t_test_independent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "from scipy.stats import mode\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('2.732367367764493202838529338721785886730')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decimal.getcontext().prec = 40\n",
    "a = decimal.Decimal(np.random.exponential(1,1)[0])\n",
    "b = decimal.Decimal(np.random.exponential(1,1)[0])\n",
    "a/b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conf_int_two_sample(x,y,alfa,alternative = 'two-tailed'):\n",
    "    '''\n",
    "    alternative == 'two-tailed','left','right'\n",
    "    '''\n",
    "    mean1 = np.mean(x)\n",
    "    mean2 = np.mean(y)\n",
    "    mean = mean1-mean2\n",
    "    n1 = np.size(x)\n",
    "    n2 = np.size(y)\n",
    "    var1 = np.var(x,ddof=1)\n",
    "    var2 = np.var(y,ddof=1)\n",
    "    se = np.sqrt(var1/n1+var2/n2)\n",
    "    ts = (np.mean(x)-np.mean(y))/se\n",
    "    df = (var1/n1+var2/n2)**2/((var1/n1)**2/(n1-1)+(var2/n2)**2/(n2-1))\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=df)*se,mean + t.ppf(1-alfa/2,df=df)*se\n",
    "    elif alternative == 'left':\n",
    "        return mean - t.ppf(1-alfa/2,df=df)*se\n",
    "    elif alternative == 'right':\n",
    "        return mean + t.ppf(1-alfa/2,df=df)*se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def t_test_for_independent_groups(x,y,diff = 0, alternative = 'two-tailed'): #Welch\n",
    "    n1 = np.size(x)\n",
    "    n2 = np.size(y)\n",
    "    var1 = np.var(x,ddof=1)\n",
    "    var2 = np.var(y,ddof=1)\n",
    "    se = np.sqrt(var1/n1+var2/n2)\n",
    "    ts = (np.mean(x)-np.mean(y)-diff)/se\n",
    "    df = (var1/n1+var2/n2)**2/((var1/n1)**2/(n1-1)+(var2/n2)**2/(n2-1)) #Welch\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",
    "    if alternative == 'less':\n",
    "        p_val = t.cdf(ts, df)\n",
    "    return p_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def independent_1(sample1,sample2,diff=0, eps = 0):\n",
    "    mean1 = np.mean(sample1)\n",
    "    mean2 = np.mean(sample2)\n",
    "    mean = mean1-mean2\n",
    "    \n",
    "    if mean > diff+eps:\n",
    "        return 'd'\n",
    "    if  mean <= diff-eps:\n",
    "        return 'm'\n",
    "    if mean < delta+eps and mean > delta-eps  : #problematyczne eps > delta\n",
    "        return 'nwd'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def independent_2(sample1, sample2, alfa, diff=0):\n",
    "    result = t_test_for_independent_groups(sample1,sample2,diff = 0, alternative = 'greater')\n",
    "    mean = np.mean(sample1) - np.mean(sample2)\n",
    "    \n",
    "    if result < alfa and mean > diff:\n",
    "        return 'd'\n",
    "    elif result > alfa and (mean <= diff):\n",
    "        return 'm'\n",
    "    else:\n",
    "        return 'nwd'    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def independent_3(sample1, sample2, alfa, diff=0):\n",
    "    result = t_test_for_independent_groups(sample1,sample2,diff = diff, alternative = 'greater')\n",
    "    result2 = t_test_for_independent_groups(sample1,sample2,diff=diff,alternative='less')\n",
    "    mean = np.mean(sample1) - np.mean(sample2)\n",
    "    \n",
    "    if result < alfa and mean > diff:\n",
    "        return 'd'\n",
    "    elif (result2 < alfa) and (mean <= diff):\n",
    "        return 'm'\n",
    "    #if result >= alfa:\n",
    "    else:\n",
    "        return 'nwd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zacznijmy od sprawdzenia podejścia numer 1:\n",
    "    # - średnia populacyjna z rozkładu prostokątnego, w zakresach\n",
    "    # - średnia populacyjna z rozkładu wykładniczego\n",
    "    # - włożyć w to wielkość próby, sd - spoko byłoby wylosować może z chi kwadrat, eps\n",
    "    # - można sprawdzić różne wielkości prób\n",
    "    # - różne sd\n",
    "# podejścia 2 i 3:\n",
    "    # dwa pierwsze kroki takie same\n",
    "    # - różne n, sd\n",
    "    # upłynnić alfę\n",
    "# potem się będzie myślało"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166\n",
      "133\n",
      "36\n",
      "0\n",
      "266\n",
      "\n",
      "\n",
      "81\n",
      "269\n",
      "3\n",
      "0\n",
      "248\n",
      "\n",
      "\n",
      "201\n",
      "0\n",
      "0\n",
      "395\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# średnia populacyjna z rozkładu prostokątnego, w zakresach\n",
    "delta = 4\n",
    "n1 = 50\n",
    "n2 = 50\n",
    "sd1 = 4\n",
    "sd2 = 4\n",
    "eps = 0.01\n",
    "alfa = 0.05\n",
    "results_podejście1 = []\n",
    "results_podejście2 = []\n",
    "results_podejście3 = []\n",
    "#lst = np.random.exponential(3,size=400)\n",
    "#for i in lst:\n",
    "for i in range(0,601):\n",
    "    mean =  i*0.01\n",
    "    sample1 = get_normal_sample(n1,mean,sd1)\n",
    "    sample2 = get_normal_sample(n2,0,sd2)\n",
    "    res = independent_1(sample1,sample2,diff=delta,eps=eps) \n",
    "    results_podejście1.append([res,mean+delta])\n",
    "    res2  = independent_2(sample1,sample2,alfa,delta)\n",
    "    res3  = independent_3(sample1,sample2,alfa,delta)\n",
    "    results_podejście2.append([res2,mean])\n",
    "    results_podejście3.append([res3,mean])\n",
    "    \n",
    "poprawne_duże_podejście1 = [i for i in results_podejście1 if i[0] == 'd' and i[1] > delta]\n",
    "poprawne_małe_podejście1 = [i for i in results_podejście1 if i[0] == 'm' and i[1] < delta]\n",
    "niepoprawne_duże_podejście1 = [i for i in results_podejście1 if i[0] == 'd' and i[1] < delta]\n",
    "niepoprawne_małe_podejście1 = [i for i in results_podejście1 if i[0] == 'm' and i[1] > delta]\n",
