{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Chapter 6 - Outlier Handling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6.2. Outlier Trimming" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", "
" ], "text/plain": [ " survived pclass sex age sibsp parch fare embarked class \\\n", "0 0 3 male 22.0 1 0 7.2500 S Third \n", "1 1 1 female 38.0 1 0 71.2833 C First \n", "2 1 3 female 26.0 0 0 7.9250 S Third \n", "3 1 1 female 35.0 1 0 53.1000 S First \n", "4 0 3 male 35.0 0 0 8.0500 S Third \n", "\n", " who adult_male deck embark_town alive alone \n", "0 man True NaN Southampton no False \n", "1 woman False C Cherbourg yes False \n", "2 woman False NaN Southampton yes True \n", "3 woman False C Southampton yes False \n", "4 man True NaN Southampton no True " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "\n", "plt.rcParams[\"figure.figsize\"] = [8,6]\n", "sns.set_style(\"darkgrid\")\n", "\n", "titanic_data = sns.load_dataset('titanic')\n", "\n", "titanic_data.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='age', data=titanic_data)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-6.6875\n", "64.8125\n" ] } ], "source": [ "IQR = titanic_data[\"age\"].quantile(0.75) - titanic_data[\"age\"].quantile(0.25)\n", "\n", "lower_age_limit = titanic_data[\"age\"].quantile(0.25) - (IQR * 1.5)\n", "upper_age_limit = titanic_data[\"age\"].quantile(0.75) + (IQR * 1.5)\n", "\n", "print(lower_age_limit)\n", "print(upper_age_limit)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "age_outliers = np.where(titanic_data[\"age\"] > upper_age_limit, True,\n", " np.where(titanic_data[\"age\"] < lower_age_limit, True, False))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((891, 15), (880, 15))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_without_age_outliers = titanic_data.loc[~(age_outliers), ]\n", "\n", "titanic_data.shape, titanic_without_age_outliers.shape\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='age', data = titanic_without_age_outliers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6.3. Outlier Capping Using IQR" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "\n", "plt.rcParams[\"figure.figsize\"] = [8,6]\n", "sns.set_style(\"darkgrid\")\n", "\n", "titanic_data = sns.load_dataset('titanic')\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='fare', data=titanic_data)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\mmalik\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IRqNUVlaybds2fD4fS5YsYe7cuVRVVVFUVMTjjz/OqVOnuOWWW7j++uuprKxkxYoVXHPNNTzyyCO89NJLlJeXf7pPPcS8ebgZgIUzLmZYnpcX9h7jlfdOKuhFROSc+g36e+65hyeeeIKvfOUrPdp+//vf9/m8PXv2MGfOHACmT59+1oY7dXV1lJaWUlhYCMCsWbOorq7mxhtvZP78+cnHmaYJwL59+5g9ezYA1157Lbt27eo36E3ToKgor7+PlbE6WjvJ83nPui8aT7D3aAv/89JiLi/tCvbPlo3g1fpTFBb6MAxnT9+bpmvI/jztoj7pSX3Sk/qkp2ztk36D/oknngD6D/XehEIhAoFA8rZpmsRiMdxuN6FQiPz8/GSb3+8nFArh9/uTz73nnntYsWIFAJZlJcPM7/fT2tra73vH4xbBYNt51ZspLMNFW3vkrPvePt5KWyTOl64czaETXZ996uh8Xj7YwO/faWDMMB95HpMch+Z9UVHekP152kV90pP6pCf1SU9O7pOSkvw+2wZ0sPu///u/qaqq4j//8z+54YYb2L59e7+PDwQChMPh5O1EIoHb7e61LRwOJ4P/2LFjfPWrX+Xmm2/mpptu6irwjOPx4XCYgoKCgZTsGEdbOjBdBpNG5fPygRO8fOAE8XgCgOf/+wNePnCCtmg8zVWKiEimGlDQP/7444wfP54f/vCH/PjHP2bz5s39Pn7mzJlUVVUBUFNTw+TJk5NtZWVl1NfXEwwGiUQiVFdXM2PGDBobG1m2bBkPPPAAt956a/LxU6ZMYffu3QBUVVVx9dVXn/eHHMpOtHZS4veetcK+yOdhhN9DXaMz/zIVEZHBM6Dz6HNychgxYgRut5uSkhIikUi/jy8vL2fXrl0sXrwYy7JYv34927dvp62tjUWLFrFq1SqWL1+OZVlUVFQwatQo1q1bR0tLC0899RRPPfUUAE8//TQrV67k4Ycf5jvf+Q4TJ0486zi+01mWxUetESaX+Hu0TRzh543DzUROj+5FRER6Y1iWZZ3rQXfffTdNTU3cdttthMNhdu/enTx+n2mi0fiQPQbTbrh48a0jydutHTH+6b8OUX5ZCf/P9ZN49Y8Nybb3GsNsefMoi2dewrLPj2eY10xHybZz8jG1C6U+6Ul90pP6pCcn90l/x+gHNKL/7ne/ywcffMCll17KO++8w4IFCwatOOnbiVAnACPzc3q0jS7ouq/h9GNERER6M6Cgb2pq4uWXX+bXv/518r6//uu/tq0o6fJR6+mgD3h7tOV53fi9ZvKPARERkd4MaDHevffeSygUori4OPlP7HeitZPCXDe5nt6n5UsCXhpa+18vISIi2W1AI3q/38/f/M3f2F2LfMKJUGev0/bdRgZyeONwM/HEOZdZiIhIlhrQiH7SpEn86le/4r333uPQoUMcOnTI7rqyXjSe4GQ42m/Ql+TnEEtYHG1uT2FlIiIylAxoRP/222/z9ttvJ28bhsEPf/hD24oSaAhFsIBR/QX96WP3h5ramDaq7xWXIiKSvQYU9M8++yytra0cOXKEsWPHJrerFfucSC7E6zvoi/1eDOB9bZwjIiJ9GFDQ/+Y3v2HTpk3E43FuvPFGDMPg61//ut21ZbVgexSXAYW+vn9EHtPFsDwP7zWF+3yMiIhktwEdo//BD37A1q1bKSoq4utf/zo7duywu66s19wRpSDXg+scV6cbGcjh/SaN6EVEpHcDCnrDMPB6vRiGgWEY+Hw+u+vKes3tMYr6Gc13K8n3cqy5g3Zd2EZERHoxoKD/zGc+w3333cdHH33EI488wlVXXWV3XVmvub1rRH8uIwM5WHRtiSsiIvJJ5xwyHjhwAJfLxb59+/jyl79MQUEBS5cuTUVtWSsWTxCKxPs9Pt9thL9r5X39qXamXpRdl/AVEZFz63dE/+KLL7J69WouueQSHnjgAQoKCti6dauO0dusuSMGQOEARvRFPg8uAz48pXPpRUSkp36HjD/84Q957rnnyMvLS953yy23cPfdd3PDDTfYXly2au6IAl0hfi6my2Bkfg4fBhX0IiLSU78jerfbfVbIAwQCAUzTmZdEzRTN7adH9AOYuge4uNDH4WCHnSWJiMgQ1W/QG32c2pVIJGwpRro0nz6HPpAzwKAvytWIXkREetVvkrz77rvcd999Z91nWRZ1dXW2FpXtBnoOfbeLC3Np6YjR3B6lcADT/SIikj36Dfp//Md/7PX+xYsX21GLnNbcHhvwtD3AJYW5ABwOtivoRUTkLP2myezZs1NVh5yhuT3KxOKBX0/gkqKuDYw+DHboFDsRETnLgDbMkdQ5n3Pou40uyMUAHacXEZEeFPQZpuU8zqHv5nW7GJWfw2EFvYiIfIKCPsOczzn0ZxozzKdNc0REpAcFfYY533Pou40tyuVDnUsvIiKfoKDPMC0dMQwg4D3foPcRbI/SenrqX0REBBT0GScUiZHnNXG5BnYOfbexp1feH27W9L2IiHxMQZ9hQp2xAe+Id6Yx3afY6Ti9iIicQUGfYcKdcQI5538tgYtPb5pztFnH6UVE5GPnP3QUW4UiMUbm55zXcwzDoNOyKPR5OHSqnVOReLItz2OSc35HAURExEEU9BkknrAIR85/RN8eS/DqHxvI87jYf6yVlw+cSLbNvXwkOV5dbVBEJFtp6j6DNLdHsayBX7Xuk4p8Hprbo4NclYiIDGUK+gzSFO4Ezv/Uum5FPg/NHVESljWYZYmIyBCmoM8gTaEIAP4LWIwHUOjzkLC6Vu6LiIiAgj6jnAx3Bf2FT913PS/Ypul7ERHpoqDPIE3dQX+Bi+e6r0Uf1O54IiJymi1Bn0gkeOSRR1i0aBFLly6lvr7+rPadO3dSUVHBokWL2Lp161ltb731FkuXLk3e3rdvH3PmzGHp0qUsXbqUf//3f7ej5IzQFI6Q63bhNi/sx9J9xbugFuSJiMhptpxet2PHDiKRCFu2bKGmpoYNGzawadMmAKLRKJWVlWzbtg2fz8eSJUuYO3cuJSUlPP300/ziF7/A5/MlX2v//v3ccccdLFu2zI5SM0pTqBP/BU7bA5gug4Jct1bei4hIki0j+j179jBnzhwApk+fTm1tbbKtrq6O0tJSCgsL8Xq9zJo1i+rqagBKS0t58sknz3qt2tpafve733H77bezevVqQqGQHSVnhKZw5IJ2xTtTYa5HI3oREUmyZUQfCoUIBALJ26ZpEovFcLvdhEIh8vPzk21+vz8Z3vPnz+fw4cNnvda0adNYsGABV155JZs2bWLjxo2sXLmyz/c2TYOiorxB/kSpcbItQrE/hzyft0eb23T1ev8n24rzc6hrCCVv5+Z4KDq9Pe5QZJquIfvztIv6pCf1SU/qk56ytU9sCfpAIEA4HE7eTiQSuN3uXtvC4fBZwf9J5eXlFBQUJL9fu3Ztv+8dj1sEg22fpvy0sCyLplCEMYW5tLVHerTH4ole7/9kW8DjoqUjRku4A7fLRUdnlGAwYWvtdioqyhuSP087qU96Up/0pD7pycl9UlLSd47aMnU/c+ZMqqqqAKipqWHy5MnJtrKyMurr6wkGg0QiEaqrq5kxY0afr7V8+XL27t0LwKuvvsrUqVPtKDntwpE4nbHEBW+W06175X1zu1bei4iITSP68vJydu3axeLFi7Esi/Xr17N9+3ba2tpYtGgRq1atYvny5ViWRUVFBaNGjerztdasWcPatWvxeDwUFxefc0Q/VDWGus+h/3TH6IuSQR9lhL/3qX4REcketgS9y+Xi29/+9ln3lZWVJb+fN28e8+bN6/W5Y8aMOeuUu6lTp7J582Y7yswojZ9ys5xu3UGvBXkiIgLaMCdjdAe9/1NO3QdyTEyXQVBT9yIigoI+YyR3xfuUU/eGYVCoc+lFROQ0BX2GaAxH8Lpd5Lg//Y+k0Kdz6UVEpIuCPkM0hiOM8HsxDONTv1aRz0OwQ0EvIiIK+ozRHfSDocjnpiOaoDMWH5TXExGRoUtBnyGaQoMZ9N0r77UgT0Qk2ynoM0RjOMLwQQr6wjPOpRcRkeymoM8AnbEErZ0xRgRyBuX1dC69iIh0U9BngO5T6wZr6j739Op9Bb2IiCjoM0DjIAd997n0CnoREVHQZ4Bk0AcGb2/6Ip9HF7YREREFfSbovqDNYC3Gg64Fec3tUSzLGrTXFBGRoUdBnwGa2iK4DCjyDe6IPpqwNH0vIpLlFPQZoCkUYXieF9P16XfF61bo67o4zvGWzkF7TRERGXoU9BmgMRyheJCvHT/s9Cl2x1s6BvV1RURkaFHQZ4DGcITiQVyIBx9vmnO8WUEvIpLNFPQZoDEcYUTe4Aa9x3Th95qauhcRyXIK+jSLJyxOtUUG9dS6boU+j6buRUSynII+zU61R0lYDPoxeui6ip1G9CIi2U1Bn2ZNp8+htyfoPXzU2kEsoXPpRUSylYI+zbp3xbMj6AtzPSQsONGqUb2ISLZS0KdZY7grhAdrn/szdV/F7qhW3ouIZC0FfZoN9gVtzlSooBcRyXoK+jRrCkcpyHWT4x78H0VBrhuXAUe08l5EJGsp6NOsMRyxZTQPYLoMSvJzNKIXEcliCvo0awwN/va3ZxpdkKugFxHJYgr6NGsKd9oc9BrRi4hkMwV9GlmWZevUPXSN6BvDETqicdveQ0REMpeCPo1aO2NE4pbtI3qAY9ohT0QkKyno06gpHAXs2Syn2+iCXECn2ImIZCsFfRp1b5Yz2JeoPVN30B9pbrftPUREJHMp6NPIzs1yug3P8+DzuPgwqBG9iEg2UtCnUaONF7TpZhgGY4t8fHhKI3oRkWykoE+jxnCEHLcLv9e09X1Kh+Xxwak2W99DREQyky1Bn0gkeOSRR1i0aBFLly6lvr7+rPadO3dSUVHBokWL2Lp161ltb731FkuXLk3erq+vZ8mSJdx22208+uijJBIJO0pOi6Zw12Y5hmHY+j6lw7o2zYnFndN3IiIyMLYE/Y4dO4hEImzZsoX77ruPDRs2JNui0SiVlZV8//vf59lnn2XLli00NDQA8PTTT/PQQw/R2fnxqWCVlZWsWLGC559/HsuyeOmll+woOS26g95upcPyiFtwRCvvRUSyji1Bv2fPHubMmQPA9OnTqa2tTbbV1dVRWlpKYWEhXq+XWbNmUV1dDUBpaSlPPvnkWa+1b98+Zs+eDcC1117LK6+8YkfJadEYjti64r7b2GE+AD7QcXoRkazjtuNFQ6EQgUAgeds0TWKxGG63m1AoRH5+frLN7/cTCoUAmD9/PocPHz7rtSzLSk5t+/1+Wltb+31v0zQoKsobrI9iq6a2KHOG5SXr7WjtJM/Xe/C7TdcFteXmeLhq/HAAGjvjQ6Zvupmma8jVbDf1SU/qk57UJz1la5/YEvSBQIBwOJy8nUgkcLvdvbaFw+Gzgv+TXC7XWY8tKCjo973jcYtgMPMXnnVE47R2xMj3uJL1WoaLtvZIr4+PxRMX1NbRGcVnmRTkujl4tHlI9M2ZioryhlzNdlOf9KQ+6Ul90pOT+6SkpJ8cteMNZ86cSVVVFQA1NTVMnjw52VZWVkZ9fT3BYJBIJEJ1dTUzZszo87WmTJnC7t27AaiqquLqq6+2o+SUS8U59GcqHebT1L2ISBayZURfXl7Orl27WLx4MZZlsX79erZv305bWxuLFi1i1apVLF++HMuyqKioYNSoUX2+1sqVK3n44Yf5zne+w8SJE5k/f74dJadcU9j+c+jPNLbIxxuHm1PyXiIikjlsCXqXy8W3v/3ts+4rKytLfj9v3jzmzZvX63PHjBlz1il3EyZM4LnnnrOjzLRKddCXDvPx4tsn6IjGyfXYe96+iIhkDm2YkybpmLoHOKytcEVEsoqCPk0awxFMA4bleVLyfqXJU+ycuRBFRER6p6BPk8ZQhOF+Ly6bd8Xr1n0ufb0W5ImIZBUFfZo0hCKUBHJS9n5+r5uRAS/vNWlELyKSTRT0aXIi1MnIFOyKd6aJxX7eawyf+4EiIuIYCvo0SfWIHmDiiDzqT7UTT1gpfV8REUkfBX0adETjtHbGKEnxiL5shJ/OWEIXtxERySIK+jRoCHWdWpfqoJ9Y3LXHs6bvRUSyh4I+DU6Eui7Dm+qp+wkjTge9FuSJiGQNBX0adI/oR6Y46P1eNxcV5PBek0b0IiLZQkGfBg3JEX1qp+4BJo7wa0QvIpJFFPRp0BCK4PO48HtTv+f8xBF5vH+yjZhW3ouIZAUFfRo0hDopCeRgpGhXvDNNLM4jGrc4HNQOeSIi2UBBnwYnQpGUb5bTbeIIP6CV9yIi2UJBnwaNoU6KU7wQr9uEEXkYQF2jjtOLiGQDBX2KWZZFQzh9I3qfx6R0mI+DJ0JpeX8REUktBX2KBdujRONWys+hP9PlowK8/VFr2t5fRERSR0GfYieS59CnZ0QPMGV0PidCEZrCkbTVICIiqaGgT7HG00GfrmP00DWiBzjwkabvRUScTkGfYt3b36ZzRH/ZyAAGsF/T9yIijqegT7GGUCcGUOxPX9D7vW7GDfdpRC8ikgUU9Cl2IhRhWJ4Ht5nerr98VD4HNKIXEXE8BX2KHW/pYHRBbrrL4IpRAU6EIjRqQZ6IiKMp6FPsWEsnFxWkbyFetytG5QNoVC8i4nAK+hRKWBbHWzq4KANG9N0L8t4+ruP0IiJOpqBPoZNtUSJxKyOCPs9rcmmJn5ojzekuRUREbKSgT6FjzR0AGTF1DzBzTCF7j7YQiyfSXYqIiNhEQZ9Cx1pOB31h+kf00BX0HbEE+3WanYiIYynoU+hYS9dmOZkyop8xphCANz4MprcQERGxjYI+hY61dFCY68bvdae7FACG5XmZMCKPPYd1nF5ExKkU9Cl0LEPOoT/TzDGF7D3SQixhpbsUERGxgYI+hTLlHPozzRxTSFs0zkGdTy8i4kgK+hSxLItjzR1cnCEL8brNHFsEwBuavhcRcSQFfYo0t8foiCVSPnVvGAanIvFe/3VaXRfXmTAij1feP5XSukREJDVsWRWWSCRYs2YNBw8exOv1sm7dOsaNG5ds37lzJxs3bsTtdlNRUcHChQv7fM6+ffu46667GD9+PABLlizhi1/8oh1l2+ro6VPrLk7x1H17LMGrf2zotW3u5SPJ8Zr86aUj+OHrHxJsj1Lk86S0PhERsZctQb9jxw4ikQhbtmyhpqaGDRs2sGnTJgCi0SiVlZVs27YNn8/HkiVLmDt3Lm+++Wavz9m/fz933HEHy5Yts6PUlDl+OugzbTEewHWXFvOD3R+y672T/PnUUekuR0REBpEtU/d79uxhzpw5AEyfPp3a2tpkW11dHaWlpRQWFuL1epk1axbV1dV9Pqe2tpbf/e533H777axevZpQaGhu7nL09Dn0F2dg0E8ZFWBkwMvv3m1MdykiIjLIbBnRh0IhAoFA8rZpmsRiMdxuN6FQiPz8/GSb3+8nFAr1+Zxp06axYMECrrzySjZt2sTGjRtZuXJln+9tmgZFRXl2fKxP5WRnjECOm7GjC/p8TEdrJ3k+b69tbtM16G25OR6KTi8O/MLU0Wx74zA5eTn4vGZ/HyWlTNOVkT/PdFKf9KQ+6Ul90lO29oktQR8IBAiHw8nbiUQCt9vda1s4HCY/P7/P55SXl1NQ0BWO5eXlrF27tt/3jsctgsG2wfw4g+K9j0JcVJDTb22W4aKtvffrw8fiiUFv6+iMEgx27XP/2bGFPLf7A/5j7xGuu7S4v4+SUkVFeRn580wn9UlP6pOe1Cc9OblPSkry+2yzZep+5syZVFVVAVBTU8PkyZOTbWVlZdTX1xMMBolEIlRXVzNjxow+n7N8+XL27t0LwKuvvsrUqVPtKNl2759sY/zwzP1LcuaYQgpy3fz2YO8L90REZGiyZURfXl7Orl27WLx4MZZlsX79erZv305bWxuLFi1i1apVLF++HMuyqKioYNSoUb0+B2DNmjWsXbsWj8dDcXHxOUf0magzluBYSwdfnDIy3aX0yW26mH/5SF74wzGCbVGK8rT6XkTECWwJepfLxbe//e2z7isrK0t+P2/ePObNm3fO5wBMnTqVzZs321FmynwYbCdhkdEjeoCKP7mIn9QcZfu+4yz9zNh0lyMiIoNAG+akQP3JrmNC44ZldtCXFfuZcUkBP9t7jISlve9FRJxAQZ8C758O+tLhvjRXcm4Vf3Ixh4Md/P79U33uqNe9q56IiGS+zLheqsO9f7Kd0fk5+DyZc9paX+ZOKmZ4nocf/fdh5rd29v2407vqiYhIZtOIPgXqM3zF/Zm8bhdLPzOWNz4McqgpfO4niIhIRlPQ28yyLOpPtjNuCEzbd1s4/WJGFeSw851GLB2rFxEZ0hT0NmsIRWiLxhk3REb00DWqX/bZcZwIRag9puvUi4gMZQp6m3UvxBs/hEb0ANdNKuaighxeeqeBlo5oussREZELpKC32fsn24HMP4f+k1yGwZevHE08YfHC3uPEE5rCFxEZihT0NvvgVBt+r0mxv/cLy2Sy4X4vfzZlFEeaO9j5Rx2vFxEZinR6nc3qGsOMH56HYRjpLuWCTBmdz5FgB9UfBHEZMG9S8ZD9LCIi2UhBb6OEZfH2RyH+7IrM3eN+IG64rBgLi9frg3TGEpRfVtLv4zstaIvGe23L85jk6O8EEZGUUdDbqP5kO+FInCmj+7584FBgGAbll5WQ43bxyqFTHAl2MHFkgM9cUtjr49uicV4+cKLXNm20IyKSWjpGb6P9x7tOTZt60dAOeugK++suLWbRzItpj8b5xua3eOTfD/DhqfZ0lyYiIv3QiN5G+463kucxM/5iNudj4gg/d35uHEdbOvm3vcf49dsnmD2uiL+46iKuu3QEHlN/O4qIZBIFvY32H2/litEBTJezDkr7PCZ/9fnx/F+fGcMLe4/zb7XHefCXb1Pk8/DnU0Yx9/L+j+GLiEjqKOhtEo0neKchxOIZl6S7FNuUBHK483PjWPY/Stldf4oX/nCczW8e4Ud7DjO2KJfZ44YxqcSvVfoiImmkoLfJHxvCROOWI47Pn4vpMvjchOF8bsJwmsIRtv3hGD/Zc4SfvnWMEX4vcyeN4NJiBb6ISDoo6G2y7/RCvKG+4v58jfB7WThzDCNyPRw4EeK/6prYVnOM8cN93HjFqHSXJyKSdRT0Ntl/vJXheR5G5+eku5S0cLkMpozO57KRAd483ExVXRPPvFaP12vyv2ZcrNG9iEiKaIm0DSzLovqDINMuLsj6QDNdBleXFrH8f5RySaGPJ16u45EXD9LRx4Y6IiIyuDSit0FdYxvHWzv5q8+WprsU2xiGwalI72Ed72VL/EKfh8UzL+Z4OMq/vlrPu41h1vz5FYw8Y8ZDu+aJiAw+Bb0N/uu9JgA+P2F4miuxT3sswat/bOi17bOTej+9zjAMvjLjEkLtEbbXfsT//aM3uWXaaEpPX9