t-test_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "# This notebook runs an A/B split test using t-test for checking"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (1.10.1)\r\n",
      "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from scipy) (1.24.2)\r\n",
      "Requirement already satisfied: numpy in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (1.24.2)\r\n",
      "Requirement already satisfied: seaborn in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (0.12.2)\r\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from seaborn) (3.7.1)\r\n",
      "Requirement already satisfied: pandas>=0.25 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from seaborn) (2.0.0)\r\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from seaborn) (1.24.2)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.39.3)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.1)\r\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\r\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\r\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (5.12.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from pandas>=0.25->seaborn) (2023.3)\r\n",
      "Requirement already satisfied: tzdata>=2022.1 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from pandas>=0.25->seaborn) (2023.3)\r\n",
      "Requirement already satisfied: zipp>=3.1.0 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.1->seaborn) (3.15.0)\r\n",
      "Requirement already satisfied: six>=1.5 in /Users/wadewilson/opt/anaconda3/envs/t-test-statistic/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\r\n"
     ]
    }
   ],
   "source": [
    "# TODO move to .py format\n",
    "!pip3 install scipy\n",
    "!pip3 install numpy\n",
    "!pip3 install seaborn\n",
    "#!pip3 install --upgrade pip"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1f262e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running t-tests can be challenging so here is a starter tool I wrote\n",
      "If we compare crop yeilds from two samples of seed, are they comparable\n",
      "1000\n",
      "1000\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns, numpy as np\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import statistics\n",
    "from scipy.stats import ttest_1samp\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# TODO add in a main to prompt user between yield calculation cycles\n",
    "# TODO add in a SQLite saved database\n",
    "# TODO add in a convert SQLite db_switch to else SQLbd_type\n",
    "# TODO create a way to track how long the analysis takes per table Batch_Name, DateTime, Batch_Number and meta c&r\n",
    "# TODO create a way to take a copy of the data, create an output folder, Data_Cleaning else if not exist\n",
    "# TODO add way to create pivot, or switch to do r^2 and pvalue analysis like https://www.rpubs.com/shubh2565/pvalue-rsquared\n",
    "\n",
    "print('Running t-tests can be challenging so here is a starter tool I wrote')\n",
    "\n",
    "print('If we compare crop yeilds from two samples of seed, are they comparable')\n",
    "\n",
    "# Our Null Hypothesis is that there is no statistically significant different harvest yield weight between YeildA and\n",
    "# YeildB seed\n",
    "Null_Hypothesis = \"YieldA = YieldB\"\n",
    "\n",
    "# Our Alternate Hypothesis is there is statistically significant different harvest yield weight between YeildA and YeildB\n",
