Data analysis 1/Insurance_company.ipynb

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    "# Homework 1 (HW1)\n",
    "\n",
    "---\n",
    "By the end of this homework we expect you to be able to:\n",
    "1. Load data from different formats using [pandas](https://pandas.pydata.org/);\n",
    "2. Navigate the documentation of Python packages by yourself;\n",
    "3. Filter and tidy up noisy data sets;\n",
    "4. Aggregate your data in different (and hopefully helpful) ways; \n",
    "5. Create meaningful visualizations to analyze the data;\n",
    "6. Critically think and write about data;\n",
    "---\n",
    "\n",
    "## Important Dates\n",
    "\n",
    "- Homework release: Fri 8 Oct 2021\n",
    "- **Homework due**: Fri 22 Oct 2021, 23:59\n",
    "- Grade release: Fri 29 Oct 2021\n",
    "\n",
    "---\n",
    "\n",
    "##  Some rules\n",
    "\n",
    "1. You are allowed to use any built-in Python library that comes with Anaconda. If you want to use an external library, \n",
    "you may do so, but must justify your choice.\n",
    "2. Make sure you use the `data` folder provided in the repository in read-only mode. (Or alternatively, be sure you \n",
    "don’t change any of the files.)\n",
    "3. Be sure to provide a textual description of your thought process, the assumptions you made, the solution you \n",
    "implemented, and explanations for your answers. A notebook that only has code cells will not suffice.\n",
    "4. For questions containing the **/Discuss:/** prefix, answer not with code, but with a textual explanation\n",
    " (**in markdown**).\n",
    "5. Back up any hypotheses and claims with data, since this is an important aspect of the course.\n",
    "6. Please write all your comments in English, and use meaningful variable names in your code. Your repo should have a \n",
    "single notebook (plus the required data files) in the *master/main* branch. If there are multiple notebooks present, \n",
    "we will **not grade** anything.\n",
    "7. We will **not run your notebook for you**! Rather, we will grade it as is, which means that only the results \n",
    "contained in your evaluated code cells will be considered, and we will not see the results in unevaluated code cells. \n",
    "Thus, be sure to hand in a **fully-run and evaluated notebook**. In order to check whether everything looks as intended,\n",
    " you can check the rendered notebook on the GitHub website once you have pushed your solution there.\n",
    "8. In continuation to the previous point, interactive plots, such as those generated using `plotly`, should be \n",
    "**strictly avoided**!\n",
    "9. Make sure to print results or dataframes that confirm you have properly addressed the task.\n",
    "\n",
    "---\n",
    "\n",
    "## Context\n",
    "\n",
    "In this project, we are going to analyze data from an experiment about *honesty*. \n",
    "Oftentimes, we are asked to confirm our honest intentions by signing **at the end** of a document. \n",
    "For example, in tax returns or insurance policy forms, we are often asked to sign our names under a text that reads \n",
    "something like \"I hereby certify that the above statements are true and correct to the best of my knowledge.\"\n",
    "\n",
    "However, when individuals sign **after** lying in the form, they may not feel the need to correct the falsehoods they \n",
    "have reported. In that context, it could be that signing at **the beginning** rather than at the end of the document \n",
    "would decrease dishonesty, as those who are filling the form would be aware of the ethical requirements *before* they \n",
    "provide the information in the form.\n",
    "\n",
    "This intuition has led researchers to partner up with a motorcycle insurance company to run a randomized experiment. \n",
    "In this insurance company (as well as in many others), customers had to report the exact *odometer kilometrage*¹ in \n",
    "order for the company to adjust the insurance premiums. Note that motorcycles with lower kilometrage are less likely to \n",
    "have issues, and thus will result in a lower *insurance premium*². Therefore, customers have an incentive to lie, \n",
    "reporting a kilometrage lower than the real value, in order to save money.\n",
    "\n",
    "In the experiment, two different forms were created: one where the signing was done at the end, and another where the \n",
    "signing was done at the beginning. The insurance company then randomized these forms (i.e., each customer received\n",
    " exactly one form, each with probability 50%) and sent back the data that customers had provided. Some noteworthy \n",
    " details on the experiment are that:\n",
    "\n",
    "- All customers involved in the experiment already had a contract with the company and were simply renewing it. \n",
    "In the data that the company provided, they also report the odometer kilometrage for their previous contract.\n",
    "Each policy, therefore, contains two contracts: the \"updated\" contract —where the experiment was conducted— and \n",
    "the \"baseline\" contract, which was the previous contract customers had with the company.\n",
    "- A single insurance policy can insure up to 4 distinct motorcycles.\n",
    "\n",
    "In this assignment, you will take the role of the researcher and work analyzing this data!\n",
    "\n",
    "*Glossary:*\n",
    "1. **odometer kilometrage:** the total distance the motorcycle has traveled.\n",
    "2. **insurance premiums:** the amount of money one pays for an insurance policy.\n",
    "\n",
    "---\n",
    "\n",
    "## The data\n",
    "\n",
    "The company provided you with a compressed `.tsv` file containing one row per policy. \n",
    "The `.tsv` has the following fields:\n",
    "\n",
    "- `condition`: either `Sign Top` or `Sign Bottom`, depending on which form was sent.\n",
    "- `id`: unique identifier for each insurance policy.\n",
    "- `motorcycle_count`: number of motorcycles covered by the insurance policy.\n",
    "- `baseline_motorcycle[1-4]`: odometer kilometrage *reported by the customer* in the *previous (baseline)* contract. \n",
    "- `update_motorcycle[1-4]`: odometer kilometrage *reported by the customer* in the *current (updated)* contract.\n",
    "\n",
    "Some comments:\n",
    "\n",
    "- Recall that a single policy can ensure up to 4 motorcycles, and thus, the suffixes [1-4] indicate that there are 4 \n",
    "versions of these columns in the data (e.g., `baseline_motorcycle1`, `baseline_motorcycle2`, `baseline_motorcycle3`, \n",
    "`baseline_motorcycle4`). \n",
    "- When a policy has fewer than 4 motorcycles, only the columns with the smaller numerals are filled (e.g., if a policy \n",
    "insures one motorcycle,  only `baseline_motorcycle1` and `update_motorcycle1` will be filled).\n",
    "- Note that we only have access to the odometer kilometrage reported by the customers, \n",
    "which may be different from the real kilometrage of the motorcycles."
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    "# Imports you may need\n",
    "import seaborn as sns\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import numpy as np"
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    "### **Task 1** — 4pts\n",
    "\n",
    "Your first task is to load the dataset into memory using pandas. \n",
    "**You should load the data directly from the compressed files.**\n",
    "\n",
    "Here, the files at hand are rather small, and you could easily uncompress the files to disk and work with them as plain \n",
    "text. Why, then, are we asking you to load the files compressed? The reason is that, in your life as a data scientist, \n",
    "this will often not be the case. Then, working with compressed files is key so that you don't receive email from your \n",
    "(often more responsible) colleagues demanding to know how you have managed to fill the entire cluster with your \n",
    "datasets. Another big advantage of compressing files is to simply read files faster. You will often find that reading\n",
    "compressed data on the fly (uncompressing it as you go), is much faster than reading uncompressed data, since reading\n",
    "and writing to disk may be your [bottleneck](https://skipperkongen.dk/2012/02/28/uncompressed-versus-compressed-read/). \n",
    "\n",
    "---\n",
    "\n",
    "**Hint:** `pandas` can open compressed files.\n",
    "\n",
    "**Hint:** In the real world (and in ADA-homework), your file often comes with some weird lines! \n",
    "This time you can safely ignore them (but in the real world you must try to understand why they are there!). \n",
    "Check the `error_bad_lines` or the `on_bad_lines` (depending on your pandas version) parameter on `read_csv`."
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    "data_folder = 'data/'\n",
    "dataframe = pd.read_csv(data_folder + 'data.tsv.gz', sep='\\t', warn_bad_lines=True, error_bad_lines=False)"
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     "text": "b'Skipping line 2142: expected 11 fields, saw 27\\n'\n",
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    "### Task 2 — 12pts\n",
    "\n",
    "As a good data scientist, the first thing you do is to clean up the data and perform some small sanity checks.\n",
    "\n",
    "1. The motorcycle insurance company mentioned that each policy has a unique identifier, but that there may be duplicate \n",
    "    rows (i.e., multiple rows with the same policy identifier). Check if there are duplicate policies and, if so, filter \n",
    "    these rows from the data (always keeping the first).\n",
    "\n",
    "2. According to the company, all policies should have kept the number of motorcycles from the previous contract (i.e., \n",
    "    the number of motorcycles recorded in baseline contracts should equal the number of motorcycles in updated contracts). \n",
    "    Also, the number of odometer readings for each contract should be the same as the number stated in the \n",
    "    `motorcycle_count` variable. Check the data to see if these two things hold. If not, filter the anomalous rows.\n",
    "\n",
    "3. Calculate the mean and the median value of each column related to kilometrages. Are the values obtained plausible? \n",
    "    Visualize the distribution of the data and remove any extreme outliers.\n",
    "\n",
    "4. **Discuss:** In cases where you cannot think of appropriate ways to remove outliers, would you prefer summarizing \n",
    "    numerical data with outliers with the mean or the median? Why?\n",
    "\n",
    "---\n",
    "\n",
    "**Hint:** Here we are considering as outliers numbers that are not plausible in the context of motorcycle kilometrages."