lPu+aJiAw+Td3b4L/qTjJldD7Fgcw+Pt89Ku/tX2+j8sEyqSTAX84eS67HxfNvHGHPh0FdGU9ExCYa0Q+yk20Rao+1cOfnxqW7lHO6kFH5YBnh9/KXs8eyvfYj/uNAAx+1dvI/J5cAGtGLiAwmjegH2SuHTmIBcyY6d9p+sOR6TCqmX8TnJgzjrSMt/O+f19IYjqS7LBERR1HQD7KqupOUBLxcNjKQ7lKGBNfpi+XcfNVo6hrC/OVzb7Dnw2C6yxIRcQwF/SA63tJBVV0T5ZeVZP1pdedryuh8/vHWq/C6Xdy9dS/f/c/3dAqeiMggUNAPoi1vHgXLYvFM5+5vb6eykgA/WjqLW6ZdxHPVh7n1B9X84q2jJLRQT0TkginoB0moM8bP9x7j+sklXFSQm+5yhqw8r8mD5ZP43qJpDPN5uG/bXhb8oJqf1BylpSOa7vJERIYcrbofJC/84TjhSJzbrx6T7lIcYeaYIv71f83g1cMtPF31Hv/vS+/ynZfrmD6mkFmlRVx1cQHji/14TZctG+10WtDWx6EDbewjIkOJgn4QNIY6+dfXP2TW2MKsu4iNnVyGwZ9fdRGfG1PA64ebeXb3h7xzIkT1B0EADAOG+TxcMTqfscPyuKgwh+JADiWBHEryvfi97n5Dub8wj1tQdfBEr23a2EdEhhIF/aeUsCweffEg7dE4K6+flO5yHMkwDCaPymfe5GLmTS6mtSPG4WA7DaEIjeEIH5xq59VDJ0l84lC+13RxUWEOJX4v+bkeCnLdFOS48eeY+DwmlmHwXkMIj+nCa7rwmAYe04XHdPHZScVE4gk8LkMLK8+DZkJEMo+C/lP619c/5PUPgnyzfBITRuSlu5yskJ/r5orR+Vxx+vZnJ5Xw+4MnCHXGaO2M0dIRo7Wj62uOx0VbZ4z6k220dsZobo8SGcC2f//frveT33tcXX8A5HpcDMvz8E5DmMnFeYwbnsfkkgD5ufo16tYWjfPyAc2EiGQS/R/qAsUSFk9Wvcfze45QflkJN181Ot0lZTXTZVDo81Do85x1/9zLRzLsE+ESiydojyY41hbhPw82EI0niMYtIvEE0XiCSNxi7Ig8Dh5rIRK3ku1tkTgn2yJs/8NxIvFE8vUuLszlspEBLh8Z4LKRAS4b6U/Z9sd9jaATlkU0lqCjI9b1uRIWsXiChAVetwtcRvJ7n8fE53HhNj9em3sho2/L6nqPzliCeMLC7TLwmJoREUk3W4I+kUiwZs0aDh48iNfrZd26dYwb9/GWsDt37mTjxo243W4qKipYuHBhn8+pr69n1apVGIbBpEmTePTRR3G50neygGVZvF4f5P9/rZ6aIy0smnExK66bqP+ZDSFu00W+6SLmMhjh9/b6mM9OKmFYTu+/HtddVkKkM8Z7TW28cyLEOydCHDgR4uU/NiYfMzzPczr0P/53SVEurkH47yQWT3C8pYMToQiHgu28WtdEa2fXLEb311BnnPh5npZoGl3B7HW7KPJ5COS4yXV3/a5ZlkXCgoQF0XiCzniCSCxB5PTXztPff/LwiQHkuF3kuF382x+OM7Yol4sKcrmoMJeLC3K4qCCXUfk55Ho00pfUyMbDS7YE/Y4dO4hEImzZsoWamho2bNjApk2bAIhGo1RWVrJt2zZ8Ph9Llixh7ty5vPnmm70+p7KykhUrVnDNNdfwyCOP8NJLL1FeXm5H2b0Ktkd5tyHM0ZYO9h1rpfrDIB+cameE38sj8ydz05UayQ+W3i59297cQYfNF9k5Xy7D6AqrgtyzrlAY6oyx70SIfcdaebchTF1DiNc/CBI/nX55XpPR+TmMDORQEvBSEvCS5+0K0xy3ixyPi3jC6grOeNfXlo4oJ9uinGqLcrItQkMowsm2SI9AdbsM8nPc5Oe6GVPkIz/XzczSIi4O5OBxu/C4DLymC4yuoD7ZEaPmwyCxuEU0kSASs07PZnT9K8rzEosl6IglMOiaMXEbBgaQ73Njnn49r+nC63Yl/0Bwmy4ON7Vhugxiia4/ADpPv47HdFF7rJUd7zQm+6Rbkc/D6PwcRuXnMLqg6+sIv5dATte6ikBu18JKr2ngPr2ewmu6cGsNxXnp7eJRn7ynt78Pe9z1iQf19uv5ydfp/TH9/2IP7HWt5B+bHdEEHbF48mtbNEG4M0ZbJE5bNE7cMDgabOdQYzj5h2r3DBRAINeDaXT9PnXNdHX9y/O6Tn81yfOY5Hnd+L2nb3vNj7/3mLhdBm6Xq+t3xjQwja6vg/FH/oWyJej37NnDnDlzAJg+fTq1tbXJtrq6OkpLSyksLARg1qxZVFdXU1NT0+tz9u3bx+zZswG49tpr2bVrV0qD/o7n3+RwsAMAv9fkTy4p4C9nj+XGy0d2TYHKoOntIjt5Pi9t7RHbL7IzGAI5biaPyufIqXauHlvI1WMLicUTNIQjfNTSiddj0twW4UQowntNYRrDPQP7kzymwfA8L8PzPBT5PEwq8VNaHKDA42JkIIfcXDdvH23B53H1CLx5V4zq83+kcQtisUSvbdD7IY9upyJ9H4f/7KSSPi+U1P2a8YRFQ6iTYy2dHGvp4HhLJx+1dnK8tYPDze1UfxgkHBn4roim0fVHokXXDAJ0nZHR7eP7zu6fM2/19v/gAQVeLz7Z54MRpBf8OnIWj2mQ6zFx0XXYymu6yD0dzgDFgRy8LoNYwqI92nWori0Spz0apz2aoC0Su+BBh8vo+oPZoOv/Fc8smc6YIt+gfbb+2BL0oVCIQODjvd5N0yQWi+F2uwmFQuTnf3wKmt/vJxQK9fkcy7KSv6B+v5/W1tZ+39vjMSkpGbxT3H6/6vpBe62B+Nr1k/tsm1Y6LGPaMq2ec9XamxJg8iVFKX1NO9z4Jxe2E+MVY4ou6HmD0W+jR8FVF/TuInK+bBmSBgIBwuFw8nYikcDtdvfaFg6Hyc/P7/M5Zx6PD4fDFBQU2FGyiIiII9kS9DNnzqSqqgqAmpoaJk/+eJRaVlZGfX09wWCQSCRCdXU1M2bM6PM5U6ZMYffu3QBUVVVx9dVX21GyiIiIIxnWuVZDXIDuFfTvvPMOlmWxfv169u/fT1tbG4sWLUquurcsi4qKCm6//fZen1NWVsahQ4d4+OGHiUajTJw4kXXr1mGaWqErIiIyELYEvYiIiGQGLRsXERFxMAW9iIiIg2kL3Axwrp0Es8Fbb73F3//93/Pss8/2uRvi1q1b2bx5M263m7vvvpu5c+emu2xbRKNRVq9ezZEjR4hEItx9991ceumlWd0n8Xichx56iEOHDmGaJpWVlViWldV9AtDU1MRXvvIVvv/97+N2u7O+PwD+4i/+InkK95gxY7jrrrvUL5ak3W9+8xtr5cqVlmVZ1ptvvmndddddaa4otf75n//Z+tKXvmQtWLDAsizL+trXvma99tprlmVZ1sMPP2z9x3/8h3XixAnrS1/6ktXZ2Wm1tLQkv3eibdu2WevWrbMsy7JOnjxpXXfddVnfJ7/97W+tVatWWZZlWa+99pp11113ZX2fRCIR6+tf/7r1hS98wXr33Xezvj8sy7I6Ojqsm2+++az71C+Wpan7DNDfToLZoLS0lCeffDJ5+5O7Ib7yyivs3buXGTNm4PV6yc/Pp7S0lAMHDqSrZFvdeOON3HvvvcnbpmlmfZ/ccMMNrF27FoCjR49SXFyc9X3y2GOPsXjxYkaOHAno9wbgwIEDtLe3s2zZMr761a9SU1OjfkHH6DNCX7sCZov58+cnN1QCet0Nsa8dFZ3I7/cTCAQIhULcc889rFixIuv7BMDtdrNy5UrWrl3L/Pnzs7pPfvaznzF8+PDkAAH0ewOQm5vL8uXLeeaZZ/jWt77F/fffr35BQZ8R+ttJMBv1thtiXzsqOtWxY8f46le/ys0338xNN92kPjntscce4ze/+Q0PP/wwnZ2dyfuzrU9++tOf8sorr7B06VLefvttVq5cycmTJ5Pt2dYf3SZMmMCXv/xlDMNgwoQJFBUV0dTUlGzP1n5R0GeA/nYSzEa97YY4bdo09uzZQ2dnJ62trdTV1Tm2nxobG1m2bBkPPPAAt956K6A+eeGFF/je974HgM/nwzAMrrzyyqztkx/96Ec899xzPPvss1xxxRU89thjXHvttVnbH922bdvGhg0bAPjoo48IhUJ8/vOfz/p+0YY5GaCvXQGzyeHDh/nbv/1btm7d2uduiFu3bmXLli1YlsXXvvY15s+fn+6ybbFu3TpefPFFJk6cmLzvm9/8JuvWrcvaPmlra+PBBx+ksbGRWCzGnXfeSVlZWVb/d9Jt6dKlrFmzBpfLlfX9EYlEePDBBzl69CiGYXD//fczbNiwrO8XBb2IiIiDaepeRETEwRT0IiIiDqagFxERcTAFvYiIiIMp6EVERBxMQS8iAxKPx1m+fDlLliyhubk53eWIyABl7/ZrInJeGhoaOHXqFD/72c/SXYqInAedRy8iA3LnnXeyZ88ebrzxRk6ePElnZyfBYJBvfOMb3HDDDXzpS19i/PjxeL1evvWtb/HNb36TU6dOAfDQQw9x2WWXpfkTiGQnBb2IDEj37oUrVqzANE2uueYa3njjDZ588kl+8IMfMG/ePP7pn/6JKVOm8Pjjj3PJJZdw22238f777/Pggw/y4x//ON0fQSQraepeRM5LSUkJmzZtYtu2bRiGcdaVFidMmADAO++8w2uvvcaLL74IQEtLS1pqFREFvYicp+9+97ssWLCA6667jp/+9Kf8/Oc/T7Z1X2Vv4sSJfPnLX+amm26iqamJn/zkJ+kqVyTradW9iJyXG2+8kb/7u7/jtttu45VXXkkehz/TXXfdxYsvvsjSpUv5q7/6KyZNmpSGSkUEdIxeRETE0TSiFxERcTAFvYiIiIMp6EVERBxMQS8iIuJgCnoREREHU9CLiIg4mIJeRETEwRT0IiIiDvZ/AFQUOT1d2HadAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(titanic_data['fare'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-26.724\n", "65.6344\n" ] } ], "source": [ "IQR = titanic_data[\"fare\"].quantile(0.75) - titanic_data[\"fare\"].quantile(0.25)\n", "\n", "lower_fare_limit = titanic_data[\"fare\"].quantile(0.25) - (IQR * 1.5)\n", "upper_fare_limit = titanic_data[\"fare\"].quantile(0.75) + (IQR * 1.5)\n", "\n", "print(lower_fare_limit)\n", "print(upper_fare_limit)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "titanic_data[\"fare\"]= np.where(titanic_data[\"fare\"] > upper_fare_limit, upper_fare_limit,\n", " np.where(titanic_data[\"fare\"] < lower_fare_limit, lower_fare_limit, titanic_data[\"fare\"]))\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='fare', data=titanic_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6.4. Outlier Capping Using Mean & Standard Deviation" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "\n", "plt.rcParams[\"figure.figsize\"] = [8,6]\n", "sns.set_style(\"darkgrid\")\n", "\n", "titanic_data = sns.load_dataset('titanic')\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAFUCAYAAADmsw94AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWXUlEQVR4nO3de2xT993H8c+JTVoSkjqo2UUKbRMaStM+0rRFydYVUzqy7KJJ3ZqNm0AbExIVUuUOugRE7HZFBJ5u2QbqBKu0VUqWZVvoLtKQNggZpmSy0DR4Ope0Kpdy6ehW4qyJg4LtnOcPhptAE1LI8cnPeb/+4sTJ+X2RMG//ju3Ysm3bFgAAmNJy3B4AAADcGMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAAXrcHGMvw8LBSKd5xBgCYPmbM8Ix525QNdiplq69v0O0xAADImOLigjFv45I4AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2gFFisV4Fgw2KxWJujwJgBIINYJSOjnb19LymPXva3R4FwAiO/GrSRCKhhoYGnT9/Xjk5OXruuefk9XrV0NAgy7JUXl6uUCiknBweLwBTSSzWq66uTtm2ra6u/Xr88aUqKipyeywAcmiHffDgQSWTSbW3t2vdunX60Y9+pKamJgUCAbW1tcm2bXV2djqxNIBb0NHRLtselnTlA3jYZQNThyM77NLSUqVSKQ0PD2tgYEBer1dHjx5VVVWVJMnv9+vw4cOqqakZ8xwejyWfL8+J8QCM4ZVXrjzYlqRkMqlDh/6iDRvWuzoTgCscCXZeXp7Onz+vL37xi4rFYtq1a5eOHDkiy7IkSfn5+erv7x/3HHxaF5B5Dz+8UAcO7FMymZTX69WCBY9wPwQyKOOf1vXSSy/p4Ycf1p/+9Cf9/ve/V0NDgxKJRPr2eDyuwsJCJ5YGcAvq6pbKsq78t5CTk6PHH1/q8kQArnIk2IWFhSoouPIo4Y477lAymVRFRYUikYgkKRwOq7Ky0omlAdyCoqLZWrToc7IsS4sWLeYFZ8AUYtm2bU/2SePxuDZt2qR///vfSiQSWrVqlR588EE1NjYqkUiorKxMW7ZskcfjGfMciUSKS3GAC2KxXv3wh/+rp56qJ9hAho13SdyRYE8Ggg0AmG4y/hw2AACYXAQbAAADEGwAAAxAsAEAMADBBgDAAAQbAAADEGwAAAxAsAEAMADBBgDAAAQbAAADEGwAAAxAsAEAMADBBgDAAAQbwCixWK+CwQbFYjG3RwEwAsEGMEpHR7t6el7Tnj3tbo8CYASCDSAtFutVV1enbNtWV9d+dtnAFEKwAaR1dLTLtoclScPDw+yygSmEYANIO3ToL0omk5KkZDKpcLjL1XkAvI9gA0hbsOAReb1eSZLX65Xfv8jliQBcRbABpNXVLZVlXflvIScnR48/vtTliQBcRbABpBUVzdaiRZ+TZVlatGixioqK3B4JwH953R4AwNRSV7dUZ8+eYXcNTDGWbdu220N8kEQipb6+QbfHAAAgY4qLC8a8jUviAAAYgGADGOXUqZNatWqJTp8+5fYoAEYg2ABG2bHj+7p0aVA7dnzf7VEAjECwAaSdOnVS586dlSSdPXuGXTYwhRBsAGnX7qrZZQNTB8EGkHZ1d33V2bNnXJoEwLUceR/2yy+/rN/+9reSpKGhIR0/flxtbW3aunWrLMtSeXm5QqGQcnJ4vABMJfn5+YrH4yOOZ7k4DYCRHCnm1772NbW0tKilpUUPPPCANm/erBdeeEGBQEBtbW2ybVudnZ1OLA3gFly+fHncYwDucXSL++qrr+rNN9/UkiVLFI1GVVVVJUny+/3q7u52cmkANyE3N3fcYwDucfRXk+7evVvr1q2TJNm2LcuyJF257Nbf3z/uz