// Numbas version: finer_feedback_settings
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If you retry this question you will need to redownload the csv.</p>\n<details>\n<summary><span style=\"text-decoration: underline;\">Help I don't know where to start!</span></summary>\nPut the <code>csv</code> file in the same folder as your Python script. You'll need to use the <code>pandas</code> package. <code>pd.readcsv</code> could be useful - remember to deal with the pesky missing header and have your data somewhere your notebook can see it. You can apply functions to columns like <code>df['height'].mean()</code>. Also don'f forget we can apply logic to columns too: <code>df[df['a'] &lt; 100]</code> gives all the items where column `a` has a value less than 100</details>", "advice": "<p>Import the <code>pandas</code> and then read in the csv.</p>\n<p>{code_block(\"import pandas as pd\\ndf = pd.read_csv('student_heights.csv', header=None, names=['name','height'])\\ndf.head()\",\"python\")}</p>\n<p>Find the mean and the standard deviation</p>\n<p>{code_block(\"mean = df['height'].mean()\\nsd = df['height'].std()\",\"python\")}</p>\n<p>filter on two standard deviations above the mean and count</p>\n<p>{code_block(\"df[df['height'] &gt; mean + 2 * sd].count()\",\"python\")}</p>", "rulesets": {}, "extensions": ["download-text-file", "programming", "random_person", "stats"], "builtin_constants": {"e": true, "pi,\u03c0": true, "i": true, "j": false}, "constants": [], "variables": {"people": {"name": "people", "group": "Ungrouped variables", "definition": "map([x['name'],trunc(normalsample(mu, sigma))],x,random_people(n))", "description": "", "templateType": "anything", "can_override": false}, "n": {"name": "n", "group": "Ungrouped variables", "definition": "random(800..1010)", "description": "", "templateType": "anything", "can_override": false}, "mu": {"name": "mu", "group": "Ungrouped variables", "definition": "random(160..180)", "description": "", "templateType": "anything", "can_override": false}, "sigma": {"name": "sigma", "group": "Ungrouped variables", "definition": "random(5..15)", "description": "", "templateType": "anything", "can_override": false}, "ans": {"name": "ans", "group": "Ungrouped variables", "definition": "sum(map(if(x[1] > (mu+2*sigma),1,0),x,people))", "description": "<p>This is the answer not based on the sample data - ignore this one</p>", "templateType": "anything", "can_override": false}, "csv": {"name": "csv", "group": "Ungrouped variables", "definition": "csv(people)", "description": "", "templateType": "anything", "can_override": false}, "x_bar": {"name": "x_bar", "group": "Ungrouped variables", "definition": "mean(map(x[1],x,people))", "description": "", "templateType": "anything", "can_override": false}, "sd": {"name": "sd", "group": "Ungrouped variables", "definition": "stdev(map(x[1],x,people),true)", "description": "", "templateType": "anything", "can_override": false}, "ans_2": {"name": "ans_2", "group": "Ungrouped variables", "definition": "sum(map(if(x[1] > (x_bar+2*sd),1,0),x,people))", "description": "", "templateType": "anything", "can_override": false}}, "variablesTest": {"condition": "", "maxRuns": 100}, "ungrouped_variables": ["people", "n", "mu", "sigma", "csv", "x_bar", "sd", "ans_2", "ans"], "variable_groups": [], "functions": {}, "preamble": {"js": "var x_ed = Numbas.extensions.programming.run_code(\"pyodide\", [\"print('hello')\"])", "css": ""}, "parts": [{"type": "numberentry", "useCustomName": false, "customName": "", "marks": 1, "scripts": {}, "customMarkingAlgorithm": "", "extendBaseMarkingAlgorithm": true, "unitTests": [], "showCorrectAnswer": true, "showFeedbackIcon": true, "variableReplacements": [], "variableReplacementStrategy": "originalfirst", "nextParts": [], "suggestGoingBack": false, "adaptiveMarkingPenalty": 0, "exploreObjective": null, "minValue": "{ans_2}", "maxValue": "{ans_2}", "correctAnswerFraction": false, "allowFractions": false, "mustBeReduced": false, "mustBeReducedPC": 0, "displayAnswer": "", "showFractionHint": true, "notationStyles": ["plain", "en", "si-en"], "correctAnswerStyle": "plain"}], "partsMode": "all", "maxMarks": 0, "objectives": [], "penalties": [], "objectiveVisibility": "always", "penaltyVisibility": "always", "contributors": [{"name": "Ed Southwood", "profile_url": "https://numbas.mathcentre.ac.uk/accounts/profile/2415/"}, {"name": "Ben McGovern", "profile_url": "https://numbas.mathcentre.ac.uk/accounts/profile/4872/"}], "resources": []}]}], "contributors": [{"name": "Ed Southwood", "profile_url": "https://numbas.mathcentre.ac.uk/accounts/profile/2415/"}, {"name": "Ben McGovern", "profile_url": "https://numbas.mathcentre.ac.uk/accounts/profile/4872/"}]}