Copy_of_spinal_exercises.ipynb

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      "name": "Copy of spinal-exercises.ipynb",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/prakshalj0512/MAT494/blob/master/Copy_of_spinal_exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "metadata": {
        "id": "9gP5BKox05KC",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "# Pandas dataframe exercises using the sprinter reaction time dataset.  \n",
        "## Goal: Get familiar with basic dataframe operations"
      ]
    },
    {
      "metadata": {
        "id": "5ipFOyif05Kf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The code block below is a preamble that loads the libraries that we need and sets things up the way we like it.  You can copy and paste this preamble and put it in any notebook to have access to the same libraries."
      ]
    },
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      "source": [
        "# Tells the notebooks to view every graph \"in line\" automatically, rather than requiring a specific `show` command\n",
        "%matplotlib inline \n",
        "# Loads the numpy library (for math stuff)\n",
        "import numpy as np \n",
        "# Load the pandas library (what we use to create, explore, and analyze datasets)\n",
        "import pandas as pd \n",
        "# Loads the matplotlib library (what we use to create graphs)\n",
        "import matplotlib.pyplot as plt \n",
        "# Loads the seaborn library (what we use to create prettier graphs)\n",
        "import seaborn as sns \n",
        "# Sets the default font size for graphs to 150% of its original value (for readability)\n",
        "sns.set(font_scale=1.5) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BBz2QnOq05Kz",
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      "cell_type": "markdown",
      "source": [
        "#### First we load the original data into a Pandas dataframe called `df`"
      ]
    },
    {
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        "id": "g1gkmxHf05K3",
        "colab_type": "code",
        "outputId": "7c4d9334-a0ed-454a-cffe-484fa3707bcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "cell_type": "code",
      "source": [
        "url = \"https://raw.githubusercontent.com/rgerkin/neuro-data-analysis-course/master/data/spinal/2004-2016-Short-Races2.csv\"\n",
        "df = pd.read_csv(url) # Loads the data from that URL into a dataframe\n",
        "df.head() # Shows the first few rows of that dataframe"
      ],
      "execution_count": 41,
      "outputs": [
        {
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              "      <td>NaN</td>\n",
              "      <td>Usain BOLT</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.20 Q</td>\n",
              "      <td>0.186</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Daniel BAILEY</td>\n",
              "      <td>ANTANT</td>\n",
              "      <td>10.24 Q</td>\n",
              "      <td>0.198</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Vicente DE LIMA</td>\n",
              "      <td>BRABRA</td>\n",
              "      <td>10.26 Q</td>\n",
              "      <td>0.168</td>\n",
              "      <td>100 m</td>\n",
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              "      <td>Henry VIZCAÍNO</td>\n",
              "      <td>CUBCUB</td>\n",
              "      <td>10.28 q</td>\n",
              "      <td>0.157</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Fabio CERUTTI</td>\n",
              "      <td>ITAITA</td>\n",
              "      <td>10.49</td>\n",
              "      <td>0.136</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
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              "   Unnamed: 0  POS  BIB          ATHLETE COUNTRY     MARK  RxnTime   Race  \\\n",
              "0           0  1.0  NaN       Usain BOLT  JAMJAM  10.20 Q    0.186  100 m   \n",
              "1           1  2.0  NaN    Daniel BAILEY  ANTANT  10.24 Q    0.198  100 m   \n",
              "2           2  3.0  NaN  Vicente DE LIMA  BRABRA  10.26 Q    0.168  100 m   \n",
              "3           3  4.0  NaN   Henry VIZCAÍNO  CUBCUB  10.28 q    0.157  100 m   \n",
              "4           4  5.0  NaN    Fabio CERUTTI  ITAITA    10.49    0.136  100 m   \n",
              "\n",
              "   Round Gender  Year  \n",
              "0  Heats      M  2008  \n",
              "1  Heats      M  2008  \n",
              "2  Heats      M  2008  \n",
              "3  Heats      M  2008  \n",
              "4  Heats      M  2008  "
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 41
        }
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    },
    {
      "metadata": {
        "id": "fYOz-7Hh05LL",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "#### Now complete the numbered tasks below.  Read the question, then use the code block to fill in your solution"
      ]
    },
    {
      "metadata": {
        "id": "tmX7Fh2Q05LP",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "1) A method is just a function that belongs to an object (like a dataframe).  The `.head()` method is a good way to preview the data that you loaded.  Sometimes people work with datasets with thousands or even millions of rows.  You usually don't need to see them all at once!  But sometimes you want to see more than 5 rows.  Try passing an argument to `.head()`, in order to see more rows.  For example, `.head(10)` to see 10 rows.  Or pick some other number."
      ]
    },
    {
      "metadata": {
        "id": "lAjsp2zg05LT",
        "colab_type": "code",
        "colab": {
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          "height": 343
        },
        "outputId": "9c89a31f-7737-4022-95c3-2ef256d9c12e"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 1 here\n",
        "df.head(10)"
      ],
      "execution_count": 42,
      "outputs": [
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              "      <td>100 m</td>\n",
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              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jurgen THEMEN</td>\n",
              "      <td>SURSUR</td>\n",
              "      <td>10.61</td>\n",
              "      <td>0.179</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Moses KAMUT</td>\n",
              "      <td>VANVAN</td>\n",
              "      <td>10.81</td>\n",
              "      <td>0.181</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Francis MANIORU</td>\n",
              "      <td>SOLSOL</td>\n",
              "      <td>11.09</td>\n",
              "      <td>0.197</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Asafa POWELL</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.16 Q</td>\n",
              "      <td>0.142</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kim COLLINS</td>\n",
              "      <td>SKNSKN</td>\n",
              "      <td>10.17 Q</td>\n",
              "      <td>0.162</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
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            "text/plain": [
              "   Unnamed: 0  POS  BIB          ATHLETE COUNTRY     MARK  RxnTime   Race  \\\n",
              "0           0  1.0  NaN       Usain BOLT  JAMJAM  10.20 Q    0.186  100 m   \n",
              "1           1  2.0  NaN    Daniel BAILEY  ANTANT  10.24 Q    0.198  100 m   \n",
              "2           2  3.0  NaN  Vicente DE LIMA  BRABRA  10.26 Q    0.168  100 m   \n",
              "3           3  4.0  NaN   Henry VIZCAÍNO  CUBCUB  10.28 q    0.157  100 m   \n",
              "4           4  5.0  NaN    Fabio CERUTTI  ITAITA    10.49    0.136  100 m   \n",
              "5           5  6.0  NaN    Jurgen THEMEN  SURSUR    10.61    0.179  100 m   \n",
              "6           6  7.0  NaN      Moses KAMUT  VANVAN    10.81    0.181  100 m   \n",
              "7           7  8.0  NaN  Francis MANIORU  SOLSOL    11.09    0.197  100 m   \n",
              "8           8  1.0  NaN     Asafa POWELL  JAMJAM  10.16 Q    0.142  100 m   \n",
              "9           9  2.0  NaN      Kim COLLINS  SKNSKN  10.17 Q    0.162  100 m   \n",
              "\n",
              "   Round Gender  Year  \n",
              "0  Heats      M  2008  \n",
              "1  Heats      M  2008  \n",
              "2  Heats      M  2008  \n",
              "3  Heats      M  2008  \n",
              "4  Heats      M  2008  \n",
              "5  Heats      M  2008  \n",
              "6  Heats      M  2008  \n",
              "7  Heats      M  2008  \n",
              "8  Heats      M  2008  \n",
              "9  Heats      M  2008  "
            ]
          },
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          "execution_count": 42
        }
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    },
    {
      "metadata": {
        "id": "j8ggp3w805Lf",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "2) You can also look at the *last* few rows of the data, using the `.tail()` method. Try viewing the last 12 rows of the dataset.  "
      ]
    },
    {
      "metadata": {
        "id": "NMBQ1qs-05Lm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "6fa17f61-53a8-46b2-e7d0-c144383d03a7"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 2 here\n",
        "df.tail(12)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>2445</th>\n",
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              "      <td>Taiwo ALADEFA-DARDEN</td>\n",
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              "      <td>4.0</td>\n",
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              "      <td>Lidiya YURKOVA</td>\n",
              "      <td>BLRBLR</td>\n",
              "      <td>13.2</td>\n",
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              "      <td>100 m H</td>\n",
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              "      <th>2447</th>\n",
              "      <td>2447</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nadezhda BODROVA</td>\n",
              "      <td>UKRUKR</td>\n",
              "      <td>13.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
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              "      <th>2448</th>\n",
              "      <td>2448</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rachael ROGERS</td>\n",
              "      <td>FIJFIJ</td>\n",
              "      <td>14.07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2449</th>\n",
              "      <td>2449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Elisabeta ANGHEL</td>\n",
              "      <td>ROUROU</td>\n",
              "      <td>DNF</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2450</th>\n",
              "      <td>2450</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gail DEVERS</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>12.73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2451</th>\n",
              "      <td>2451</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gillian RUSSELL-LOVE</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>12.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>2452</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yuliya GRAUDYN</td>\n",
              "      <td>RUSRUS</td>\n",
              "      <td>12.95</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2453</th>\n",
              "      <td>2453</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ime AKPAN</td>\n",
              "      <td>NGRNGR</td>\n",
              "      <td>13.11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>2454</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Olena KRASOVSKA</td>\n",
              "      <td>UKRUKR</td>\n",
              "      <td>13.23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2455</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Véronique LINSTER</td>\n",
              "      <td>LUXLUX</td>\n",
              "      <td>13.47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2456</th>\n",
              "      <td>2456</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Carla TUZZI</td>\n",
              "      <td>ITAITA</td>\n",
              "      <td>DNF</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  POS  BIB               ATHLETE COUNTRY   MARK  RxnTime  \\\n",
              "2445        2445  3.0  NaN  Taiwo ALADEFA-DARDEN  NGRNGR  13.06      NaN   \n",
              "2446        2446  4.0  NaN        Lidiya YURKOVA  BLRBLR   13.2      NaN   \n",
              "2447        2447  5.0  NaN      Nadezhda BODROVA  UKRUKR  13.22      NaN   \n",
              "2448        2448  6.0  NaN        Rachael ROGERS  FIJFIJ  14.07      NaN   \n",
              "2449        2449  NaN  NaN      Elisabeta ANGHEL  ROUROU    DNF      NaN   \n",
              "2450        2450  1.0  NaN           Gail DEVERS  USAUSA  12.73      NaN   \n",
              "2451        2451  2.0  NaN  Gillian RUSSELL-LOVE  JAMJAM  12.85      NaN   \n",
              "2452        2452  3.0  NaN        Yuliya GRAUDYN  RUSRUS  12.95      NaN   \n",
              "2453        2453  4.0  NaN             Ime AKPAN  NGRNGR  13.11      NaN   \n",
              "2454        2454  5.0  NaN       Olena KRASOVSKA  UKRUKR  13.23      NaN   \n",
              "2455        2455  6.0  NaN     Véronique LINSTER  LUXLUX  13.47      NaN   \n",
              "2456        2456  NaN  NaN           Carla TUZZI  ITAITA    DNF      NaN   \n",
              "\n",
              "         Race  Round Gender  Year  \n",
              "2445  100 m H  Heats      F  1996  \n",
              "2446  100 m H  Heats      F  1996  \n",
              "2447  100 m H  Heats      F  1996  \n",
              "2448  100 m H  Heats      F  1996  \n",
              "2449  100 m H  Heats      F  1996  \n",
              "2450  100 m H  Heats      F  1996  \n",
              "2451  100 m H  Heats      F  1996  \n",
              "2452  100 m H  Heats      F  1996  \n",
              "2453  100 m H  Heats      F  1996  \n",
              "2454  100 m H  Heats      F  1996  \n",
              "2455  100 m H  Heats      F  1996  \n",
              "2456  100 m H  Heats      F  1996  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "kDEpr7dk05Ly",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "3) How much data do you have?  You can find how many rows and columns you have with the `.shape` attribute.  Note the lack of parentheses here.  Attributes differ from methods in that they don't actually do anything (like print out information, or make a plot), but you can access their value to get information about your data.  The `.shape` attribute reports the number of rows and columns of the dataframe.  How many columns does the dataframe have?  "
      ]
    },
    {
      "metadata": {
        "id": "QHnMKKzG05L1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d4098613-455a-4d9d-b4dc-533df222fe81"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 3 here\n",
        "df.shape"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2457, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "L1XN9iQI05M1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "4) Sometimes you only care about one column in your dataframe.  You can access it be name.  For example, `df['ATHLETE']` will access the \"ATHLETE\" column.  Access the column for reaction time. "