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   "source": [
    "# 2.1 Dropping rows with duplicate id\n",
    "dataframe = dataframe.drop_duplicates(subset=['id'], keep='first')\n",
    "dataframe.set_index('id')"
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         "condition": "Sign Top",
         "baseline_motorcycle1": 499,
         "update_motorcycle1": 45488,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13449
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 37000,
         "update_motorcycle1": 69266,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13450
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 8555,
         "update_motorcycle1": 37195,
         "baseline_motorcycle2": 716,
         "update_motorcycle2": 24164,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13452
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 39948,
         "update_motorcycle1": 63647,
         "baseline_motorcycle2": 56200,
         "update_motorcycle2": 61272,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13453
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 977,
         "update_motorcycle1": 5332,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13454
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 57895,
         "update_motorcycle1": 100111,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13455
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 35084,
         "update_motorcycle1": 62127,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13456
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 49916,
         "update_motorcycle1": 55985,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13457
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 28600,
         "update_motorcycle1": 31463,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13458
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 91775,
         "update_motorcycle1": 125091,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13459
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 101143,
         "update_motorcycle1": 110251,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": 976,
         "update_motorcycle3": 6909,
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13460
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 21277,
         "update_motorcycle1": 54002,
         "baseline_motorcycle2": 3059,
         "update_motorcycle2": 50223,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13461
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 5100,
         "update_motorcycle1": 53596,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": 207000,
         "update_motorcycle3": 214473,
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13462
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 45268,
         "update_motorcycle1": 85452,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13463
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 129450,
         "update_motorcycle1": 145317,
         "baseline_motorcycle2": 65803,
         "update_motorcycle2": 96285,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13464
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 106243,
         "update_motorcycle1": 133721,
         "baseline_motorcycle2": 124357,
         "update_motorcycle2": 126203,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13465
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 151600,
         "update_motorcycle1": 171929,
         "baseline_motorcycle2": 113100,
         "update_motorcycle2": 138714,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": 57237,
         "update_motorcycle4": 84566,
         "motorcycle_count": 3,
         "_deepnote_index_column": 13466
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 60746,
         "update_motorcycle1": 96253,
         "baseline_motorcycle2": 75219,
         "update_motorcycle2": 98352,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13467
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 15301,
         "update_motorcycle1": 53807,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13468
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 135260,
         "update_motorcycle1": 181200,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13469
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 88779,
         "update_motorcycle1": 123286,
         "baseline_motorcycle2": 50714,
         "update_motorcycle2": 72763,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13470
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 143885,
         "update_motorcycle1": 149071,
         "baseline_motorcycle2": 172000,
         "update_motorcycle2": 188581,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13471
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 46000,
         "update_motorcycle1": 91161,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13472
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 121024,
         "update_motorcycle1": 165396,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": 4000,
         "update_motorcycle4": 45390,
         "motorcycle_count": 2,
         "_deepnote_index_column": 13473
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 123505,
         "update_motorcycle1": 143009,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13474
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 16,
         "update_motorcycle1": 1489,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13475
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 110366,
         "update_motorcycle1": 118707,
         "baseline_motorcycle2": 23118,
         "update_motorcycle2": 61993,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13476
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 0,
         "update_motorcycle1": 3630,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13477
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 100467,
         "update_motorcycle1": 117836,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": 20,
         "update_motorcycle3": 32650,
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13478
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 250,
         "update_motorcycle1": 10476,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13479
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 121739,
         "update_motorcycle1": 152607,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13480
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 59624,
         "update_motorcycle1": 71545,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": 140198,
         "update_motorcycle4": 151643,
         "motorcycle_count": 2,
         "_deepnote_index_column": 13481
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 440,
         "update_motorcycle1": 17569,
         "baseline_motorcycle2": 58863,
         "update_motorcycle2": 88571,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13482
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 32050,
         "update_motorcycle1": 33346,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13483
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 20970,
         "update_motorcycle1": 61552,
         "baseline_motorcycle2": 42391,
         "update_motorcycle2": 46815,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13484
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 22606,
         "update_motorcycle1": 56382,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": 467,
         "update_motorcycle4": 27441,
         "motorcycle_count": 2,
         "_deepnote_index_column": 13485
        },
        {
         "condition": "Sign Bottom",
         "baseline_motorcycle1": 30700,
         "update_motorcycle1": 32916,
         "baseline_motorcycle2": 89680,
         "update_motorcycle2": 139570,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "_deepnote_index_column": 13486
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 29884,
         "update_motorcycle1": 35459,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13487
        },
        {
         "condition": "Sign Top",
         "baseline_motorcycle1": 22930,
         "update_motorcycle1": 37888,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "_deepnote_index_column": 13488
        }
       ]
      },
      "text/plain": "         condition  baseline_motorcycle1  update_motorcycle1  \\\nid                                                             \n1         Sign Top                   896               39198   \n2      Sign Bottom                 21396               63511   \n3      Sign Bottom                 21340               37460   \n4      Sign Bottom                 23912               59136   \n5      Sign Bottom                 16862               59292   \n...            ...                   ...                 ...   \n13484     Sign Top                 20970               61552   \n13485     Sign Top                 22606               56382   \n13486  Sign Bottom                 30700               32916   \n13487     Sign Top                 29884               35459   \n13488     Sign Top                 22930               37888   \n\n       baseline_motorcycle2  update_motorcycle2  baseline_motorcycle3  \\\nid                                                                      \n1                       NaN                 NaN                   NaN   \n2                   32659.0             47605.0                   NaN   \n3                   44998.0             59002.0                   NaN   \n4                       NaN                 NaN                   NaN   \n5                       NaN                 NaN                   NaN   \n...                     ...                 ...                   ...   \n13484               42391.0             46815.0                   NaN   \n13485                   NaN                 NaN                   NaN   \n13486               89680.0            139570.0                   NaN   \n13487                   NaN                 NaN                   NaN   \n13488                   NaN                 NaN                   NaN   \n\n       update_motorcycle3  baseline_motorcycle4  update_motorcycle4  \\\nid                                                                    \n1                     NaN                   NaN                 NaN   \n2                     NaN                   NaN                 NaN   \n3                     NaN                   NaN                 NaN   \n4                     NaN                   NaN                 NaN   \n5                     NaN                   NaN                 NaN   \n...                   ...                   ...                 ...   \n13484                 NaN                   NaN                 NaN   \n13485                 NaN                 467.0             27441.0   \n13486                 NaN                   NaN                 NaN   \n13487                 NaN                   NaN                 NaN   \n13488                 NaN                   NaN                 NaN   \n\n       motorcycle_count  \nid                       \n1                     1  \n2                     2  \n3                     2  \n4                     1  \n5                     1  \n...                 ...  \n13484                 2  \n13485                 2  \n13486                 2  \n13487                 1  \n13488                 1  \n\n[13488 rows x 10 columns]",