    "niewiadomo_podejście1 = [i for i in results_podejście1 if i[0] == 'nwd']\n",
    "\n",
    "poprawne_duże_podejście2 = [i for i in results_podejście2 if i[0] == 'd' and i[1] > delta]\n",
    "poprawne_małe_podejście2 = [i for i in results_podejście2 if i[0] == 'm' and i[1] < delta]\n",
    "niepoprawne_duże_podejście2 = [i for i in results_podejście2 if i[0] == 'd' and i[1] < delta]\n",
    "niepoprawne_małe_podejście2 = [i for i in results_podejście2 if i[0] == 'm' and i[1] > delta]\n",
    "niewiadomo_podejście2 = [i for i in results_podejście2 if i[0] == 'nwd']\n",
    "\n",
    "poprawne_duże_podejście3 = [i for i in results_podejście3 if i[0] == 'd' and i[1] > delta]\n",
    "poprawne_małe_podejście3 = [i for i in results_podejście3 if i[0] == 'm' and i[1] < delta]\n",
    "niepoprawne_duże_podejście3 = [i for i in results_podejście3 if i[0] == 'd' and i[1] < delta]\n",
    "niepoprawne_małe_podejście3 = [i for i in results_podejście3 if i[0] == 'm' and i[1] > delta]\n",
    "niewiadomo_podejście3 = [i for i in results_podejście3 if i[0] == 'nwd']\n",
    "\n",
    "\n",
    "\n",
    "print(len(poprawne_duże_podejście2))\n",
    "print(len(poprawne_małe_podejście2))\n",
    "print(len(niepoprawne_duże_podejście2))\n",
    "print(len(niepoprawne_małe_podejście2))\n",
    "#print(np.mean([i[1] for i in niepoprawne_małe_podejście2]))\n",
    "print(len(niewiadomo_podejście2))\n",
    "print('\\n')\n",
    "print(len(poprawne_duże_podejście3))\n",
    "print(len(poprawne_małe_podejście3))\n",
    "print(len(niepoprawne_duże_podejście3))\n",
    "print(len(niepoprawne_małe_podejście3))\n",
    "print(len(niewiadomo_podejście3))\n",
    "print('\\n')\n",
    "print(len(poprawne_duże_podejście1))\n",
    "print(len(poprawne_małe_podejście1))\n",
    "print(len(niepoprawne_duże_podejście1))\n",
    "print(len(niepoprawne_małe_podejście1))\n",
    "print(len(niewiadomo_podejście1))\n",
    "#print(np.mean([i[1] for i in niepoprawne_małe_podejście1]))\n",
    "#print(niepoprawne_małe_podejście1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 0., 0., 0., 0., 0., 1., 1., 0., 1.]),\n",
       " array([7.32 , 7.414, 7.508, 7.602, 7.696, 7.79 , 7.884, 7.978, 8.072,\n",
       "        8.166, 8.26 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1,sharex=True)\n",
    "z = [i for i in range(len(niewiadomo_podejście1))]\n",
    "ax1.hist([i[1] for i in niewiadomo_podejście1],bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 33.4 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rysuj():\n",
    "    fig, (ax1,ax2) = plt.subplots(2,sharex=True)\n",
    "    n1 = 50\n",
    "    n2 = 50\n",
    "    sd1 = 2\n",
    "    sd2 = 2\n",
    "    delta = 3\n",
    "    mean1 = 0\n",
    "    #res2 = []\n",
    "    #res3 = []\n",
    "    ax1.set_xlim(0,6)\n",
    "    diffs = [i*0.01 for i in range(0,601)]\n",
    "    for diff in diffs:\n",
    "        sample1 = get_normal_sample(n1,mean1+diff,sd1)\n",
    "        sample2 = get_normal_sample(n2,mean1,sd2)\n",
    "        res2=[independent_2(sample1,sample2,i*0.01,delta) for i in range(1,101)]\n",
    "        res3=[independent_3(sample1,sample2,i*0.01,delta) for i in range(1,101)]\n",
    "        result = diff - delta\n",
    "        res2_vals = {'m':[1-(i+1)*0.01 for i,x in enumerate(res2) if x == 'm'],'d':[1-(i+1)*0.01 for i,x in enumerate(res2) if x == 'd'],'nwd':[1-(i+1)*0.01 for i,x in enumerate(res2) if x == 'nwd']}\n",
    "        res3_vals = {'m':[1-(i+1)*0.01 for i,x in enumerate(res3) if x == 'm'],'d':[1-(i+1)*0.01 for i,x in enumerate(res3) if x == 'd'],'nwd':[1-(i+1)*0.01 for i,x in enumerate(res3) if x == 'nwd']}\n",
    "        \n",
    "        \n",
    "        if result > 0:\n",
    "\n",
    "            if len(res2_vals['m']) != 0:\n",
    "                ax1.axvline(diff,ymin=res2_vals['m'][-1],ymax=res2_vals['m'][0], color = 'red')\n",
    "            if len(res2_vals['d']) != 0:\n",
    "                ax1.axvline(diff,ymin=res2_vals['d'][-1],ymax=res2_vals['d'][0], color = 'green')#,label = 'efekt duży')\n",
    "            if len(res2_vals['nwd']) != 0:\n",
    "                ax1.axvline(diff,ymin=res2_vals['nwd'][-1],ymax=res2_vals['nwd'][0], color = 'black')#,label = 'nie wiadomo')            \n",
    "            if len(res3_vals['m']) != 0:\n",
    "                ax2.axvline(diff,ymin=res3_vals['m'][-1],ymax=res3_vals['m'][0], color = 'red')\n",
    "            if len(res3_vals['d']) != 0:\n",
    "                ax2.axvline(diff,ymin=res3_vals['d'][-1],ymax=res3_vals['d'][0], color = 'green')#,label = 'efekt duży')\n",
    "            if len(res3_vals['nwd']) != 0:\n",
    "                ax2.axvline(diff,ymin=res3_vals['nwd'][-1],ymax=res3_vals['nwd'][0], color = 'black')#,label = 'nie wiadomo')\n",
    "\n",
    "        else:\n",
    "\n",
    "            if len(res2_vals['m']) != 0:\n",
    "                ax1.axvline(diff,ymin=res2_vals['m'][-1],ymax=res2_vals['m'][0], color = 'blue')#,label = 'efekt mały')\n",
    "            if len(res2_vals['d']) != 0:\n",
    "                ax1.axvline(diff,ymin=res2_vals['d'][-1],ymax=res2_vals['d'][0], color = 'red')\n",
    "            if len(res2_vals['nwd']) != 0:\n",
    "                ax1.axvline(diff,ymin=res2_vals['nwd'][-1],ymax=res2_vals['nwd'][0], color = 'black')#,label = 'nie wiadomo')            \n",
    "            if len(res3_vals['m']) != 0:\n",
    "                ax2.axvline(diff,ymin=res3_vals['m'][-1],ymax=res3_vals['m'][0], color = 'blue')#,label = 'efekt mały')\n",
    "            if len(res3_vals['d']) != 0:\n",