3o8lny+PCfHA3CNkZfDrxwPcD8EpgjHgv3ee+/p5MmT+vSnPy1Jo56vjsfjKiwsHPfnUymbX00KZFhJyZxRLzybM+cu7odABrnyq0mPHDmihx56KH1cUVGhSCQiSQqHw6qsrHRqaQA36cknN4x7DMA9jgX71KlTKikpSR/X19dr586dWrJkiRKJhGpra51aGsBNKi0tU0nJHElXdtf33FPq8kQAruLTugCMcurUSYVCG/W9720j2ECGjXdJnGADADBF8PGaAAAYjmADAGAAgg0AgAEINgAABiDYAAAYgGADGCUW61Uw2KBYLOb2KABGINgARunoaFdPz2vas6fd7VEAjECwAaTFYr3q6uqUbdvq6trPLhuYQgg2gLSOjnYlkwlJUiKRYJcNTCEEG0DaoUN/0dVffmjbtsLhLlfnAfA+gg0g7f77Hxh1XFHxPy5NAuBaBBtAWk/Pa6OOjx+PujQJgGsRbABpg4OD1xzHXZoEwLUINoC0/Pz8a45nuTQJgGsRbABpTz1VP+r4O99pcGkSANfi87BhjIMHD+jAgX1uj5H1XnstKslWTk6O5s+vcHucrPXoozVauPBRt8fAFMPnYQOYsNtuy5UklZTMcXkSACOxwwYwSii0UZL07LNNLk8CTD/ssAEAMBzBBgDAAAQbAAADEGwAAAxAsAEAMADBBgDAAAQbAAADEGwAAAxAsAEAMADBBgDAAF6nTrx7924dOHBAiURCy5YtU1VVlRoaGmRZlsrLyxUKhZSTw+MFAAAmwpFiRiIR/f3vf9cvf/lLtbS06MKFC2pqalIgEFBbW5ts21ZnZ6cTSwMAkJUcCfYrr7yiefPmad26dVq7dq0eeeQRRaNRVVVVSZL8fr+6u7udWBoAgKzkyCXxWCymt99+W7t27dK5c+f0xBNPyLZtWZYlScrPz1d/f/+45/B4LPl8eU6MB2AcXq9Hkrj/AVOMI8H2+XwqKytTbm6uysrKdNttt+nChQvp2+PxuAoLC8c9Rypl8/GagAuSyZQkcf8DXJDxj9f81Kc+pUOHDsm2bb3zzju6dOmSPvOZzygSiUiSwuGwKisrnVgaAICs5MgOe9GiRTpy5Ijq6upk27aCwaBKSkrU2Nio5uZmlZWVqba21omlAQDISo69reu73/3udV9rbW11ajkAALIab4QGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAAXqdO/Nhjj6mgoECSVFJSorVr16qhoUGWZam8vFyhUEg5OTxeAABgIhwJ9tDQkCSppaUl/bW1a9cqEAiourpawWBQnZ2dqqmpcWJ5AACyjiNb3J6eHl26dEmrV6/WqlWrdPToUUWjUVVVVUmS/H6/uru7nVgaAICs5MgO+/bbb9e3v/1tff3rX9fp06e1Zs0a2bYty7IkSfn5+erv7x/3HB6PJZ8vz4nxAIzD6/VIEvc/YIpxJNilpaW6++67ZVmWSktL5fP5FI1G07fH43EVFhaOe45UylZf36AT4wEYRzKZkiTuf4ALiosLxrzNkUviHR0d2rZtmyTpnXfe0cDAgD772c8qEolIksLhsCorK51YGgCArOTIDruurk4bN27UsmXLZFmWtm7dqqKiIjU2Nqq5uVllZWWqra11YmkAALKSI8HOzc3VD37wg+u+3tra6sRyAABkPd4IDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGmHCwT58+rYMHD+rChQuybdvJmQAAwDW8E/mm1tZW7du3T//5z3/02GOP6cyZMwoGg07PBgAA/mtCO+w//vGPeumll1RQUKBvfvObOnbsmNNzAQCAESYU7KuXwC3LkiTl5uY6NxEAALjOhIL95S9/WStWrNCZM2e0Zs0aLV68+IY/c/HiRS1cuFAnTpzQW2+9pWXLlmn58uUKhUIaHh6+5cEBAJhOJvQc9sqVK/XQQw/pjTfeUFlZme67775xvz+RSCgYDOr222+XJDU1NSkQCKi6ulrBYFCdnZ2qqam59ekBAJgmJhTsjRs3pv8cDoc1Y8YMfexjH9OKFSt0xx13XPf927dv19KlS/XTn/5UkhSNRlVVVSVJ8vv9Onz4MMEGAOBDmFCwh4aGNGfOHFVWVurYsWN69dVXNXv2bNXX12vXrl2jvvfll1/W7NmztWDBgnSwbdtOP/+dn5+v/v7+G67p8Vjy+fI+7N8HwC3yej2SxP0PmGImFOze3l41NzdLkhYsWKDVq1crEAhoxYoV133vnj17ZFmW/vrXv+r48eOqr69Xb29v+vZ4PK7CwsIbrplK2errG5zo3wPAJEkmU5LE/Q9wQXFxwZi3TehFZwMDAzpx4oQk6cSJE4rH44rFYhocvP4O/Ytf/EKtra1qaWnR/fffr+3bt8vv9ysSiUi6ckm9srLyZv4eAABMWxPaYQeDQT399NP617/+pY9//ONqbGzU3r17tXbt2gktUl9fr8bGRjU3N6usrEy1tbW3NDQAANPNhIIdjUYVj8eVm5urixcvasOGDfrzn/98w59raWlJ/7m1tfXmpwQAYJqb0CXx3/zmN2ppadHChQvV1NSke++91+m5AADACBPaYRcVFekjH/mI4vG4qqurtWPHDqfnMs7Pf/6iTp8+6fYYwC27+u84FNp4g+8EprZ77inTt761xu0xJs2Egl1QUKD9+/fLsiy1t7ePetU3rjh9+qSir7+hVN5st0cBbok1PEOS9H9n33V5EuDmeQazr1MTCvaWLVt05swZrV+/Xj/72c/0zDPPODyWmVJ5s3Vp/pfcHgMApr2ZPXvdHmHSTSjYs2bNUkVFhSSpoaHB0YEAAMD1JvSiMwAA4C6CDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABiAYAMAYACCDQCAAQg2AAAGINgAABjA68RJU6mUNm/erFOnTsnj8aipqUm2bauhoUGWZam8vFyhUEg5OTxeAABgIhwJdldXlySpvb1dkUgkHexAIKDq6moFg0F1dnaqpqbGieUBAMg6jmxxFy9erOeee06S9Pbbb+vOO+9UNBpVVVWVJMnv96u7u9uJpQEAyEqO7LAlyev1qr6+Xvv27dOOHTvU1dUly7IkSfn5+erv7x/35z0eSz5fnlPjTTqv1+P2CACAEbxej1EduRHHgi1J27dv14YNG/SNb3xDQ0ND6a/H43EVFhaO+7OplK2+vkEnx5tUyWTK7REAACMkkymjOiJJxcUFY97myCXx3/3ud9q9e7ckaebMmbIsSw8++KAikYgkKRwOq7Ky0omlAQDISo7ssD//+c9r48aNWrFihZLJpDZt2qS5c+eqsbFRzc3NKisrU21trRNLAwCQlRwJdl5enn784x9f9/XW1lYnlgMAIOvxRmgAAAzg6IvOppO+vpg8gxc1s2ev26MAwLTnGbyovr7sevcOO2wAAAzADnuS+HxFOtOf0qX5X3J7FACY9mb27JXPV+T2GJOKHTYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAbwTvYJE4mENm3apPPnz+vy5ct64okndO+996qhoUGWZam8vFyhUEg5OTxWAABgoiY92H/4wx/k8/n0/PPPKxaL6atf/armz5+vQCCg6upqBYNBdXZ2qqamZrKXBgAga016sL/whS+otrY2fezxeBSNRlVVVSVJ8vv9Onz4cFYG2zPYq5k9e90eA7glVuKSJMmeMdPlSYCb5xnslXSn22NMqkkPdn5+viRpYGBATz75pAKBgLZv3y7LstK39/f33/A8Ho8lny9vssdzzH33zZPX63F7DOCWnTjxpiRpbulHXZ4EuBUf1dy5c43qyI1MerAl6Z///KfWrVun5cuX6ytf+Yqef/759G3xeFyFhYU3PEcqZauvb9CJ8RyxfPm33B4BmBSh0