   23     1    3  \n",
       "28                        50.0   26     5    4  \n",
       "29                        83.0   21     7    5  \n",
       "..                         ...  ...   ...  ...  \n",
       "72                        70.0   31     1    1  \n",
       "73                        40.0   27     4    4  \n",
       "74                        67.0   32     1    2  \n",
       "75                         NaN   28     8    9  \n",
       "76                        61.5   25     4    1  \n",
       "77                        83.0   30     2    2  \n",
       "78                        40.0   21     7   12  \n",
       "79                        53.0   29     4    6  \n",
       "80                        55.0   20     1    5  \n",
       "81                        81.0   22     7    5  \n",
       "82                        64.0   23     4    4  \n",
       "83                        71.0   20     5    9  \n",
       "84                        59.0   17     6    3  \n",
       "85                        67.0   27     4    8  \n",
       "86                        58.0   27     2    6  \n",
       "87                        50.0   22     3   10  \n",
       "88                        91.0   25     4   21  \n",
       "89                        50.0   30     3    5  \n",
       "90                        85.0   32     1    5  \n",
       "91                        64.0   30     5    9  \n",
       "92                        82.0   32     2    1  \n",
       "93                        59.5   28     1    8  \n",
       "94                        60.0   31     1   11  \n",
       "95                        75.0   29     3    5  \n",
       "96                        59.5   26     6    7  \n",
       "97                        74.0   20     8   10  \n",
       "98                        80.0   19     8    7  \n",
       "99                        81.0   31     2   15  \n",
       "100                       51.0   22     4    7  \n",
       "101                       73.0   24     5    7  \n",
       "\n",
       "[102 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a nice representation of the data, but we really do not need to display that much. Instead, we can use the `head()` method of `DataFrame`s to look at the first few rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant number</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>correct hit percentage</th>\n",
       "      <th>correct reject percentage</th>\n",
       "      <th>percent correct</th>\n",
       "      <th>confidence when correct hit</th>\n",
       "      <th>confidence incorrect hit</th>\n",
       "      <th>confidence correct reject</th>\n",
       "      <th>confidence incorrect reject</th>\n",
       "      <th>confidence when correct</th>\n",
       "      <th>confidence when incorrect</th>\n",
       "      <th>sci</th>\n",
       "      <th>psqi</th>\n",
       "      <th>ess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>39</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>72.5</td>\n",
       "      <td>91.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>m</td>\n",
       "      <td>42</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>f</td>\n",
       "      <td>31</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>92.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>f</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>87.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>f</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "      <td>68.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   participant number gender  age  correct hit percentage  \\\n",
       "0                   8      f   39                      65   \n",
       "1                  16      m   42                      90   \n",
       "2                  18      f   31                      90   \n",
       "3                  22      f   35                     100   \n",
       "4                  27      f   74                      60   \n",
       "\n",
       "   correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "0                         80             72.5                         91.0   \n",
       "1                         90             90.0                         75.5   \n",
       "2                         95             92.5                         89.5   \n",
       "3                         75             87.5                         89.5   \n",
       "4                         65             62.5                         68.5   \n",
       "\n",
       "   confidence incorrect hit  confidence correct reject  \\\n",
       "0                      90.0                       93.0   \n",
       "1                      55.5                       70.5   \n",
       "2                      90.0                       86.0   \n",
       "3                       NaN                       71.0   \n",
       "4                      49.0                       61.0   \n",
       "\n",
       "   confidence incorrect reject  confidence when correct  \\\n",
       "0                         83.5                     93.0   \n",
       "1                         50.0                     75.0   \n",
       "2                         81.0                     89.0   \n",
       "3                         80.0                     88.0   \n",
       "4                         49.0                     65.0   \n",
       "\n",
       "   confidence when incorrect  sci  psqi  ess  \n",
       "0                       90.0    9    13    2  \n",
       "1                       50.0    4    11    7  \n",
       "2                       88.0   10     9    3  \n",
       "3                       80.0   13     8   20  \n",