    "                ax2.axvline(diff,ymin=res3_vals['d'][-1],ymax=res3_vals['d'][0], color = 'red')\n",
    "            if len(res3_vals['nwd']) != 0:\n",
    "                ax2.axvline(diff,ymin=res3_vals['nwd'][-1],ymax=res3_vals['nwd'][0], color = 'black')#,label = 'nie wiadomo')\n",
    "    \n",
    "    ax1.axvline(0,ymin=0,ymax=0,color='red',label = 'błędna odpowiedź')\n",
    "    ax1.axvline(0,ymin=0,ymax=0,color='green',label = 'efekt duży')\n",
    "    ax1.axvline(0,ymin=0,ymax=0,color='blue',label = 'efekt mały')\n",
    "    ax1.axvline(0,ymin=0,ymax=0,color='black',label = 'nie wiadomo')\n",
    "    ax1.set_ylabel('poziom ufności')\n",
    "    ax2.set_xlabel('poziom ufności dla poszczególnych odpowiedzi w zależności od średniej populacyjnej, delta=3')\n",
    "    mlt.rcParams.update(mlt.rcParamsDefault)\n",
    "\n",
    "    fig.legend(frameon=False, loc = 'center', bbox_to_anchor = (0.5,1))\n",
    "        \n",
    "        \n",
    "%time rysuj()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 = 4\n",
    "mean = 0\n",
    "eps = 1\n",
    "sds = [i*0.1 for i in range(20,50)]\n",
    "for k in range(1,25): \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 j in range(0,60):\n",
    "                pop_mean = j*0.1\n",
    "                alfa = 0.05\n",
    "                sample1 = get_normal_sample(n,mean+pop_mean,sd)\n",
    "                sample2 = get_normal_sample(n,mean,sd)\n",
    "                pod1.append([independent_1(sample1,sample2,delta,eps),pop_mean,eps])\n",
    "                pod2.append([independent_2(sample1,sample2,alfa,delta),pop_mean,alfa])\n",
    "                pod3.append([independent_3(sample1,sample2,alfa,delta),pop_mean,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 np.abs(i[1]) > np.abs(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 np.abs(i[1]) > np.abs(delta)]\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 np.abs(i[1]) > np.abs(delta)]\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 (np.abs(i[1]) < np.abs(delta))]\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 np.abs(i[1]) < np.abs(delta)]\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 np.abs(i[1]) < np.abs(delta)]\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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdss = sds*25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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",
       "      <th>mały_podejście1</th>\n",
       "      <th>mały_podejście2</th>\n",
       "      <th>mały_podejście3</th>\n",
       "      <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",
       "      <th>źle_małe3</th>\n",
       "      <th>niewiadomo_małe1</th>\n",
       "      <th>niewiadomo_małe2</th>\n",
       "      <th>niewiadomo_małe3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.905263</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.094737</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.884211</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.115789</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.484211</td>\n",
       "      <td>0.873684</td>\n",
       "      <td>0.431579</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.126316</td>\n",
       "      <td>0.547368</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>0.831579</td>\n",
       "      <td>0.326316</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557895</td>\n",
       "      <td>0.168421</td>\n",
       "      <td>0.673684</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.320</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",
       "      <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>715</th>\n",
       "      <td>135</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.905263</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.094737</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>135</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.557895</td>\n",
       "      <td>0.926316</td>\n",
       "      <td>0.589474</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>135</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.884211</td>\n",
       "      <td>0.536842</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.115789</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>135</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.494737</td>\n",
       "      <td>0.905263</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>0.094737</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>135</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.536842</td>\n",
       "      <td>0.915789</td>\n",
       "      <td>0.547368</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>0.084211</td>\n",
       "      <td>0.442105</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wielkość próby   sd  efekt duży, podejście1  efekt duży, podejście2  \\\n",
       "0                20  2.0                0.505263                0.905263   \n",
       "1                20  2.1                0.505263                0.894737   \n",
       "2                20  2.2                0.526316                0.884211   \n",
       "3                20  2.3                0.484211                0.873684   \n",
       "4                20  2.4                0.442105                0.831579   \n",
       "..              ...  ...                     ...                     ...   \n",
       "715             135  4.5                0.494737                0.905263   \n",
       "716             135  4.6                0.557895                0.926316   \n",