    "# seeds\n",
    "Alternate_Hypothesis = \"YieldA != YieldB\"\n",
    "\n",
    "\n",
    "# TODO create data digestion function to test YeildA vs YeildB\n",
    "# TODO copy github data access from healthcare data from github url string.\n",
    "# https://calcpark.com/random-number-generator/9-5040\n",
    "YieldA = [4618, 3094, 158, 4701, 2692, 2012, 2569, 1233, 1895, 181, 2045, 4078, 4483, 2970, 1180, 513, 1679, 333, 2815, 4038, 792, 2940, 4140, 1697, 1965, 3569, 1132, 741, 3087, 3447, 3902, 1658, 1252, 1210, 4729, 1387, 3938, 3816, 1156, 885, 47, 1875, 3375, 2359, 4199, 2599, 3970, 3409, 3304, 4142, 3887, 205, 516, 2446, 890, 4250, 3936, 1901, 4401, 1664, 1695, 4536, 1581, 4703, 4433, 692, 4281, 3834, 2602, 4382, 4800, 725, 3468, 4340, 4988, 3559, 1738, 3827, 116, 4537, 4628, 2324, 572, 1362, 3942, 287, 1735, 3297, 3462, 4546, 3309, 1151, 2798, 1297, 7, 1614, 2335, 4394, 2606, 2767, 4221, 2139, 2050, 3818, 1259, 4544, 1228, 2379, 3289, 3703, 44, 1119, 3914, 2742, 4318, 2665, 3728, 2408, 2516, 1599, 4774, 2653, 4450, 1600, 4525, 872, 3314, 1439, 479, 3438, 162, 4682, 1872, 3655, 3693, 449, 1845, 3193, 3514, 51, 3878, 3795, 3452, 875, 4939, 4192, 3868, 227, 4830, 4864, 1072, 4672, 1685, 2184, 2564, 850, 2754, 366, 2026, 455, 866, 4456, 1219, 575, 4115, 2310, 2624, 2200, 467, 4414, 706, 183, 1052, 1890, 4074, 2174, 2409, 239, 1876, 3070, 235, 4163, 4658, 1017, 4955, 4119, 4604, 369, 3802, 566, 1197, 1972, 3326, 3720, 918, 1733, 1682, 3596, 1703, 1691, 2085, 1057, 2863, 3747, 1508, 771, 3804, 541, 1371, 2541, 2732, 4865, 4408, 4704, 2824, 1699, 3478, 1221, 2447, 4108, 4137, 4850, 2920, 3620, 4286, 3467, 3649, 3905, 1257, 3055, 4818, 1010, 698, 580, 579, 3030, 1389, 4475, 26, 4096, 1675, 1313, 2737, 426, 1764, 1457, 389, 2290, 2301, 453, 3584, 2179, 1265, 1806, 3059, 2609, 4968, 1488, 2011, 1298, 1231, 3101, 760, 1914, 1044, 1645, 2128, 2820, 1799, 1629, 2119, 3494, 36, 3699, 1261, 4977, 4033, 3011, 2055, 1760, 3039, 3239, 535, 1515, 4635, 3517, 2155, 1110, 910, 4370, 4010, 1573, 2605, 385, 4531, 830, 4692, 3920, 1498, 228, 1129, 1612, 3003, 3471, 4333, 1496, 3108, 4201, 2462, 1404, 2649, 4895, 978, 5018, 1109, 1497, 2977, 4056, 3474, 664, 3958, 4256, 2717, 3764, 1880, 1671, 1782, 2169, 2209, 329, 2562, 4312, 4278, 4328, 4788, 893, 653, 2582, 892, 4623, 15, 3597, 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1428, 751, 887, 3417, 4667, 4587, 3278, 1042, 4473, 1113, 2731, 3243, 4951, 1111, 1624, 3292, 203, 4533, 1332, 1276, 1820, 673, 1544, 2170, 2691, 3896, 3592, 3980, 683, 4332, 4490, 4090, 93, 633, 320, 1061, 4912, 4136, 4700, 3310, 1321, 4046, 4223, 3671, 2427, 256, 1244, 3510, 2225, 1028, 19, 3414, 3688, 749, 975, 4772, 543, 133, 3821, 2855, 4805, 2743, 2253, 1199, 2252, 1302, 3412, 1598, 3009, 3179, 870, 595, 517, 76, 4061, 2480, 2637, 2909, 1661, 1161, 772, 4508, 2172, 3580, 4067, 2574, 946, 3639, 2414, 3095, 4086, 2298, 2397, 1286, 784, 2520, 141, 2442, 3166, 1681, 2616, 2749, 1962, 2281, 1100, 824, 2278, 742, 3664, 3112, 120, 1910, 2728, 4110, 1731, 4529, 2575, 4813, 1279, 3451, 2129, 4156, 2572, 3858, 1393, 3109, 4087, 4636, 4389, 3352, 1358, 1842, 3153, 1755, 4590, 1882, 4295, 2041, 3726, 2919, 4726, 2978, 3306, 3064, 608, 3735, 3853, 629, 869, 1464, 2077, 3186, 2052, 4768, 2706, 5015, 2693, 825, 3690, 418, 1853, 2425, 4570, 164, 4497, 376, 2533, 894, 2953, 2030, 2972, 2991, 1730, 1349, 3577, 1495, 1224, 2571, 121, 4541, 