t_test_dependent_d_cohena.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mlt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.stats.api as sms\n",
    "import statsmodels.stats.power as smp\n",
    "import scipy.stats\n",
    "from scipy.stats import t, norm, ttest_ind, ttest_rel, ttest_1samp\n",
    "import decimal\n",
    "import random\n",
    "#decimal.getcontext().prec = 4\n",
    "import itertools\n",
    "from scipy.stats import nct\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normal_sample(n,mean,sd):\n",
    "    return np.random.normal(loc=mean,scale=sd,size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conf_int_one_sample(x,alfa,alternative = 'two-tailed'):\n",
    "    '''\n",
    "    alternative == 'two-tailed','left','right'\n",
    "    '''\n",
    "    mean = np.mean(x)\n",
    "    se = np.std(x,ddof=1)/np.sqrt(len(x))\n",
    "    if alternative == 'two-tailed':\n",
    "        return mean - t.ppf(1-alfa/2,df=len(x)-1)*se,mean + t.ppf(1-alfa/2,df=len(x)-1)*se\n",
    "    elif alternative == 'left':\n",
    "        return mean - t.ppf(1-alfa,df=len(x)-1)*se\n",
    "    elif alternative == 'right':\n",
    "        return mean + t.ppf(1-alfa,df=len(x)-1)*se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def t_test_for_dependent_groups(x,diff = 0, alternative='two-tailed'):\n",
    "    n = np.size(x)\n",
    "    x = np.array(x)\n",
    "    std = np.std(x,ddof=1)\n",
    "    mean = np.mean(x)\n",
    "    se = std/np.sqrt(n)\n",
    "    ts = (mean-diff)/se\n",
    "    df = n-1\n",
    "    if alternative == 'two-tailed':\n",
    "        p_val = 2*t.sf(np.abs(ts),df)\n",
    "    if alternative == 'greater':\n",
    "        p_val = t.sf(ts,df)\n",
    "    elif alternative == 'lesser':\n",
    "        p_val = t.cdf(ts, df)\n",
    "    return p_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dependent_1(pop_mean,sample,diff=0,eps = 0):\n",
    "    mean = np.mean(sample)\n",
    "    if mean > diff+eps:\n",
    "        return 'd'\n",
    "    elif mean < diff - eps:\n",
    "        return 'm'\n",
    "    elif (mean <= eps+diff and mean >= diff-eps):\n",
    "        return 'nwd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dependent_2(pop_mean,sample,alfa,diff=0):\n",
    "    mean = np.mean(sample)\n",
    "    result = t_test_for_dependent_groups(sample,diff=0)\n",
    "    std = np.std(sample)\n",
    "    d_cohena = (mean-delta)/std\n",
    "    if result < alfa and d_cohena > diff:\n",
    "        return 'd'\n",
    "    elif result > alfa and (d_cohena >= 0 and d_cohena <= diff):\n",
    "        return 'm'\n",
    "    elif mean < 0:\n",
    "        return 'm'\n",
    "    \n",
    "    else:\n",
    "        return 'nwd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dependent_3(pop_mean,sampe,alfa,diff=0):\n",
    "    mean = np.mean(sample)\n",
    "    result = t_test_for_dependent_groups(sample,diff=diff)\n",
    "    std = np.std(sample)\n",
    "    d_cohena = mean/std\n",
    "    if result < alfa and d_cohena > diff:\n",
    "        return 'd'\n",
    "    elif result < alfa and (d_cohena >= 0 and d_cohena <= diff):\n",
    "        return 'm'\n",
    "    else:\n",
    "        return 'nwd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = 0.5\n",
    "duże1 = []\n",
    "małe1 = []\n",
    "duże2 = []\n",
    "duże3 = []\n",
    "małe2 = []\n",
    "małe3 = []\n",
    "źle_duże1 = []\n",
    "niewiadomo_duże1 = []\n",
    "źle_duże2 = []\n",
    "niewiadomo_duże2 = []\n",
    "źle_duże3 = []\n",
    "niewiadomo_duże3 = []\n",
    "źle_małe1 = []\n",
    "niewiadomo_małe1 = []\n",
    "źle_małe2 = []\n",
    "niewiadomo_małe2 = []\n",
    "źle_małe3 = []\n",
    "niewiadomo_małe3 = []\n",
    "przedziały_n = []\n",
    "ile_dużych = []\n",
    "ile_małych = []\n",
    "means = []\n",
    "delta = 0.5\n",
    "sds = [i*0.1 for i in range(10,45)]\n",
    "for k in range(15): \n",
    "    p1,p2 = 15+k*5,20+k*5\n",
    "    for sd in sds:\n",
    "        przedziały_n.append(p1)\n",
    "        pod1 = []\n",
    "        pod2 = []\n",
    "        pod3 = []\n",
    "        for n in range(p1,p2):\n",
    "            for i in range(-20,21):\n",
    "                pop_mean = sd*delta+i*0.1\n",
    "                #for j in range(1,5):\n",
    "                alfa = 0.05\n",
    "                sample = get_normal_sample(n,pop_mean,sd)\n",
    "                d_cohena = np.mean(sample)/np.std(sample)\n",
    "                pod1.append([dependent_1(pop_mean,sample,delta,eps),pop_mean,eps])\n",
