From a114d6731786278053b3cb4e7b75f4604efb4b12 Mon Sep 17 00:00:00 2001 From: Chris Mekelburg Date: Sat, 2 Nov 2024 14:40:49 -0400 Subject: [PATCH] final_submission of Pokemon lab MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Doing this a little out of order, as I already submitted my proposal, but I’m looking forward to working with a dataset on teen technology use and exploring the connections between hours spent on technology and perceptions of fitting in, mental health, etc. I found a good data set online from the Pew Research center that I am going to use. I did have one question about this lab too- The first time I run codeblock 46, it runs fine and then gives me the graph I want for block 47, but if I run it again it gives an error. This did not happen with the similar codeblock in the pokemon section, and I am just wondering why? It does not seem to affect my graph when I get the error message the second time. --- lab_pokemon.ipynb | 253 +++++++++++++++++++++------------------------- 1 file changed, 115 insertions(+), 138 deletions(-) diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb index 67619d5..f366f89 100644 --- a/lab_pokemon.ipynb +++ b/lab_pokemon.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "0e2a2927-f6d1-4b13-97ae-ff97416723e9", "metadata": { "tags": [] @@ -66,7 +66,7 @@ "10" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "69dd7908-b213-4d0f-8016-e46a4a491961", "metadata": { "tags": [] @@ -91,7 +91,7 @@ "20" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "579d8dda-ca39-48b1-8819-b17651029729", "metadata": {}, "outputs": [ @@ -413,7 +413,7 @@ "[800 rows x 12 columns]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -436,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f", "metadata": {}, "outputs": [ @@ -664,7 +664,7 @@ "[166425 rows x 11 columns]" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -687,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "9afca362-9edc-423c-981b-dc42107d5de0", "metadata": {}, "outputs": [ @@ -703,7 +703,7 @@ "Name: generation, dtype: float64" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -722,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479", "metadata": {}, "outputs": [ @@ -736,7 +736,7 @@ "dtype: float64" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -755,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f", "metadata": {}, "outputs": [ @@ -765,7 +765,7 @@ "0.08125" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -794,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a", "metadata": {}, "outputs": [ @@ -810,7 +810,7 @@ "Name: age, dtype: float64" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -830,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "b7f910c8-3d40-49ae-b270-678734c04100", "metadata": {}, "outputs": [ @@ -840,7 +840,7 @@ "(1.7050816283611236, 83.05358750187773)" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -859,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "f3891188-a85f-4089-8388-d4d81c7438ad", "metadata": {}, "outputs": [ @@ -869,7 +869,7 @@ "0.7858014120474688" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -893,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "12c0c6c9-c07b-4183-82f6-5e346c74aac9", "metadata": {}, "outputs": [ @@ -1133,7 +1133,7 @@ "[65 rows x 12 columns]" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1153,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "5d089acf-7b76-4f91-8803-42a4a9a11e3e", "metadata": {}, "outputs": [ @@ -1174,7 +1174,7 @@ "Name: type, Length: 800, dtype: bool" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1193,7 +1193,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "510fa0fc-2b38-4725-9bbf-ec57d62792be", "metadata": {}, "outputs": [ @@ -1651,7 +1651,7 @@ "789 44 46 28 6 False " ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1671,7 +1671,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "05d4c5c2-c6b4-4795-9799-c884b15445a1", "metadata": {}, "outputs": [ @@ -1840,7 +1840,7 @@ "676 95 135 105 5 False " ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1870,7 +1870,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "id": "198cb0c6-3f43-43c2-9eee-3939c12ea537", "metadata": {}, "outputs": [ @@ -2098,7 +2098,7 @@ "[13784 rows x 11 columns]" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2126,7 +2126,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851", "metadata": {}, "outputs": [ @@ -2354,7 +2354,7 @@ "[142249 rows x 11 columns]" ] }, - "execution_count": 25, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2381,7 +2381,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 20, "id": "315682ae-7d54-4d78-9a63-d23c83ba1576", "metadata": {}, "outputs": [ @@ -2609,7 +2609,7 @@ "[2321 rows x 11 columns]" ] }, - "execution_count": 30, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2635,7 +2635,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 21, "id": "069ea0ab-eff6-4985-9f46-db956fe1df91", "metadata": {}, "outputs": [ @@ -2664,7 +2664,7 @@ "Name: speed, dtype: float64" ] }, - "execution_count": 31, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2683,7 +2683,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 22, "id": "5c420c0e-b5d2-49ae-ab98-3305ee076169", "metadata": {}, "outputs": [ @@ -2855,7 +2855,7 @@ "Dragon 83.312500 112.125000 86.375000" ] }, - "execution_count": 32, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2875,7 +2875,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "id": "444a580d-e70c-48a1-bf87-77f98b8c9f85", "metadata": {}, "outputs": [ @@ -2902,7 +2902,7 @@ "Name: legendary, dtype: float64" ] }, - "execution_count": 6, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2935,7 +2935,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 24, "id": "75c1ac4f-3914-4c0a-a156-2e084002df66", "metadata": {}, "outputs": [ @@ -2954,7 +2954,7 @@ "Name: sleep, dtype: float64" ] }, - "execution_count": 92, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2974,7 +2974,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21", "metadata": {}, "outputs": [ @@ -3072,7 +3072,7 @@ " male 7.190582 3.826512" ] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -3080,8 +3080,7 @@ "source": [ "income_health = people.groupby([\"education\",\"sex\"])\n", "list = [\"income\",\"health\"]\n", - "income_health[list].mean()\n", - " " + "income_health[list].mean()" ] }, { @@ -3098,7 +3097,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce", "metadata": {}, "outputs": [], @@ -3121,7 +3120,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "id": "5ce066fe-f81d-4b78-a394-c5c2f4dc9f46", "metadata": {}, "outputs": [ @@ -3131,7 +3130,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, @@ -3152,7 +3151,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 28, "id": "bceb253b-ef4f-4aa2-aef4-cab2b3ca6d59", "metadata": {}, "outputs": [ @@ -3162,7 +3161,7 @@ "" ] }, - "execution_count": 8, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, @@ -3193,7 +3192,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "id": "92be1ad0-12bb-49f0-a3f6-85fcfd98e943", "metadata": {}, "outputs": [ @@ -3203,7 +3202,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, @@ -3232,7 +3231,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 30, "id": "17f1c289-5990-4420-bfcb-e50eee0b8af6", "metadata": {}, "outputs": [ @@ -3242,7 +3241,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, @@ -3273,7 +3272,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 45, "id": "3c8e9f47-9aea-4bf0-a628-7aa1a66a8eee", "metadata": {}, "outputs": [], @@ -3285,7 +3284,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 32, "id": "22f78bec-3d18-4133-ba9f-6595d7181ded", "metadata": {}, "outputs": [ @@ -3295,7 +3294,7 @@ "" ] }, - "execution_count": 20, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, @@ -3326,7 +3325,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "id": "444d9832-bd57-4238-9ea4-5ee898847170", "metadata": {}, "outputs": [ @@ -3336,7 +3335,7 @@ "" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, @@ -3365,7 +3364,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "id": "86f9747b-00a3-407f-9b73-0bce40bac50d", "metadata": {}, "outputs": [ @@ -3375,7 +3374,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, @@ -3404,17 +3403,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "id": "7385237c-6a5c-4041-af46-559d6d84d1fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, @@ -3446,7 +3445,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 36, "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c", "metadata": {}, "outputs": [ @@ -3456,7 +3455,7 @@ "" ] }, - "execution_count": 23, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, @@ -3485,7 +3484,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, "id": "ee30c851-14b1-4901-9182-4304d54d53a6", "metadata": {}, "outputs": [ @@ -3495,7 +3494,7 @@ "" ] }, - "execution_count": 27, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, @@ -3524,7 +3523,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 38, "id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80", "metadata": {}, "outputs": [ @@ -3534,7 +3533,7 @@ "" ] }, - "execution_count": 30, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, @@ -3563,7 +3562,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 39, "id": "4ee2eb69-2f9a-42e7-b5d3-9499631bfd06", "metadata": {}, "outputs": [ @@ -3573,7 +3572,7 @@ "" ] }, - "execution_count": 90, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, @@ -3602,7 +3601,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 40, "id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e", "metadata": {}, "outputs": [ @@ -3612,7 +3611,7 @@ "" ] }, - "execution_count": 93, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, @@ -3641,17 +3640,17 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 41, "id": "b00dd7d6-226b-469c-86d8-b71b328aa576", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 100, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, @@ -3693,17 +3692,41 @@ "id": "4a274316-a134-4bd8-88fd-e0cd455c09d5", "metadata": {}, "source": [ - "Research question: Do people who exercise more and sleep more have greater health?\n", + "Research question: At what point (if any) does reduced sleep outweigh the health benefit of exercise?\n", + "\n", + "Have you ever wondered if you should really do that morning or evening workout at the expense of sleep? Is it healthier to skip that 5 AM gym session for some extra zzz's?\n", "\n", "This question can best be analyzed using a barplot that shows sleep and exercise on the x axis and health on the y-axis.\n", "\n", - "To create the barplot, run the following code:" + "First sleep is going to be categorized into 6 categories: Less than 5 hours, 5 hours, 6 hours, 7 hours, 8 hours, or 9 or more hours\"" ] }, { "cell_type": "code", - "execution_count": 113, - "id": "f32ec27a-0552-4af1-ae0e-ff604e6b779a", + "execution_count": 42, + "id": "83155603-33ac-4d80-b74e-b4cdb6ced5a1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "bins = [0,5,6,7,8,9,10]\n", + "labels = [\"less than 5\", \"5 hours\", \"6 hours\", \"7 hours\", \"8 hours\", \"9 or more hours\"]\n", + "people[\"sleep\"] = pd.cut(people.sleep, bins=bins, labels=labels)" + ] + }, + { + "cell_type": "raw", + "id": "ac4ddb7d-4aac-4ac1-8866-9d06098d7676", + "metadata": {}, + "source": [ + "Then a barplot can be constructed using sleep categories on the x-axis and mean health on the y-axis. This is further classified by bars for exercise (true) and non-exercise (false)." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "0668eb7c-8cca-421e-b51f-ac942a0063b9", "metadata": { "tags": [] }, @@ -3714,13 +3737,13 @@ "" ] }, - "execution_count": 113, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3730,65 +3753,19 @@ } ], "source": [ - "sns.barplot(data=people, x=\"sleep\", y=\"health\", hue=\"exercise\", ci=None)" + "sns.barplot(data=people, x=\"sleep\", y=\"health\", hue=\"exercise\")" ] }, { - "cell_type": "code", - "execution_count": 110, - "id": "12f0e389-e4ab-4c6a-bef3-ea0d83d1d90e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "sleep\n", - "1 3.466667\n", - "2 2.878282\n", - "3 2.719080\n", - "4 2.872056\n", - "5 3.209646\n", - "6 3.488253\n", - "7 3.769927\n", - "8 3.689101\n", - "9 3.562742\n", - "10 3.160417\n", - "11 2.970732\n", - "12 2.779868\n", - "13 2.620000\n", - "14 2.694215\n", - "15 2.505747\n", - "16 2.848214\n", - "17 3.100000\n", - "18 2.465116\n", - "19 2.333333\n", - "20 2.666667\n", - "22 1.833333\n", - "23 2.000000\n", - "24 3.454545\n", - "Name: health, dtype: float64" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sleep = people.groupby([\"sleep\"])\n", - "sleep[\"health\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7368e5ec-d63b-4fe2-bc66-578d8c0913cd", + "cell_type": "raw", + "id": "24dd8d12-439b-4e75-9a5f-331d8565a45a", "metadata": {}, - "outputs": [], "source": [ - "#Why are there up to 24 hours of sleep now when previousyly there was never more than " + "Within each sleep category, the exercise group always has a higher health score than the non-exercise group. \n", + "\n", + "The health scores of the exercise groups are also always higher than the non-exercise groups, even when you look across sleep categories. Even among the lowest health score group that exercises (sleeps less than 5 hours and exercises), they still have a higher health score than the highest health score group that doesn't exercise (sleeps 6 hours and does not exercise)\n", + "\n", + "So skip those zzz's and get out for that run or to the gym! (Caution: Correalation does not prove causation; use this advice at your own risk.)" ] } ],