4227, 843, 4200, 1429, 3084, 3177, 557, 3198, 2654, 4390, 1924, 752, 433, 1878, 2589, 2221, 998, 358, 1030, 1762, 2144, 3222, 3543, 3965, 1527, 1831, 1318, 2898, 1422, 3068, 4022, 2997, 2460, 524, 1977, 125, 2661, 4419, 2230, 992, 398, 4214, 2799, 321, 207, 4101, 3431, 561, 2411, 3328, 539, 4791, 4242, 3588, 618, 4684, 200, 1181, 2800, 2152, 3787, 1065, 3302, 4054, 243, 2297, 2682, 4528, 3623, 3353, 665, 4586, 4513, 4519, 3308, 3739, 814, 2903, 2971, 1826, 2632, 4929, 596, 1083, 2646, 3565, 1684, 4832, 3294, 4371, 3025, 3293, 4051, 2921, 565, 223, 4062, 2364, 4219, 3450, 2984, 4423, 2231, 1022, 1603, 1192, 2876, 3625, 273, 2345, 4662, 1331, 2561, 4898, 1226, 3076, 3562, 3563, 4677, 1376, 3724, 3463, 1032, 4871, 2773, 2764, 1425, 4147, 5009, 4240, 5010, 3727, 4963, 3753, 1688, 886, 1637, 3683, 229, 761, 2733, 3831, 1234, 514, 3561, 463, 1378, 714, 1160, 606, 5002, 3892, 4424, 3733, 4642, 4522, 2451, 3307, 3658, 2812, 3492, 356, 2673, 3549, 4690, 989, 2381, 4606, 2275, 1616, 2286, 1609, 4698, 1673, 1194, 604, 1541, 371, 838, 3856, 2734, 1253, 628, 4739, 635, 2251, 4737, 3833, 2496, 3716, 29, 3566, 250, 1266, 658, 3940, 2197, 2234, 2120, 1571, 1974, 1568, 4158, 2615, 1993, 2805, 2989, 500, 902, 4787, 4487, 4098, 2816, 2164, 2040, 1094, 1791, 4767, 2220, 1846, 4877, 5003, 4847, 4298, 2424, 2378, 1950, 4357, 1430, 915, 3918, 4550, 4453, 4753, 2375, 2617, 3378, 408, 2488, 4495, 961, 4035, 1281, 4427, 1345, 3065, 4907, 1309, 2435, 443, 1204, 4297, 1869, 2598, 2293, 3184, 1802, 1830, 2341, 55, 855, 1487, 1759, 2703, 4558, 1881, 744, 3515, 231, 4121, 4626, 2208, 2091, 1049, 3785, 4245, 2705, 4649, 4491, 388, 293, 2406, 3027, 392, 2775, 863, 2603, 4925, 1558, 2965, 1413, 300, 3697, 2875, 166, 2072, 1079, 936, 737, 3507, 267, 1407, 379]\n",
    "\n",
    "YieldB = [3352, 174, 1661, 1984, 1518, 3594, 3346, 1834, 3300, 3816, 517, 32, 3308, 3554, 4669, 2074, 1022, 2227, 4417, 3087, 2937, 1884, 4680, 228, 2719, 3257, 3078, 1007, 4352, 3706, 3894, 4609, 3172, 1582, 2634, 519, 3625, 2090, 1617, 3462, 3638, 3544, 3938, 382, 2272, 3670, 563, 4930, 429, 1403, 4187, 2280, 289, 2295, 4340, 3994, 2000, 4410, 2792, 4660, 3539, 1409, 507, 1762, 159, 3306, 522, 690, 4436, 2533, 1684, 2928, 2528, 2310, 3584, 880, 3269, 798, 2764, 4823, 2493, 3857, 2145, 3386, 248, 681, 1964, 1628, 440, 2875, 3908, 1713, 3692, 1626, 3837, 4945, 2840, 67, 4744, 165, 546, 920, 3898, 3288, 3572, 3575, 3852, 281, 4100, 276, 1766, 4804, 4022, 2895, 444, 1785, 2485, 826, 1042, 4019, 2605, 4387, 4581, 4613, 3610, 1655, 3615, 2271, 1944, 1824, 3933, 1577, 2120, 4211, 1407, 550, 2506, 4106, 1636, 2080, 2933, 3374, 2216, 2987, 1877, 3170, 2223, 1600, 4924, 2786, 115, 534, 4050, 291, 2711, 565, 4735, 1938, 2154, 1169, 1119, 3177, 4936, 2981, 4315, 2820, 1224, 4462, 3191, 1126, 3179, 3133, 4287, 3637, 325, 524, 2751, 2714, 2574, 1295, 2607, 2467, 1036, 2962, 2270, 1845, 3952, 3225, 3696, 4505, 2648, 912, 4429, 4173, 4333, 210, 2101, 4396, 4017, 1912, 1044, 3326, 3276, 614, 4500, 3779, 2552, 1753, 4619, 4510, 4994, 4973, 259, 4339, 2083, 4134, 4921, 4842, 1488, 180, 1402, 4589, 4898, 1738, 2945, 807, 1020, 3298, 3828, 1953, 1767, 1981, 1859, 4128, 730, 902, 3946, 3488, 2523, 2278, 4893, 443, 227, 1406, 1069, 4361, 428, 2361, 1597, 2568, 3436, 3006, 2624, 3509, 2355, 313, 2414, 288, 2007, 331, 4105, 3004, 715, 813, 14, 2881, 461, 3715, 4038, 1545, 1968, 732, 1865, 2934, 4843, 2785, 3703, 505, 4205, 1032, 3216, 2659, 