    "                pod2.append([dependent_2(pop_mean,sample,alfa,delta),d_cohena,alfa])\n",
    "                pod3.append([dependent_3(pop_mean,sample,alfa,delta),d_cohena,alfa])\n",
    "\n",
    "    # --------------------------------------------------      wśród wyników które były duże, ile z nich wskazał test - (wyniki wskazane przez test)/(faktycznie duże testy\n",
    "    #---------------------------------------------------      ile razy test wskazał wynik mały, gdy ten w rzeczywistości był duży\n",
    "    #---------------------------------------------------      ile razy test wskazał \"nie wiadomo\", gdy efekt w rzeczywistości był duży\n",
    "    #podejście 1\n",
    "        duże_pod1 = [i for i in pod1 if i[1] > delta]\n",
    "\n",
    "        if len(duże_pod1) != 0:\n",
    "            pod1_dobrze_duże = len([i for i in duże_pod1 if i[0] == 'd'])/len(duże_pod1)\n",
    "            pod1_źle_duże = len([i for i in duże_pod1 if i[0] == 'm'])/len(duże_pod1)\n",
    "            pod1_niewiadomo_duże = len([i for i in duże_pod1 if i[0] == 'nwd'])/len(duże_pod1)\n",
    "        else:\n",
    "            pod1_dobrze_duże = 'brak'\n",
    "            pod1_źle_duże = 'brak'\n",
    "            pod1_niewiadomo_duże = 'brak'\n",
    "        #podejście 2\n",
    "        duże_pod2 = [i for i in pod2 if i[1] - delta > 0]\n",
    "        if len(duże_pod2) != 0:\n",
    "            pod2_dobrze_duże = len([i for i in duże_pod2 if i[0] == 'd'])/len(duże_pod2)\n",
    "            pod2_źle_duże = len([i for i in duże_pod2 if i[0] == 'm'])/len(duże_pod2)\n",
    "            pod2_niewiadomo_duże = len([i for i in duże_pod2 if i[0] == 'nwd'])/len(duże_pod2)\n",
    "        else:\n",
    "            pod2_dobrze_duże = 'brak'\n",
    "            pod2_źle_duże = 'brak'\n",
    "            pod2_niewiadomo_duże = 'brak'\n",
    "        #podejście 3\n",
    "        duże_pod3 = [i for i in pod3 if i[1] - delta > 0]\n",
    "        if len(duże_pod3) != 0:\n",
    "            pod3_dobrze_duże = len([i for i in duże_pod3 if i[0] == 'd'])/len(duże_pod3)\n",
    "            pod3_źle_duże = len([i for i in duże_pod3 if i[0] == 'm'])/len(duże_pod3)\n",
    "            pod3_niewiadomo_duże = len([i for i in duże_pod3 if i[0] == 'nwd'])/len(duże_pod3)\n",
    "        else:\n",
    "            pod3_dobrze_duże = 'brak'\n",
    "            pod3_źle_duże = 'brak'\n",
    "            pod3_niewiadomo_duże = 'brak'\n",
    "\n",
    "        # --------------------------------------------------      wśród wyników które były małe, ile z nich wskazał test - (wyniki małe wskazane przez test)/(faktycznie małe wyniki)\n",
    "        #---------------------------------------------------      ile razy test wskazał wynik duży, gdy ten w rzeczywistości był mały\n",
    "        #---------------------------------------------------      ile razy test wskazał \"nie wiadomo\", gdy efekt w rzeczywistości był duży\n",
    "        #podejście 1\n",
    "        małe_pod1 = [i for i in pod1 if (i[1] < delta-eps)]\n",
    "        if len(małe_pod1) != 0:\n",
    "            pod1_dobrze_małe = len([i for i in małe_pod1 if i[0] == 'm'])/len(małe_pod1)\n",
    "            pod1_źle_małe = len([i for i in małe_pod1 if i[0] == 'd'])/len(małe_pod1)\n",
    "            pod1_niewiadomo_małe = len([i for i in małe_pod1 if i[0] == 'nwd'])/len(małe_pod1)\n",
    "        else:\n",
    "            pod1_dobrze_małe = 'brak'\n",
    "            pod1_źle_małe = 'brak'\n",
    "            pod1_niewiadomo_małe = 'brak'\n",
    "\n",
    "        #podejście 2\n",
    "        małe_pod2 = [i for i in pod2 if i[1] - delta < 0]\n",
    "        if len(małe_pod2) != 0:\n",
    "            pod2_dobrze_małe = len([i for i in małe_pod2 if i[0] == 'm'])/len(małe_pod2)\n",
    "            pod2_źle_małe = len([i for i in małe_pod2 if i[0] == 'd'])/len(małe_pod2)\n",
    "            pod2_niewiadomo_małe = len([i for i in małe_pod2 if i[0] == 'nwd'])/len(małe_pod2)\n",
    "        else:\n",
    "            pod2_dobrze_małe = 'brak'\n",
    "            pod2_źle_małe = 'brak'\n",
    "            pod2_niewiadomo_małe = 'brak'\n",
    "\n",
    "        #podejście 3\n",
    "        małe_pod3 = [i for i in pod3 if i[1] - delta < 0]\n",
    "        if len(małe_pod3) != 0:\n",
    "            pod3_dobrze_małe = len([i for i in małe_pod3 if i[0] == 'm'])/len(małe_pod3)\n",
    "            pod3_źle_małe = len([i for i in małe_pod3 if i[0] == 'd'])/len(małe_pod3)\n",
    "            pod3_niewiadomo_małe = len([i for i in małe_pod3 if i[0] == 'nwd'])/len(małe_pod3)\n",