      "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>condition</th>\n      <th>baseline_motorcycle1</th>\n      <th>update_motorcycle1</th>\n      <th>baseline_motorcycle2</th>\n      <th>update_motorcycle2</th>\n      <th>baseline_motorcycle3</th>\n      <th>update_motorcycle3</th>\n      <th>baseline_motorcycle4</th>\n      <th>update_motorcycle4</th>\n      <th>motorcycle_count</th>\n    </tr>\n    <tr>\n      <th>id</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      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Sign Top</td>\n      <td>896</td>\n      <td>39198</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Sign Bottom</td>\n      <td>21396</td>\n      <td>63511</td>\n      <td>32659.0</td>\n      <td>47605.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Sign Bottom</td>\n      <td>21340</td>\n      <td>37460</td>\n      <td>44998.0</td>\n      <td>59002.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Sign Bottom</td>\n      <td>23912</td>\n      <td>59136</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Sign Bottom</td>\n      <td>16862</td>\n      <td>59292</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13484</th>\n      <td>Sign Top</td>\n      <td>20970</td>\n      <td>61552</td>\n      <td>42391.0</td>\n      <td>46815.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>13485</th>\n      <td>Sign Top</td>\n      <td>22606</td>\n      <td>56382</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>467.0</td>\n      <td>27441.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>13486</th>\n      <td>Sign Bottom</td>\n      <td>30700</td>\n      <td>32916</td>\n      <td>89680.0</td>\n      <td>139570.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>13487</th>\n      <td>Sign Top</td>\n      <td>29884</td>\n      <td>35459</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13488</th>\n      <td>Sign Top</td>\n      <td>22930</td>\n      <td>37888</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>13488 rows × 10 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 58,
    "execution_start": 1634805505204,
    "source_hash": "fc7debbf",
    "tags": [],
    "cell_id": "00006-d3d2e46c-8b34-4fd9-af3b-7613c2d9b540",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# baseline and updated kilometrages columns\n",
    "baselines = ['baseline_motorcycle1', 'baseline_motorcycle2', 'baseline_motorcycle3', 'baseline_motorcycle4']\n",
    "updates = ['update_motorcycle1', 'update_motorcycle2', 'update_motorcycle3', 'update_motorcycle4']\n",
    "\n",
    "# Wong colorblind-safe palette\n",
    "colours = [\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\"]"
   ],
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1,
    "execution_start": 1634805505262,
    "source_hash": "ae4a868d",
    "cell_id": "00007-a38ae56d-ce7d-49fb-b4e2-89a17fa64623",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 2.2 Dropping anomalous rows: number of motorcycles should be the same across \n",
    "# motorcycle_count, baseline_motorcycle, and update_motorcycle\n",
    "\n",
    "# count baseline values and updated values\n",
    "dataframe['baseline_count'] = dataframe[baselines].count(axis = 1)\n",
    "dataframe['update_count'] = dataframe[updates].count(axis = 1)\n",
    "\n",
    "# keep rows where count is equal to the motorcycle_count provided\n",
    "dataframe = dataframe[(dataframe[\"motorcycle_count\"] == dataframe[\"baseline_count\"]) & (dataframe[\"motorcycle_count\"] == dataframe[\"update_count\"])]\n",
    "dataframe = dataframe.drop(['baseline_count', 'update_count'], axis = 1)"
   ],
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "allow_embed": false,
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 11970,
    "execution_start": 1634805505263,
    "source_hash": "e5d1d445",
    "cell_id": "00008-40707552-6bf5-4db6-86ff-969d380b2fcc",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 2.3a Calculating mean and median of each column and visualizing data\n",
    "# interleave the columns \n",
    "\n",
    "def plot_all_kilometrages(title):\n",
    "    \"\"\"\n",
    "    Plots histograms of all 4 baseline and updated kilometrages.\n",
    "    \"\"\"\n",
    "    kilo_cols = [val for pair in zip(baselines, updates) for val in pair]\n",
    "    kilometrages = dataframe[kilo_cols]\n",
    "\n",
    "    mean = kilometrages.mean(axis = 0)\n",
    "    median = kilometrages.median(axis = 0)\n",
    "\n",
    "    fig, axs = plt.subplots(4,2, sharex = True, sharey = True, figsize = (20,10))\n",
    "    fig.suptitle(title, fontsize=16)\n",
    "\n",
    "    for i, ax in enumerate(axs.flat):\n",
    "        data = dataframe[kilo_cols[i]].values\n",
    "        ax.hist(data, color = colours[i], bins=np.logspace(0, np.log10(10**7), 100))\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')    \n",
    "        \n",
    "        ax.set_title(kilo_cols[i])\n",
    "        ax.axvline(mean[i], color = 'green', label = f\"mean: {np.round(mean[i], 1)}\")\n",
    "        ax.axvline(median[i], color = 'red', label = f\"median: {np.round(median[i], 1)}\")\n",
    "        ax.legend()\n",
    "\n",
    "    fig.supxlabel(\"Kilometers\")\n",
    "    fig.supylabel(\"Frequency\")\n",
    "\n",
    "plot_all_kilometrages(title='Data with outliers')"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x720 with 8 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 1281,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17206,
    "execution_start": 1634805517289,
    "source_hash": "8ed09792",
    "cell_id": "00009-c770677a-3283-4bbc-b6db-cd30dcb3c956",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 2.3b Dropping outliers, data inside 1-percent quantile and outside 99-percent quantile, and visualizing data\n",
    "\n",
    "def remove_outliers(df, cols, bottom_percentile, upper_percentile):\n",
    "    \"\"\"\n",
    "    In the given DataFrame, remove rows where the specified column values don't fall between the percentile bounds.\n",
    "    \"\"\"\n",
    "    Q1 = df[cols].quantile(bottom_percentile)\n",
    "    Q3 = df[cols].quantile(upper_percentile)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    df = df[~((df[cols] < (Q1 - 1.5 * IQR)) |(df[cols] > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "    return df\n",
    "    \n",
    "\n",
    "dataframe = remove_outliers(dataframe, baselines+updates,0.01,0.99)\n",
    "\n",
    "plot_all_kilometrages(title='Data without outliers')"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x720 with 8 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 1281,
       "height": 712
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2.4\n",
    "Since the outliers are only a few and their values are extreme, it would be better to represent data with the median, as it is less influenced by the value of those outliers (more robust).\n"
   ],
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1,
    "execution_start": 1633966912539,
    "source_hash": "dd0d7a77",
    "cell_id": "00010-703d4257-8af8-4df7-a0b1-a1988ca223b1",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 3 — 12pts\n",
    "\n",
    "One of the challenges with the current data is that it is not clear how to handle policies with multiple motorcycles.\n",
    "\n",
    "1. Create three additional columns in the dataframe, `baseline_average`, `update_average`, and `diff_average`. These \n",
    "    should contain, respectively, the average value for `baseline_motorcycle[1-4]` for all motorcycles insured; the average\n",
    "    value for `update_motorcycle[1-4]`; and the difference between the average updated value and the average baseline \n",
    "    value.\n",
    "2. **Discuss:** Propose another way of aggregating distinct motorcycles from a single policy. Point out a way in which \n",
    "    this aggregation may be either better or worse than taking the average for the purpose of comparing the two scenarios in\n",
    "    the study (sign at the top vs. sign at the bottom).\n",
    "3. To get a sense of the impact that policies with multiple motorcycles may have in the study, let's perform some \n",
    "    additional analyses. Plot the distribution of motorcycles per policy in the data. Your plot should allow for the \n",
    "    meaningful comparison of the values shown.\n",
    "4. In 3.1, you calculated the average kilometrage for each policy for the updated contract. Calculate the mean and the \n",
    "    standard deviation of these values (which should be in the column `update_average`).\n",
    "5. Another meaningful metric is the average kilometrage **per motorcycle**, not distinguishing motorcycles in different \n",
    "    policies (i.e., pooled across policies). **Considering only policies with 4 motorcycles insured,** calculate the mean \n",
    "    and the standard deviation of the kilometrage per motorcycle in the updated contract.\n",
    "6. **Discuss:** To analyze whether signing at the top decreases dishonesty, we could consider the data in different \n",
    "    ways. We could first calculate the average value per motorcycle within each policy and have one data point per policy **or** we could analyze the data at the motorcycle level and have one data point per motorcycle in the dataset. \n",
    "    According to questions 3.4 and 3.5, how may this decision influence the results of the study?\n",
    "---"
   ],
   "metadata": {
    "cell_id": "00011-7735aeaa-0d2b-435e-a92a-23954915ad8d",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 22,
    "execution_start": 1634805534502,
    "source_hash": "aa541137",
    "cell_id": "00012-d6054c4c-55bd-4adc-8918-4ec089cf93e5",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 3.1 Creating additional columns\n",
    "dataframe['baseline_average'] = dataframe[baselines].mean(axis = 1)\n",
    "dataframe['update_average'] = dataframe[updates].mean(axis = 1)\n",
    "dataframe['diff_average'] = dataframe['update_average'] - dataframe['baseline_average']"
   ],
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.2  \n",
    "We could calculate the median value for both baseline and update average. Median could be better because,as stated before, it would be more robust to outliers and, calculating the average of the medians would give more significant values. In this case  though, outliers have already been removed so it wouldn't make much difference. \n",