EZJUmPjFpcnAW6dSR2RpOLigjFvm/RXfr377rtavXq1nn76adXV1UmSKioqFIlEJEnhcFiVlZWTvSwAAFlt0oO9a9cuvffee/rJT36ilStXauXKlQoEAtq5c6eWLFmiRCIx6jluAABwY5Zt27bbQ3yQRCJl3KUMIBtcvST+7LNNLk8CTD8ZvSQOAAAmH8EGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAAMQbAAADECwAQAwgGPBPnbsmFauXClJeuutt7Rs2TItX75coVBIw8PDTi0LAEBWciTYL774ojZv3qyhoSFJUlNTkwKBgNra2mTbtjo7O51YFgCArOVIsO+66y7t3LkzfRyNRlVVVSVJ8vv96u7udmJZAACylteJk9bW1urcuXPpY9u2ZVmWJCk/P1/9/f03PIfHY8nny3NiPADj8Ho9ksT9D5hiHAn2tXJy3t/Ix+NxFRYW3vBnUilbfX2DTo4F4AMkkylJ4v4HuKC4uGDM2zLyKvGKigpFIhFJUjgcVmVlZSaWBQAga2Qk2PX19dq5c6eWLFmiRCKh2traTCwLAEDWcOySeElJiX79619LkkpLS9Xa2urUUgAAZD1+cQoAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGIBgAwBgAIINAIABCDYAAAYg2AAAGMCybdvOxELDw8N65pln9Prrrys3N1dbtmzR3XffPeb3JxIp9fUNZmI0GOLgwQM6cGCf22NkvdOnT0qS7rmnzOVJstujj9Zo4cJH3R4DU0xxccGYt2Vsh71//35dvnxZv/rVr7R+/Xpt27YtU0sD+BB8vtny+Wa7PQaAa3gztdDf/vY3LViwQJL0iU98Qv/4xz8ytTSyxMKFj7IjATBtZSzYAwMDmjVrVvrY4/EomUzK6/3gETweSz5fXqbGAwBgSstYsGfNmqV4PJ4+Hh4eHjPWkpRK2TyHDQCYVqbEc9if/OQnFQ6HJUlHjx7VvHnzMrU0AADGy9gOu6amRocPH9bSpUtl27a2bt2aqaUBADBext7W9WHxti4AwHQzJS6JAwCAm0ewAQAwAMEGAMAABBsAAAMQbAAADECwAQAwAMEGAMAABBsAAANM2V+cAgAA3scOGwAAAxBsAAAMQLABADAAwQYAwAAEGwAAAxBsAAAM8P8hnaZTHk7VQgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='age', data=titanic_data)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-13.88037434994331\n", "73.27860964406095\n" ] } ], "source": [ "lower_age_limit = titanic_data[\"age\"].mean() - (3 * titanic_data[\"age\"].std())\n", "upper_age_limit = titanic_data[\"age\"].mean() + (3 * titanic_data[\"age\"].std())\n", "\n", "print(lower_age_limit)\n", "print(upper_age_limit)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "titanic_data[\"age\"]= np.where(titanic_data[\"age\"] > upper_age_limit, upper_age_limit,\n", " np.where(titanic_data[\"age\"] < lower_age_limit, lower_age_limit, titanic_data[\"age\"]))\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='age', data=titanic_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6.5. Outlier Capping Using Quantiles" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "\n", "plt.rcParams[\"figure.figsize\"] = [8,6]\n", "sns.set_style(\"darkgrid\")\n", "\n", "titanic_data = sns.load_dataset('titanic')\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='fare', data=titanic_data)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.225\n", "112.07915\n" ] } ], "source": [ "lower_fare_limit = titanic_data[\"fare\"].quantile(0.05) \n", "upper_fare_limit = titanic_data[\"fare\"].quantile(0.95)\n", "\n", "print(lower_fare_limit)\n", "print(upper_fare_limit)\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "titanic_data[\"fare\"]= np.where(titanic_data[\"fare\"] > upper_fare_limit, upper_fare_limit,\n", " np.where(titanic_data[\"fare\"] < lower_fare_limit, lower_fare_limit, titanic_data[\"fare\"]))\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='fare', data=titanic_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6.6. Outlier Capping Using Custom Values" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "80.0\n", "0.42\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "\n", "plt.rcParams[\"figure.figsize\"] = [8,6]\n", "\n", "sns.set_style(\"darkgrid\")\n", "\n", "titanic_data = sns.load_dataset('titanic')\n", "print(titanic_data.age.max())\n", "print(titanic_data.age.min())\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "titanic_data[\"age\"]= np.where(titanic_data[\"age\"] > 50, 50,\n", " np.where(titanic_data[\"age\"] < 10, 10, titanic_data[\"age\"]))\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50.0\n", "10.0\n" ] } ], "source": [ "print(titanic_data.age.max())\n", "print(titanic_data.age.min())\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot( y='age', data=titanic_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 6.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 1\n", "\n", "Which of the following techniques can be used to remove outliers from a dataset?