       "717             135  4.7                0.505263                0.884211   \n",
       "718             135  4.8                0.494737                0.905263   \n",
       "719             135  4.9                0.536842                0.915789   \n",
       "\n",
       "     efekt duży, podejście3  mały_podejście1  mały_podejście2  \\\n",
       "0                  0.505263            0.740            0.285   \n",
       "1                  0.494737            0.750            0.300   \n",
       "2                  0.494737            0.775            0.280   \n",
       "3                  0.431579            0.745            0.300   \n",
       "4                  0.326316            0.735            0.295   \n",
       "..                      ...              ...              ...   \n",
       "715                0.578947            0.750            0.225   \n",
       "716                0.589474            0.730            0.225   \n",
       "717                0.536842            0.760            0.250   \n",
       "718                0.526316            0.730            0.255   \n",
       "719                0.547368            0.750            0.260   \n",
       "\n",
       "     mały_podejście3  źle_duże1  źle_duże2  źle_duże3  nie wiadomo duże1  \\\n",
       "0              0.720   0.000000        0.0   0.000000           0.494737   \n",
       "1              0.735   0.000000        0.0   0.000000           0.494737   \n",
       "2              0.755   0.000000        0.0   0.000000           0.473684   \n",
       "3              0.705   0.021053        0.0   0.021053           0.494737   \n",
       "4              0.675   0.000000        0.0   0.000000           0.557895   \n",
       "..               ...        ...        ...        ...                ...   \n",
       "715            0.770   0.000000        0.0   0.000000           0.505263   \n",
       "716            0.750   0.000000        0.0   0.010526           0.442105   \n",
       "717            0.775   0.000000        0.0   0.000000           0.494737   \n",
       "718            0.750   0.000000        0.0   0.000000           0.505263   \n",
       "719            0.750   0.000000        0.0   0.010526           0.463158   \n",
       "\n",
       "     nie wiadomo duże2  nie wiadomo duże3  źle_małe1  źle_małe2  źle_małe3  \\\n",
       "0             0.094737           0.494737      0.000      0.075      0.000   \n",
       "1             0.105263           0.505263      0.010      0.085      0.000   \n",
       "2             0.115789           0.505263      0.005      0.040      0.005   \n",
       "3             0.126316           0.547368      0.005      0.065      0.000   \n",
       "4             0.168421           0.673684      0.010      0.055      0.005   \n",
       "..                 ...                ...        ...        ...        ...   \n",
       "715           0.094737           0.421053      0.005      0.050      0.010   \n",
       "716           0.073684           0.400000      0.000      0.045      0.000   \n",
       "717           0.115789           0.463158      0.000      0.045      0.005   \n",
       "718           0.094737           0.473684      0.000      0.055      0.000   \n",
       "719           0.084211           0.442105      0.015      0.085      0.005   \n",
       "\n",
       "     niewiadomo_małe1  niewiadomo_małe2  niewiadomo_małe3  \n",
       "0               0.260             0.640             0.280  \n",
       "1               0.240             0.615             0.265  \n",
       "2               0.220             0.680             0.240  \n",
       "3               0.250             0.635             0.295  \n",
       "4               0.255             0.650             0.320  \n",
       "..                ...               ...               ...  \n",
       "715             0.245             0.725             0.220  \n",
       "716             0.270             0.730             0.250  \n",
       "717             0.240             0.705             0.220  \n",
       "718             0.270             0.690             0.250  \n",
       "719             0.235             0.655             0.245  \n",
       "\n",
       "[720 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2576ae9f100>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1106.75x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2576ae9fb20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1085.5x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "duży_źle = ['źle_duże1','źle_duże2','źle_duże3']\n",
    "mały_dobrze = ['mały_podejście1','mały_podejście2','mały_podejście3']\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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2576ae9f6a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1019.12x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży_źle = ['źle_duże1','źle_duże2','źle_duże3']\n",
    "mały_dobrze = ['mały_podejście1','mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','źle_duże1','źle_duże2','źle_duże3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['źle_duże1','źle_duże2','źle_duże3'],value_name='procent źle wskazanych',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 źle wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2576be523d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1066.12x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "mały_dobrze = ['mały_podejście1','mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','mały_podejście1','mały_podejście2','mały_podejście3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['mały_podejście1','mały_podejście2','mały_podejście3'],value_name='procent dobrze wskazanych',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 dobrze wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2576be475b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1084.88x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "mały_niewiadomo = ['niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3'],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": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25771c121c0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "źle_małe = ['źle_małe1','źle_małe2','źle_małe3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','źle_małe1','źle_małe2','źle_małe3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['źle_małe1','źle_małe2','źle_małe3'],value_name='procent źle wskazanych',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 źle wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 = 3\n",
    "mean = 0\n",
    "sds = [i*0.1 for i in range(20,50)]\n",
    "#n1 = 45\n",
    "#n2 = 30\n",
    "#sd1 = 2\n",
    "#sd2 = 2\n",
    "n1 = 60\n",
    "for n2 in range(61,121):   \n",
    "    for sd in sds:\n",
    "        przedziały_n.append(n2-n1)\n",
    "        pod1 = []\n",
    "        pod2 = []\n",
    "        pod3 = []\n",
    "        for j in range(0,61):\n",
    "            pop_mean = j*0.1\n",
    "            alfa = 0.05\n",
    "            sample1 = get_normal_sample(n1,mean+pop_mean,sd)\n",
    "            sample2 = get_normal_sample(n2,mean,sd)\n",
    "            #p1 = mode([independent_1(sample1,sample2,delta,eps) for i in range(10)])[0][0]\n",
    "            #p2 = mode([independent_2(sample1,sample2,alfa,delta) for i in range(10)])[0][0]\n",
    "            #p3 = mode([independent_3(sample1,sample2,alfa,delta) for i in range(10)])[0][0]\n",
    "            pod1.append([independent_1(sample1,sample2,delta,eps),pop_mean,eps])\n",
    "            pod2.append([independent_2(sample1,sample2,alfa,delta),pop_mean,alfa])\n",
    "            pod3.append([independent_3(sample1,sample2,alfa,delta),pop_mean,alfa])\n",
    "            #pod1.append([p1,pop_mean,eps])\n",
    "            #pod2.append([p2,pop_mean,alfa])\n",
    "            #pod3.append([p3,pop_mean,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 np.abs(i[1]) > np.abs(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 np.abs(i[1]) > np.abs(delta)]\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 np.abs(i[1]) > np.abs(delta)]\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 (np.abs(i[1]) < np.abs(delta))]\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 np.abs(i[1]) < np.abs(delta)]\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 np.abs(i[1]) < np.abs(delta)]\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdss = sds*60\n",
    "#przedziały_n = przedziały_n*len(sds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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",
       "      <th>mały_podejście1</th>\n",
       "      <th>mały_podejście2</th>\n",
       "      <th>mały_podejście3</th>\n",
       "      <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",
       "      <th>źle_małe3</th>\n",
       "      <th>niewiadomo_małe1</th>\n",
       "      <th>niewiadomo_małe2</th>\n",
       "      <th>niewiadomo_małe3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.200000</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",
       "      <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>1795</th>\n",
       "      <td>60</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>60</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>60</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>60</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>60</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1800 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      wielkość próby   sd  efekt duży, podejście1  efekt duży, podejście2  \\\n",
       "0                  1  2.0                0.833333                0.966667   \n",
       "1                  1  2.1                0.833333                0.966667   \n",
       "2                  1  2.2                0.900000                0.966667   \n",
       "3                  1  2.3                0.833333                1.000000   \n",
       "4                  1  2.4                0.800000                0.900000   \n",
       "...              ...  ...                     ...                     ...   \n",
       "1795              60  4.5                0.866667                0.933333   \n",
       "1796              60  4.6                0.766667                0.866667   \n",
       "1797              60  4.7                0.766667                0.866667   \n",
       "1798              60  4.8                0.900000                0.933333   \n",
       "1799              60  4.9                0.833333                0.866667   \n",
       "\n",
       "      efekt duży, podejście3  mały_podejście1  mały_podejście2  \\\n",
       "0                   0.800000         0.933333         0.166667   \n",
       "1                   0.800000         0.933333         0.300000   \n",
       "2                   0.833333         0.866667         0.233333   \n",
       "3                   0.833333         0.833333         0.300000   \n",
       "4                   0.800000         0.833333         0.233333   \n",
       "...                      ...              ...              ...   \n",
       "1795                0.666667         0.900000         0.433333   \n",