       "4                       49.0   13     9   12  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is more manageable and gives us an overview of what the columns are. Note also the the missing data was populated with NaN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing data frames\n",
    "\n",
    "The `DataFrame` is a convenient data structure for many reasons that will become clear as we start exploring. Let's start by looking at how `DataFrame`s are indexed. Let's try to look at the first row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-ad11118bc8f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "df[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes! Lots of errors. The problem is that we tried to index numerically by row. **We index `DataFrame`s, by columns.** And there is no column that has the name `0` in this `DataFrame`, though there could be. Instead, a might want to look at the column with the percentage of correct face matching tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       72.5\n",
       "1       90.0\n",
       "2       92.5\n",
       "3       87.5\n",
       "4       62.5\n",
       "5       50.0\n",
       "6       82.5\n",
       "7       67.5\n",
       "8       90.0\n",
       "9       75.0\n",
       "10      62.5\n",
       "11      95.0\n",
       "12      80.0\n",
       "13      80.0\n",
       "14      65.0\n",
       "15      70.0\n",
       "16      70.0\n",
       "17      80.0\n",
       "18      85.0\n",
       "19      75.0\n",
       "20      70.0\n",
       "21      97.5\n",
       "22      87.5\n",
       "23      45.0\n",
       "24      70.0\n",
       "25      72.5\n",
       "26      85.0\n",
       "27      75.0\n",
       "28      67.5\n",
       "29      95.0\n",
       "       ...  \n",
       "72      87.5\n",
       "73      97.5\n",
       "74      92.5\n",
       "75     100.0\n",
       "76      60.0\n",
       "77      95.0\n",
       "78      77.5\n",
       "79      77.5\n",
       "80      82.5\n",
       "81      40.0\n",
       "82      87.5\n",
       "83      85.0\n",
       "84      87.5\n",
       "85      77.5\n",
       "86      62.5\n",
       "87      72.5\n",
       "88      85.0\n",
       "89      92.5\n",
       "90      85.0\n",
       "91      72.5\n",
       "92      90.0\n",
       "93      90.0\n",
       "94      55.0\n",
       "95      87.5\n",
       "96      60.0\n",
       "97      77.5\n",
       "98      87.5\n",
       "99      75.0\n",
       "100     70.0\n",
       "101     62.5\n",
       "Name: percent correct, Length: 102, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['percent correct']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gave us the numbers we were after. Notice that when it was printed, the index of the rows came along with it. If we wanted to pull out a single percentage correct, say corresponding to index `4`, we can do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['percent correct'][4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this is **not** the preferred way to do this. It is better to use **`.loc`**. This give the location in the `DataFrame` we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[4, 'percent correct']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also important to note that **row indices need not be integers**. And you should not count on them being integers. In practice you will almost never use row indices, but rather use **Boolean indexing**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean indexing of data frames\n",
    "\n",
    "Let's say I wanted the percent correct of participant number 42. I can use Boolean indexing to specify the row. Specifically, I want the row for which `df['participant number'] == 42`. You can essentially plop this syntax directly when using `.loc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54    85.0\n",
       "Name: percent correct, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['participant number'] == 42, 'percent correct']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I want to pull the whole record for that participant, I can use `:` for the column index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>42</td>\n",
       "      <td>m</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
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      ],
      "text/plain": [
       "    participant number gender  age  correct hit percentage  \\\n",
       "54                  42      m   29                     100   \n",
       "\n",
       "    correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "54                         70             85.0                         75.0   \n",
       "\n",
       "    confidence incorrect hit  confidence correct reject  \\\n",
       "54                       NaN                       64.5   \n",
       "\n",
       "    confidence incorrect reject  confidence when correct  \\\n",
       "54                         43.0                     74.0   \n",
       "\n",
       "    confidence when incorrect  sci  psqi  ess  \n",
       "54                       43.0   32     1    6  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['participant number'] == 42, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the index, `54`, comes along for the ride, but we do not need it.\n",
    "\n",
    "Now, let's pull out all records of females under the age of 21. We can again use Boolean indexing, but we need to use an `&` operator. We did not cover this bitwise operator before, but the syntax is self-explanatory in the example below. Note that it is important that each Boolean operation you are doing is in parentheses because of the precedence of the operators involved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>participant number</th>\n",