    "        else:\n",
    "            pod3_dobrze_małe = 'brak'\n",
    "            pod3_źle_małe = 'brak'\n",
    "            pod3_niewiadomo_małe = 'brak'\n",
    "\n",
    "\n",
    "        duże1.append(pod1_dobrze_duże)\n",
    "        małe1.append(pod1_dobrze_małe)\n",
    "        duże2.append(pod2_dobrze_duże)\n",
    "        duże3.append(pod3_dobrze_duże)\n",
    "        małe2.append(pod2_dobrze_małe)\n",
    "        małe3.append(pod3_dobrze_małe)\n",
    "\n",
    "        źle_duże1.append(pod1_źle_duże)\n",
    "        źle_duże2.append(pod2_źle_duże)\n",
    "        źle_duże3.append(pod3_źle_duże)\n",
    "        niewiadomo_duże1.append(pod1_niewiadomo_duże)\n",
    "        niewiadomo_duże2.append(pod2_niewiadomo_duże)\n",
    "        niewiadomo_duże3.append(pod3_niewiadomo_duże)\n",
    "\n",
    "        źle_małe1.append(pod1_źle_małe)\n",
    "        źle_małe2.append(pod2_źle_małe)\n",
    "        źle_małe3.append(pod3_źle_małe)\n",
    "        niewiadomo_małe1.append(pod1_niewiadomo_małe)\n",
    "        niewiadomo_małe2.append(pod2_niewiadomo_małe)\n",
    "        niewiadomo_małe3.append(pod3_niewiadomo_małe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdss = sds*15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wielkość próby</th>\n",
       "      <th>sd</th>\n",
       "      <th>efekt duży, podejście1</th>\n",
       "      <th>efekt duży, podejście2</th>\n",
       "      <th>efekt duży, podejście3</th>\n",
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       "      <th>źle_duże1</th>\n",
       "      <th>źle_duże2</th>\n",
       "      <th>źle_duże3</th>\n",
       "      <th>nie wiadomo duże1</th>\n",
       "      <th>nie wiadomo duże2</th>\n",
       "      <th>nie wiadomo duże3</th>\n",
       "      <th>źle_małe1</th>\n",
       "      <th>źle_małe2</th>\n",
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       "      <td>0.788462</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>0.801980</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.182692</td>\n",
       "      <td>0.211538</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0533333</td>\n",
       "      <td>0.198020</td>\n",
       "      <td>0.990099</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.721154</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>0.801980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009524</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.259615</td>\n",
       "      <td>0.278846</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0533333</td>\n",
       "      <td>0.198020</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.754545</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.774510</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.747573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.236364</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.225490</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.252427</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.736364</td>\n",
       "      <td>0.738318</td>\n",
       "      <td>0.728972</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.263636</td>\n",
       "      <td>0.242991</td>\n",
       "      <td>0.271028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0571429</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.763636</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.745098</td>\n",
       "      <td>0.953846</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.009091</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.235294</td>\n",
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       "      <td>0.679245</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.262626</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131429</td>\n",
       "      <td>0.320755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.737374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>85</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.817308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.297030</td>\n",
       "      <td>0.306931</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.182692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.702970</td>\n",
       "      <td>0.693069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>85</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.844444</td>\n",
       "      <td>0.783019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.202020</td>\n",