       "1796                0.533333         0.833333         0.533333   \n",
       "1797                0.566667         0.833333         0.433333   \n",
       "1798                0.633333         0.800000         0.466667   \n",
       "1799                0.600000         0.733333         0.466667   \n",
       "\n",
       "      mały_podejście3  źle_duże1  źle_duże2  źle_duże3  nie wiadomo duże1  \\\n",
       "0            0.900000   0.000000   0.000000   0.000000           0.166667   \n",
       "1            0.900000   0.000000   0.000000   0.000000           0.166667   \n",
       "2            0.766667   0.000000   0.000000   0.000000           0.100000   \n",
       "3            0.833333   0.000000   0.000000   0.000000           0.166667   \n",
       "4            0.800000   0.000000   0.000000   0.000000           0.200000   \n",
       "...               ...        ...        ...        ...                ...   \n",
       "1795         0.733333   0.033333   0.000000   0.000000           0.100000   \n",
       "1796         0.633333   0.066667   0.000000   0.000000           0.166667   \n",
       "1797         0.533333   0.000000   0.000000   0.000000           0.233333   \n",
       "1798         0.633333   0.033333   0.000000   0.000000           0.066667   \n",
       "1799         0.600000   0.100000   0.033333   0.066667           0.066667   \n",
       "\n",
       "      nie wiadomo duże2  nie wiadomo duże3  źle_małe1  źle_małe2  źle_małe3  \\\n",
       "0              0.033333           0.200000   0.000000   0.033333        0.0   \n",
       "1              0.033333           0.200000   0.000000   0.033333        0.0   \n",
       "2              0.033333           0.166667   0.000000   0.033333        0.0   \n",
       "3              0.000000           0.166667   0.000000   0.033333        0.0   \n",
       "4              0.100000           0.200000   0.033333   0.066667        0.0   \n",
       "...                 ...                ...        ...        ...        ...   \n",
       "1795           0.066667           0.333333   0.000000   0.000000        0.0   \n",
       "1796           0.133333           0.466667   0.000000   0.033333        0.0   \n",
       "1797           0.133333           0.433333   0.000000   0.133333        0.0   \n",
       "1798           0.066667           0.366667   0.033333   0.033333        0.0   \n",
       "1799           0.100000           0.333333   0.066667   0.066667        0.0   \n",
       "\n",
       "      niewiadomo_małe1  niewiadomo_małe2  niewiadomo_małe3  \n",
       "0             0.066667          0.800000          0.100000  \n",
       "1             0.066667          0.666667          0.100000  \n",
       "2             0.133333          0.733333          0.233333  \n",
       "3             0.166667          0.666667          0.166667  \n",
       "4             0.133333          0.700000          0.200000  \n",
       "...                ...               ...               ...  \n",
       "1795          0.100000          0.566667          0.266667  \n",
       "1796          0.166667          0.433333          0.366667  \n",
       "1797          0.166667          0.433333          0.466667  \n",
       "1798          0.166667          0.500000          0.366667  \n",
       "1799          0.200000          0.466667          0.400000  \n",
       "\n",
       "[1800 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d849c86a0>"
      ]
     },
     "execution_count": 21,
     "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, podejście1','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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d84944ee0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 767.25x216 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 = ['mały_podejście1','mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','mały_podejście1','mały_podejście2','mały_podejście3']]\n",
    "dtt = dt[dt['sd'].isin([2,3,4])]\n",
    "nowe = dtt.melt(value_vars=['mały_podejście1','mały_podejście2','mały_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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21374094fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780.625x216 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 = ['niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3']]\n",
    "dtt = dt[dt['sd'].isin([2,3,4])]\n",
    "nowe = dtt.melt(value_vars=['niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3'],value_name='procent nie wiadomo',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 nie wiadomo')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d85cd6550>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.25x216 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 = ['źle_małe1','źle_małe2','źle_małe3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','źle_małe1','źle_małe2','źle_małe3']]\n",
    "dtt = dt[dt['sd'].isin([2,3,4])]\n",
    "nowe = dtt.melt(value_vars=['źle_małe1','źle_małe2','źle_małe3'],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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d85cd6850>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 781.25x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "duży_dobrze = ['wielkość próby','sd','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['sd'].isin([2,3,4])]\n",
    "nowe = dtt.melt(value_vars=['nie wiadomo duże1','nie wiadomo duże2','nie wiadomo duże3'],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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d84a200d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 733.625x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "duży_dobrze = ['wielkość próby','sd','źle_duże1','źle_duże2','źle_duże3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','źle_duże1','źle_duże2','źle_duże3']]\n",