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       "      <th>sci</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
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       "      <td>7</td>\n",
       "      <td>5</td>\n",
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       "      <th>66</th>\n",
       "      <td>58</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
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       "      <td>18</td>\n",
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       "      <td>75</td>\n",
       "      <td>77.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>51.5</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>85</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>91.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    participant number gender  age  correct hit percentage  \\\n",
       "27                   3      f   16                      70   \n",
       "29                   5      f   18                      90   \n",
       "66                  58      f   16                      85   \n",
       "79                  72      f   18                      80   \n",
       "88                  85      f   18                      85   \n",
       "\n",
       "    correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "27                         80             75.0                         70.0   \n",
       "29                        100             95.0                         76.5   \n",
       "66                         85             85.0                         55.0   \n",
       "79                         75             77.5                         67.5   \n",
       "88                         85             85.0                         93.0   \n",
       "\n",
       "    confidence incorrect hit  confidence correct reject  \\\n",
       "27                      57.0                       54.0   \n",
       "29                      83.0                       80.0   \n",
       "66                      30.0                       50.0   \n",
       "79                      51.5                       66.0   \n",
       "88                      92.0                       91.0   \n",
       "\n",
       "    confidence incorrect reject  confidence when correct  \\\n",
       "27                         53.0                     57.0   \n",
       "29                          NaN                     80.0   \n",
       "66                         40.0                     52.5   \n",
       "79                         57.0                     67.0   \n",
       "88                         89.0                     91.5   \n",
       "\n",
       "    confidence when incorrect  sci  psqi  ess  \n",
       "27                       54.5   23     1    3  \n",
       "29                       83.0   21     7    5  \n",
       "66                       35.0   29     2   11  \n",
       "79                       53.0   29     4    6  \n",
       "88                       91.0   25     4   21  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['age'] < 21) & (df['gender'] == 'f'), :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do something even more complicated, like pull out all females under 30 who got more than 85% of the face matching tasks correct. The code is clearer if we set up our Boolean indexing first, as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5      False\n",
       "6      False\n",
       "7      False\n",
       "8      False\n",
       "9      False\n",
       "10     False\n",
       "11     False\n",
       "12     False\n",
       "13     False\n",
       "14     False\n",
       "15     False\n",
       "16     False\n",
       "17     False\n",
       "18     False\n",
       "19     False\n",
       "20     False\n",
       "21     False\n",
       "22      True\n",
       "23     False\n",
       "24     False\n",
       "25     False\n",
       "26     False\n",
       "27     False\n",
       "28     False\n",
       "29      True\n",
       "       ...  \n",
       "72     False\n",
       "73     False\n",
       "74     False\n",
       "75      True\n",
       "76     False\n",
       "77     False\n",
       "78     False\n",
       "79     False\n",
       "80     False\n",
       "81     False\n",
       "82     False\n",
       "83     False\n",
       "84     False\n",
       "85     False\n",
       "86     False\n",
       "87     False\n",
       "88     False\n",
       "89     False\n",
       "90     False\n",
       "91     False\n",
       "92     False\n",
       "93     False\n",
       "94     False\n",
       "95     False\n",
       "96     False\n",
       "97     False\n",
       "98     False\n",
       "99     False\n",
       "100    False\n",
       "101    False\n",
       "Length: 102, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inds = (df['age'] < 30) & (df['gender'] == 'f') & (df['percent correct'] > 85)\n",
    "\n",
    "# Take a look\n",
    "inds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `inds` is an array (actually a Pandas `Series`, essentially a `DataFrame` with one column) of `True`s and `False`s. When we index with it using `.loc`, we get back rows where `inds` is `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>85</td>\n",
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       "      <td>97.0</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    participant number gender  age  correct hit percentage  \\\n",
       "22                  93      f   28                     100   \n",
       "29                   5      f   18                      90   \n",
       "30                   6      f   28                      95   \n",