      ]
    },
    {
      "metadata": {
        "id": "tLUoF7Ap05M6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1058
        },
        "outputId": "090e096a-9b5e-4e79-d2f3-6003e6cd5175"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 4 here\n",
        "df['RxnTime']"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0.186\n",
              "1       0.198\n",
              "2       0.168\n",
              "3       0.157\n",
              "4       0.136\n",
              "5       0.179\n",
              "6       0.181\n",
              "7       0.197\n",
              "8       0.142\n",
              "9       0.162\n",
              "10      0.174\n",
              "11      0.178\n",
              "12      0.144\n",
              "13      0.144\n",
              "14      0.159\n",
              "15      0.190\n",
              "16      0.188\n",
              "17      0.162\n",
              "18      0.140\n",
              "19      0.177\n",
              "20      0.140\n",
              "21      0.175\n",
              "22      0.123\n",
              "23      0.152\n",
              "24      0.156\n",
              "25      0.141\n",
              "26      0.139\n",
              "27      0.160\n",
              "28      0.184\n",
              "29      0.171\n",
              "        ...  \n",
              "2427      NaN\n",
              "2428      NaN\n",
              "2429      NaN\n",
              "2430      NaN\n",
              "2431      NaN\n",
              "2432      NaN\n",
              "2433      NaN\n",
              "2434      NaN\n",
              "2435      NaN\n",
              "2436      NaN\n",
              "2437      NaN\n",
              "2438      NaN\n",
              "2439      NaN\n",
              "2440      NaN\n",
              "2441      NaN\n",
              "2442      NaN\n",
              "2443      NaN\n",
              "2444      NaN\n",
              "2445      NaN\n",
              "2446      NaN\n",
              "2447      NaN\n",
              "2448      NaN\n",
              "2449      NaN\n",
              "2450      NaN\n",
              "2451      NaN\n",
              "2452      NaN\n",
              "2453      NaN\n",
              "2454      NaN\n",
              "2455      NaN\n",
              "2456      NaN\n",
              "Name: RxnTime, Length: 2457, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "xsEQKOU505NB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "5) Notice that the above doesn't look like a dataframe anymore.  That's because you've only accessed one column, so the two-dimensional dataset (rows x columns) is now only one-dimensional. One-dimensional data in Pandas is convered into a so-called \"Series\".  Many of the same methods that belong to dataframes also belong to a series, e.g. `.head()` or `.mean()`.  Find the median reaction time as you did in the earlier homework assignment, but operating directly on the series containing the reaction times.  "
      ]
    },
    {
      "metadata": {
        "id": "6XMt_r2s05NE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ac9a0208-3eb1-4f02-e787-1ad2aee2aea5"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 5 here\n",
        "df['RxnTime'].median()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.165"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "w1VzFPDP05NM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "6) Sometimes you want to keep a dataframe, but just with fewer columns.  For example, only the name and reaction time.  You do this with an extra set of brackets, i.e. `df[['col1','col2']]` if you wanted to access hypothetical columns named 'col1' and 'col2'.  Try generating a dataframe that has only the name and reaction time columns from the original dataset.  "
      ]
    },
    {
      "metadata": {
        "id": "FxenbiGB05NO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1882
        },
        "outputId": "de9e6ddf-24e9-4f56-a764-c83af3514987"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Fill in your answer to 6 here\n",
        "df[['ATHLETE','RxnTime']]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ATHLETE</th>\n",
              "      <th>RxnTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Usain BOLT</td>\n",
              "      <td>0.186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Daniel BAILEY</td>\n",
              "      <td>0.198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Vicente DE LIMA</td>\n",
              "      <td>0.168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Henry VIZCAÍNO</td>\n",
              "      <td>0.157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fabio CERUTTI</td>\n",
              "      <td>0.136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jurgen THEMEN</td>\n",
              "      <td>0.179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Moses KAMUT</td>\n",
              "      <td>0.181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Francis MANIORU</td>\n",
              "      <td>0.197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Asafa POWELL</td>\n",
              "      <td>0.142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kim COLLINS</td>\n",
              "      <td>0.162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Craig PICKERING</td>\n",
              "      <td>0.174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Daniel GRUESO</td>\n",
              "      <td>0.178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Dariusz KUC</td>\n",
              "      <td>0.144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Berenger Aymard BOSSE</td>\n",
              "      <td>0.144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Aisea TOHI</td>\n",
              "      <td>0.159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Roman CRESS</td>\n",
              "      <td>0.190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Richard THOMPSON</td>\n",
              "      <td>0.188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Martial MBANDJOCK</td>\n",
              "      <td>0.162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Simone COLLIO</td>\n",
              "      <td>0.140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Aziz ZAKARI</td>\n",
              "      <td>0.177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Andrew HINDS</td>\n",
              "      <td>0.140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Suryo Agung WIBOWO</td>\n",
              "      <td>0.175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Jared LEWIS</td>\n",
              "      <td>0.123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Rabangaki NAWAI</td>\n",
              "      <td>0.152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Michael FRATER</td>\n",
              "      <td>0.156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Pierre BROWNE</td>\n",
              "      <td>0.141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Darrel BROWN</td>\n",
              "      <td>0.139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Nobuharu ASAHARA</td>\n",
              "      <td>0.160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Holder DA SILVA</td>\n",
              "      <td>0.184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Idrissa SANOU</td>\n",
              "      <td>0.171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2427</th>\n",
              "      <td>Lesley TASHLIN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2428</th>\n",
              "      <td>Ludmila ENGQUIST</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2429</th>\n",
              "      <td>Angela ATEDE</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2430</th>\n",
              "      <td>María José MARDOMINGO</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2431</th>\n",
              "      <td>Monique ÉWANJÉ-ÉPÉE TOURRET</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2432</th>\n",
              "      <td>Lena SOLLI-REIMANN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2433</th>\n",
              "      <td>Natalya GRIGORYEVA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2434</th>\n",
              "      <td>Sonia PAQUETTE</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2435</th>\n",
              "      <td>Julie BAUMANN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2436</th>\n",
              "      <td>Angela THORP</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2437</th>\n",
              "      <td>Lynda TOLBERT-GOODE</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2438</th>\n",
              "      <td>Kristin PATZWAHL</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2439</th>\n",
              "      <td>Tatyana RESHETNIKOVA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2440</th>\n",
              "      <td>Cécile CINÉLU</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2441</th>\n",
              "      <td>Bich Huong VU</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2442</th>\n",
              "      <td>Aminata CAMARA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2443</th>\n",
              "      <td>Brigita BUKOVEC</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2444</th>\n",
              "      <td>Dionne ROSE-HENLEY</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2445</th>\n",
              "      <td>Taiwo ALADEFA-DARDEN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2446</th>\n",
              "      <td>Lidiya YURKOVA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2447</th>\n",
              "      <td>Nadezhda BODROVA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2448</th>\n",
              "      <td>Rachael ROGERS</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2449</th>\n",
              "      <td>Elisabeta ANGHEL</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2450</th>\n",
              "      <td>Gail DEVERS</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2451</th>\n",
              "      <td>Gillian RUSSELL-LOVE</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>Yuliya GRAUDYN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2453</th>\n",
              "      <td>Ime AKPAN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>Olena KRASOVSKA</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>Véronique LINSTER</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2456</th>\n",
              "      <td>Carla TUZZI</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2457 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                          ATHLETE  RxnTime\n",
              "0                      Usain BOLT    0.186\n",
              "1                   Daniel BAILEY    0.198\n",
              "2                 Vicente DE LIMA    0.168\n",
              "3                  Henry VIZCAÍNO    0.157\n",
              "4                   Fabio CERUTTI    0.136\n",
              "5                   Jurgen THEMEN    0.179\n",
              "6                     Moses KAMUT    0.181\n",
              "7                 Francis MANIORU    0.197\n",
              "8                    Asafa POWELL    0.142\n",
              "9                     Kim COLLINS    0.162\n",
              "10                Craig PICKERING    0.174\n",
              "11                  Daniel GRUESO    0.178\n",
              "12                    Dariusz KUC    0.144\n",
              "13          Berenger Aymard BOSSE    0.144\n",
              "14                     Aisea TOHI    0.159\n",
              "15                    Roman CRESS    0.190\n",
              "16               Richard THOMPSON    0.188\n",
              "17              Martial MBANDJOCK    0.162\n",
              "18                  Simone COLLIO    0.140\n",
              "19                    Aziz ZAKARI    0.177\n",
              "20                   Andrew HINDS    0.140\n",
              "21             Suryo Agung WIBOWO    0.175\n",
              "22                    Jared LEWIS    0.123\n",
              "23                Rabangaki NAWAI    0.152\n",
              "24                 Michael FRATER    0.156\n",
              "25                  Pierre BROWNE    0.141\n",
              "26                   Darrel BROWN    0.139\n",
              "27               Nobuharu ASAHARA    0.160\n",
              "28                Holder DA SILVA    0.184\n",
              "29                  Idrissa SANOU    0.171\n",
              "...                           ...      ...\n",
              "2427               Lesley TASHLIN      NaN\n",
              "2428             Ludmila ENGQUIST      NaN\n",
              "2429                 Angela ATEDE      NaN\n",
              "2430        María José MARDOMINGO      NaN\n",
              "2431  Monique ÉWANJÉ-ÉPÉE TOURRET      NaN\n",
              "2432           Lena SOLLI-REIMANN      NaN\n",
              "2433           Natalya GRIGORYEVA      NaN\n",
              "2434               Sonia PAQUETTE      NaN\n",
              "2435                Julie BAUMANN      NaN\n",
              "2436                 Angela THORP      NaN\n",
              "2437          Lynda TOLBERT-GOODE      NaN\n",
              "2438             Kristin PATZWAHL      NaN\n",
              "2439         Tatyana RESHETNIKOVA      NaN\n",
              "2440                Cécile CINÉLU      NaN\n",
              "2441                Bich Huong VU      NaN\n",
              "2442               Aminata CAMARA      NaN\n",
              "2443              Brigita BUKOVEC      NaN\n",
              "2444           Dionne ROSE-HENLEY      NaN\n",
              "2445         Taiwo ALADEFA-DARDEN      NaN\n",
              "2446               Lidiya YURKOVA      NaN\n",
              "2447             Nadezhda BODROVA      NaN\n",
              "2448               Rachael ROGERS      NaN\n",
              "2449             Elisabeta ANGHEL      NaN\n",
              "2450                  Gail DEVERS      NaN\n",
              "2451         Gillian RUSSELL-LOVE      NaN\n",
              "2452               Yuliya GRAUDYN      NaN\n",
              "2453                    Ime AKPAN      NaN\n",
              "2454              Olena KRASOVSKA      NaN\n",
              "2455            Véronique LINSTER      NaN\n",
              "2456                  Carla TUZZI      NaN\n",
              "\n",
              "[2457 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "kmnUnhd205NU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "7) Columns have names ('ATHLETE', 'COUNTRY', etc.), but so do rows.  The row names are part of the so-called \"index\" of the dataframe (or series), and allow you to access data more intuitively than just by row number.  In this case though, the names in the index are the row numbers.  You can extract row 144 with `df.loc[144]`.  Note the use of the `.loc` attribute, and the brackets instead of parentheses.  Use this extract the row containing Usain Bolt's 2008 preliminary heats data (hint, it is at the beginning of the dataframe)."