    "dtt = dt[dt['sd'].isin([2,3,4])]\n",
    "nowe = dtt.melt(value_vars=['źle_duże1','źle_duże2','źle_duże3'],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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 = 3\n",
    "mean = 0\n",
    "eps = 1\n",
    "#sds = [i*0.1 for i in range(20,50)] \n",
    "sds = []\n",
    "sd1 = 2\n",
    "for k in range(1,25): \n",
    "    p1,p2 = 15+k*5,20+k*5\n",
    "    #for sd in sds:\n",
    "    for sd2 in range(20,50):\n",
    "        sd2 = sd2*0.1\n",
    "        sds.append(sd2-sd1)\n",
    "        przedziały_n.append(p1)\n",
    "        pod1 = []\n",
    "        pod2 = []\n",
    "        pod3 = []\n",
    "        for n in range(p1,p2):\n",
    "            for j in range(20,61):\n",
    "                pop_mean = j*0.1\n",
    "                alfa = 0.05\n",
    "                sample1 = get_normal_sample(n,mean+pop_mean,sd1)\n",
    "                sample2 = get_normal_sample(n,mean,sd2)\n",
    "                pod1.append([independent_1(sample1,sample2,delta,eps),pop_mean,eps])\n",
    "                pod2.append([independent_2(sample1,sample2,alfa,delta),pop_mean,alfa])\n",
    "                pod3.append([independent_3(sample1,sample2,alfa,delta),pop_mean,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 np.abs(i[1]) > np.abs(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 np.abs(i[1]) > np.abs(delta)]\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 np.abs(i[1]) > np.abs(delta)]\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 (np.abs(i[1]) < np.abs(delta))]\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 np.abs(i[1]) < np.abs(delta)]\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 np.abs(i[1]) < np.abs(delta)]\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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",
       "      <th>mały_podejście1</th>\n",
       "      <th>mały_podejście2</th>\n",
       "      <th>mały_podejście3</th>\n",
       "      <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",
       "      <th>źle_małe3</th>\n",
       "      <th>niewiadomo_małe1</th>\n",
       "      <th>niewiadomo_małe2</th>\n",
       "      <th>niewiadomo_małe3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.686667</td>\n",
       "      <td>0.913333</td>\n",
       "      <td>0.686667</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.313333</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>0.313333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</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",
       "      <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>715</th>\n",
       "      <td>135</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.953333</td>\n",
       "      <td>0.753333</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.246667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>135</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.713333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.806667</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.193333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>135</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>135</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.673333</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.773333</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.326667</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>135</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>0.753333</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wielkość próby   sd  efekt duży, podejście1  efekt duży, podejście2  \\\n",
       "0                20  2.0                0.653333                0.926667   \n",
       "1                20  2.1                0.686667                0.913333   \n",
       "2                20  2.2                0.653333                0.920000   \n",
       "3                20  2.3                0.720000                0.940000   \n",
       "4                20  2.4                0.640000                0.900000   \n",
       "..              ...  ...                     ...                     ...   \n",
       "715             135  4.5                0.680000                0.953333   \n",
       "716             135  4.6                0.713333                0.966667   \n",
       "717             135  4.7                0.680000                0.966667   \n",
       "718             135  4.8                0.673333                0.960000   \n",
       "719             135  4.9                0.653333                0.946667   \n",
       "\n",
       "     efekt duży, podejście3  mały_podejście1  mały_podejście2  \\\n",
       "0                  0.653333             0.30             0.04   \n",
       "1                  0.686667             0.24             0.02   \n",
       "2                  0.640000             0.22             0.02   \n",
       "3                  0.700000             0.30             0.04   \n",
       "4                  0.620000             0.24             0.06   \n",
       "..                      ...              ...              ...   \n",
       "715                0.753333             0.14             0.00   \n",
       "716                0.806667             0.30             0.00   \n",
       "717                0.760000             0.20             0.00   \n",
       "718                0.773333             0.24             0.00   \n",
