data-explore-practice.ipynb

{"cells":[{"cell_type":"code","source":["import numpy as np\nimport pandas as pd"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ac61f3ad-7611-4248-b449-1ad08f23627f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\ndf_students = pd.read_csv('grades.csv',delimiter=',',header='infer')\ndf_students.head()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"830a14fe-6690-4652-90bc-ad6bd369303d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">--2021-11-01 11:11:11--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\r\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\r\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\r\nHTTP request sent, awaiting response... 200 OK\r\nLength: 322 [text/plain]\r\nSaving to: ‘grades.csv.1’\r\n\r\n\rgrades.csv.1          0%[                    ]       0  --.-KB/s               \rgrades.csv.1        100%[===================&gt;]     322  --.-KB/s    in 0s      \r\n\r\n2021-11-01 11:11:11 (7.75 MB/s) - ‘grades.csv.1’ saved [322/322]\r\n\r\nOut[4]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">--2021-11-01 11:11:11--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\r\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\r\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\r\nHTTP request sent, awaiting response... 200 OK\r\nLength: 322 [text/plain]\r\nSaving to: ‘grades.csv.1’\r\n\r\n\rgrades.csv.1          0%[                    ]       0  --.-KB/s               \rgrades.csv.1        100%[===================&gt;]     322  --.-KB/s    in 0s      \r\n\r\n2021-11-01 11:11:11 (7.75 MB/s) - ‘grades.csv.1’ saved [322/322]\r\n\r\nOut[4]: </div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dan</td>\n      <td>10.00</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.50</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pedro</td>\n      <td>9.00</td>\n      <td>47.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.00</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ethan</td>\n      <td>9.25</td>\n      <td>49.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dan</td>\n      <td>10.00</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.50</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pedro</td>\n      <td>9.00</td>\n      <td>47.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.00</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ethan</td>\n      <td>9.25</td>\n      <td>49.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["df_students.isnull().sum()\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4787d63b-5444-4943-9c34-0cdf1b64a730"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[5]: Name          0\nStudyHours    1\nGrade         2\ndtype: int64</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[5]: Name          0\nStudyHours    1\nGrade         2\ndtype: int64</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["df_students[df_students.isnull().any(axis=1)]"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"02bb053f-5b93-4b33-b984-c54d6f1fe755"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[6]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[6]: </div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>22</th>\n      <td>Bill</td>\n      <td>8.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Ted</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>22</th>\n      <td>Bill</td>\n      <td>8.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Ted</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["df_students.StudyHours = df_students.StudyHours.fillna(df_students.StudyHours.mean())\ndf_students"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"bdd43626-4a49-4e3a-bcc7-9f16b08d0643"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[7]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[7]: </div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dan</td>\n      <td>10.000000</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.500000</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pedro</td>\n      <td>9.000000</td>\n      <td>47.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.000000</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ethan</td>\n      <td>9.250000</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Vicky</td>\n      <td>1.000000</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Frederic</td>\n      <td>11.500000</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Jimmie</td>\n      <td>9.000000</td>\n      <td>42.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Rhonda</td>\n      <td>8.500000</td>\n      <td>26.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Giovanni</td>\n      <td>14.500000</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Francesca</td>\n      <td>15.500000</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Rajab</td>\n      <td>13.750000</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Naiyana</td>\n      <td>9.000000</td>\n      <td>37.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Kian</td>\n      <td>8.000000</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Jenny</td>\n      <td>15.500000</td>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Jakeem</td>\n      <td>8.000000</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Helena</td>\n      <td>9.000000</td>\n      <td>36.