4988, 4260, 5032, 316, 1595, 484, 562, 1781, 1275, 395, 1388, 3286, 2976, 829, 3579, 1538, 4346, 329, 2309, 2063, 1074, 36, 2183, 799, 1657, 1610, 939, 4158, 4928, 628, 1866, 4249, 2497, 3841, 3830, 221, 2776, 85, 3506, 1170, 4739, 887, 2076, 4495, 1355, 797, 4968, 4825, 9, 2567, 1128, 2022, 577, 651, 2296, 3676, 3161, 4378, 1144, 2322, 4597, 4645, 185, 1554, 1963, 2119, 4689, 3786, 4257, 4656, 2660, 4465, 1292, 4706, 3652, 2925, 3883, 1775, 1506, 3077, 2264, 2569, 1905, 2026, 2674, 4319, 3194, 1639, 1445, 1023, 1769, 3142, 2208, 4873, 3749, 2932, 1471, 1939, 3669, 783, 219, 2207, 1246, 1462, 2589, 1483, 147, 1040, 4715, 3806, 4180, 4578, 974, 1747, 683, 1241, 1849, 947, 4030, 709, 3093, 3884, 4638, 3590, 4590, 478, 4321, 2012, 4543, 2058, 1710, 3649, 4704, 2107, 1716, 1154, 2847, 2396, 1795, 438, 272, 411, 86, 1603, 4895, 1188, 688, 2456, 3110, 4468, 2357, 1536, 104, 3729, 3902, 4037, 96, 3446, 1424, 4952, 2039, 4049, 1203, 3971, 3608, 3296, 4550, 1587, 899, 3922, 3479, 879, 3899, 1377, 1779, 4776, 90, 2637, 3865, 1028, 4649, 2440, 4977, 1408, 4668, 2631, 4275, 3445, 123, 126, 2625, 378, 516, 1701, 2771, 4457, 3456, 576, 2512, 4357, 279, 541, 1736, 4165, 2865, 3862, 2949, 2583, 677, 3745, 1838, 1731, 2885, 94, 4251, 3465, 578, 1965, 435, 3657, 963, 4809, 2490, 3394, 4004, 707, 133, 3223, 1198, 406, 2266, 3241, 11, 2212, 4802, 2524, 2633, 1058, 3335, 3322, 4135, 2754, 1818, 1218, 2898, 3494, 4474, 56, 1842, 4542, 951, 1512, 4892, 1559, 2805, 4875, 2304, 1103, 3954, 3823, 4539, 1525, 727, 222, 1519, 3410, 1978, 2950, 2986, 4293, 4351, 3452, 2919, 2554, 3516, 2502, 4461, 80, 3686, 3153, 3683, 295, 338, 4541, 138, 529, 4087, 4593, 3931, 3641, 4122, 900, 2450, 5031, 778, 3737, 1541, 413, 4370, 4521, 812, 3963, 2545, 2532, 336, 4719, 4816, 109, 4604, 2258, 2558, 2136, 206, 979, 2948, 1244, 2277, 362, 4394, 883, 1916, 2086, 2627, 3180, 495, 4516, 3470, 3068, 46, 4031, 2908, 1072, 2204, 867, 2324, 716, 2233, 448, 3813, 2662, 1405, 2899, 3183, 463, 1867, 2761, 1786, 4473, 3722, 3253, 3147, 1908, 780, 1396, 4766, 353, 1008, 3840, 2369, 1893, 2829, 1031, 2535, 3371, 1003, 101, 4010, 4228, 1097, 2931, 4054, 3925, 140, 2872, 3944, 4691, 3956, 3709, 2744, 4140, 4690, 3794, 658, 1429, 4851, 1211, 2189, 3477, 4818, 2349, 4381, 3204, 1729, 1301, 1986, 650, 1979, 1726, 2167, 1794, 3115, 1588, 4002, 110, 3560, 268, 957, 4714, 557, 750, 906, 449, 3772, 2468, 2124, 2481, 2602, 4667, 3037, 3538, 3959, 4836, 1921, 2861, 3116, 4496, 65, 4878, 575, 1857, 1546, 2910, 1172, 1495, 4127, 472, 352, 1568, 4212, 2837, 693, 3799, 2388, 3042, 1385, 1685, 3790, 4859, 1067, 3788, 2621, 1245, 2300, 636, 721, 4570, 3049, 1021, 348, 1892, 527, 634, 2670, 3336, 4272, 3210, 83, 3970, 1220, 2131, 616, 163, 4951, 3209, 2433, 3315, 1936, 301, 4036, 3382, 2883, 4777, 3255, 1216, 3533, 611, 3328, 1728, 4978, 2699, 3206, 2707, 2265, 494, 2983, 1173, 3796, 539, 3098, 1832, 2731, 4840, 398, 2613, 3282, 4422, 1134, 1840, 5033, 1551, 3855, 1706, 310, 4432, 564, 1127, 4452, 4224, 3662, 907, 3695, 2721, 984, 1029, 1529, 2566, 1278, 931, 1511, 1189, 601, 2085, 5037, 2673, 4617, 992, 633, 2887, 3226, 2964, 4610, 3331, 4931, 1282, 60, 1581, 282, 1117, 4213, 1593, 240, 4408, 1966, 2692, 1810, 1026, 4993, 3468, 2391, 4479, 4919, 1935, 591, 4, 4664, 4383, 41, 475, 1890, 39, 4012, 4234, 2006, 4166, 526, 1854, 1654, 531, 3975, 590, 2815, 360, 1622, 4305, 4485, 3463, 1354, 2996, 766, 29, 1263, 187, 3984, 1996, 3020, 1071, 1778, 