      ]
    },
    {
      "metadata": {
        "id": "NS0KOfPp05NX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "98ff66b2-53f4-42c9-fb69-bbe5d9a7c629"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 7 here\n",
        "df.loc[144]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              144\n",
              "POS                       1\n",
              "BIB                     NaN\n",
              "ATHLETE       Torri EDWARDS\n",
              "COUNTRY              USAUSA\n",
              "MARK                11.26 Q\n",
              "RxnTime               0.169\n",
              "Race                  100 m\n",
              "Round                 Heats\n",
              "Gender                    F\n",
              "Year                   2008\n",
              "Name: 144, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "l15sMXci05Nd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "8) You can access specific cells of the dataframe by row and column using `.loc`, e.g. `df.loc[7,'Country']`.  You can also edit data the same way.  Maybe Usain Bolt's preliminary heats time was faster than reported, say 9.5 seconds?  Edit the dataframe with `.loc` to make that change.  Be sure to use the `=` sign or you won't actually make the change!"
      ]
    },
    {
      "metadata": {
        "id": "1WvKayQM05Ni",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "7d4907dd-4f10-408d-91c1-07e86ad9c6c9"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 8 here\n",
        "df.loc[144,\"MARK\"] = \"11.26 Q\"\n",
        "df.loc[144]"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              144\n",
              "POS                       1\n",
              "BIB                     NaN\n",
              "ATHLETE       Torri EDWARDS\n",
              "COUNTRY              USAUSA\n",
              "MARK                11.26 Q\n",
              "RxnTime               0.169\n",
              "Race                  100 m\n",
              "Round                 Heats\n",
              "Gender                    F\n",
              "Year                   2008\n",
              "Name: 144, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "QzYrIgDb05Nq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "9) I don't like the way this data is organized.  Accessing it by arbitrary row numbers is not intuitive at all.  Let's organize the data by athlete name and year instead, using the `set_index()` method.  Inside the parentheses, put a list containing the column name indicating athlete name and the column name indicating the year.  Don't forget quotes and brackets!  "
      ]
    },
    {
      "metadata": {
        "id": "hjwrcY1y05Ns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1911
        },
        "outputId": "16a8b4cb-5ff1-4239-aa19-89da10998ced"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 9 here\n",
        "df.set_index(['ATHLETE','Year'])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>POS</th>\n",
              "      <th>BIB</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>MARK</th>\n",
              "      <th>RxnTime</th>\n",
              "      <th>Race</th>\n",
              "      <th>Round</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ATHLETE</th>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Usain BOLT</th>\n",
              "      <th>2008</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.20 Q</td>\n",
              "      <td>0.186</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daniel BAILEY</th>\n",
              "      <th>2008</th>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ANTANT</td>\n",
              "      <td>10.24 Q</td>\n",
              "      <td>0.198</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vicente DE LIMA</th>\n",
              "      <th>2008</th>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BRABRA</td>\n",
              "      <td>10.26 Q</td>\n",
              "      <td>0.168</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Henry VIZCAÍNO</th>\n",
              "      <th>2008</th>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CUBCUB</td>\n",
              "      <td>10.28 q</td>\n",
              "      <td>0.157</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fabio CERUTTI</th>\n",
              "      <th>2008</th>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ITAITA</td>\n",
              "      <td>10.49</td>\n",
              "      <td>0.136</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jurgen THEMEN</th>\n",
              "      <th>2008</th>\n",
              "      <td>5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SURSUR</td>\n",
              "      <td>10.61</td>\n",
              "      <td>0.179</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Moses KAMUT</th>\n",
              "      <th>2008</th>\n",
              "      <td>6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>VANVAN</td>\n",
              "      <td>10.81</td>\n",
              "      <td>0.181</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Francis MANIORU</th>\n",
              "      <th>2008</th>\n",
              "      <td>7</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SOLSOL</td>\n",
              "      <td>11.09</td>\n",
              "      <td>0.197</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asafa POWELL</th>\n",
              "      <th>2008</th>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.16 Q</td>\n",
              "      <td>0.142</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kim COLLINS</th>\n",
              "      <th>2008</th>\n",
              "      <td>9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SKNSKN</td>\n",
              "      <td>10.17 Q</td>\n",
              "      <td>0.162</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Craig PICKERING</th>\n",
              "      <th>2008</th>\n",
              "      <td>10</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GBRGBR</td>\n",
              "      <td>10.21 Q</td>\n",
              "      <td>0.174</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daniel GRUESO</th>\n",
              "      <th>2008</th>\n",
              "      <td>11</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>COLCOL</td>\n",
              "      <td>10.35 q</td>\n",
              "      <td>0.178</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dariusz KUC</th>\n",
              "      <th>2008</th>\n",
              "      <td>12</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POLPOL</td>\n",
              "      <td>10.44 q</td>\n",
              "      <td>0.144</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Berenger Aymard BOSSE</th>\n",
              "      <th>2008</th>\n",
              "      <td>13</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CAFCAF</td>\n",
              "      <td>10.51</td>\n",
              "      <td>0.144</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aisea TOHI</th>\n",
              "      <th>2008</th>\n",
              "      <td>14</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TGATGA</td>\n",
              "      <td>11.17</td>\n",
              "      <td>0.159</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roman CRESS</th>\n",
              "      <th>2008</th>\n",
              "      <td>15</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MHLMHL</td>\n",
              "      <td>11.18</td>\n",
              "      <td>0.190</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Richard THOMPSON</th>\n",
              "      <th>2008</th>\n",
              "      <td>16</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TTOTTO</td>\n",
              "      <td>10.24 Q</td>\n",
              "      <td>0.188</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Martial MBANDJOCK</th>\n",
              "      <th>2008</th>\n",
              "      <td>17</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FRAFRA</td>\n",
              "      <td>10.26 Q</td>\n",
              "      <td>0.162</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Simone COLLIO</th>\n",
              "      <th>2008</th>\n",
              "      <td>18</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ITAITA</td>\n",
              "      <td>10.32 Q</td>\n",
              "      <td>0.140</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aziz ZAKARI</th>\n",
              "      <th>2008</th>\n",
              "      <td>19</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GHAGHA</td>\n",
              "      <td>10.34 q</td>\n",
              "      <td>0.177</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andrew HINDS</th>\n",
              "      <th>2008</th>\n",
              "      <td>20</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BARBAR</td>\n",
              "      <td>10.35 q</td>\n",
              "      <td>0.140</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suryo Agung WIBOWO</th>\n",
              "      <th>2008</th>\n",
              "      <td>21</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>INAINA</td>\n",
              "      <td>10.46</td>\n",
              "      <td>0.175</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jared LEWIS</th>\n",
              "      <th>2008</th>\n",
              "      <td>22</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>VINVIN</td>\n",
              "      <td>11</td>\n",
              "      <td>0.123</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rabangaki NAWAI</th>\n",
              "      <th>2008</th>\n",
              "      <td>23</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KIRKIR</td>\n",
              "      <td>11.29</td>\n",
              "      <td>0.152</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Michael FRATER</th>\n",
              "      <th>2008</th>\n",
              "      <td>24</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.15 Q</td>\n",
              "      <td>0.156</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pierre BROWNE</th>\n",
              "      <th>2008</th>\n",
              "      <td>25</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>10.22 Q</td>\n",
              "      <td>0.141</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Darrel BROWN</th>\n",
              "      <th>2008</th>\n",
              "      <td>26</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TTOTTO</td>\n",
              "      <td>10.22 Q</td>\n",
              "      <td>0.139</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nobuharu ASAHARA</th>\n",
              "      <th>2008</th>\n",
              "      <td>27</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JPNJPN</td>\n",
              "      <td>10.25 q</td>\n",
              "      <td>0.160</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Holder DA SILVA</th>\n",
              "      <th>2008</th>\n",
              "      <td>28</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GBSGBS</td>\n",
              "      <td>10.58</td>\n",
              "      <td>0.184</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Idrissa SANOU</th>\n",
              "      <th>2008</th>\n",
              "      <td>29</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BURBUR</td>\n",
              "      <td>10.63</td>\n",
              "      <td>0.171</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lesley TASHLIN</th>\n",
              "      <th>1996</th>\n",
              "      <td>2427</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>13.61</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ludmila ENGQUIST</th>\n",
              "      <th>1996</th>\n",
              "      <td>2428</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SWESWE</td>\n",
              "      <td>12.66</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angela ATEDE</th>\n",
              "      <th>1996</th>\n",
              "      <td>2429</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NGRNGR</td>\n",
              "      <td>12.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>María José MARDOMINGO</th>\n",
              "      <th>1996</th>\n",
              "      <td>2430</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ESPESP</td>\n",
              "      <td>12.91</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monique ÉWANJÉ-ÉPÉE TOURRET</th>\n",
              "      <th>1996</th>\n",
              "      <td>2431</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FRAFRA</td>\n",
              "      <td>13.12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lena SOLLI-REIMANN</th>\n",
              "      <th>1996</th>\n",
              "      <td>2432</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NORNOR</td>\n",
              "      <td>13.13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Natalya GRIGORYEVA</th>\n",
              "      <th>1996</th>\n",
              "      <td>2433</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UKRUKR</td>\n",
              "      <td>13.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sonia PAQUETTE</th>\n",
              "      <th>1996</th>\n",
              "      <td>2434</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>13.29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Julie BAUMANN</th>\n",
              "      <th>1996</th>\n",
              "      <td>2435</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SUISUI</td>\n",
              "      <td>12.86</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angela THORP</th>\n",
              "      <th>1996</th>\n",
              "      <td>2436</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GBRGBR</td>\n",
              "      <td>12.93</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lynda TOLBERT-GOODE</th>\n",
              "      <th>1996</th>\n",
              "      <td>2437</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>12.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kristin PATZWAHL</th>\n",
              "      <th>1996</th>\n",