       "33                  10      f   25                     100   \n",
       "56                  44      f   21                      85   \n",
       "58                  48      f   23                      90   \n",
       "60                  51      f   24                      85   \n",
       "75                  67      f   25                     100   \n",
       "\n",
       "    correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "22                         75             87.5                         89.5   \n",
       "29                        100             95.0                         76.5   \n",
       "30                         80             87.5                        100.0   \n",
       "33                        100            100.0                         90.0   \n",
       "56                         90             87.5                         66.0   \n",
       "58                         85             87.5                         67.0   \n",
       "60                         95             90.0                         97.0   \n",
       "75                        100            100.0                         61.5   \n",
       "\n",
       "    confidence incorrect hit  confidence correct reject  \\\n",
       "22                       NaN                       67.0   \n",
       "29                      83.0                       80.0   \n",
       "30                      85.0                       94.0   \n",
       "33                       NaN                       85.0   \n",
       "56                      29.0                       70.0   \n",
       "58                      47.0                       69.0   \n",
       "60                      41.0                       74.0   \n",
       "75                       NaN                       58.5   \n",
       "\n",
       "    confidence incorrect reject  confidence when correct  \\\n",
       "22                         60.0                     80.0   \n",
       "29                          NaN                     80.0   \n",
       "30                         61.0                     99.0   \n",
       "33                          NaN                     90.0   \n",
       "56                         29.0                     67.0   \n",
       "58                         40.0                     67.0   \n",
       "60                         73.0                     83.0   \n",
       "75                          NaN                     60.5   \n",
       "\n",
       "    confidence when incorrect  sci  psqi  ess  \n",
       "22                       60.0   16     7    4  \n",
       "29                       83.0   21     7    5  \n",
       "30                       65.0   19     7   12  \n",
       "33                        NaN   17    10   11  \n",
       "56                       29.0   26     7   18  \n",
       "58                       40.0   18     6    8  \n",
       "60                       55.5   29     1    7  \n",
       "75                        NaN   28     8    9  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[inds, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of interest in this exercise in Boolean indexing is that we never had to write a loop. To produce our indices, we could have done the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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      "text/plain": [
       "    participant number gender  age  correct hit percentage  \\\n",
       "22                  93      f   28                     100   \n",
       "29                   5      f   18                      90   \n",
       "30                   6      f   28                      95   \n",
       "33                  10      f   25                     100   \n",
       "56                  44      f   21                      85   \n",
       "58                  48      f   23                      90   \n",
       "60                  51      f   24                      85   \n",
       "75                  67      f   25                     100   \n",
       "\n",
       "    correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "22                         75             87.5                         89.5   \n",
       "29                        100             95.0                         76.5   \n",
       "30                         80             87.5                        100.0   \n",
       "33                        100            100.0                         90.0   \n",
       "56                         90             87.5                         66.0   \n",
       "58                         85             87.5                         67.0   \n",
       "60                         95             90.0                         97.0   \n",
       "75                        100            100.0                         61.5   \n",
       "\n",
       "    confidence incorrect hit  confidence correct reject  \\\n",
       "22                       NaN                       67.0   \n",
       "29                      83.0                       80.0   \n",
       "30                      85.0                       94.0   \n",
       "33                       NaN                       85.0   \n",
       "56                      29.0                       70.0   \n",
       "58                      47.0                       69.0   \n",
       "60                      41.0                       74.0   \n",
       "75                       NaN                       58.5   \n",
       "\n",
       "    confidence incorrect reject  confidence when correct  \\\n",
       "22                         60.0                     80.0   \n",
       "29                          NaN                     80.0   \n",