    "Another kind of aggregation might be the difference between the sum of updated values and the sum of baseline values. This gives the actual amount of kilometres percurred in a year by the person interested by the insurance policy. Results aren't much different, but if previously the difference between signed at the top and signed at the bottom averages in updated policies was 2427, now it increased to 3327. This gives us an even stronger proof that signed at the bottom documents have lower kilometrage's values. "
   ],
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 0,
    "execution_start": 1633966912586,
    "source_hash": "b8086366",
    "cell_id": "00013-6f2b425c-54da-4c38-90b3-1c25aa54dce1",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 75,
    "execution_start": 1634805534532,
    "source_hash": "9f756bd8",
    "tags": [],
    "cell_id": "00014-f04ba5a3-8965-4003-8358-db55628bcac2",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "#3.2 proof\n",
    "dataframe['diff_sum']= abs(dataframe[updates].sum(axis=1)-dataframe[baselines].sum(axis=1))\n",
    "print(dataframe.groupby('condition').agg('mean')['diff_sum'])\n",
    "print(dataframe.groupby('condition').agg('mean')['diff_average'])\n",
    "dataframe=dataframe.drop('diff_sum', axis= 1)"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "text": "condition\nSign Bottom    36677.924545\nSign Top       40004.264995\nName: diff_sum, dtype: float64\ncondition\nSign Bottom    23673.122877\nSign Top       26100.566078\nName: diff_average, dtype: float64\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 361,
    "execution_start": 1634805534611,
    "source_hash": "6cc3af90",
    "cell_id": "00015-683e514d-845e-4119-83bd-18595a363ba2",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 3.3 Distribution of number of motorcycles\n",
    "axs = dataframe['motorcycle_count'].value_counts().plot.bar();\n",
    "plt.title(\"Insured motorcycles per policy\")\n",
    "axs.set_xlabel(\"Motorcycle number\")\n",
    "axs.set_ylabel(\"Frequency\");"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 395,
       "height": 275
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1634805534976,
    "source_hash": "bc7631f1",
    "tags": [],
    "cell_id": "00016-f07e408f-15ee-4a6d-9aa1-c155daa8e64d",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 3.4 Calculating mean and standard deviation for average kilometrage per updated policy\n",
    "update_average_mean, update_average_sd = dataframe['update_average'].agg(['mean', 'std'])\n",
    "\n",
    "print(f\"\"\"Average kilometrage per updated policy -\n",
    "    mean: {update_average_mean:.0f} km\n",
    "    sd: {update_average_sd:.0f} km\n",
    "    normalized_sd: \"\"\" + str(update_average_sd/update_average_mean))"
   ],
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "text": "Average kilometrage per updated policy -\n    mean: 91721 km\n    sd: 49860 km\n    normalized_sd: 0.5436077467063912\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 32,
    "execution_start": 1634805535001,
    "source_hash": "212db04b",
    "tags": [],
    "cell_id": "00017-22a60d2c-3adc-4885-8aa9-c9c71e7b0bcd",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 3.5 Calculating mean and standard deviation for kilometrage per motorcycle (considering updated policies with 4 motorcycles only)\n",
    "kilometrage_mean, kilometrage_sd = dataframe[dataframe['motorcycle_count'] == 4][updates].agg(['mean', 'std']).values\n",
    "\n",
    "for i, (mn, sd) in enumerate(zip(kilometrage_mean, kilometrage_sd)):\n",
    "    print(f\"\"\"Average kilometrage motorcycle {i+1} -\n",
    "    mean: {mn :.0f} km\n",
    "    sd: {sd :.0f} km\n",
    "    normalized_sd: \"\"\" + str(sd/mn))\n",
    "    "
   ],
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "text": "Average kilometrage motorcycle 1 -\n    mean: 67635 km\n    sd: 42535 km\n    normalized_sd: 0.6288919596617312\nAverage kilometrage motorcycle 2 -\n    mean: 121408 km\n    sd: 74218 km\n    normalized_sd: 0.6113129874621938\nAverage kilometrage motorcycle 3 -\n    mean: 103314 km\n    sd: 64274 km\n    normalized_sd: 0.6221203642074731\nAverage kilometrage motorcycle 4 -\n    mean: 129108 km\n    sd: 45750 km\n    normalized_sd: 0.3543531044069662\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6\n",
    "Considering one data point per policy is better, since we obtain more homogeneous data with a lower standard deviation.\n",
    "\n",
    "Looking at values from question __3.5__ doesn't make sense because they are calculated considering only policies with 4 motorcycles,which constitute only a small part of the dataset (as we can see in question __3.3__). If we calculate the same values considering all policies we obtain similar averages and similar standard deviations, all higher than the standard deviation obtained by consider one data point per policy.\n",
    " \n",
    "We might instead divide the policies into four populations, based on `motorcycle_count`. This way we can then calculate the average difference between updated policies and baseline ones, dividing the result among signing position of the updated policies. Dividing the data into four populations considering one data point per motorcycle is also worse because populations wouldn't be independent, since kilometrage values of motorcycles from the same policy are all from the same document with the same signing position.  \n",
    "\n",
    "On the other hand, this decision would probably not influence the result of the study, since both ways prove that signed at the top documents have always sligthly lower values."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00018-fc0670d6-1518-4f60-921e-0c0f2cba52e3",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 288,
    "execution_start": 1634805535043,
    "source_hash": "97f543f8",
    "tags": [],
    "cell_id": "00019-e5f46fb7-92dd-40a1-92da-eb45100c42b7",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "#proof and calculus to support 3.6\n",
    "kilometrage_mean, kilometrage_sd = dataframe[updates].agg(['mean', 'std']).values\n",
    "for i, (mn, sd) in enumerate(zip(kilometrage_mean, kilometrage_sd)):\n",
    "    print(f\"\"\"Average kilometrage motorcycle {i+1} -\n",
    "    mean: {mn :.0f} km\n",
    "    sd: {sd :.0f} km\n",
    "    normalized_sd: \"\"\" + str(sd/mn))\n",
    "policy_type = [\"Sign Bottom\", \"Sign Top\"]\n",
    "\n",
    "print(dataframe.groupby(['condition','motorcycle_count']).agg('mean')['diff_average'])\n",
    "dataframe['diff_of_1']= dataframe[updates[0]]-dataframe[baselines[0]]\n",
    "print(dataframe.groupby('condition').agg('mean')['diff_of_1'])\n",
    "dataframe['diff_of_2']= dataframe[updates[1]]-dataframe[baselines[1]]\n",
    "print(dataframe.groupby('condition').agg('mean')['diff_of_2'])\n",
    "dataframe['diff_of_3']= dataframe[updates[2]]-dataframe[baselines[2]]\n",
    "print(dataframe.groupby('condition').agg('mean')['diff_of_3'])\n",
    "dataframe['diff_of_4']= dataframe[updates[3]]-dataframe[baselines[3]]\n",
    "print(dataframe.groupby('condition').agg('mean')['diff_of_4'])\n",
    "\n"
   ],
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "text": "Average kilometrage motorcycle 1 -\n    mean: 91934 km\n    sd: 56558 km\n    normalized_sd: 0.6152009067934034\nAverage kilometrage motorcycle 2 -\n    mean: 91338 km\n    sd: 55240 km\n    normalized_sd: 0.604786183154566\nAverage kilometrage motorcycle 3 -\n    mean: 91306 km\n    sd: 57938 km\n    normalized_sd: 0.6345414902225907\nAverage kilometrage motorcycle 4 -\n    mean: 92753 km\n    sd: 55957 km\n    normalized_sd: 0.603292239900256\ncondition    motorcycle_count\nSign Bottom  1                   23276.944780\n             2                   24048.274373\n             3                   24602.005479\n             4                   23282.476190\nSign Top     1                   26179.650279\n             2                   26037.316445\n             3                   25802.350638\n             4                   26405.964286\nName: diff_average, dtype: float64\ncondition\nSign Bottom    23622.255073\nSign Top       26211.199589\nName: diff_of_1, dtype: float64\ncondition\nSign Bottom    24151.039928\nSign Top       25751.245884\nName: diff_of_2, dtype: float64\ncondition\nSign Bottom    24884.054773\nSign Top       25487.075949\nName: diff_of_3, dtype: float64\ncondition\nSign Bottom    23283.945122\nSign Top       27670.462500\nName: diff_of_4, dtype: float64\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 4 — 18pts\n",
    "\n",
    "Now that we have compressed the kilometrage reported in each contract into a single number, it is time to analyze the \n",
    "million-dollar question: ***does signing at the top decrease dishonesty?***\n",
    "\n",
    "1. Considering the updated contracts, calculate the mean and the standard deviation of the average kilometrage in \n",
    "    1. policies where the signing was done at the bottom; and \n",
    "    2. policies where the signing was done at the top. Additionally, plot the two kilometrage distributions.\n",
    "2. Determine whether the difference between the two scenarios mentioned in 1. is statistically significant.\n",
    "3. **Discuss:** Recall that the assignment of the different contracts (*sign at the top* vs. *sign at the bottom*) was \n",
    "    done at random. According to the results in 1. and 2., does signing at the top decrease dishonesty? \n",
    "4.  **Discuss:** Given that no intervention was performed in the baseline contract (i.e., everyone signed at the \n",
    "    bottom). Would you expect any differences between the average values between *sign at the top* vs. *sign at the bottom*\n",
    "    conditions for the baseline contract? Note that this is related to columns `baseline_motorcycle[1-4]`.\n",
    "5. Calculate the mean and the standard deviation for the baseline contracts of the two groups (sign at the top vs. sign \n",
    "    at the bottom). Additionally, plot the two kilometrage distributions.\n",
    "6. Determine whether the difference between the two scenarios mentioned in 5. is statistically significant.\n",
    "\n",
    "---\n",
    "\n",
    "**Hint:** Recall that the column `condition` encodes the experimental condition in which the customer fell when \n",
    "reporting the values shown in columns `update_motorcycle[1-4]`!\n",
    "\n",
    "**Hint:** To determine if the means of two sets of data are significantly different from each other, you may want to use\n",
    " the `ttest_ind` function implemented in the \n",
    " [scipy library](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html). To learn more about \n",
    " Student's t-test, check out this [Wikipedia page](https://en.wikipedia.org/wiki/Student%27s_t-test)."