       "      <td>0.242424</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.216981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>85</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.859459</td>\n",
       "      <td>0.776699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.372549</td>\n",
       "      <td>0.010811</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129730</td>\n",
       "      <td>0.223301</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.627451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>85</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.875676</td>\n",
       "      <td>0.826531</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.196262</td>\n",
       "      <td>0.448598</td>\n",
       "      <td>0.005405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.118919</td>\n",
       "      <td>0.173469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>brak</td>\n",
       "      <td>0.803738</td>\n",
       "      <td>0.551402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wielkość próby   sd  efekt duży, podejście1  efekt duży, podejście2  \\\n",
       "0                15  1.0                0.820000                0.807692   \n",
       "1                15  1.1                0.733333                0.730769   \n",
       "2                15  1.2                0.754545                0.794118   \n",
       "3                15  1.3                0.736364                0.738318   \n",
       "4                15  1.4                0.763636                0.735294   \n",
       "..              ...  ...                     ...                     ...   \n",
       "520              85  4.0                0.862857                0.679245   \n",
       "521              85  4.1                0.850000                0.817308   \n",
       "522              85  4.2                0.844444                0.783019   \n",
       "523              85  4.3                0.859459                0.776699   \n",
       "524              85  4.4                0.875676                0.826531   \n",
       "\n",
       "     efekt duży, podejście3 mały_podejście1  mały_podejście2  mały_podejście3  \\\n",
       "0                  0.788462        0.946667         0.801980         0.009901   \n",
       "1                  0.721154        0.946667         0.801980         0.000000   \n",
       "2                  0.774510             0.9         0.747573         0.000000   \n",
       "3                  0.728972        0.942857         0.816327         0.000000   \n",
       "4                  0.745098        0.953846         0.757282         0.000000   \n",
       "..                      ...             ...              ...              ...   \n",
       "520                1.000000            brak         0.272727         0.262626   \n",
       "521                1.000000            brak         0.297030         0.306931   \n",
       "522                1.000000            brak         0.202020         0.242424   \n",
       "523                1.000000            brak         0.235294         0.372549   \n",
       "524                1.000000            brak         0.196262         0.448598   \n",
       "\n",
       "     źle_duże1  źle_duże2  źle_duże3  nie wiadomo duże1  nie wiadomo duże2  \\\n",
       "0     0.010000   0.009615        0.0           0.170000           0.182692   \n",
       "1     0.009524   0.009615        0.0           0.257143           0.259615   \n",
       "2     0.009091   0.000000        0.0           0.236364           0.205882   \n",
       "3     0.000000   0.018692        0.0           0.263636           0.242991   \n",
       "4     0.009091   0.029412        0.0           0.227273           0.235294   \n",
       "..         ...        ...        ...                ...                ...   \n",
       "520   0.005714   0.000000        0.0           0.131429           0.320755   \n",
       "521   0.011111   0.000000        0.0           0.138889           0.182692   \n",
       "522   0.005556   0.000000        0.0           0.150000           0.216981   \n",
       "523   0.010811   0.000000        0.0           0.129730           0.223301   \n",
       "524   0.005405   0.000000        0.0           0.118919           0.173469   \n",
       "\n",
       "     nie wiadomo duże3 źle_małe1  źle_małe2  źle_małe3 niewiadomo_małe1  \\\n",
       "0             0.211538         0        0.0        0.0        0.0533333   \n",
       "1             0.278846         0        0.0        0.0        0.0533333   \n",