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Ismat</td>\n      <td>6.000000</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Anila</td>\n      <td>10.000000</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Skye</td>\n      <td>12.000000</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Daniel</td>\n      <td>12.500000</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Aisha</td>\n      <td>12.000000</td>\n      <td>64.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Bill</td>\n      <td>8.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Ted</td>\n      <td>10.413043</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dan</td>\n      <td>10.000000</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.500000</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pedro</td>\n      <td>9.000000</td>\n      <td>47.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.000000</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ethan</td>\n      <td>9.250000</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Vicky</td>\n      <td>1.000000</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Frederic</td>\n      <td>11.500000</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Jimmie</td>\n      <td>9.000000</td>\n      <td>42.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Rhonda</td>\n      <td>8.500000</td>\n      <td>26.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Giovanni</td>\n      <td>14.500000</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Francesca</td>\n      <td>15.500000</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Rajab</td>\n      <td>13.750000</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Naiyana</td>\n      <td>9.000000</td>\n      <td>37.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Kian</td>\n      <td>8.000000</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Jenny</td>\n      <td>15.500000</td>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Jakeem</td>\n      <td>8.000000</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Helena</td>\n      <td>9.000000</td>\n      <td>36.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Ismat</td>\n      <td>6.000000</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Anila</td>\n      <td>10.000000</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Skye</td>\n      <td>12.000000</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Daniel</td>\n      <td>12.500000</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Aisha</td>\n      <td>12.000000</td>\n      <td>64.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Bill</td>\n      <td>8.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Ted</td>\n      <td>10.413043</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["df_students = df_students.dropna(axis=0, how='any')\ndf_students"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"775ad800-7b95-41ab-9fbd-941b89c13fd7"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[8]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[8]: </div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dan</td>\n      <td>10.00</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.50</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pedro</td>\n      <td>9.00</td>\n      <td>47.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.00</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ethan</td>\n      <td>9.25</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Vicky</td>\n      <td>1.00</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Frederic</td>\n      <td>11.50</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Jimmie</td>\n      <td>9.00</td>\n      <td>42.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Rhonda</td>\n      <td>8.50</td>\n      <td>26.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Giovanni</td>\n      <td>14.50</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Francesca</td>\n      <td>15.50</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Rajab</td>\n      <td>13.75</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Naiyana</td>\n      <td>9.00</td>\n      <td>37.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Kian</td>\n      <td>8.00</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Jenny</td>\n      <td>15.50</td>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Jakeem</td>\n      <td>8.00</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Helena</td>\n      <td>9.00</td>\n      <td>36.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Ismat</td>\n      <td>6.00</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Anila</td>\n      <td>10.00</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Skye</td>\n      <td>12.00</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Daniel</td>\n      <td>12.50</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Aisha</td>\n      <td>12.00</td>\n      <td>64.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dan</td>\n      <td>10.00</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.50</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pedro</td>\n      <td>9.00</td>\n      <td>47.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.00</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ethan</td>\n      <td>9.25</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Vicky</td>\n      <td>1.00</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Frederic</td>\n      <td>11.50</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Jimmie</td>\n      <td>9.00</td>\n      <td>42.