132, 2428, 4769, 3034, 2573, 1166, 1153, 306, 2318, 2305, 2401, 2141, 1773, 4316, 569, 2034, 3964, 1472, 2406, 4576, 1479, 2782, 1000, 172, 4086, 2796, 477, 3129, 243, 3398, 3397, 4700, 3122, 3388, 4748, 417, 2195, 169, 1686, 4200, 747, 3487, 2114, 2683, 1851, 4324, 1430, 2706, 3682, 2641, 1139, 1664, 340, 5025, 604, 5012, 4331, 4798, 779, 1544, 3419, 2617, 371, 3945, 4153, 713, 414, 2712, 3128, 1380, 4124, 4209, 2418, 4759, 2383, 4639, 656, 4838, 1933, 5021, 2004, 3711, 3553, 2698, 2036, 3055, 2447, 1140, 473, 1158, 2453, 1399, 2494, 2116, 31, 3174, 1737, 720, 270, 3165, 3052, 3074, 3032, 3012, 1880, 2449, 1922, 854, 4592, 4323, 828, 3050, 1757, 3929, 4472, 2072, 4707, 1758, 403, 492, 4042]\n",
    "\n",
    "# Checking that YeildA and Yeild B have the same number of harvest samples\n",
    "# TODO add test that len(YieldA) = len(YieldB)\n",
    "print(len(YieldA))\n",
    "print(len(YieldB))\n",
    "\n",
    "# TODO in future add a NLP phrasing to parse yes samples are same, or no and remove imbalance in larger sample"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "run a program from terminal that gives a list of running .py files and which folder on the computer\n",
    "saves it to sbox Active_Awareness/ folder\n",
    "\n",
    "create a program as part of nightly schedule to test similar what I did with iTunes sorted by\n",
    "'recently listened to'='90-days',\\\n",
    "                       ''\\\n",
    "        least"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yeild A is <bound method NDFrame.describe of         0\n",
      "0    4618\n",
      "1    3094\n",
      "2     158\n",
      "3    4701\n",
      "4    2692\n",
      "..    ...\n",
      "995   737\n",
      "996  3507\n",
      "997   267\n",
      "998  1407\n",
      "999   379\n",
      "\n",
      "[1000 rows x 1 columns]> here is the list as a dateframe of df_A         0\n",
      "0    4618\n",
      "1    3094\n",
      "2     158\n",
      "3    4701\n",
      "4    2692\n",
      "..    ...\n",
      "995   737\n",
      "996  3507\n",
      "997   267\n",
      "998  1407\n",
      "999   379\n",
      "\n",
      "[1000 rows x 1 columns]\n",
      "here is the list as a dateframe of df_B         0\n",
      "0    3352\n",
      "1     174\n",
      "2    1661\n",
      "3    1984\n",
      "4    1518\n",
      "..    ...\n",
      "995  4707\n",
      "996  1758\n",
      "997   403\n",
      "998   492\n",
      "999  4042\n",
      "\n",
      "[1000 rows x 1 columns] Yeild B is <bound method NDFrame.describe of         0\n",
      "0    3352\n",
      "1     174\n",
      "2    1661\n",
      "3    1984\n",
      "4    1518\n",
      "..    ...\n",
      "995  4707\n",
      "996  1758\n",
      "997   403\n",
      "998   492\n",
      "999  4042\n",
      "\n",
      "[1000 rows x 1 columns]>\n"
     ]
    }
   ],
   "source": [
    "# TODO create convert list_to_csv function for Data_Cleaner\n",
    "# TODO create initial output to convert to pandas.dataframe as pd and pandas.data_reader as pdr\n",
    "# TODO create a test of all functions in directory, those that work show green, those that do are yellow, red is\n",
    "#  problem so its turned itself off\n",
    "# TODO create a mstables style menu and display\n",
    "\n",
    "\n",
    "df_A = df = pd.DataFrame(YieldA)\n",
    "df_B = df = pd.DataFrame(YieldB)\n",
    "\n",
    "\n",