       "719                0.753333             0.14             0.00   \n",
       "\n",
       "     mały_podejście3  źle_duże1  źle_duże2  źle_duże3  nie wiadomo duże1  \\\n",
       "0               0.34   0.000000        0.0   0.000000           0.346667   \n",
       "1               0.26   0.000000        0.0   0.000000           0.313333   \n",
       "2               0.20   0.000000        0.0   0.000000           0.346667   \n",
       "3               0.26   0.000000        0.0   0.000000           0.280000   \n",
       "4               0.16   0.000000        0.0   0.000000           0.360000   \n",
       "..               ...        ...        ...        ...                ...   \n",
       "715             0.28   0.000000        0.0   0.000000           0.320000   \n",
       "716             0.40   0.000000        0.0   0.000000           0.286667   \n",
       "717             0.46   0.000000        0.0   0.000000           0.320000   \n",
       "718             0.40   0.000000        0.0   0.013333           0.326667   \n",
       "719             0.30   0.006667        0.0   0.006667           0.340000   \n",
       "\n",
       "     nie wiadomo duże2  nie wiadomo duże3  źle_małe1  źle_małe2  źle_małe3  \\\n",
       "0             0.073333           0.346667       0.00       0.14       0.00   \n",
       "1             0.086667           0.313333       0.00       0.20       0.00   \n",
       "2             0.080000           0.360000       0.02       0.16       0.02   \n",
       "3             0.060000           0.300000       0.06       0.22       0.04   \n",
       "4             0.100000           0.380000       0.00       0.22       0.00   \n",
       "..                 ...                ...        ...        ...        ...   \n",
       "715           0.046667           0.246667       0.00       0.12       0.00   \n",
       "716           0.033333           0.193333       0.00       0.20       0.00   \n",
       "717           0.033333           0.240000       0.00       0.16       0.02   \n",
       "718           0.040000           0.213333       0.02       0.14       0.02   \n",
       "719           0.053333           0.240000       0.00       0.14       0.00   \n",
       "\n",
       "     niewiadomo_małe1  niewiadomo_małe2  niewiadomo_małe3  \n",
       "0                0.70              0.82              0.66  \n",
       "1                0.76              0.78              0.74  \n",
       "2                0.76              0.82              0.78  \n",
       "3                0.64              0.74              0.70  \n",
       "4                0.76              0.72              0.84  \n",
       "..                ...               ...               ...  \n",
       "715              0.86              0.88              0.72  \n",
       "716              0.70              0.80              0.60  \n",
       "717              0.80              0.84              0.52  \n",
       "718              0.74              0.86              0.58  \n",
       "719              0.86              0.86              0.70  \n",
       "\n",
       "[720 rows x 20 columns]"
      ]
     },
     "execution_count": 28,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25771540a00>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2577622ab80>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "duży_źle = ['źle_duże1','źle_duże2','źle_duże3']\n",
    "mały_dobrze = ['mały_podejście1','mały_podejście2','mały_podejście3']\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d85f84340>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 733.625x432 with 6 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_źle = ['źle_duże1','źle_duże2','źle_duże3']\n",
    "mały_dobrze = ['mały_podejście1','mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','źle_duże1','źle_duże2','źle_duże3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['źle_duże1','źle_duże2','źle_duże3'],value_name='procent źle wskazanych',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 źle wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d8460a0d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 767.25x432 with 6 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",
    "mały_dobrze = ['mały_podejście1','mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','mały_podejście1','mały_podejście2','mały_podejście3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['mały_podejście1','mały_podejście2','mały_podejście3'],value_name='procent dobrze wskazanych',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 dobrze wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d83b16a60>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780.625x432 with 6 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",
    "mały_niewiadomo = ['niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['niewiadomo_małe1','niewiadomo_małe2','niewiadomo_małe3'],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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19d883df4f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.25x432 with 6 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",
    "źle_małe = ['źle_małe1','źle_małe2','źle_małe3']\n",
    "\n",
    "dt = trzy_podejścia[['wielkość próby','sd','źle_małe1','źle_małe2','źle_małe3']]\n",
    "dtt = dt[dt['wielkość próby'].isin([20,40,60,80,100,120])]\n",
    "nowe = dtt.melt(value_vars=['źle_małe1','źle_małe2','źle_małe3'],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"
   ]
  },
  {
   "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
}