              "      <td>2438</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GERGER</td>\n",
              "      <td>12.98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tatyana RESHETNIKOVA</th>\n",
              "      <th>1996</th>\n",
              "      <td>2439</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RUSRUS</td>\n",
              "      <td>13.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cécile CINÉLU</th>\n",
              "      <th>1996</th>\n",
              "      <td>2440</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FRAFRA</td>\n",
              "      <td>13.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bich Huong VU</th>\n",
              "      <th>1996</th>\n",
              "      <td>2441</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>VIEVIE</td>\n",
              "      <td>13.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aminata CAMARA</th>\n",
              "      <th>1996</th>\n",
              "      <td>2442</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MLIMLI</td>\n",
              "      <td>14.94</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brigita BUKOVEC</th>\n",
              "      <th>1996</th>\n",
              "      <td>2443</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SLOSLO</td>\n",
              "      <td>12.72</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dionne ROSE-HENLEY</th>\n",
              "      <th>1996</th>\n",
              "      <td>2444</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>12.81</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Taiwo ALADEFA-DARDEN</th>\n",
              "      <th>1996</th>\n",
              "      <td>2445</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NGRNGR</td>\n",
              "      <td>13.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lidiya YURKOVA</th>\n",
              "      <th>1996</th>\n",
              "      <td>2446</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BLRBLR</td>\n",
              "      <td>13.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nadezhda BODROVA</th>\n",
              "      <th>1996</th>\n",
              "      <td>2447</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UKRUKR</td>\n",
              "      <td>13.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rachael ROGERS</th>\n",
              "      <th>1996</th>\n",
              "      <td>2448</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FIJFIJ</td>\n",
              "      <td>14.07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Elisabeta ANGHEL</th>\n",
              "      <th>1996</th>\n",
              "      <td>2449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ROUROU</td>\n",
              "      <td>DNF</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gail DEVERS</th>\n",
              "      <th>1996</th>\n",
              "      <td>2450</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>12.73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gillian RUSSELL-LOVE</th>\n",
              "      <th>1996</th>\n",
              "      <td>2451</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>12.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yuliya GRAUDYN</th>\n",
              "      <th>1996</th>\n",
              "      <td>2452</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RUSRUS</td>\n",
              "      <td>12.95</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ime AKPAN</th>\n",
              "      <th>1996</th>\n",
              "      <td>2453</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NGRNGR</td>\n",
              "      <td>13.11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Olena KRASOVSKA</th>\n",
              "      <th>1996</th>\n",
              "      <td>2454</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UKRUKR</td>\n",
              "      <td>13.23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Véronique LINSTER</th>\n",
              "      <th>1996</th>\n",
              "      <td>2455</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LUXLUX</td>\n",
              "      <td>13.47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carla TUZZI</th>\n",
              "      <th>1996</th>\n",
              "      <td>2456</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ITAITA</td>\n",
              "      <td>DNF</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2457 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  Unnamed: 0  POS  BIB COUNTRY     MARK  \\\n",
              "ATHLETE                     Year                                          \n",
              "Usain BOLT                  2008           0  1.0  NaN  JAMJAM  10.20 Q   \n",
              "Daniel BAILEY               2008           1  2.0  NaN  ANTANT  10.24 Q   \n",
              "Vicente DE LIMA             2008           2  3.0  NaN  BRABRA  10.26 Q   \n",
              "Henry VIZCAÍNO              2008           3  4.0  NaN  CUBCUB  10.28 q   \n",
              "Fabio CERUTTI               2008           4  5.0  NaN  ITAITA    10.49   \n",
              "Jurgen THEMEN               2008           5  6.0  NaN  SURSUR    10.61   \n",
              "Moses KAMUT                 2008           6  7.0  NaN  VANVAN    10.81   \n",
              "Francis MANIORU             2008           7  8.0  NaN  SOLSOL    11.09   \n",
              "Asafa POWELL                2008           8  1.0  NaN  JAMJAM  10.16 Q   \n",
              "Kim COLLINS                 2008           9  2.0  NaN  SKNSKN  10.17 Q   \n",
              "Craig PICKERING             2008          10  3.0  NaN  GBRGBR  10.21 Q   \n",
              "Daniel GRUESO               2008          11  4.0  NaN  COLCOL  10.35 q   \n",
              "Dariusz KUC                 2008          12  5.0  NaN  POLPOL  10.44 q   \n",
              "Berenger Aymard BOSSE       2008          13  6.0  NaN  CAFCAF    10.51   \n",
              "Aisea TOHI                  2008          14  7.0  NaN  TGATGA    11.17   \n",
              "Roman CRESS                 2008          15  8.0  NaN  MHLMHL    11.18   \n",
              "Richard THOMPSON            2008          16  1.0  NaN  TTOTTO  10.24 Q   \n",
              "Martial MBANDJOCK           2008          17  2.0  NaN  FRAFRA  10.26 Q   \n",
              "Simone COLLIO               2008          18  3.0  NaN  ITAITA  10.32 Q   \n",
              "Aziz ZAKARI                 2008          19  4.0  NaN  GHAGHA  10.34 q   \n",
              "Andrew HINDS                2008          20  5.0  NaN  BARBAR  10.35 q   \n",
              "Suryo Agung WIBOWO          2008          21  6.0  NaN  INAINA    10.46   \n",
              "Jared LEWIS                 2008          22  7.0  NaN  VINVIN       11   \n",
              "Rabangaki NAWAI             2008          23  8.0  NaN  KIRKIR    11.29   \n",
              "Michael FRATER              2008          24  1.0  NaN  JAMJAM  10.15 Q   \n",
              "Pierre BROWNE               2008          25  2.0  NaN  CANCAN  10.22 Q   \n",
              "Darrel BROWN                2008          26  3.0  NaN  TTOTTO  10.22 Q   \n",
              "Nobuharu ASAHARA            2008          27  4.0  NaN  JPNJPN  10.25 q   \n",
              "Holder DA SILVA             2008          28  5.0  NaN  GBSGBS    10.58   \n",
              "Idrissa SANOU               2008          29  6.0  NaN  BURBUR    10.63   \n",
              "...                                      ...  ...  ...     ...      ...   \n",
              "Lesley TASHLIN              1996        2427  7.0  NaN  CANCAN    13.61   \n",
              "Ludmila ENGQUIST            1996        2428  1.0  NaN  SWESWE    12.66   \n",
              "Angela ATEDE                1996        2429  2.0  NaN  NGRNGR    12.88   \n",
              "María José MARDOMINGO       1996        2430  3.0  NaN  ESPESP    12.91   \n",
              "Monique ÉWANJÉ-ÉPÉE TOURRET 1996        2431  4.0  NaN  FRAFRA    13.12   \n",
              "Lena SOLLI-REIMANN          1996        2432  5.0  NaN  NORNOR    13.13   \n",
              "Natalya GRIGORYEVA          1996        2433  6.0  NaN  UKRUKR    13.16   \n",
              "Sonia PAQUETTE              1996        2434  7.0  NaN  CANCAN    13.29   \n",
              "Julie BAUMANN               1996        2435  1.0  NaN  SUISUI    12.86   \n",
              "Angela THORP                1996        2436  2.0  NaN  GBRGBR    12.93   \n",
              "Lynda TOLBERT-GOODE         1996        2437  3.0  NaN  USAUSA    12.97   \n",
              "Kristin PATZWAHL            1996        2438  4.0  NaN  GERGER    12.98   \n",
              "Tatyana RESHETNIKOVA        1996        2439  5.0  NaN  RUSRUS    13.01   \n",
              "Cécile CINÉLU               1996        2440  6.0  NaN  FRAFRA    13.05   \n",
              "Bich Huong VU               1996        2441  7.0  NaN  VIEVIE    13.85   \n",
              "Aminata CAMARA              1996        2442  8.0  NaN  MLIMLI    14.94   \n",
              "Brigita BUKOVEC             1996        2443  1.0  NaN  SLOSLO    12.72   \n",
              "Dionne ROSE-HENLEY          1996        2444  2.0  NaN  JAMJAM    12.81   \n",
              "Taiwo ALADEFA-DARDEN        1996        2445  3.0  NaN  NGRNGR    13.06   \n",
              "Lidiya YURKOVA              1996        2446  4.0  NaN  BLRBLR     13.2   \n",
              "Nadezhda BODROVA            1996        2447  5.0  NaN  UKRUKR    13.22   \n",
              "Rachael ROGERS              1996        2448  6.0  NaN  FIJFIJ    14.07   \n",
              "Elisabeta ANGHEL            1996        2449  NaN  NaN  ROUROU      DNF   \n",
              "Gail DEVERS                 1996        2450  1.0  NaN  USAUSA    12.73   \n",
              "Gillian RUSSELL-LOVE        1996        2451  2.0  NaN  JAMJAM    12.85   \n",
              "Yuliya GRAUDYN              1996        2452  3.0  NaN  RUSRUS    12.95   \n",
              "Ime AKPAN                   1996        2453  4.0  NaN  NGRNGR    13.11   \n",
              "Olena KRASOVSKA             1996        2454  5.0  NaN  UKRUKR    13.23   \n",
              "Véronique LINSTER           1996        2455  6.0  NaN  LUXLUX    13.47   \n",
              "Carla TUZZI                 1996        2456  NaN  NaN  ITAITA      DNF   \n",
              "\n",
              "                                  RxnTime     Race  Round Gender  \n",
              "ATHLETE                     Year                                  \n",
              "Usain BOLT                  2008    0.186    100 m  Heats      M  \n",
              "Daniel BAILEY               2008    0.198    100 m  Heats      M  \n",
              "Vicente DE LIMA             2008    0.168    100 m  Heats      M  \n",
              "Henry VIZCAÍNO              2008    0.157    100 m  Heats      M  \n",
              "Fabio CERUTTI               2008    0.136    100 m  Heats      M  \n",
              "Jurgen THEMEN               2008    0.179    100 m  Heats      M  \n",
              "Moses KAMUT                 2008    0.181    100 m  Heats      M  \n",
              "Francis MANIORU             2008    0.197    100 m  Heats      M  \n",
              "Asafa POWELL                2008    0.142    100 m  Heats      M  \n",
              "Kim COLLINS                 2008    0.162    100 m  Heats      M  \n",
              "Craig PICKERING             2008    0.174    100 m  Heats      M  \n",
              "Daniel GRUESO               2008    0.178    100 m  Heats      M  \n",
              "Dariusz KUC                 2008    0.144    100 m  Heats      M  \n",
              "Berenger Aymard BOSSE       2008    0.144    100 m  Heats      M  \n",
              "Aisea TOHI                  2008    0.159    100 m  Heats      M  \n",
              "Roman CRESS                 2008    0.190    100 m  Heats      M  \n",
              "Richard THOMPSON            2008    0.188    100 m  Heats      M  \n",
              "Martial MBANDJOCK           2008    0.162    100 m  Heats      M  \n",
              "Simone COLLIO               2008    0.140    100 m  Heats      M  \n",
              "Aziz ZAKARI                 2008    0.177    100 m  Heats      M  \n",
              "Andrew HINDS                2008    0.140    100 m  Heats      M  \n",
              "Suryo Agung WIBOWO          2008    0.175    100 m  Heats      M  \n",
              "Jared LEWIS                 2008    0.123    100 m  Heats      M  \n",
              "Rabangaki NAWAI             2008    0.152    100 m  Heats      M  \n",
              "Michael FRATER              2008    0.156    100 m  Heats      M  \n",
              "Pierre BROWNE               2008    0.141    100 m  Heats      M  \n",
              "Darrel BROWN                2008    0.139    100 m  Heats      M  \n",
              "Nobuharu ASAHARA            2008    0.160    100 m  Heats      M  \n",
              "Holder DA SILVA             2008    0.184    100 m  Heats      M  \n",
              "Idrissa SANOU               2008    0.171    100 m  Heats      M  \n",
              "...                                   ...      ...    ...    ...  \n",
              "Lesley TASHLIN              1996      NaN  100 m H  Heats      F  \n",
              "Ludmila ENGQUIST            1996      NaN  100 m H  Heats      F  \n",
              "Angela ATEDE                1996      NaN  100 m H  Heats      F  \n",
              "María José MARDOMINGO       1996      NaN  100 m H  Heats      F  \n",
              "Monique ÉWANJÉ-ÉPÉE TOURRET 1996      NaN  100 m H  Heats      F  \n",
              "Lena SOLLI-REIMANN          1996      NaN  100 m H  Heats      F  \n",
              "Natalya GRIGORYEVA          1996      NaN  100 m H  Heats      F  \n",
              "Sonia PAQUETTE              1996      NaN  100 m H  Heats      F  \n",
              "Julie BAUMANN               1996      NaN  100 m H  Heats      F  \n",
              "Angela THORP                1996      NaN  100 m H  Heats      F  \n",
              "Lynda TOLBERT-GOODE         1996      NaN  100 m H  Heats      F  \n",