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Rhonda</td>\n      <td>8.50</td>\n      <td>26.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Giovanni</td>\n      <td>14.50</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Francesca</td>\n      <td>15.50</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Rajab</td>\n      <td>13.75</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Naiyana</td>\n      <td>9.00</td>\n      <td>37.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Kian</td>\n      <td>8.00</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Jenny</td>\n      <td>15.50</td>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Jakeem</td>\n      <td>8.00</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Helena</td>\n      <td>9.00</td>\n      <td>36.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Ismat</td>\n      <td>6.00</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Anila</td>\n      <td>10.00</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Skye</td>\n      <td>12.00</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Daniel</td>\n      <td>12.50</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Aisha</td>\n      <td>12.00</td>\n      <td>64.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Get the mean study hours using to column name as an index\nmean_study = df_students['StudyHours'].mean()\n\n# Get the mean grade using the column name as a property (just to make the point!)\nmean_grade = df_students.Grade.mean()\n\n# Print the mean study hours and mean grade\nprint('Average weekly study hours: {:.2f}\\nAverage grade: {:.2f}'.format(mean_study, mean_grade))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a2fc6875-de08-408f-9275-270ee37bf884"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Average weekly study hours: 10.52\nAverage grade: 49.18\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Average weekly study hours: 10.52\nAverage grade: 49.18\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["df_students[df_students.StudyHours > mean_study]"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"62af2595-0a30-4817-8a5a-1529d93b217a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[10]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[10]: </div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.50</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.00</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Frederic</td>\n      <td>11.50</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Giovanni</td>\n      <td>14.50</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Francesca</td>\n      <td>15.50</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Rajab</td>\n      <td>13.75</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Jenny</td>\n      <td>15.50</td>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Skye</td>\n      <td>12.00</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Daniel</td>\n      <td>12.50</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Aisha</td>\n      <td>12.00</td>\n      <td>64.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","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>Name</th>\n      <th>StudyHours</th>\n      <th>Grade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Joann</td>\n      <td>11.50</td>\n      <td>50.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosie</td>\n      <td>16.00</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Frederic</td>\n      <td>11.50</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Giovanni</td>\n      <td>14.50</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Francesca</td>\n      <td>15.50</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Rajab</td>\n      <td>13.75</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Jenny</td>\n      <td>15.50</td>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Skye</td>\n      <td>12.00</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Daniel</td>\n      <td>12.50</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Aisha</td>\n      <td>12.00</td>\n      <td>64.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["df_students[df_students.StudyHours > mean_study].Grade.mean()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4e10f0d5-50c0-41d3-ba76-59ce10271064"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[11]: 66.7</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[11]: 66.7</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["passes = pd.Series(df_students[\"Grade\"]>=60)\nprint(passes)\ndf_students = pd.concat([df_students,passes.rename(\"Pass\")],axis=1)\nprint(df_students)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"12be12b2-146f-4f7d-85ea-7a2c14be49db"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">0     False\n1     False\n2     False\n3      True\n4     False\n5     False\n6     False\n7     False\n8     False\n9      True\n10     True\n11     True\n12    False\n13    False\n14     True\n15    False\n16    False\n17    False\n18    False\n19    False\n20     True\n21     True\nName: Grade, dtype: bool\n         Name  StudyHours  Grade   Pass\n0         Dan       10.00   50.0  False\n1       Joann       11.50   50.0  False\n2       Pedro        9.00   47.0  False\n3       Rosie       16.00   97.0   True\n4       Ethan        9.25   49.0  False\n5       Vicky        1.00    3.0  False\n6    Frederic       