   ],
   "metadata": {
    "cell_id": "00020-42317c61-9ed3-4eaa-bba5-03bef4347631",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1,
    "execution_start": 1634805535183,
    "source_hash": "463c23b0",
    "tags": [],
    "cell_id": "00021-6267b3b7-83aa-4823-80e4-2942cd26ad6a",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "def plot_mean_std(ax, mean, std):\n",
    "    \"\"\"\n",
    "    Adds the mean and standard deviation lines to the given axes of a plot.\n",
    "    \"\"\"\n",
    "    lower, upper = mean - std, mean + std\n",
    "    ax.axvspan(lower, upper, alpha=0.15, color='red')\n",
    "    ax.axvline(lower, linestyle = \"--\", color = 'red', label = 'mean - std: '+ str(np.round(lower, 1)))\n",
    "    ax.axvline(upper, linestyle = \"--\", color = 'red', label = 'mean + std: '+ str(np.round(upper, 1)))\n",
    "    ax.axvline(mean, color = 'orange', label = 'mean: '+ str(np.round(mean, 1)))"
   ],
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2542,
    "execution_start": 1634805535185,
    "source_hash": "9449bf3d",
    "cell_id": "00022-d08c269e-353e-4f5c-a137-7029208473b9",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 4.1 mean and std for average kilometrage per updated policy and seperated on condition\n",
    "\n",
    "policy_type = [\"Sign Bottom\", \"Sign Top\"]\n",
    "\n",
    "# for each condition, aggregate the mean and standard deviation of the average kilometrage per updated policy\n",
    "mean, std = dataframe.groupby('condition')['update_average'].agg(['mean', 'std']).T.values\n",
    "\n",
    "fig, axs = plt.subplots(1,2, sharex = True, sharey = True, figsize = (10,5))\n",
    "fig.suptitle('Average kilometrage per updated policy', fontsize=16)\n",
    "\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    ax.hist(dataframe[dataframe['condition'] == policy_type[i]]['update_average'].values, bins = 100, color = colours[i+1])\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_title(policy_type[i])\n",
    "    \n",
    "    # plot std as a range around the mean\n",
    "    plot_mean_std(ax, mean[i], std[i])\n",
    "    ax.legend()\n",
    "\n",
    "\n",
    "\n",
    "fig.supxlabel(\"Kilometers\")\n",
    "fig.supylabel(\"Frequency\");"
   ],
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 649,
       "height": 363
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 4,
    "execution_start": 1634805537818,
    "source_hash": "deedd08e",
    "tags": [],
    "cell_id": "00023-96c4be15-8a87-4595-9c28-cdd1858bde51",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 4.2 Testing if the two independent datasets of 'updated contract kilometrage' have identical average values using the ttest_ind function from scipy\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "ttest_ind(\n",
    "    dataframe[dataframe['condition'] == policy_type[0]]['update_average'].values,\n",
    "    dataframe[dataframe['condition'] == policy_type[1]]['update_average'].values\n",
    "    )"
   ],
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "Ttest_indResult(statistic=14.925373355138145, pvalue=5.650578748070613e-50)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.2 \n",
    "`p_value` is less than 0.05, therefore we reject the null hypothesis that the kilometrages are the same, there is a significant difference."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00024-541b6a76-828e-4f06-a73b-bbf9f3acd04b",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.3\n",
    "The `p_value` is less than 0.05, therefore we can reject the null hypothesis that the kilometrages are the same. This means that there is a significant difference between the two populations.\n",
    "\n",
    "Considering this and that contracts signed at the bottom have a remarkable higher kilometrage, we can assume that signing at the top does indeed decrease dishonesty."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00025-f9aed3bb-139a-444d-994b-92c5ded3b269",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.4\n",
    "We would expect a statistically insignificant difference in the average kilometrage among the baseline contracts, since the Signed Bot vs Signed Top difference didn't exist in those contracts, i.e. they should be part of the same population."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00026-a27585ab-d0d6-4344-b8b8-c6807882f147",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2594,
    "execution_start": 1634805537819,
    "source_hash": "2b935aed",
    "tags": [],
    "cell_id": "00027-2ac66a32-0afb-455b-8771-9548be7b390f",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 4.5 mean and std for average kilometrage per baseline policy and seperated on condition\n",
    "\n",
    "policy_type = [\"Sign Bottom\", \"Sign Top\"]\n",
    "\n",
    "# for each condition, aggregare the mean and standard deviation of the average baseline kilometrage per contract\n",
    "mean, std = dataframe.groupby('condition')['baseline_average'].agg(['mean', 'std']).T.values\n",
    "\n",
    "fig, axs = plt.subplots(1,2, sharex = True, sharey = True, figsize = (10,5))\n",
    "fig.suptitle('Average kilometrage per baseline policy (seperated based on updated contract condition)', fontsize=16)\n",
    "\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    ax.hist(dataframe[dataframe['condition'] == policy_type[i]]['baseline_average'].values, bins = 100, color = colours[i+1])\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_title(policy_type[i])\n",
    "    plot_mean_std(ax, mean[i], std[i])\n",
    "    ax.legend()\n",
    "\n",
    "fig.supxlabel(\"Kilometers\")\n",
    "fig.supylabel(\"Frequency\")"
   ],
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 17,
     "data": {
      "text/plain": "Text(0.02, 0.5, 'Frequency')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 740,
       "height": 363
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 29,
    "execution_start": 1634805540417,
    "source_hash": "6d0730a0",
    "tags": [],
    "cell_id": "00028-ce368a76-ef46-4c89-b7b5-154b274c8c31",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 4.6 Testing if the two independent datasets of 'baseline contract kilometrage' have identical average values using the ttest_ind function from scipy\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "ttest_ind(\n",
    "    dataframe[dataframe['condition'] == policy_type[0]]['baseline_average'].values,\n",
    "    dataframe[dataframe['condition'] == policy_type[1]]['baseline_average'].values\n",
    "    )"
   ],
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "Ttest_indResult(statistic=18.453226727861935, pvalue=4.1293195903442773e-75)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.6 \n",
    "`p_value` is less than 0.05, therefore we reject the null hypothesis that the kilometrages are the same, there is a significant difference."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00029-0aed18bd-4641-4431-a630-8e9fdfbee6bc",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Interlude: Something is dodgy about this data...\n",
    "\n",
    "---\n",
    "\n",
    "Noticing the oddities in the data, you send an email to your contact at the insurance company asking about the integrity\n",
    " of the data. Their reply seems like something out of a detective movie:\n",
    "\n",
    "> Dear researcher, I believe that this data is not to be trusted. I can't talk right now but will send further hints \n",
    ">throughout the week.\n",
    "\n",
    "As a researcher, you know that [data integrity](https://en.wikipedia.org/wiki/Data_integrity) is a big deal, and decide\n",
    " to investigate."
   ],
   "metadata": {
    "cell_id": "00030-464c4341-3e28-4262-b520-fe99a43e4739",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 5 — 9pts\n",
    "\n",
    "The first hint you receive is: *there is something off about the kilometrage values in the data.*\n",
    "\n",
    "1. For the baseline contract and for the updated contract, plot the distribution of odometer kilometrage reported for \n",
    "    the first motorcycle insured in each policy. \n",
    "2. For the first motorcycle insured by the company in each policy, plot the difference between the number of kilometers\n",
    "    reported in the baseline and in the updated contract.\n",
    "3. **Discuss:** why is this data unlikely to have originated from real odometer readings?\n",
    "\n",
    "---"
   ],
   "metadata": {
    "cell_id": "00031-bc5ffd24-f04c-4667-a351-42f01acec222",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2545,
    "execution_start": 1634805540446,
    "source_hash": "d614389f",
    "cell_id": "00032-1a674766-e354-43fa-8b69-772dfac7b36e",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 5.1 Plotting the updated and baseline kilometrage for the first motorcycle of each policy\n",
    "\n",
    "contract_type = ['baseline contract', 'updated contract']\n",
    "\n",
    "kilometrages = [dataframe[baselines[0]], dataframe[updates[0]]]\n",
    "\n",
    "fig, axs = plt.subplots(1,2, sharex = True, sharey = True, figsize = (10,5))\n",
    "fig.suptitle('First motorcycle kilometrage per policy', fontsize=16)\n",
    "\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    ax.hist(kilometrages[i].values, bins = 100, color = colours[i+1])\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_title(contract_type[i])\n",
    "\n",
    "fig.supxlabel(\"Kilometers\")\n",
    "fig.supylabel(\"Frequency\");"
   ],
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 649,
       "height": 363
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 638,
    "execution_start": 1634805543065,
    "source_hash": "79377ba4",
    "tags": [],
    "cell_id": "00033-cef98952-54a1-4ff0-8ace-0a7025b542f4",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 5.2 Plotting the difference in updated and baseline kilometrage for the first motorcycle of each policy\n",
    "\n",
    "kilometrages_diff = dataframe[updates[0]] - dataframe[baselines[0]]\n",
    "\n",
    "plt.hist(x=kilometrages_diff, bins=100, color=colours[1])\n",
    "plt.suptitle('Difference bw updated and baseline kilometrage per policy \\n(first motorcycle only)', fontsize=16)\n",
    "plt.xlabel(\"Kilometers\")\n",
    "plt.ylabel(\"Frequency\");"
   ],
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 497,
       "height": 291
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 453,
    "execution_start": 1634805543718,
    "source_hash": "25bdbd7b",
    "tags": [],
    "cell_id": "00034-606c96ca-071a-4240-a6ed-f56a787a3792",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "stats.probplot(kilometrages_diff, dist='norm', plot=plt)\n",
    "\n",
    "plt.suptitle(\"Normal distribution\");"
   ],
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 409,
       "height": 291
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 5.3 \n",
    "We would expect the data in the histogram above to follow a normal distribution (bell curve), but it doesn't, as it can be seen in the probplot above.  \n",
    "\n",
    "A smaller number of people should have used their motorcycle very little or very much, in the duration between the baseline and updated contract, while the large majority of people would have driven the motorcycles around some average distance. Instead, we can see how the population seems to follow some sort of uniform distribution, which is unlikely."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00035-7a661855-8865-48c4-8033-1ead42a1b241",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 6 — 15pts\n",
    "\n",
    "The second hint you receive is: *there is something off with the last digits of the reported kilometrages.*\n",
    "\n",
    "1. Considering the baseline kilometrage of the first motorcycle in each contract (`baseline_motorcycle1`), plot the \n",
    "    distribution of occurrences of the last three digits. Repeat the same for the updated kilometrage of the first \n",
    "    motorcycle in each contract (`update_motorcycle1`).\n",
    "\n",
    "3. **Discuss:** What are the differences between the two distributions?\n",
    "\n",
    "4. **Discuss:** Hypothesize a **plausible** cause for the patterns observed in the digit distribution of the baseline\n",
    "    kilometrage of the first motorcycle.\n",
    "    \n",
    "---\n",
    "\n",
    "**Hint:** The last three digits of the number 12314 are 314. Note that you should consider the three last digits together,\n",
    " not separately!"