              "Kristin PATZWAHL            1996      NaN  100 m H  Heats      F  \n",
              "Tatyana RESHETNIKOVA        1996      NaN  100 m H  Heats      F  \n",
              "Cécile CINÉLU               1996      NaN  100 m H  Heats      F  \n",
              "Bich Huong VU               1996      NaN  100 m H  Heats      F  \n",
              "Aminata CAMARA              1996      NaN  100 m H  Heats      F  \n",
              "Brigita BUKOVEC             1996      NaN  100 m H  Heats      F  \n",
              "Dionne ROSE-HENLEY          1996      NaN  100 m H  Heats      F  \n",
              "Taiwo ALADEFA-DARDEN        1996      NaN  100 m H  Heats      F  \n",
              "Lidiya YURKOVA              1996      NaN  100 m H  Heats      F  \n",
              "Nadezhda BODROVA            1996      NaN  100 m H  Heats      F  \n",
              "Rachael ROGERS              1996      NaN  100 m H  Heats      F  \n",
              "Elisabeta ANGHEL            1996      NaN  100 m H  Heats      F  \n",
              "Gail DEVERS                 1996      NaN  100 m H  Heats      F  \n",
              "Gillian RUSSELL-LOVE        1996      NaN  100 m H  Heats      F  \n",
              "Yuliya GRAUDYN              1996      NaN  100 m H  Heats      F  \n",
              "Ime AKPAN                   1996      NaN  100 m H  Heats      F  \n",
              "Olena KRASOVSKA             1996      NaN  100 m H  Heats      F  \n",
              "Véronique LINSTER           1996      NaN  100 m H  Heats      F  \n",
              "Carla TUZZI                 1996      NaN  100 m H  Heats      F  \n",
              "\n",
              "[2457 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "6SD08Ot705N2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "10) That's OK, but it doesn't look much nicer.  Maybe if it the index was sorted?  If you've ever done a hierarchical sort in Excel, this will be similar.  Try the same command as above, but append `.sort_index()`  to it"
      ]
    },
    {
      "metadata": {
        "id": "qoEQQNUn05N8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 10 here\n",
        "df = df.set_index(['ATHLETE','Year']).sort_index()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2XEXxO_G05OC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "11) Display the dataframe again by entering `df` on an empty line (e.g. in the code cell below, or create a new code cell).  Does it reflect the changes you made above?  If not, it's because `.set_index()`, `.sort_index()` (and most other methods) only return modified copies of your data; they don't overwrite it.  If you want to work with that modified copy, set the original dataframe equal to it, i.e. `df = ...` where `...` would contain the command you used above.  "
      ]
    },
    {
      "metadata": {
        "id": "_nkTz2u605OH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "35441754-2d0a-47db-e96c-944fa8834752"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 11 here\n",
        "df.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>POS</th>\n",
              "      <th>BIB</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>MARK</th>\n",
              "      <th>RxnTime</th>\n",
              "      <th>Race</th>\n",
              "      <th>Round</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ATHLETE</th>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aaron BROWN</th>\n",
              "      <th>2016</th>\n",
              "      <td>1147</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2192.0</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>10.24</td>\n",
              "      <td>0.135</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdul GHAFOOR</th>\n",
              "      <th>1996</th>\n",
              "      <td>2257</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AFGAFG</td>\n",
              "      <td>12.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdul RASHID</th>\n",
              "      <th>2008</th>\n",
              "      <td>378</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PAKPAK</td>\n",
              "      <td>14.52</td>\n",
              "      <td>0.172</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdul Wahab ZAHIRI</th>\n",
              "      <th>2016</th>\n",
              "      <td>1068</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>AFGAFG</td>\n",
              "      <td>11.56</td>\n",
              "      <td>0.170</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Preliminary</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdullah AL-SOOLI</th>\n",
              "      <th>2008</th>\n",
              "      <td>44</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OMAOMA</td>\n",
              "      <td>10.53</td>\n",
              "      <td>0.153</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Unnamed: 0  POS     BIB COUNTRY   MARK  RxnTime  \\\n",
              "ATHLETE            Year                                                    \n",
              "Aaron BROWN        2016        1147  3.0  2192.0  CANCAN  10.24    0.135   \n",
              "Abdul GHAFOOR      1996        2257  8.0     NaN  AFGAFG   12.2      NaN   \n",
              "Abdul RASHID       2008         378  8.0     NaN  PAKPAK  14.52    0.172   \n",
              "Abdul Wahab ZAHIRI 2016        1068  7.0  2000.0  AFGAFG  11.56    0.170   \n",
              "Abdullah AL-SOOLI  2008          44  5.0     NaN  OMAOMA  10.53    0.153   \n",
              "\n",
              "                            Race        Round Gender  \n",
              "ATHLETE            Year                               \n",
              "Aaron BROWN        2016    100 m        Heats      M  \n",
              "Abdul GHAFOOR      1996    100 m        Heats      M  \n",
              "Abdul RASHID       2008  110 m H        Heats      M  \n",
              "Abdul Wahab ZAHIRI 2016    100 m  Preliminary      M  \n",
              "Abdullah AL-SOOLI  2008    100 m        Heats      M  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "sQDjdVTm05OO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now that `df` has been changed, some of the commands in the earlier questions won't work the same.  So you won't be able to access e.g. `df.loc[7]` anymore, because there is no longer a row named 7!  This is why you should always execute notebooks from top to bottom.  Each notebook cell can change the data in a way that a cell above it doesn't know about.  "
      ]
    },
    {
      "metadata": {
        "id": "iN_Ix2Zx05OQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "12) But now we have an intuitive index for the data frame.  Instead of accessing something like `df.loc[7]` we can access by name and year (using the same `.loc` attribute).  First just try accessing Usain Bolt's data by name (note that his last name is all capital letters in this dataset).  Note that it will contain results from several races across several years.  "
      ]
    },
    {
      "metadata": {
        "id": "6NqO5ydc05OT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "16b6458a-5b2a-40e6-9e7f-a7d648d64b10"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 12 here\n",
        "df.loc['Usain BOLT']"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>POS</th>\n",
              "      <th>BIB</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>MARK</th>\n",
              "      <th>RxnTime</th>\n",
              "      <th>Race</th>\n",
              "      <th>Round</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.20 Q</td>\n",
              "      <td>0.186</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>104</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.92 Q</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Quarter</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>120</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.85 Q</td>\n",
              "      <td>0.161</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Semi</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>136</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>630</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2170.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.09 Q</td>\n",
              "      <td>0.178</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>670</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2170.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.87 Q</td>\n",
              "      <td>0.180</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Semi</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>686</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2170.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.63</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1136</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2612.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.07 Q</td>\n",
              "      <td>0.156</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1162</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2612.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.86 Q</td>\n",
              "      <td>0.143</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Semi</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1178</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2612.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.81</td>\n",
              "      <td>0.155</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  POS     BIB COUNTRY     MARK  RxnTime   Race    Round Gender\n",
              "Year                                                                          \n",
              "2008           0  1.0     NaN  JAMJAM  10.20 Q    0.186  100 m    Heats      M\n",
              "2008         104  1.0     NaN  JAMJAM   9.92 Q    0.165  100 m  Quarter      M\n",
              "2008         120  1.0     NaN  JAMJAM   9.85 Q    0.161  100 m     Semi      M\n",
              "2008         136  1.0     NaN  JAMJAM     9.69    0.165  100 m    Final      M\n",
              "2012         630  1.0  2170.0  JAMJAM  10.09 Q    0.178  100 m    Heats      M\n",
              "2012         670  1.0  2170.0  JAMJAM   9.87 Q    0.180  100 m     Semi      M\n",
              "2012         686  1.0  2170.0  JAMJAM     9.63    0.165  100 m    Final      M\n",
              "2016        1136  1.0  2612.0  JAMJAM  10.07 Q    0.156  100 m    Heats      M\n",
              "2016        1162  1.0  2612.0  JAMJAM   9.86 Q    0.143  100 m     Semi      M\n",
              "2016        1178  1.0  2612.0  JAMJAM     9.81    0.155  100 m    Final      M"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "luDzsvXh05Ob",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "13) You can further restrict the data to a single year by using the `.loc` attribute again (\"chaining\" operations together), and now including a specific year.  Access Usain Bolt's data for 2008.  Hint: it will look just like the answer above, but with an extra `.loc` appended on.  "
      ]
    },
    {
      "metadata": {
        "id": "xcKorPEo05Od",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "aa865311-3b9c-4ed1-bd2a-50e0684c0fdb"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 13 here\n",
        "df.loc['Usain BOLT'].loc[2008]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>POS</th>\n",
              "      <th>BIB</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>MARK</th>\n",
              "      <th>RxnTime</th>\n",
              "      <th>Race</th>\n",
              "      <th>Round</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.20 Q</td>\n",
              "      <td>0.186</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>104</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.92 Q</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Quarter</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>120</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.85 Q</td>\n",
              "      <td>0.161</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Semi</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>136</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  POS  BIB COUNTRY     MARK  RxnTime   Race    Round Gender\n",
              "Year                                                                       \n",
              "2008           0  1.0  NaN  JAMJAM  10.20 Q    0.186  100 m    Heats      M\n",
              "2008         104  1.0  NaN  JAMJAM   9.92 Q    0.165  100 m  Quarter      M\n",
              "2008         120  1.0  NaN  JAMJAM   9.85 Q    0.161  100 m     Semi      M\n",
              "2008         136  1.0  NaN  JAMJAM     9.69    0.165  100 m    Final      M"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "id": "ibtXVHra05Ol",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "14) How fast does Usain Bolt react to the starting gun?  Find his minimum Olympic sprint reaction time by acessing his data using `.loc`, accessing reaction time by column name, and then computing the minimum.  Hint: the *maximum* is obtained with the `.max()` method.  "
      ]
    },
    {
      "metadata": {
        "id": "x_GZCCGj05Op",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4f51eaf1-b86d-4a07-872f-868b23239aff"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 14 here\n",
        "df.loc['Usain BOLT']['RxnTime'].min()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14300000000000002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "5_oYMuah05Oz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "15) Usain Bolt was the fastest 100m sprinter of all time, but he didn't try his hardest until the finals of each meet (he didn't need to).  Let's extract only his data from the finals.  This takes only one step, but we'll do it in two to make the idea clear.  First, let's generate a series that tells us whether it is True or False that a given race was a final.  Just paste `df['Round'] == 'Final'` in the cell below.  You will see a series containig some `True` values and some `False` values (mostly the latter), reflecting rows containing data from finals vs. other rounds.  Note the ellipsis halfway down, which represents all the data (hundreds of rows) it isn't showing you to save space.  "