       "2             0.225490         0        0.0        0.0              0.1   \n",
       "3             0.271028         0        0.0        0.0        0.0571429   \n",
       "4             0.254902         0        0.0        0.0        0.0461538   \n",
       "..                 ...       ...        ...        ...              ...   \n",
       "520           0.000000      brak        0.0        0.0             brak   \n",
       "521           0.000000      brak        0.0        0.0             brak   \n",
       "522           0.000000      brak        0.0        0.0             brak   \n",
       "523           0.000000      brak        0.0        0.0             brak   \n",
       "524           0.000000      brak        0.0        0.0             brak   \n",
       "\n",
       "     niewiadomo_małe2  niewiadomo_małe3  \n",
       "0            0.198020          0.990099  \n",
       "1            0.198020          1.000000  \n",
       "2            0.252427          1.000000  \n",
       "3            0.183673          1.000000  \n",
       "4            0.242718          1.000000  \n",
       "..                ...               ...  \n",
       "520          0.727273          0.737374  \n",
       "521          0.702970          0.693069  \n",
       "522          0.797980          0.757576  \n",
       "523          0.764706          0.627451  \n",
       "524          0.803738          0.551402  \n",
       "\n",
       "[525 rows x 20 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trzy_podejścia = pd.DataFrame(zip(przedziały_n,sdss,duże1,duże2,duże3,małe1,małe2,małe3,źle_duże1,źle_duże2,źle_duże3,niewiadomo_duże1,niewiadomo_duże2,niewiadomo_duże3,źle_małe1,źle_małe2,źle_małe3,niewiadomo_małe1,\n",
    "                                  niewiadomo_małe2,niewiadomo_małe3), columns = ['wielkość próby','sd','efekt duży, podejście1','efekt duży, podejście2','efekt duży, podejście3','mały_podejście1','mały_podejście2','mały_podejście3',\n",
    "                                                                                 'źle_duże1','źle_duże2','źle_duże3','nie wiadomo duże1','nie wiadomo duże2','nie wiadomo duże3','źle_małe1','źle_małe2','źle_małe3',\n",
    "                                                                                 'niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3'])\n",
    "trzy_podejścia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11d9ae8c5b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 796.5x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży_dobrze = ['efekt duży, podejście1','efekt duży, podejście2','efekt duży, podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','efekt duży, podejście1','efekt duży, podejście2','efekt duży, podejście3']]\n",
    "dtt = dt[dt['sd'].isin([2,3,4])]\n",
    "nowe = dtt.melt(value_vars=['efekt duży, podejście2','efekt duży, podejście3'],value_name='procent dużych',id_vars = ['wielkość próby','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='sd',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'wielkość próby','procent dużych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11d9ae8c730>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 781.25x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży_niewiadomo = ['nie wiadomo duże1','nie wiadomo duże2','nie wiadomo duże3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','nie wiadomo duże1','nie wiadomo duże2','nie wiadomo duże3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['nie wiadomo duże1','nie wiadomo duże2','nie wiadomo duże3'],value_name='procent nie wiadomo',id_vars = ['wielkość próby','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='wielkość próby',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent nie wiadomo')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11d9b690760>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 796.5x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży = ['efekt duży, podejście1','efekt duży, podejście2','efekt duży, podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','efekt duży, podejście1','efekt duży, podejście2','efekt duży, podejście3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['efekt duży, podejście1','efekt duży, podejście2','efekt duży, podejście3'],value_name='procent nie wiadomo',id_vars = ['wielkość próby','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='wielkość próby',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent nie wiadomo')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}