   ],
   "metadata": {
    "cell_id": "00036-d6a8e80e-e75e-4cf2-9f69-20725934d972",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1899,
    "execution_start": 1634805544193,
    "source_hash": "55be1fd3",
    "cell_id": "00037-c4d4dc6e-cdd7-4930-82f7-1e047c6cdd1a",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 6.1 Plotting the distribution of occurrences of the last three digits\n",
    "\n",
    "def plot_3digits(data1, data2, title, label1, label2):\n",
    "    \"\"\"\n",
    "    Plot the distribution of last three digits in the values of the two provided Series.\n",
    "    \"\"\"\n",
    "    # calculate last 3 digits of the kilometrages using the modulo operator\n",
    "    data1_last3digits = data1 % 1000\n",
    "    data2_last3digits = data2 % 1000\n",
    "\n",
    "    # construct the plot\n",
    "    plt.yscale(\"log\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Last 3 digits\")\n",
    "    plt.ylabel(\"Number of occurrences\")\n",
    "\n",
    "    # plot the histograms\n",
    "    data1_last3digits.hist(bins = 100, alpha = 1, label = label1, color=colours[1])\n",
    "    data2_last3digits.hist(bins = 100, alpha = 0.6, label = label2, color=colours[2])\n",
    "\n",
    "    # show the plot\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    # to support the plot, please see the most common values:\n",
    "    print(\"Value counts for the baseline: \\n\", data1_last3digits.value_counts().iloc[:5])\n",
    "    print(\"Value counts for the update: \\n\", data2_last3digits.value_counts().iloc[:5])\n",
    "\n",
    "plot_3digits(dataframe[\"baseline_motorcycle1\"], dataframe[\"update_motorcycle1\"], \"Kilometrages - last 3 digits\", \"baseline\", \"update\")"
   ],
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 388,
       "height": 278
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "text": "Value counts for the baseline: \n 0      1397\n500     143\n200      73\n600      64\n100      57\nName: baseline_motorcycle1, dtype: int64\nValue counts for the update: \n 60     26\n738    25\n517    24\n902    24\n980    23\nName: update_motorcycle1, dtype: int64\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 6.2\n",
    "The last 3 digits of the `update_motorcycle1` column follow a uniform distribution. The last 3 digits of the `baseline_motorcycle1` column also follow a distribution close to normal, with an exception of some clearly visible peaks: the largest one is for value `000` and some other peaks around all the other 'round' numbers (`100`, `200`, ..., `900`).\n",
    "\n",
    "### 6.3\n",
    "The large peak around `000` may be a result of people registering new motorcycles with no kilometrage yet. The peaks around 'round' numbers can be explained as follows: it is possible that some users entered approximate, rounded values for the baseline contract, which would explain the peaks in the histogram for `baseline_motorcycle1`. On the other hand, for the contract update, the first motorcycle does not have a peak around 0 anymore because none of the motorcyles are new, all of them have already had a previous contract i.e. have been driven already. Moreover, the users were explicitly asked to report the *'exact odometer kilometrage'*, so the peaks for the round values  in the histogram for `update_motorcycle1` are gone and the values follow the expected uniform distribution."
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00038-61527b4c-13f4-4a63-9dec-0b919b741f42",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 7 — 15pts\n",
    "\n",
    "The third (and last) hint you receive is: *there is something wrong with the fonts.*\n",
    "\n",
    "Your anonymous source explains that the `.tsv` file sent to you was derived from an Excel sheet. There, some of the rows\n",
    " were written in the font *Helvetica* while others were written in the font *Arial*. Your anonymous source sends you a \n",
    " file with the relation between policy id and the font used in the respective row (`fonts.csv`), and tells you that the\n",
    "  rows with *Helvetica* font were fabricated.\n",
    "\n",
    "1. Load `fonts.csv` and merge the `font` column into your pandas dataframe.\n",
    "2. Considering only the kilometrage of the first motorcycle in each policy, calculate the number of rows with the Arial  and the Helvetica font, respectively. Also, plot the distribution of baseline kilometers for rows with the Arial font  and for rows with the Helvetica font.\n",
    "3. Similar to what was done in task 6. Plot the distribution of the last three digits for 1) the first motorcycle in  each baseline contract (`baseline_motorcycle1`); and 2) the first motorcycle in each updated contract  (`update_motorcycle1`).\n",
    "4. **Discuss:** Why do these analyses provide evidence that the rows with Helvetica font were fabricated?\n",
    "\n",
    "---"
   ],
   "metadata": {
    "cell_id": "00039-03bdccfe-b8af-4b77-8cdd-b7b63afbc619",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 232,
    "execution_start": 1634805546105,
    "source_hash": "d10f9794",
    "cell_id": "00040-ca6d6a04-08d1-44c9-b26b-42b63eab7cd7",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 7.1 Load fonts.csv and merge the font column into your pandas dataframe.\n",
    "\n",
    "# load the data\n",
    "fonts = pd.read_csv(\"data/fonts.csv\")\n",
    "fonts = fonts.set_index('id')\n",
    "\n",
    "# and perform the join\n",
    "dataframe = dataframe.join(fonts, on='id', how='left')\n",
    "dataframe.head()"
   ],
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 5,
       "column_count": 19,
       "columns": [
        {
         "name": "condition",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Sign Bottom",
            "count": 4
           },
           {
            "name": "Sign Top",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "id",
         "dtype": "int64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "1",
          "max": "5",
          "histogram": [
           {
            "bin_start": 1,
            "bin_end": 1.4,
            "count": 1
           },
           {
            "bin_start": 1.4,
            "bin_end": 1.8,
            "count": 0
           },
           {
            "bin_start": 1.8,
            "bin_end": 2.2,
            "count": 1
           },
           {
            "bin_start": 2.2,
            "bin_end": 2.6,
            "count": 0
           },
           {
            "bin_start": 2.6,
            "bin_end": 3,
            "count": 0
           },
           {
            "bin_start": 3,
            "bin_end": 3.4000000000000004,
            "count": 1
           },
           {
            "bin_start": 3.4000000000000004,
            "bin_end": 3.8000000000000003,
            "count": 0
           },
           {
            "bin_start": 3.8000000000000003,
            "bin_end": 4.2,
            "count": 1
           },
           {
            "bin_start": 4.2,
            "bin_end": 4.6,
            "count": 0
           },
           {
            "bin_start": 4.6,
            "bin_end": 5,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "baseline_motorcycle1",
         "dtype": "uint64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "896",
          "max": "23912",
          "histogram": [
           {
            "bin_start": 896,
            "bin_end": 3197.6,
            "count": 1
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         "diff_average": 38302,
         "diff_of_1": 38302,
         "diff_of_2": "nan",
         "diff_of_3": "nan",
         "diff_of_4": "nan",
         "font": "Helvetica",
         "_deepnote_index_column": 0
        },
        {
         "condition": "Sign Bottom",
         "id": 2,
         "baseline_motorcycle1": 21396,
         "update_motorcycle1": 63511,
         "baseline_motorcycle2": 32659,
         "update_motorcycle2": 47605,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "baseline_average": 27027.5,
         "update_average": 55558,
         "diff_average": 28530.5,
         "diff_of_1": 42115,
         "diff_of_2": 14946,
         "diff_of_3": "nan",
         "diff_of_4": "nan",
         "font": "Helvetica",
         "_deepnote_index_column": 1
        },
        {
         "condition": "Sign Bottom",
         "id": 3,
         "baseline_motorcycle1": 21340,
         "update_motorcycle1": 37460,
         "baseline_motorcycle2": 44998,
         "update_motorcycle2": 59002,
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 2,
         "baseline_average": 33169,
         "update_average": 48231,
         "diff_average": 15062,
         "diff_of_1": 16120,
         "diff_of_2": 14004,
         "diff_of_3": "nan",
         "diff_of_4": "nan",
         "font": "Helvetica",
         "_deepnote_index_column": 2
        },
        {
         "condition": "Sign Bottom",
         "id": 4,
         "baseline_motorcycle1": 23912,
         "update_motorcycle1": 59136,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "baseline_average": 23912,
         "update_average": 59136,
         "diff_average": 35224,
         "diff_of_1": 35224,
         "diff_of_2": "nan",
         "diff_of_3": "nan",
         "diff_of_4": "nan",
         "font": "Helvetica",
         "_deepnote_index_column": 3
        },
        {
         "condition": "Sign Bottom",
         "id": 5,
         "baseline_motorcycle1": 16862,
         "update_motorcycle1": 59292,
         "baseline_motorcycle2": "nan",