      ]
    },
    {
      "metadata": {
        "id": "AqU1XdjZ05O3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1075
        },
        "outputId": "861273d5-6111-48ce-8289-1f483099e38e"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 15 here\n",
        "df['Round']=='Final'"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ATHLETE                    Year\n",
              "Aaron BROWN                2016    False\n",
              "Abdul GHAFOOR              1996    False\n",
              "Abdul RASHID               2008    False\n",
              "Abdul Wahab ZAHIRI         2016    False\n",
              "Abdullah AL-SOOLI          2008    False\n",
              "Abdullah Abkar MOHAMMED    2016    False\n",
              "Abiodun OYEPITAN           2004    False\n",
              "                           2004    False\n",
              "                           2004    False\n",
              "                           2012    False\n",
              "                           2012    False\n",
              "Abraham KEPSIN             2000    False\n",
              "Adam GEMILI                2012    False\n",
              "                           2012    False\n",
              "Adrian DURANT              2004    False\n",
              "Adrian GRIFFITH            2016    False\n",
              "Adrian WOODLEY             2000    False\n",
              "                           2000    False\n",
              "Adrien DEGHELT             2012    False\n",
              "                           2012    False\n",
              "Afa ISMAIL                 2012    False\n",
              "Affoué Amandine ALLOU      2004    False\n",
              "                           2008    False\n",
              "Agné EGGERTH               2000    False\n",
              "                           2004    False\n",
              "Ahmad HAZER                2012    False\n",
              "                           2016    False\n",
              "Ahmed DOUHOU               1996    False\n",
              "Aisea TOHI                 2008    False\n",
              "Aissata TOURÉ              2012    False\n",
              "                                   ...  \n",
              "Yuniel HERNÁNDEZ           2004    False\n",
              "Yuriy ARISTOV              1996    False\n",
              "Yvette LEWIS               2016    False\n",
              "Yvonne KANAZAWA            1996    False\n",
              "                           2000    False\n",
              "                           2000    False\n",
              "                           2000    False\n",
              "Zaidatul Husniah ZULKIFLI  2016    False\n",
              "                           2016    False\n",
              "Zhanna BLOCK               1996    False\n",
              "                           2000    False\n",
              "                           2000    False\n",
              "                           2000    False\n",
              "                           2000     True\n",
              "                           2004    False\n",
              "                           2004    False\n",
              "                           2004    False\n",
              "Zhenye XIE                 2016    False\n",
              "                           2016    False\n",
              "Zhivko VIDENOV             2000    False\n",
              "Zlatka GEORGIEVA           1996    False\n",
              "Ággelos PAVLAKÁKIS         2000    False\n",
              "Ángel David RODRÍGUEZ      2008    False\n",
              "                           2008    False\n",
              "                           2012    False\n",
              "Ángela TENORIO             2016    False\n",
              "                           2016    False\n",
              "Éder Antonio SOUZA         2016    False\n",
              "                           2016    False\n",
              "Édson RIBEIRO              1996    False\n",
              "Name: Round, Length: 2457, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "id": "sDpzYp2A05O_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "16) The expression above can be used as a \"selector\", to select data from the dataframe which matches the desired condition (i.e. only 200 m races).  Place it inside brackets (i.e. inside `df[]`) to access only the 200 m races from the original data.  "
      ]
    },
    {
      "metadata": {
        "id": "aYQsuh8i05PB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1911
        },
        "outputId": "ef2b8462-7a5b-46a6-c14e-b484ab731384"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 16 here\n",
        "df[df['Round']=='Final']"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>POS</th>\n",
              "      <th>BIB</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>MARK</th>\n",
              "      <th>RxnTime</th>\n",
              "      <th>Race</th>\n",
              "      <th>Round</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ATHLETE</th>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Akani SIMBINE</th>\n",
              "      <th>2016</th>\n",
              "      <td>1182</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2909.0</td>\n",
              "      <td>RSARSA</td>\n",
              "      <td>9.94</td>\n",
              "      <td>0.128</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aleen BAILEY</th>\n",
              "      <th>2004</th>\n",
              "      <td>1374</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>11.05</td>\n",
              "      <td>0.208</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aliuska LÓPEZ</th>\n",
              "      <th>2000</th>\n",
              "      <td>2181</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CUBCUB</td>\n",
              "      <td>12.83</td>\n",
              "      <td>0.179</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allen JOHNSON</th>\n",
              "      <th>2000</th>\n",
              "      <td>2094</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>13.23</td>\n",
              "      <td>0.182</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allyson FELIX</th>\n",
              "      <th>2012</th>\n",
              "      <td>573</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3287.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>10.89</td>\n",
              "      <td>0.176</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andre DE GRASSE</th>\n",
              "      <th>2016</th>\n",
              "      <td>1180</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2196.0</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>9.91</td>\n",
              "      <td>0.141</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angela WHYTE</th>\n",
              "      <th>2004</th>\n",
              "      <td>1585</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>12.81</td>\n",
              "      <td>0.155</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Anier GARCÍA</th>\n",
              "      <th>2000</th>\n",
              "      <td>2091</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CUBCUB</td>\n",
              "      <td>13</td>\n",
              "      <td>0.172</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>1686</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CUBCUB</td>\n",
              "      <td>13.2</td>\n",
              "      <td>0.169</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aries MERRITT</th>\n",
              "      <th>2012</th>\n",
              "      <td>854</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3236.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>12.92</td>\n",
              "      <td>0.143</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Artur NOGA</th>\n",
              "      <th>2008</th>\n",
              "      <td>452</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POLPOL</td>\n",
              "      <td>13.36</td>\n",
              "      <td>0.169</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Asafa POWELL</th>\n",
              "      <th>2004</th>\n",
              "      <td>1522</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.94</td>\n",
              "      <td>0.166</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>140</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.95</td>\n",
              "      <td>0.134</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>692</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2184.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>11.99</td>\n",
              "      <td>0.155</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ato BOLDON</th>\n",
              "      <th>2000</th>\n",
              "      <td>1850</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TTOTTO</td>\n",
              "      <td>9.99</td>\n",
              "      <td>0.136</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Aziz ZAKARI</th>\n",
              "      <th>2000</th>\n",
              "      <td>1856</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GHAGHA</td>\n",
              "      <td>DNF</td>\n",
              "      <td>0.180</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>1525</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GHAGHA</td>\n",
              "      <td>DNF</td>\n",
              "      <td>0.178</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beate SCHROTT</th>\n",
              "      <th>2012</th>\n",
              "      <td>774</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1093.0</td>\n",
              "      <td>AUTAUT</td>\n",
              "      <td>13.07</td>\n",
              "      <td>0.156</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ben Youssef MEITÉ</th>\n",
              "      <th>2016</th>\n",
              "      <td>1183</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2245.0</td>\n",
              "      <td>CIVCIV</td>\n",
              "      <td>9.96</td>\n",
              "      <td>0.156</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Blessing OKAGBARE</th>\n",
              "      <th>2012</th>\n",
              "      <td>576</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2584.0</td>\n",
              "      <td>NGRNGR</td>\n",
              "      <td>11.01</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brianna ROLLINS</th>\n",
              "      <th>2016</th>\n",
              "      <td>862</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1369.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>12.48</td>\n",
              "      <td>0.158</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Brigitte FOSTER-HYLTON</th>\n",
              "      <th>2000</th>\n",
              "      <td>2184</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>13.49</td>\n",
              "      <td>0.217</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>354</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>12.66</td>\n",
              "      <td>0.167</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Carmelita JETER</th>\n",
              "      <th>2012</th>\n",
              "      <td>570</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3299.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>10.78</td>\n",
              "      <td>0.153</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chandra STURRUP</th>\n",
              "      <th>2000</th>\n",
              "      <td>1992</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BAHBAH</td>\n",
              "      <td>11.21</td>\n",
              "      <td>0.193</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charles ALLEN</th>\n",
              "      <th>2004</th>\n",
              "      <td>1689</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>13.48</td>\n",
              "      <td>0.139</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Christania WILLIAMS</th>\n",
              "      <th>2016</th>\n",
              "      <td>1061</td>\n",
              "      <td>8.0</td>\n",
              "      <td>905.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>11.8</td>\n",
              "      <td>0.163</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Churandy MARTINA</th>\n",
              "      <th>2008</th>\n",
              "      <td>139</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AHOAHO</td>\n",
              "      <td>9.93</td>\n",
              "      <td>0.169</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>690</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2547.0</td>\n",
              "      <td>NEDNED</td>\n",
              "      <td>9.94</td>\n",
              "      <td>0.139</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cindy OFILI</th>\n",
              "      <th>2016</th>\n",
              "      <td>865</td>\n",
              "      <td>4.0</td>\n",
              "      <td>713.0</td>\n",
              "      <td>GBR</td>\n",
              "      <td>12.63</td>\n",
              "      <td>0.161</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sally PEARSON</th>\n",
              "      <th>2012</th>\n",
              "      <td>768</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1079.0</td>\n",
              "      <td>AUSAUS</td>\n",
              "      <td>12.35</td>\n",
              "      <td>0.137</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sarah CLAXTON</th>\n",
              "      <th>2008</th>\n",
              "      <td>356</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GBRGBR</td>\n",
              "      <td>12.94</td>\n",
              "      <td>0.163</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savatheda FYNES</th>\n",
              "      <th>2000</th>\n",
              "      <td>1993</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BAHBAH</td>\n",