       "30                         61.0                     99.0   \n",
       "33                          NaN                     90.0   \n",
       "56                         29.0                     67.0   \n",
       "58                         40.0                     67.0   \n",
       "60                         73.0                     83.0   \n",
       "75                          NaN                     60.5   \n",
       "\n",
       "    confidence when incorrect  sci  psqi  ess  \n",
       "22                       60.0   16     7    4  \n",
       "29                       83.0   21     7    5  \n",
       "30                       65.0   19     7   12  \n",
       "33                        NaN   17    10   11  \n",
       "56                       29.0   26     7   18  \n",
       "58                       40.0   18     6    8  \n",
       "60                       55.5   29     1    7  \n",
       "75                        NaN   28     8    9  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize array of Boolean indices\n",
    "inds = [False] * len(df)\n",
    "\n",
    "# Iterate over the rows of the DataFrame to check if the row should be included\n",
    "for i, r in df.iterrows():\n",
    "    if r['age'] < 30 and r['gender'] == 'f' and r['percent correct'] > 85:\n",
    "        inds[i] = True\n",
    "\n",
    "# Make our seleciton with Boolean indexing\n",
    "df.loc[inds, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature, where the looping is done automatically on Pandas objects like `DataFrame`s, is very powerful and saves us writing lots of lines of code. This example also showed how to use the `iterrows()` method of a `DataFrame` to iterate over the rows of a `DataFrame`. It is actually rare that you will need to do that, as we'll show next when computing with `DataFrames`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating with data frames\n",
    "\n",
    "Recall that a subject is said to suffer from insomnia if he or she has an SCI of 16 or below. We might like to add a column to the `DataFrame` that specifies whether or not the subject suffers from insomnia. We can conveniently compute with columns. This is done elementwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "5       True\n",
       "6       True\n",
       "7       True\n",
       "8       True\n",
       "9       True\n",
       "10      True\n",
       "11      True\n",
       "12      True\n",
       "13      True\n",
       "14      True\n",
       "15      True\n",
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       "28     False\n",
       "29     False\n",
       "       ...  \n",
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       "75     False\n",
       "76     False\n",
       "77     False\n",
       "78     False\n",
       "79     False\n",
       "80     False\n",
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       "82     False\n",
       "83     False\n",
       "84     False\n",
       "85     False\n",
       "86     False\n",
       "87     False\n",
       "88     False\n",
       "89     False\n",
       "90     False\n",
       "91     False\n",
       "92     False\n",
       "93     False\n",
       "94     False\n",
       "95     False\n",
       "96     False\n",
       "97     False\n",
       "98     False\n",
       "99     False\n",
       "100    False\n",
       "101    False\n",
       "Name: sci, Length: 102, dtype: bool"
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['sci'] <= 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells use who is an insomniac. We can simply add this back to the `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   participant number gender  age  correct hit percentage  \\\n",
       "0                   8      f   39                      65   \n",
       "1                  16      m   42                      90   \n",
       "2                  18      f   31                      90   \n",
       "3                  22      f   35                     100   \n",
       "4                  27      f   74                      60   \n",
       "\n",
       "   correct reject percentage  percent correct  confidence when correct hit  \\\n",
       "0                         80             72.5                         91.0   \n",
       "1                         90             90.0                         75.5   \n",
       "2                         95             92.5                         89.5   \n",
       "3                         75             87.5                         89.5   \n",
       "4                         65             62.5                         68.5   \n",
       "\n",
       "   confidence incorrect hit  confidence correct reject  \\\n",
       "0                      90.0                       93.0   \n",
       "1                      55.5                       70.5   \n",
       "2                      90.0                       86.0   \n",
       "3                       NaN                       71.0   \n",
       "4                      49.0                       61.0   \n",
       "\n",
       "   confidence incorrect reject  confidence when correct  \\\n",
       "0                         83.5                     93.0   \n",
       "1                         50.0                     75.0   \n",
       "2                         81.0                     89.0   \n",
       "3                         80.0                     88.0   \n",
       "4                         49.0                     65.0   \n",
       "\n",
       "   confidence when incorrect  sci  psqi  ess  insomnia  \n",
       "0                       90.0    9    13    2      True  \n",
       "1                       50.0    4    11    7      True  \n",