    "print(f\"Yeild A is\", df_A.describe, \"here is the list as a dateframe of df_A\", df_A)\n",
    "print(\"here is the list as a dateframe of df_B\", df_B, \"Yeild B is\", df_B.describe)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PlotA and PlotB seperately\n",
      "PlotA and PlotB together\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = YieldA\n",
    "x2 = YieldB\n",
    "\n",
    "Vx1 = YieldA\n",
    "Vx2 = YieldB\n",
    "\n",
    "# TODO try to fix hist=True as its been deprecated\n",
    "print(\"PlotA and PlotB seperately\")\n",
    "sns.displot(YieldA, kind=\"kde\")\n",
    "sns.displot(YieldB, kind=\"kde\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v0/7gtj3bhx74z6w7kx78fhqlmm0000gn/T/ipykernel_22957/3498676917.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(YieldA,hist=True)\n",
      "/var/folders/v0/7gtj3bhx74z6w7kx78fhqlmm0000gn/T/ipykernel_22957/3498676917.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(YieldB,hist=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PlotA and PlotB together\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(YieldA,hist=True)\n",
    "sns.distplot(YieldB,hist=True)\n",
    "print(\"PlotA and PlotB together\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "<bound method NDFrame.describe of         0\n0    3352\n1     174\n2    1661\n3    1984\n4    1518\n..    ...\n995  4707\n996  1758\n997   403\n998   492\n999  4042\n\n[1000 rows x 1 columns]>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change to pandas dataframes\n",
    "# TODO update \"AttributeError: 'list' object has no attribute 'describe'\"\n",
    "df_A.describe\n",
    "df_B.describe"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "015f5956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Statistical Analysis:\n",
      "\n",
      "Here are my current t test results ==> Ttest_indResult(statistic=0.25917648415439404, pvalue=0.7955257735681125)\n"
     ]
    }
   ],
   "source": [
    "print(\"Calculating Statistical Analysis:\")\n",
    "alpha = 0.03                                                 # significance level = 5%\n",
    "n1, n2 = len(x1), len(x2)                                    # sample sizes\n",
    "s1, s2 = np.var(x1, ddof=1), np.var(x2, ddof=1)              # sample variances\n",
    "s = np.sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2)) # pooled standard deviation\n",
    "df = n1 + n2 - 2                                             # degrees of freedom\n",
    "t = stats.t.ppf(1 - alpha/2, df)                             # t-critical value for 95% CI\n",
    "test_results = stats.ttest_ind(x1, x2, equal_var=True)\n",
    "print(\"\\nHere are my current t test results ==>\", test_results)\n",
    "\n",
    "\n",
    "#lower = (np.mean(x1) - np.mean(x2)) - t * np.sqrt(1 / len(x1) + 1 / len(x2)) * s\n",
    "#upper = (np.mean(x1) - np.mean(x2)) + t * np.sqrt(1 / len(x1) + 1 / len(x2)) * s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "79de4dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generate Statistical Analysis Readout:\n",
      "YieldA Sample Size = 1000\n",
      "YieldB Sample Size = 1000\n",
      "YieldA Sample Variance = 2082470.5916306307\n",
      "YieldB Sample Variance = 2130764.585269269\n",
      "Alpha = 0.03\n",
      "Pooled Standard Deviation = 1451.4191635947038\n",
      "Degrees of Freedom = 1998\n",
      "Alpha = 0.03\n",