              "      <td>11.22</td>\n",
              "      <td>0.253</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shawn CRAWFORD</th>\n",
              "      <th>2004</th>\n",
              "      <td>1521</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>9.89</td>\n",
              "      <td>0.161</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Shelly-Ann FRASER-PRYCE</th>\n",
              "      <th>2008</th>\n",
              "      <td>285</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.78</td>\n",
              "      <td>0.190</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>569</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2197.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.75</td>\n",
              "      <td>0.153</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1056</td>\n",
              "      <td>3.0</td>\n",
              "      <td>887.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.86</td>\n",
              "      <td>0.138</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sherone SIMPSON</th>\n",
              "      <th>2004</th>\n",
              "      <td>1375</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>11.07</td>\n",
              "      <td>0.164</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>286</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.98</td>\n",
              "      <td>0.155</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stanislavs OLIJARS</th>\n",
              "      <th>2004</th>\n",
              "      <td>1688</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LATLAT</td>\n",
              "      <td>13.21</td>\n",
              "      <td>0.174</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tayna LAWRENCE</th>\n",
              "      <th>2000</th>\n",
              "      <td>1989</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>11.18</td>\n",
              "      <td>0.163</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Terrence TRAMMELL</th>\n",
              "      <th>2000</th>\n",
              "      <td>2092</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>13.16</td>\n",
              "      <td>0.221</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>1685</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>13.18</td>\n",
              "      <td>0.154</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tianna BARTOLETTA</th>\n",
              "      <th>2012</th>\n",
              "      <td>572</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3306.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>10.85</td>\n",
              "      <td>0.171</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tiffany PORTER</th>\n",
              "      <th>2016</th>\n",
              "      <td>868</td>\n",
              "      <td>7.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>GBR</td>\n",
              "      <td>12.76</td>\n",
              "      <td>0.142</td>\n",
              "      <td>100 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tori BOWIE</th>\n",
              "      <th>2016</th>\n",
              "      <td>1055</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>10.83</td>\n",
              "      <td>0.112</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Torri EDWARDS</th>\n",
              "      <th>2008</th>\n",
              "      <td>292</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.179</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trayvon BROMELL</th>\n",
              "      <th>2016</th>\n",
              "      <td>1185</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3054.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>10.06</td>\n",
              "      <td>0.135</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tyson GAY</th>\n",
              "      <th>2012</th>\n",
              "      <td>693</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3217.0</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>DQ</td>\n",
              "      <td>0.145</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Usain BOLT</th>\n",
              "      <th>2008</th>\n",
              "      <td>136</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>686</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2170.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.63</td>\n",
              "      <td>0.165</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1178</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2612.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.81</td>\n",
              "      <td>0.155</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Veronica CAMPBELL-BROWN</th>\n",
              "      <th>2004</th>\n",
              "      <td>1372</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.97</td>\n",
              "      <td>0.199</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>571</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2194.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>10.81</td>\n",
              "      <td>0.143</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Walter DIX</th>\n",
              "      <th>2008</th>\n",
              "      <td>138</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USAUSA</td>\n",
              "      <td>9.91</td>\n",
              "      <td>0.133</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Xiang LIU</th>\n",
              "      <th>2004</th>\n",
              "      <td>1684</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CHNCHN</td>\n",
              "      <td>12.91</td>\n",
              "      <td>0.139</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Yohan BLAKE</th>\n",
              "      <th>2012</th>\n",
              "      <td>687</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2169.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.75</td>\n",
              "      <td>0.179</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1181</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2611.0</td>\n",
              "      <td>JAMJAM</td>\n",
              "      <td>9.93</td>\n",
              "      <td>0.145</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yuliya NESTSIARENKA</th>\n",
              "      <th>2004</th>\n",
              "      <td>1370</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BLRBLR</td>\n",
              "      <td>10.93</td>\n",
              "      <td>0.186</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zhanna BLOCK</th>\n",
              "      <th>2000</th>\n",
              "      <td>1991</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UKRUKR</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.223</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Final</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Unnamed: 0  POS     BIB COUNTRY   MARK  RxnTime  \\\n",
              "ATHLETE                 Year                                                    \n",
              "Akani SIMBINE           2016        1182  5.0  2909.0  RSARSA   9.94    0.128   \n",
              "Aleen BAILEY            2004        1374  5.0     NaN  JAMJAM  11.05    0.208   \n",
              "Aliuska LÓPEZ           2000        2181  5.0     NaN  CUBCUB  12.83    0.179   \n",
              "Allen JOHNSON           2000        2094  4.0     NaN  USAUSA  13.23    0.182   \n",
              "Allyson FELIX           2012         573  5.0  3287.0  USAUSA  10.89    0.176   \n",
              "Andre DE GRASSE         2016        1180  3.0  2196.0  CANCAN   9.91    0.141   \n",
              "Angela WHYTE            2004        1585  6.0     NaN  CANCAN  12.81    0.155   \n",
              "Anier GARCÍA            2000        2091  1.0     NaN  CUBCUB     13    0.172   \n",
              "                        2004        1686  3.0     NaN  CUBCUB   13.2    0.169   \n",
              "Aries MERRITT           2012         854  1.0  3236.0  USAUSA  12.92    0.143   \n",
              "Artur NOGA              2008         452  5.0     NaN  POLPOL  13.36    0.169   \n",
              "Asafa POWELL            2004        1522  5.0     NaN  JAMJAM   9.94    0.166   \n",
              "                        2008         140  5.0     NaN  JAMJAM   9.95    0.134   \n",
              "                        2012         692  7.0  2184.0  JAMJAM  11.99    0.155   \n",
              "Ato BOLDON              2000        1850  2.0     NaN  TTOTTO   9.99    0.136   \n",
              "Aziz ZAKARI             2000        1856  NaN     NaN  GHAGHA    DNF    0.180   \n",
              "                        2004        1525  NaN     NaN  GHAGHA    DNF    0.178   \n",
              "Beate SCHROTT           2012         774  7.0  1093.0  AUTAUT  13.07    0.156   \n",
              "Ben Youssef MEITÉ       2016        1183  6.0  2245.0  CIVCIV   9.96    0.156   \n",
              "Blessing OKAGBARE       2012         576  8.0  2584.0  NGRNGR  11.01    0.165   \n",
              "Brianna ROLLINS         2016         862  1.0  1369.0     USA  12.48    0.158   \n",
              "Brigitte FOSTER-HYLTON  2000        2184  8.0     NaN  JAMJAM  13.49    0.217   \n",
              "                        2008         354  6.0     NaN  JAMJAM  12.66    0.167   \n",
              "Carmelita JETER         2012         570  2.0  3299.0  USAUSA  10.78    0.153   \n",
              "Chandra STURRUP         2000        1992  5.0     NaN  BAHBAH  11.21    0.193   \n",
              "Charles ALLEN           2004        1689  6.0     NaN  CANCAN  13.48    0.139   \n",
              "Christania WILLIAMS     2016        1061  8.0   905.0  JAMJAM   11.8    0.163   \n",
              "Churandy MARTINA        2008         139  4.0     NaN  AHOAHO   9.93    0.169   \n",
              "                        2012         690  5.0  2547.0  NEDNED   9.94    0.139   \n",
              "Cindy OFILI             2016         865  4.0   713.0     GBR  12.63    0.161   \n",
              "...                                  ...  ...     ...     ...    ...      ...   \n",
              "Sally PEARSON           2012         768  1.0  1079.0  AUSAUS  12.35    0.137   \n",
              "Sarah CLAXTON           2008         356  8.0     NaN  GBRGBR  12.94    0.163   \n",
              "Savatheda FYNES         2000        1993  6.0     NaN  BAHBAH  11.22    0.253   \n",
              "Shawn CRAWFORD          2004        1521  4.0     NaN  USAUSA   9.89    0.161   \n",
              "Shelly-Ann FRASER-PRYCE 2008         285  1.0     NaN  JAMJAM  10.78    0.190   \n",
              "                        2012         569  1.0  2197.0  JAMJAM  10.75    0.153   \n",
              "                        2016        1056  3.0   887.0  JAMJAM  10.86    0.138   \n",
              "Sherone SIMPSON         2004        1375  6.0     NaN  JAMJAM  11.07    0.164   \n",
              "                        2008         286  2.0     NaN  JAMJAM  10.98    0.155   \n",
              "Stanislavs OLIJARS      2004        1688  5.0     NaN  LATLAT  13.21    0.174   \n",
              "Tayna LAWRENCE          2000        1989  2.0     NaN  JAMJAM  11.18    0.163   \n",
              "Terrence TRAMMELL       2000        2092  2.0     NaN  USAUSA  13.16    0.221   \n",
              "                        2004        1685  2.0     NaN  USAUSA  13.18    0.154   \n",
              "Tianna BARTOLETTA       2012         572  4.0  3306.0  USAUSA  10.85    0.171   \n",
              "Tiffany PORTER          2016         868  7.0   719.0     GBR  12.76    0.142   \n",
              "Tori BOWIE              2016        1055  2.0  1326.0  USAUSA  10.83    0.112   \n",
              "Torri EDWARDS           2008         292  8.0     NaN  USAUSA   11.2    0.179   \n",
              "Trayvon BROMELL         2016        1185  8.0  3054.0  USAUSA  10.06    0.135   \n",
              "Tyson GAY               2012         693  NaN  3217.0  USAUSA     DQ    0.145   \n",
              "Usain BOLT              2008         136  1.0     NaN  JAMJAM   9.69    0.165   \n",
              "                        2012         686  1.0  2170.0  JAMJAM   9.63    0.165   \n",
              "                        2016        1178  1.0  2612.0  JAMJAM   9.81    0.155   \n",
              "Veronica CAMPBELL-BROWN 2004        1372  3.0     NaN  JAMJAM  10.97    0.199   \n",
              "                        2012         571  3.0  2194.0  JAMJAM  10.81    0.143   \n",
              "Walter DIX              2008         138  3.0     NaN  USAUSA   9.91    0.133   \n",
              "Xiang LIU               2004        1684  1.0     NaN  CHNCHN  12.91    0.139   \n",
              "Yohan BLAKE             2012         687  2.0  2169.0  JAMJAM   9.75    0.179   \n",
              "                        2016        1181  4.0  2611.0  JAMJAM   9.93    0.145   \n",
              "Yuliya NESTSIARENKA     2004        1370  1.0     NaN  BLRBLR  10.93    0.186   \n",
              "Zhanna BLOCK            2000        1991  4.0     NaN  UKRUKR   11.2    0.223   \n",
              "\n",
              "                                 Race  Round Gender  \n",
              "ATHLETE                 Year                         \n",
              "Akani SIMBINE           2016    100 m  Final      M  \n",
              "Aleen BAILEY            2004    100 m  Final      F  \n",
              "Aliuska LÓPEZ           2000  100 m H  Final      F  \n",
              "Allen JOHNSON           2000  110 m H  Final      M  \n",
              "Allyson FELIX           2012    100 m  Final      F  \n",
              "Andre DE GRASSE         2016    100 m  Final      M  \n",
              "Angela WHYTE            2004  100 m H  Final      F  \n",
              "Anier GARCÍA            2000  110 m H  Final      M  \n",
              "                        2004  110 m H  Final      M  \n",
              "Aries MERRITT           2012  110 m H  Final      M  \n",
              "Artur NOGA              2008  110 m H  Final      M  \n",
              "Asafa POWELL            2004    100 m  Final      M  \n",