       "2                       88.0   10     9    3      True  \n",
       "3                       80.0   13     8   20      True  \n",
       "4                       49.0   13     9   12      True  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the column to the DataFrame\n",
    "df['insomnia'] = df['sci'] <= 16\n",
    "\n",
    "# Take a look\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A note about vectorization\n",
    "\n",
    "Notice how applying the `<=` operator to a `Series` resulted in **elementwise** application. This is called `vectorization`. It means that we do not have to write a **`for`** loop to do operations on the elements of a `Series` or other array-like object. Imagine if we had to do that with a **`for`** loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "insomnia = []\n",
    "for sci in df['sci']:\n",
    "    insomnia.append(sci <= 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is cumbersome. The vectorization allows for much more convenient calculation. Beyond that, the vectorized code is almost always faster when using Pandas and Numpy because the looping is done with compiled code under the hood. This can be done with many operators, including those you've already seen, like `+`, `-`, `*`, `/`, `**`, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying functions to Pandas objects\n",
    "\n",
    "Remember when we briefly saw the `np.mean()` function? We can compute with that as well. Let's compare the mean percent correct for insomniacs versus those who are not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insomniacs: 76.1\n",
      "Control:    81.46103896103897\n"
     ]
    }
   ],
   "source": [
    "print('Insomniacs:', np.mean(df.loc[df['insomnia'], 'percent correct']))\n",
    "print('Control:   ', np.mean(df.loc[~df['insomnia'], 'percent correct']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that I used the `~` operator, which is a bit switcher. It changes all `True`s to `False`s and vice versa. In this case, it functions like NOT.\n",
    "\n",
    "We will do a lot more computing with Pandas `DataFrame`s in the next lessons. For our last demonstration in this lesson, we can quickly compute summary statistics about each column of a `DataFrame` using its `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant number</th>\n",
       "      <th>age</th>\n",
       "      <th>correct hit percentage</th>\n",
       "      <th>correct reject percentage</th>\n",
       "      <th>percent correct</th>\n",
       "      <th>confidence when correct hit</th>\n",
       "      <th>confidence incorrect hit</th>\n",
       "      <th>confidence correct reject</th>\n",
       "      <th>confidence incorrect reject</th>\n",
       "      <th>confidence when correct</th>\n",
       "      <th>confidence when incorrect</th>\n",
       "      <th>sci</th>\n",
       "      <th>psqi</th>\n",
       "      <th>ess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.049020</td>\n",
       "      <td>37.921569</td>\n",
       "      <td>83.088235</td>\n",
       "      <td>77.205882</td>\n",
       "      <td>80.147059</td>\n",
       "      <td>74.990196</td>\n",
       "      <td>58.565476</td>\n",
       "      <td>71.137255</td>\n",
       "      <td>61.220430</td>\n",
       "      <td>74.642157</td>\n",
       "      <td>61.979798</td>\n",
       "      <td>22.245098</td>\n",
       "      <td>5.274510</td>\n",
       "      <td>7.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.020909</td>\n",
       "      <td>14.029450</td>\n",
       "      <td>15.091210</td>\n",
       "      <td>17.569854</td>\n",
       "      <td>12.047881</td>\n",
       "      <td>14.165916</td>\n",
       "      <td>19.560653</td>\n",
       "      <td>14.987479</td>\n",
       "      <td>17.671283</td>\n",
       "      <td>13.619725</td>\n",
       "      <td>15.921670</td>\n",
       "      <td>7.547128</td>\n",
       "      <td>3.404007</td>\n",
       "      <td>4.426715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.250000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>46.375000</td>\n",
       "      <td>64.625000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.500000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>71.250000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>75.750000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.750000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>82.375000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       participant number         age  correct hit percentage  \\\n",
       "count          102.000000  102.000000              102.000000   \n",
       "mean            52.049020   37.921569               83.088235   \n",
       "std             30.020909   14.029450               15.091210   \n",
       "min              1.000000   16.000000               35.000000   \n",
       "25%             26.250000   26.500000               75.000000   \n",
       "50%             52.500000   36.500000               90.000000   \n",
       "75%             77.750000   45.000000               95.000000   \n",
       "max            103.000000   74.000000              100.000000   \n",
       "\n",
       "       correct reject percentage  percent correct  \\\n",
       "count                 102.000000       102.000000   \n",
       "mean                   77.205882        80.147059   \n",
       "std                    17.569854        12.047881   \n",
       "min                    20.000000        40.000000   \n",
       "25%                    70.000000        72.500000   \n",
       "50%                    80.000000        83.750000   \n",
       "75%                    90.000000        87.500000   \n",