      "t-critical value for 97% CI = 2.1716417099989087\n"
     ]
    }
   ],
   "source": [
    "print(\"Generate Statistical Analysis Readout:\")\n",
    "#print(\"Lower = \" + str(lower))\n",
    "#print(\"Upper = \" + str(upper))\n",
    "print(\"YieldA Sample Size = \" + str(n1))\n",
    "print(\"YieldB Sample Size = \" + str(n2))\n",
    "print(\"YieldA Sample Variance = \" + str(s1))\n",
    "print(\"YieldB Sample Variance = \" + str(s2))\n",
    "print(\"Alpha = \" + str(alpha))\n",
    "print(\"Pooled Standard Deviation = \" + str(s))\n",
    "print(\"Degrees of Freedom = \" + str(df))\n",
    "print(\"Alpha = \" + str(alpha))\n",
    "print(\"t-critical value for 97% CI = \" + str(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4a336f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Ttest_indResult(statistic=0.25917648415439404, pvalue=0.7955257770758422)"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = stats.ttest_ind(YieldA, YieldB, equal_var=False)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "(0.25917648415439404, 0.7955257770758422)"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.statistic , r.pvalue"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r stat =  0.25917648415439404\n",
      "The r pvalue =  0.7955257770758422\n",
      "0.25917648415439404 0.7955257770758422\n",
      "Mean Points for A = 2483.469\n",
      "Mean Points for B = 2466.646\n",
      "The T Stat = 52.01, and the P Value = 0.0000\n",
      "T Stat = 52.01, P Value = 0.0000\n",
      "You should reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "print(\"The r stat = \", r.statistic)\n",
    "print(\"The r pvalue = \", r.pvalue)\n",
    "print(r.statistic, r.pvalue)\n",
    "\n",
    "A_mean_pts = statistics.mean(Vx1)\n",
    "B_mean_pts = statistics.mean(Vx2)\n",
    "\n",
    "print(\"Mean Points for A =\", A_mean_pts)\n",
    "print(\"Mean Points for B =\", B_mean_pts)\n",
    "\n",
    "tstat, pval = ttest_1samp(YieldA, 110)\n",
    "\n",
    "print(\"The T Stat = %.2f, and the P Value = %.4f\" % (tstat, pval))\n",
    "print(\"T Stat = %.2f, P Value = %.4f\" % (tstat, pval))\n",
    "if pval < 0.03:\n",
    "    print(\"You should reject the null hypothesis\")\n",
    "else:\n",
    "    print(\"You should accept the null hypothesis\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1268a5e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You should reject the null hypothesis which again was... that YieldA was equal to YieldBso we can say we have statistically significant proof of number of subjects in population sample imbalance\n"
     ]
    }
   ],
   "source": [
    "population = 'number of subjects in population sample'\n",
    "unit_measure = population\n",
    "\n",
    "if pval < 0.03:\n",
    "    print(\"You should reject the null hypothesis which again was... that YieldA was equal to YieldB\"\n",
    "          f\"so we can say we have statistically significant proof of {unit_measure} imbalance\")\n",
    "# TODO create a valence switch\n",
    "else:\n",
    "    print(f\"You should accept the null hypothesis which again was... that YieldA was not equal to YieldB\"\n",
    "          \"so we would have statistically significant data, from a sufficent sample to say that \"\n",
    "          \"YieldA is either greater than or less than YieldB and worth the risk or investment\")\n"
   ]
  }
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