              "                        2008    100 m  Final      M  \n",
              "                        2012    100 m  Final      M  \n",
              "Ato BOLDON              2000    100 m  Final      M  \n",
              "Aziz ZAKARI             2000    100 m  Final      M  \n",
              "                        2004    100 m  Final      M  \n",
              "Beate SCHROTT           2012  100 m H  Final      F  \n",
              "Ben Youssef MEITÉ       2016    100 m  Final      M  \n",
              "Blessing OKAGBARE       2012    100 m  Final      F  \n",
              "Brianna ROLLINS         2016  100 m H  Final      F  \n",
              "Brigitte FOSTER-HYLTON  2000  100 m H  Final      F  \n",
              "                        2008  100 m H  Final      F  \n",
              "Carmelita JETER         2012    100 m  Final      F  \n",
              "Chandra STURRUP         2000    100 m  Final      F  \n",
              "Charles ALLEN           2004  110 m H  Final      M  \n",
              "Christania WILLIAMS     2016    100 m  Final      F  \n",
              "Churandy MARTINA        2008    100 m  Final      M  \n",
              "                        2012    100 m  Final      M  \n",
              "Cindy OFILI             2016  100 m H  Final      F  \n",
              "...                               ...    ...    ...  \n",
              "Sally PEARSON           2012  100 m H  Final      F  \n",
              "Sarah CLAXTON           2008  100 m H  Final      F  \n",
              "Savatheda FYNES         2000    100 m  Final      F  \n",
              "Shawn CRAWFORD          2004    100 m  Final      M  \n",
              "Shelly-Ann FRASER-PRYCE 2008    100 m  Final      F  \n",
              "                        2012    100 m  Final      F  \n",
              "                        2016    100 m  Final      F  \n",
              "Sherone SIMPSON         2004    100 m  Final      F  \n",
              "                        2008    100 m  Final      F  \n",
              "Stanislavs OLIJARS      2004  110 m H  Final      M  \n",
              "Tayna LAWRENCE          2000    100 m  Final      F  \n",
              "Terrence TRAMMELL       2000  110 m H  Final      M  \n",
              "                        2004  110 m H  Final      M  \n",
              "Tianna BARTOLETTA       2012    100 m  Final      F  \n",
              "Tiffany PORTER          2016  100 m H  Final      F  \n",
              "Tori BOWIE              2016    100 m  Final      F  \n",
              "Torri EDWARDS           2008    100 m  Final      F  \n",
              "Trayvon BROMELL         2016    100 m  Final      M  \n",
              "Tyson GAY               2012    100 m  Final      M  \n",
              "Usain BOLT              2008    100 m  Final      M  \n",
              "                        2012    100 m  Final      M  \n",
              "                        2016    100 m  Final      M  \n",
              "Veronica CAMPBELL-BROWN 2004    100 m  Final      F  \n",
              "                        2012    100 m  Final      F  \n",
              "Walter DIX              2008    100 m  Final      M  \n",
              "Xiang LIU               2004  110 m H  Final      M  \n",
              "Yohan BLAKE             2012    100 m  Final      M  \n",
              "                        2016    100 m  Final      M  \n",
              "Yuliya NESTSIARENKA     2004    100 m  Final      F  \n",
              "Zhanna BLOCK            2000    100 m  Final      F  \n",
              "\n",
              "[160 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "Z7Nvh5xz05PJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "17) Now use the above, plus chaining with `.loc[]` and `max()` to find Usain Bolt's *worst* reaction time in an Olympics final.   "
      ]
    },
    {
      "metadata": {
        "id": "v1dTk0My05PM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c2e1052d-57b9-4192-f9cd-a8b4b6345750"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 17 here\n",
        "df[df['Round']=='Final'].loc['Usain BOLT'][\"RxnTime\"].max()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.165"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "pyg3rhdf05PU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "18) Let's clean up this data a little bit more.  See all the `NaN` values above?  Those correspond to missing data (NaN stands for \"Not a Number\").  We can get rid of rows that have missing data with the `.dropna()` method.  But some missing data is irrelevant to our analysis.  Who cares if the bib number is missing?  Select only those columns we care about (let's say reaction time, round, and gender) using the approach from question 6, and then use `dropna()` on that subset of the columns.  Chain that with `.shape` to see how many rows the remaining data has.  It should be less than the number of rows we started with (because we removed some rows), but still well more than 0!"
      ]
    },
    {
      "metadata": {
        "id": "h3_mTm7Y05PZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "fbd9e0b7-51c7-4ac1-8778-258270f3dc8e"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 18 here\n",
        "#print(df.shape)\n",
        "df = df.dropna(subset=['RxnTime'],axis=0)\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2123, 9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>POS</th>\n",
              "      <th>BIB</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>MARK</th>\n",
              "      <th>RxnTime</th>\n",
              "      <th>Race</th>\n",
              "      <th>Round</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ATHLETE</th>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aaron BROWN</th>\n",
              "      <th>2016</th>\n",
              "      <td>1147</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2192.0</td>\n",
              "      <td>CANCAN</td>\n",
              "      <td>10.24</td>\n",
              "      <td>0.135</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdul RASHID</th>\n",
              "      <th>2008</th>\n",
              "      <td>378</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PAKPAK</td>\n",
              "      <td>14.52</td>\n",
              "      <td>0.172</td>\n",
              "      <td>110 m H</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdul Wahab ZAHIRI</th>\n",
              "      <th>2016</th>\n",
              "      <td>1068</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>AFGAFG</td>\n",
              "      <td>11.56</td>\n",
              "      <td>0.170</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Preliminary</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdullah AL-SOOLI</th>\n",
              "      <th>2008</th>\n",
              "      <td>44</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OMAOMA</td>\n",
              "      <td>10.53</td>\n",
              "      <td>0.153</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abdullah Abkar MOHAMMED</th>\n",
              "      <th>2016</th>\n",
              "      <td>1106</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2717.0</td>\n",
              "      <td>KSAKSA</td>\n",
              "      <td>10.26</td>\n",
              "      <td>0.154</td>\n",
              "      <td>100 m</td>\n",
              "      <td>Heats</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Unnamed: 0  POS     BIB COUNTRY   MARK  RxnTime  \\\n",
              "ATHLETE                 Year                                                    \n",
              "Aaron BROWN             2016        1147  3.0  2192.0  CANCAN  10.24    0.135   \n",
              "Abdul RASHID            2008         378  8.0     NaN  PAKPAK  14.52    0.172   \n",
              "Abdul Wahab ZAHIRI      2016        1068  7.0  2000.0  AFGAFG  11.56    0.170   \n",
              "Abdullah AL-SOOLI       2008          44  5.0     NaN  OMAOMA  10.53    0.153   \n",
              "Abdullah Abkar MOHAMMED 2016        1106  5.0  2717.0  KSAKSA  10.26    0.154   \n",
              "\n",
              "                                 Race        Round Gender  \n",
              "ATHLETE                 Year                               \n",
              "Aaron BROWN             2016    100 m        Heats      M  \n",
              "Abdul RASHID            2008  110 m H        Heats      M  \n",
              "Abdul Wahab ZAHIRI      2016    100 m  Preliminary      M  \n",
              "Abdullah AL-SOOLI       2008    100 m        Heats      M  \n",
              "Abdullah Abkar MOHAMMED 2016    100 m        Heats      M  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "kb1uJCag05Pj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "19) Do Americans sprinters react faster than Canadian sprinters?  No one would ever fund such a study, but you can answer that question right here for free!  Use the `.groupby()` method to aggregate sprinters by country `.groupby('COUNTRY')`, select the reaction time column, and chain it with `.mean()` to compare mean reaction time across countries.  You can further optionally append `[['USAUSA','CANCAN']]` to focus on only those two countries.  Why are all the country names doubled?  I think it is because the person who compiled this dataset likes to say, \"U-S-A!  U-S-A!\" and just repeated that for every country.  "
      ]
    },
    {
      "metadata": {
        "id": "1zbAvEfX05Pl",
        "colab_type": "code",
        "outputId": "874ae9c3-2c81-4916-b02e-acb16fa01d0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 19 here\n",
        "df.groupby('COUNTRY')['RxnTime'].mean()[['USAUSA','CANCAN']]"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "COUNTRY\n",
              "USAUSA    0.170185\n",
              "CANCAN    0.159889\n",
              "Name: RxnTime, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "metadata": {
        "id": "ivZmOOf505Pt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Be sure to run this code cell!\n",
        "from scipy.stats import ttest_ind"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gdc7bFeL05Pw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "20) So *do* Americans react faster (or slower) than Canadians? In the previous question we only computed the means.  What about the variability?  Let's assume for the moment that a t-test is a good way to compare the two groups.  Extract the two groups using the data using the selector approach from question 16, extract the reaction time from each, drop the missing values, and assigning one to the variable `usa` and the other to the variable `can`.  Don't use `groupby()` here.  Having loaded a t-test function (`ttest_ind` in the cell above), call that function using your selected groups (`ttest_ind(usa,can)`).  Note that you can have multiple command in a code cell, as long each one is on its own line.  "
      ]
    },
    {
      "metadata": {
        "id": "ePsG1uS505Py",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8edfeac-3874-496b-89c0-f13ec76452b1"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 20 here\n",
        "usa = df[df[\"COUNTRY\"]==\"USAUSA\"]['RxnTime']\n",
        "usa.head()\n",
        "can = df[df[\"COUNTRY\"]==\"CANCAN\"]['RxnTime']\n",
        "can.head()\n",
        "ttest_ind(usa,can)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=2.627641765259621, pvalue=0.009140575516656623)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "metadata": {
        "id": "mbPaZkR805P2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "21) Oh my, are we going to win a Nobel Prize for this discovery?  Or have we failed to control for something?  Maybe one country has more male vs female athletes, or more sprinters vs hurdlers.  Repeat the above, restricting the data to men (21a), then to women (21b), then to only 100 m sprinters (21c).  You can have multiple `==` expressions in your selector, but you should use parentheses to keep them separated e.g. `(df['COUNTRY']=='USA') && (df['Gender']=='F')`.  "
      ]
    },
    {
      "metadata": {
        "id": "baHSOO3505P3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c5f720d4-d418-4fe3-d549-beeb50121e73"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 21a here\n",
        "usam = df[(df[\"COUNTRY\"]==\"USAUSA\") & (df['Gender']==\"M\")]['RxnTime']\n",
        "usam.head()\n",
        "canm = df[(df[\"COUNTRY\"]==\"CANCAN\") & (df['Gender']==\"M\")]['RxnTime']\n",
        "canm.head()\n",
        "ttest_ind(usam,canm)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=2.0779762126756274, pvalue=0.0397416990780365)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "id": "Ye2KD1QH05P7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0113965-23fa-4503-f3c9-ecfdcfd728d9"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 21b here\n",
        "usaw = df[(df[\"COUNTRY\"]==\"USAUSA\") & (df['Gender']==\"F\")]['RxnTime']\n",
        "usaw.head()\n",
        "canw = df[(df[\"COUNTRY\"]==\"CANCAN\") & (df['Gender']==\"F\")]['RxnTime']\n",
        "canw.head()\n",
        "ttest_ind(usaw,canw)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=1.80019192856523, pvalue=0.074407006123811)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "metadata": {
        "id": "ggkEHISb05P_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9943b0e5-4b05-4355-8d76-4db5eaa1aed3"
      },
      "cell_type": "code",
      "source": [
        "# Fill in your answer to 21c here\n",
        "usah = df[(df[\"COUNTRY\"]==\"USAUSA\") & (df['Race']==\"100 m\")]['RxnTime']\n",
        "usah.head()\n",
        "canh = df[(df[\"COUNTRY\"]==\"CANCAN\") & (df['Race']==\"100 m\")]['RxnTime']\n",
        "canh.head()\n",
        "ttest_ind(usah,canh)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=1.4573584046126689, pvalue=0.14754531648779756)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "metadata": {
        "id": "9HsNo7NN05QG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Congratulations, you are now familiar with the basic of programmatic operations on structured data!"
      ]
    }
  ]
}