       "max                   100.000000       100.000000   \n",
       "\n",
       "       confidence when correct hit  confidence incorrect hit  \\\n",
       "count                   102.000000                 84.000000   \n",
       "mean                     74.990196                 58.565476   \n",
       "std                      14.165916                 19.560653   \n",
       "min                      29.500000                  7.000000   \n",
       "25%                      66.000000                 46.375000   \n",
       "50%                      75.000000                 56.250000   \n",
       "75%                      86.500000                 73.500000   \n",
       "max                     100.000000                 92.000000   \n",
       "\n",
       "       confidence correct reject  confidence incorrect reject  \\\n",
       "count                 102.000000                    93.000000   \n",
       "mean                   71.137255                    61.220430   \n",
       "std                    14.987479                    17.671283   \n",
       "min                    19.000000                    17.000000   \n",
       "25%                    64.625000                    50.000000   \n",
       "50%                    71.250000                    61.000000   \n",
       "75%                    80.000000                    74.000000   \n",
       "max                   100.000000                   100.000000   \n",
       "\n",
       "       confidence when correct  confidence when incorrect         sci  \\\n",
       "count               102.000000                  99.000000  102.000000   \n",
       "mean                 74.642157                  61.979798   22.245098   \n",
       "std                  13.619725                  15.921670    7.547128   \n",
       "min                  24.000000                  24.500000    0.000000   \n",
       "25%                  66.000000                  51.000000   17.000000   \n",
       "50%                  75.750000                  61.500000   23.500000   \n",
       "75%                  82.375000                  73.000000   29.000000   \n",
       "max                 100.000000                 100.000000   32.000000   \n",
       "\n",
       "             psqi         ess  \n",
       "count  102.000000  102.000000  \n",
       "mean     5.274510    7.294118  \n",
       "std      3.404007    4.426715  \n",
       "min      0.000000    0.000000  \n",
       "25%      3.000000    4.000000  \n",
       "50%      5.000000    7.000000  \n",
       "75%      7.000000   10.000000  \n",
       "max     15.000000   21.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a `DataFrame` with summary statistics. Note that in this `DataFrame`, the row indices are not integers, but are the names of the summary statistics. If we wanted to extract the median value of each entry, we could do that with `.loc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "participant number             52.50\n",
       "age                            36.50\n",
       "correct hit percentage         90.00\n",
       "correct reject percentage      80.00\n",
       "percent correct                83.75\n",
       "confidence when correct hit    75.00\n",
       "confidence incorrect hit       56.25\n",
       "confidence correct reject      71.25\n",
       "confidence incorrect reject    61.00\n",
       "confidence when correct        75.75\n",
       "confidence when incorrect      61.50\n",
       "sci                            23.50\n",
       "psqi                            5.00\n",
       "ess                             7.00\n",
       "Name: 50%, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().loc['50%', :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outputting a new CSV file\n",
    "\n",
    "Now that we added the insomniac column, we might like to save our `DataFrame` as a new CSV that we can reload later. We use `df.to_csv()` for this with the `index` kwarg to ask Pandas not to explicitly write the indices to the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('gfmt_sleep_with_insomnia.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what this file looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "participant number,gender,age,correct hit percentage,correct reject percentage,percent correct,confidence when correct hit,confidence incorrect hit,confidence correct reject,confidence incorrect reject,confidence when correct,confidence when incorrect,sci,psqi,ess,insomnia\n",
      "8,f,39,65,80,72.5,91.0,90.0,93.0,83.5,93.0,90.0,9,13,2,True\n",
      "16,m,42,90,90,90.0,75.5,55.5,70.5,50.0,75.0,50.0,4,11,7,True\n",
      "18,f,31,90,95,92.5,89.5,90.0,86.0,81.0,89.0,88.0,10,9,3,True\n",
      "22,f,35,100,75,87.5,89.5,,71.0,80.0,88.0,80.0,13,8,20,True\n",
      "27,f,74,60,65,62.5,68.5,49.0,61.0,49.0,65.0,49.0,13,9,12,True\n",
      "28,f,61,80,20,50.0,71.0,63.0,31.0,72.5,64.5,70.5,15,14,2,True\n",
      "30,m,32,90,75,82.5,67.0,56.5,66.0,65.0,66.0,64.0,16,9,3,True\n",
      "33,m,62,45,90,67.5,54.0,37.0,65.0,81.5,62.0,61.0,14,9,9,True\n",
      "34,f,33,80,100,90.0,70.5,76.5,64.5,,68.0,76.5,14,12,10,True\n"
     ]
    }
   ],
   "source": [
    "!head gfmt_sleep_with_insomnia.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very nice. Notice that by default Pandas leaves an empty field for NaNs, and we do not need the `na_values` kwarg when we load in this CSV file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.8\n",
      "IPython 7.1.1\n",
      "\n",
      "numpy 1.16.4\n",
      "pandas 0.24.2\n",
      "jupyterlab 1.0.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,pandas,jupyterlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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