         "update_motorcycle2": "nan",
         "baseline_motorcycle3": "nan",
         "update_motorcycle3": "nan",
         "baseline_motorcycle4": "nan",
         "update_motorcycle4": "nan",
         "motorcycle_count": 1,
         "baseline_average": 16862,
         "update_average": 59292,
         "diff_average": 42430,
         "diff_of_1": 42430,
         "diff_of_2": "nan",
         "diff_of_3": "nan",
         "diff_of_4": "nan",
         "font": "Arial",
         "_deepnote_index_column": 4
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "     condition  id  baseline_motorcycle1  update_motorcycle1  \\\n0     Sign Top   1                   896               39198   \n1  Sign Bottom   2                 21396               63511   \n2  Sign Bottom   3                 21340               37460   \n3  Sign Bottom   4                 23912               59136   \n4  Sign Bottom   5                 16862               59292   \n\n   baseline_motorcycle2  update_motorcycle2  baseline_motorcycle3  \\\n0                   NaN                 NaN                   NaN   \n1               32659.0             47605.0                   NaN   \n2               44998.0             59002.0                   NaN   \n3                   NaN                 NaN                   NaN   \n4                   NaN                 NaN                   NaN   \n\n   update_motorcycle3  baseline_motorcycle4  update_motorcycle4  \\\n0                 NaN                   NaN                 NaN   \n1                 NaN                   NaN                 NaN   \n2                 NaN                   NaN                 NaN   \n3                 NaN                   NaN                 NaN   \n4                 NaN                   NaN                 NaN   \n\n   motorcycle_count  baseline_average  update_average  diff_average  \\\n0                 1             896.0         39198.0       38302.0   \n1                 2           27027.5         55558.0       28530.5   \n2                 2           33169.0         48231.0       15062.0   \n3                 1           23912.0         59136.0       35224.0   \n4                 1           16862.0         59292.0       42430.0   \n\n   diff_of_1  diff_of_2  diff_of_3  diff_of_4       font  \n0      38302        NaN        NaN        NaN  Helvetica  \n1      42115    14946.0        NaN        NaN  Helvetica  \n2      16120    14004.0        NaN        NaN  Helvetica  \n3      35224        NaN        NaN        NaN  Helvetica  \n4      42430        NaN        NaN        NaN      Arial  ",
      "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>condition</th>\n      <th>id</th>\n      <th>baseline_motorcycle1</th>\n      <th>update_motorcycle1</th>\n      <th>baseline_motorcycle2</th>\n      <th>update_motorcycle2</th>\n      <th>baseline_motorcycle3</th>\n      <th>update_motorcycle3</th>\n      <th>baseline_motorcycle4</th>\n      <th>update_motorcycle4</th>\n      <th>motorcycle_count</th>\n      <th>baseline_average</th>\n      <th>update_average</th>\n      <th>diff_average</th>\n      <th>diff_of_1</th>\n      <th>diff_of_2</th>\n      <th>diff_of_3</th>\n      <th>diff_of_4</th>\n      <th>font</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sign Top</td>\n      <td>1</td>\n      <td>896</td>\n      <td>39198</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>896.0</td>\n      <td>39198.0</td>\n      <td>38302.0</td>\n      <td>38302</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Helvetica</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Sign Bottom</td>\n      <td>2</td>\n      <td>21396</td>\n      <td>63511</td>\n      <td>32659.0</td>\n      <td>47605.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>27027.5</td>\n      <td>55558.0</td>\n      <td>28530.5</td>\n      <td>42115</td>\n      <td>14946.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Helvetica</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Sign Bottom</td>\n      <td>3</td>\n      <td>21340</td>\n      <td>37460</td>\n      <td>44998.0</td>\n      <td>59002.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>33169.0</td>\n      <td>48231.0</td>\n      <td>15062.0</td>\n      <td>16120</td>\n      <td>14004.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Helvetica</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Sign Bottom</td>\n      <td>4</td>\n      <td>23912</td>\n      <td>59136</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>23912.0</td>\n      <td>59136.0</td>\n      <td>35224.0</td>\n      <td>35224</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Helvetica</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Sign Bottom</td>\n      <td>5</td>\n      <td>16862</td>\n      <td>59292</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>16862.0</td>\n      <td>59292.0</td>\n      <td>42430.0</td>\n      <td>42430</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Arial</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 85,
    "execution_start": 1634805546255,
    "source_hash": "e43c7ba6",
    "tags": [],
    "cell_id": "00041-ac3950f2-75d7-44b7-a41c-1e5b06c58e86",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 7.2 Considering only the kilometrage of the first motorcycle in each policy, calculate the number of rows with the Arial.\n",
    "\n",
    "# split the data, check the number of rows\n",
    "arial = dataframe[dataframe['font'] == 'Arial']\n",
    "helvetica = dataframe[dataframe['font'] == 'Helvetica']\n",
    "\n",
    "print(f\"\"\"Number of rows:\n",
    "    Arial:       {arial.shape[0]}\n",
    "    Helvetica:   {helvetica.shape[0]}\"\"\")"
   ],
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "text": "Number of rows:\n    Arial:       6737\n    Helvetica:   6735\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2027,
    "execution_start": 1634805546282,
    "source_hash": "3249f423",
    "tags": [],
    "cell_id": "00042-34cf8822-f93b-4c91-ac1c-6bcb68e877a7",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# Also, plot the distribution of baseline kilometers for rows with the Arial font and for rows with the Helvetica font. \n",
    "\n",
    "def plot_hist_array(title, datas, labels, xlabel, ylabel):\n",
    "    \"\"\"\n",
    "    Plot histograms for the array of data series provided with respective labels.\n",
    "    \"\"\"\n",
    "    assert len(datas) == len(labels), \"The number of data lists and labels has to be the same.\"\n",
    "    N = len(datas)\n",
    "\n",
    "    fig, axs = plt.subplots(1,N, sharex = True, sharey = True, figsize = (N * 6,6))\n",
    "    fig.suptitle(title, fontsize=12)\n",
    "\n",
    "    for ax, data, label, color in zip(axs.flat, datas, labels, colours[1:]):\n",
    "        ax.hist(data.values, bins = 100, color = color)\n",
    "        ax.set_yscale('log')\n",
    "        ax.set_title(label)\n",
    "\n",
    "    fig.supxlabel(xlabel)\n",
    "    fig.supylabel(ylabel);\n",
    "\n",
    "datas = [arial[\"baseline_motorcycle1\"], helvetica[\"baseline_motorcycle1\"]]\n",
    "labels = [\"Arial\", \"Helvetica (fabricated data)\"]\n",
    "plot_hist_array(\"Baseline kilometrages\", datas, labels, \"kilometrages\", \"number of occurrences\")\n"
   ],
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 774,
       "height": 433
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2204,
    "execution_start": 1634805548315,
    "source_hash": "f3722369",
    "tags": [],
    "cell_id": "00043-5eb18ea4-81c5-4074-89cf-31aeaf0da35b",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# Also plot the update\n",
    "datas = [arial[\"update_motorcycle1\"], helvetica[\"update_motorcycle1\"]]\n",
    "labels = [\"Arial\", \"Helvetica (fabricated data)\"]\n",
    "plot_hist_array(\"Updated kilometrages\", datas, labels, \"kilometrages\", \"number of occurrences\")"
   ],
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 774,
       "height": 433
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 3267,
    "execution_start": 1634805550721,
    "source_hash": "848b5496",
    "tags": [],
    "cell_id": "00044-67f0bf67-d5c6-4043-8b88-f35220d7c421",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "#  7.3 Similar to what was done in task 6. Plot the distribution of the last three digits for \n",
    "#       1) the first motorcycle in each baseline contract (baseline_motorcycle1); and \n",
    "#       2) the first motorcycle in each updated contract (update_motorcycle1).\n",
    "\n",
    "def compare_3_digits(title, xlabel, ylabel, baseline1, update1, label1, baseline2, update2, label2):\n",
    "    fig, axs = plt.subplots(1, 2, sharex = True, sharey = True, figsize = (12,6))\n",
    "    fig.suptitle(title, fontsize=12)\n",
    "\n",
    "    for ax, baseline, update, label, color in zip(axs.flat, [baseline1, baseline2], [update1, update2], [label1, label2], colours):\n",
    "        ax.set_yscale('log')\n",
    "        ax.set_title(label)\n",
    "        ax.hist(baseline, bins = 100, alpha = 1, label = \"baseline\", color=colours[1])\n",
    "        ax.hist(update, bins = 100, alpha = 0.6, label = \"update\", color=colours[2])\n",
    "        ax.legend()\n",
    "    \n",
    "    fig.supxlabel(xlabel)\n",
    "    fig.supylabel(ylabel);\n",
    "\n",
    "compare_3_digits(\"Kilometrages, last 3 digits\", \"Last 3 digits\", \"Frequency\", \n",
    "arial[\"baseline_motorcycle1\"] % 1000, arial[\"update_motorcycle1\"] % 1000 , \"Arial\",\n",