11.50   53.0  False\n7      Jimmie        9.00   42.0  False\n8      Rhonda        8.50   26.0  False\n9    Giovanni       14.50   74.0   True\n10  Francesca       15.50   82.0   True\n11      Rajab       13.75   62.0   True\n12    Naiyana        9.00   37.0  False\n13       Kian        8.00   15.0  False\n14      Jenny       15.50   70.0   True\n15     Jakeem        8.00   27.0  False\n16     Helena        9.00   36.0  False\n17      Ismat        6.00   35.0  False\n18      Anila       10.00   48.0  False\n19       Skye       12.00   52.0  False\n20     Daniel       12.50   63.0   True\n21      Aisha       12.00   64.0   True\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">0     False\n1     False\n2     False\n3      True\n4     False\n5     False\n6     False\n7     False\n8     False\n9      True\n10     True\n11     True\n12    False\n13    False\n14     True\n15    False\n16    False\n17    False\n18    False\n19    False\n20     True\n21     True\nName: Grade, dtype: bool\n         Name  StudyHours  Grade   Pass\n0         Dan       10.00   50.0  False\n1       Joann       11.50   50.0  False\n2       Pedro        9.00   47.0  False\n3       Rosie       16.00   97.0   True\n4       Ethan        9.25   49.0  False\n5       Vicky        1.00    3.0  False\n6    Frederic       11.50   53.0  False\n7      Jimmie        9.00   42.0  False\n8      Rhonda        8.50   26.0  False\n9    Giovanni       14.50   74.0   True\n10  Francesca       15.50   82.0   True\n11      Rajab       13.75   62.0   True\n12    Naiyana        9.00   37.0  False\n13       Kian        8.00   15.0  False\n14      Jenny       15.50   70.0   True\n15     Jakeem        8.00   27.0  False\n16     Helena        9.00   36.0  False\n17      Ismat        6.00   35.0  False\n18      Anila       10.00   48.0  False\n19       Skye       12.00   52.0  False\n20     Daniel       12.50   63.0   True\n21      Aisha       12.00   64.0   True\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["print(df_students.groupby(df_students.Pass).Name.count())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8cacdbe3-879a-4b71-87bc-040b14d434b2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Pass\nFalse    15\nTrue      7\nName: Name, dtype: int64\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Pass\nFalse    15\nTrue      7\nName: Name, dtype: int64\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["print(df_students.groupby(df_students.Pass)[\"Grade\"].mean())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c3f3e780-bc9f-4de3-9dc2-7c4617aaec0a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Pass\nFalse    38.000000\nTrue     73.142857\nName: Grade, dtype: float64\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Pass\nFalse    38.000000\nTrue     73.142857\nName: Grade, dtype: float64\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n%matplotlib inline"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"6e42ef6d-41df-47b0-b470-8a24242c7a99"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Create a bar plot of name vs grade\nplt.bar(x=df_students.Name, height=df_students.Grade)\n\n# Customize the chart\nplt.title('Student Grades')\nplt.xlabel('Student')\nplt.ylabel('Grade')\nplt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\nplt.xticks(rotation=90)\n\n# Display the plot\nplt.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"43e2f704-5b27-479d-95fb-37cc6cff73ee"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/cf3e23d8-6ccf-470b-9b9c-7e9a7eaf6779.png","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Create a figure for 2 subplots (1 row, 2 columns)\nfig, ax= plt.subplots(1,2,figsize=(10,4))\n#fig, ax = plt.subplots(1, 2, figsize = (10,4))\n\n# Create a bar plot of name vs grade on the first axis\nax[0].bar(x=df_students.Name, height=df_students.Grade, color='orange')\nax[0].set_title('Grades')\nax[0].set_xticklabels(df_students.Name, rotation=90)\n\n# Create a pie chart of pass counts on the second axis\npass_counts = df_students['Pass'].value_counts()\nax[1].pie(pass_counts, labels=pass_counts)\nax[1].set_title('Passing Grades')\nax[1].legend(pass_counts.keys().tolist())\n\n# Add a title to the Figure\nfig.suptitle('Student Data')\n\n# Show the figure\nfig.show()\n\nprint(pass_counts)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"59e9cf5d-b62f-45a0-853c-b3e191243024"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">False    15\nTrue      7\nName: Pass, dtype: int64\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">False    15\nTrue      7\nName: Pass, dtype: int64\n</div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/5dc2dd0b-e74c-4611-97f6-3f7ac7982d97.png","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Get the variable to examine\nvar_data = df_students['Grade']\n\n# Create a Figure\nfig = plt.figure(figsize=(10,4))\n\n# Plot a histogram\nplt.hist(var_data)\n\n# Add titles and labels\nplt.title('Data Distribution')\nplt.xlabel('Value')\nplt.ylabel('Frequency')\n\n# Show the figure\nfig.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4d13b738-e2f9-48ad-90cb-3f8a3b76b48e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/989b22d1-70ed-4d42-a485-97c67da65c52.png","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Get the variable to examine\nvar = df_students['Grade']\n\n# Get statistics\nmin_val = var.min()\nmax_val = var.max()\nmean_val = var.mean()\nmed_val = var.median()\nmod_val = var.mode()[0]\n\nprint('Minimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n                                                                                        mean_val,\n                                                                                        med_val,\n                                                                                        mod_val,\n                                                                                        max_val))\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"3498ecae-f71c-449c-86f5-d90a063eff18"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Minimum:3.00\nMean:49.18\nMedian:49.50\nMode:50.00\nMaximum:97.00\n\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Minimum:3.00\nMean:49.18\nMedian:49.50\nMode:50.00\nMaximum:97.00\n\n</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Create a Figure\nfig = plt.figure(figsize=(10,4))\n\n# Plot a histogram\nplt.hist(var)\n\n# Add lines for the statistics\nplt.axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\nplt.axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\nplt.axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\nplt.axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\nplt.axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n\n# Add titles and labels\nplt.title('Data Distribution')\nplt.xlabel('Value')\nplt.ylabel('Frequency')\n\n# Show the figure\nfig.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f4c09c81-e039-4968-b3ac-a8fe55377983"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/5be6bc09-eed2-4459-9fdc-fc3b8848ab8f.png","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"},"transient":null}],"execution_count":0},{"cell_type":"code","source":["# Get the variable to examine\nvar = df_students['Grade']\n\n# Create a Figure\nfig = plt.figure(figsize=(10,4))\n\n# Plot a histogram\nplt.boxplot(var)\n\n# Add titles and labels\nplt.title('Data Distribution')\n\n# Show the figure\nfig.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"91e71dd9-2545-442c-909f-edcc9490bef8"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/9908ef6e-0f82-4a71-920b-a39e2f514a8d.png","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"},"transient":null}],"execution_count":0},{"cell_type":"code","source":["def show_density(var_data):\n    from matplotlib import pyplot as plt\n\n    fig = plt.figure(figsize=(10,4))\n\n    # Plot density\n    var_data.plot.density()\n\n    # Add titles and labels\n    plt.title('Data Density')\n    \n\n    # Show the mean, median, and mode\n    plt.axvline(x=var_data.mean(), color = 'cyan', linestyle='dashed', linewidth = 2)\n    plt.axvline(x=var_data.median(), color = 'red', linestyle='dashed', linewidth = 2)\n    plt.axvline(x=var_data.mode()[0], color = 'yellow', linestyle='dashed', linewidth = 2)\n\n    # Show the figure\n    plt.show()\n\n# Get the density of Grade\ncol = df_students['Grade']\nshow_density(col)\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d35cccc3-4b59-420c-bd85-9267a3447b41"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]},"transient":null},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">NameError</span>                                 Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-716471686085128&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">     20</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">     21</span> <span class=\"ansi-red-fg\"># Get the density of Grade</span>\n<span class=\"ansi-green-fg\">---&gt; 22</span><span class=\"ansi-red-fg\"> </span>col <span class=\"ansi-blue-fg\">=</span> df_students<span class=\"ansi-blue-fg\">[</span><span class=\"ansi-blue-fg\">&#39;Grade&#39;</span><span class=\"ansi-blue-fg\">]</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">     23</span> show_density<span class=\"ansi-blue-fg\">(</span>col<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-red-fg\">NameError</span>: name &#39;df_students&#39; is not defined</div>","errorSummary":"<span class=\"ansi-red-fg\">NameError</span>: name &#39;df_students&#39; is not defined","metadata":{},"errorTraceType":"html","type":"ipynbError","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">NameError</span>                                 Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-716471686085128&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">     20</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">     21</span> <span class=\"ansi-red-fg\"># Get the density of Grade</span>\n<span class=\"ansi-green-fg\">---&gt; 22</span><span class=\"ansi-red-fg\"> </span>col <span class=\"ansi-blue-fg\">=</span> df_students<span class=\"ansi-blue-fg\">[</span><span class=\"ansi-blue-fg\">&#39;Grade&#39;</span><span class=\"ansi-blue-fg\">]</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">     23</span> show_density<span class=\"ansi-blue-fg\">(</span>col<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-red-fg\">NameError</span>: name &#39;df_students&#39; is not defined</div>"]},"transient":null}],"execution_count":0},{"cell_type":"code","source":[""],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a59ab4d8-1e2f-45cc-a020-26e735d53fbe"}},"outputs":[],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"data-explore-practice","dashboards":[],"notebookMetadata":{"pythonIndentUnit":4},"language":"python","widgets":{},"notebookOrigID":3138489542641193}},"nbformat":4,"nbformat_minor":0}