    "helvetica[\"baseline_motorcycle1\"] % 1000, helvetica[\"update_motorcycle1\"] % 1000, \"Helvetica (fabricated data)\")"
   ],
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 774,
       "height": 433
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 7.4\n",
    "__Q: Why do these analyses provide evidence that the rows with Helvetica font were fabricated?__\n",
    "\n",
    "Suspiciously, the *baseline* and *update* distributions seem almost identical for Arial and Helvetica -- it seems like someone has taken the data from Arial, and generated more data following the same distribution. To support this theory, consider the last 3 digits plots: in the first plot (for the Arial distribution), we can observe the difference between the *baseline* and the *update* distributions, explained in tasks __6.3__ and __6.4__ - the *baseline* has several peaks, but the *update* follows a uniform distribution. However, for the Helvetica data there is no such effect, indicating that the data was generated automatically for both *baseline* and *update* columns.\n",
    "\n",
    "<!-- TODO: finish -->"
   ],
   "metadata": {
    "tags": [],
    "cell_id": "00045-237ffeae-783f-4396-9aba-8f06b9b3c634",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Task 8 — 15pts\n",
    "\n",
    "You've had enough! You decide to write an email to your PI (principal investigator, also known as \"your boss\") reporting\n",
    " the oddities you have observed in the data. Write a (brief) text (in markdown) of up to 300 words. Your text should:\n",
    "\n",
    "- Use the analyses you did to make a convincing case that the data has been tampered with. \n",
    "- Grounded in your analyses, provide a plausible hypothesis about how the data has been tampered with.\n",
    "- Discuss whether the data leads to the conclusion that signing at the top decreases dishonesty.\n",
    "\n",
    "Structure your text into three bullets, analogous to the ones above.\n",
    "\n",
    "You can reference images present in the notebook by indicating a task (e.g., image plotted in Task 3.3) or a cell \n",
    "number.\n",
    "\n",
    "Note that you do not need to conduct any additional analysis to write this text. If you do, the code should be at the \n",
    "end of this notebook, i.e., after your answer.\n",
    "\n",
    "---\n",
    "\n",
    "**Hint:** The data has been tampered with in more than one way."
   ],
   "metadata": {
    "cell_id": "00046-d00609e7-11df-4cf5-b729-4cfa6355cc4b",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### The data is not convincing\n",
    "- The data provided for the study can not be used since different analyses prove that it has been tampered with. Firstly, kilometrage differences between updated and baseline policies follow a uniform distribution. This is highly suspect since people's usage of motorcycles should be around the average, and only a few travel a lot or little (normal distribution). Additionally, some policies were written using “Helvetica” font, whereas others used “Arial”. Dividing the data according to the font we find two identically distributed populations but radical differences in the last 3-digits. Arial-written data mostly have kilometrages rounded to “000”, as well as “200”,”300”, etc. Conversely, Helvetica’s data is equally distributed among all 3-digits numbers. Finally, statistical hypothesis tests prove that, dividing baseline kilometrages by the policy’s signing position, we obtain two populations with different means, even though all baseline policies had to be signed at the bottom and should have had, therefore, an equal mean. Considering only Arial-written data doesn’t change the result.\n",
    "- Due to what was stated above, some hypotheses might be made. According to the 3 digits distribution, Helvetica’s data has been generated following real data distribution, but without considering the human’s tendency to round values. This tendency disappears in updated values since *'exact odometer kilometrage'* were asked. Even without considering Helvetica’s data, many signed at the top policies have null baseline kilometrage values, whereas policies signed at the bottom don’t. This may contradict the claim that updated policies were divided randomly among baseline contracts.\n",
    "- To conclude, data insights seem to prove that signing at the top decreases dishonesty with slightly higher values in kilometrage declarations. On the other hand, since policies appear to come from different populations, we can’t state that data is telling the truth. Therefore, nothing can be proved.\n"
   ],
   "metadata": {
    "deepnote_to_be_reexecuted": true,
    "execution_millis": 1,
    "execution_start": 1634628206945,
    "source_hash": "b623e53d",
    "cell_id": "00047-f01f4bfd-b14e-44e8-9d68-f87bd4dddf62",
    "deepnote_cell_type": "markdown",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "00048-08ce75f1-ed90-4261-a2de-75db73a7dd44",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8610be8b",
    "execution_start": 1634805554053,
    "execution_millis": 845,
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# Task 8 Additional analysis \n",
    "\n",
    "dataframe = dataframe.drop(dataframe[dataframe.font == 'Helvetica'].index)\n",
    "kilometrages_diff = dataframe[updates[0]] - dataframe[baselines[0]]\n",
    "plt.hist(x=kilometrages_diff, bins=100, color=colours[1])\n",
    "plt.suptitle('Difference bw updated and baseline kilometrage per policy, Arial-data only', fontsize=16)\n",
    "plt.xlabel(\"Kilometers\")\n",
    "plt.ylabel(\"Frequency\")"
   ],
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 28,
     "data": {
      "text/plain": "Text(0, 0.5, 'Frequency')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 620,
       "height": 291
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2670,
    "execution_start": 1634805554926,
    "source_hash": "727dff37",
    "tags": [],
    "cell_id": "00049-aec9ce0a-de88-4721-951f-fe9a1594751e",
    "deepnote_cell_type": "code",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "\n",
    "print(f\"\"\"Test to show that Helvetica and Arial populations have the same average value :\n",
    "\n",
    "baseline_1 kilometrages : {ttest_ind(arial[baselines[0]].values, helvetica[baselines[0]].values)}\n",
    "updated_1 kilometrages  : {ttest_ind(arial[updates[0]].values, helvetica[updates[0]].values)}\n",
    "\n",
    "p-values > 0.05, therefore we cannot reject the null hypothesis, \n",
    "i.e. there is no statistically significant difference in the avg values.\"\"\")\n",
    "print(u'\\u2500' * 100)\n",
    "\n",
    "\n",
    "print(f\"\"\"\\nTest to show that Arial-only data still has different means between baseline values seperated by sign position :\n",
    "\n",
    "{ttest_ind(\n",
    "    arial[arial['condition'] == policy_type[0]]['baseline_average'].values,\n",
    "    arial[arial['condition'] == policy_type[1]]['baseline_average'].values)}\n",
    "\n",
    "p-values < 0.05, therefore we can reject the null hypothesis, \n",
    "i.e. there is a statistically significant difference in the avg values.\"\"\")\n",
    "print(u'\\u2500' * 100)\n",
    "\n",
    "\n",
    "policy_type = [\"Sign Bottom\", \"Sign Top\"]\n",
    "\n",
    "# per condition, aggregate mean and standard deviation for average kilometrages per baseline policy\n",
    "mean, std = dataframe.groupby('condition')['baseline_average'].agg(['mean', 'std']).T.values\n",
    "\n",
    "fig, axs = plt.subplots(1,2, sharex = True, sharey = True, figsize = (10,5))\n",
    "fig.suptitle('Average kilometrage per baseline policy (seperated based on updated contract groups), Arial-data only', fontsize=16)\n",
    "\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    ax.hist(dataframe[dataframe['condition'] == policy_type[i]]['baseline_average'].values, bins = 100, color = colours[i+1])\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_title(policy_type[i])\n",
    "    plot_mean_std(ax, mean[i], std[i])\n",
    "    ax.legend()\n",
    "\n",
    "fig.supxlabel(\"Kilometers\")\n",
    "fig.supylabel(\"Frequency\");"
   ],
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "text": "Test to show that Helvetica and Arial populations have the same average value :\n\nbaseline_1 kilometrages : Ttest_indResult(statistic=-0.5078907625572127, pvalue=0.6115382695303345)\nupdated_1 kilometrages  : Ttest_indResult(statistic=-0.865286631422312, pvalue=0.3868968965203279)\n\np-values > 0.05, therefore we cannot reject the null hypothesis, \ni.e. there is no statistically significant difference in the avg values.\n────────────────────────────────────────────────────────────────────────────────────────────────────\n\nTest to show that Arial-only data still has different means between baseline values seperated by sign position :\n\nTtest_indResult(statistic=12.69568787359053, pvalue=1.6333976432950103e-36)\n\np-values < 0.05, therefore we can reject the null hypothesis, \ni.e. there is a statistically significant difference in the avg values.\n────────────────────────────────────────────────────────────────────────────────────────────────────\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 2 Axes>",
      "image/png": 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\n"
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    "Created in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>"
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