\n", "\n", "A. Trimming \\\n", "B. Censoring \\\n", "C. Discretization \\\n", "D. All of the above\n", "\n", "Answer: D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 2\n", "\n", "What IQR distance is normally used to cap outliers via IQR.\n", "\n", "A. 2.0 \\\n", "B. 3.0\\\n", "C. 1.5 \\\n", "D. 1.0\n", "\n", "Answer: C" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 3\n", "\n", "What quartile distance is normally used to cap outliers via mean and standard deviation.\n", "\n", "A. 2.0 \\\n", "B. 3.0\\\n", "C. 1.5 \\\n", "D. 1.0\n", "\n", "Answer: B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 6.2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the `price` column of the following `diamonds`dataset, apply outlier capping via IQR. Display box plot for the `price` column after outlier capping." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
\n", "
" ], "text/plain": [ " carat cut color clarity depth table price x y z\n", "0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n", "1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n", "2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n", "3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n", "4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "plt.rcParams[\"figure.figsize\"] = [8,6]\n", "sns.set_style(\"darkgrid\")\n", "\n", "diamond_data = sns.load_dataset('diamonds')\n", "\n", "diamond_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Solution" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.225\n", "112.07915\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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W/wMEAACTOCzLsm606dFHH1U0GtX09LTWrFmjvLw8DQ4OZmO+W5JMprikCNggEHhektTR0WnzJIB5rndJf0m/pb9t2zbl5eWprKxMTqdTHR0dyzocAADIrCVd0u/p6dFvfvMbrVmzRhcvXtQzzzyj2traTM8GAACWyZJO+D6fL/1Je3feeac8Hk9GhwIAAMtrSSd8j8ejJ598Uvfff7/Gx8c1OzurYDAo6T930gMAALe3JQX/W9/6Vvp/f+Yzn8nYMAAAIDOWFPyrf48PAABy0ye77R0AAMgpBB8AAAMQfAAADEDwAQAwAMEHAMAABB8AAAMs6eY5uSrXbp7zq18d0Tvv/M3uMYBbdvWf43vuKbN5EuDW3HNPmZ54YrPdY9yU6908Z0l/h4/seOedv2n87f9TanWx3aMAt8SxkC9JGjt30eZJgE/OeXna7hGWFcG/zaRWF+vKl75t9xgAYLxVE8ftHmFZ8R4+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGIPgAABiA4AMAYACCDwCAAQg+AAAGyHrwL126pAcffFCTk5N699131dzcrJaWFgUCAS0sLEiSBgYGtGHDBm3cuFEnT56UJM3Ozmrbtm1qaWnR5s2bNT09ne3RAQDIWVkNfjKZ1L59+/SpT31KktTZ2am2tjaFw2FZlqXBwUFNTU0pFAqpv79fvb29CgaDSiQS6uvrk9/vVzgcVlNTk3p6erI5OgAAOS2rwe/q6tKmTZt01113SZLGx8dVU1MjSaqvr9fp06c1NjamiooKFRQUyOv1qrS0VBMTE4pEIqqrq0vvHRkZyeboAADkNFe2nuh3v/udiouLVVdXp5dfflmSZFmWHA6HJMntdisajSoWi8nr9aa/z+12KxaLLVq/uvdGnE6HfL7VGXg1meFyOe0eAQDwES6XM6c68nGyFvzf/va3cjgcGhkZ0VtvvaX29vZF78PH43EVFRXJ4/EoHo8vWvd6vYvWr+69kVTK0szM5eV/MRkyP5+yewQAwEfMz6dyqiOSVFLiveZ61i7pv/rqqzp69KhCoZDuu+8+dXV1qb6+XqOjo5Kk4eFhVVdXq7y8XJFIRHNzc4pGo5qcnJTf71dlZaWGhobSe6uqqrI1OgAAOS9rJ/xraW9v1969exUMBlVWVqbGxkY5nU61traqpaVFlmVp+/btKiwsVHNzs9rb29Xc3Kz8/Hx1d3fbOToAADnFYVmWZfcQmZJM5talmEDgeY2du6grX/q23aMAgPFWTRxX+d13qqOj0+5Rbortl/QBAIB9CD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGMBl9wD4r5mZD+W8fEmrJo7bPQoAGM95+ZJmZpx2j7FsOOEDAGAATvi3EZ/v03ovmtKVL33b7lEAwHirJo7L5/u03WMsG074AAAYgOADAGAAgg8AgAEIPgAABiD4AAAYIGu/pZ9MJvXCCy/owoULSiQS2rJli774xS9q9+7dcjgcWrt2rQKBgPLy8jQwMKD+/n65XC5t2bJFDQ0Nmp2d1a5du3Tp0iW53W51dXWpuLg4W+MDAJDTsnbC/8Mf/iCfz6dwOKwjR47oxRdfVGdnp9ra2hQOh2VZlgYHBzU1NaVQKKT+/n719vYqGAwqkUior69Pfr9f4XBYTU1N6unpydboAADkvKyd8B955BE1NjamHzudTo2Pj6umpkaSVF9fr9dff115eXmqqKhQQUGBCgoKVFpaqomJCUUiET311FPpvQQfAICly1rw3W63JCkWi+m5555TW1uburq65HA40l+PRqOKxWLyer2Lvi8Wiy1av7r3RpxOh3y+1Rl4NZnhcq2cj3AEgJXA5XLmVEc+TlY/ae/999/X1q1b1dLSokcffVQ///nP01+Lx+MqKiqSx+NRPB5ftO71ehetX917I6mUpZmZy8v/QjJkfj5l9wgAgI+Yn0/lVEckqaTEe831rL2Hf/HiRf3whz/Url279Nhjj0mS1q1bp9HRUUnS8PCwqqurVV5erkgkorm5OUWjUU1OTsrv96uyslJDQ0PpvVVVVdkaHQCAnJe1E/7hw4f173//Wz09Pen333/84x9r//79CgaDKisrU2Njo5xOp1pbW9XS0iLLsrR9+3YVFhaqublZ7e3tam5uVn5+vrq7u7M1OgAAOc9hWZZl9xCZkkzm1qWYQOB5jZ27yM1zAOA2sGriuMrvvlMdHZ12j3JTbL+kDwAA7EPwAQAwAMEHAMAABB8AAAMQfAAADEDwAQAwAMEHAMAABB8AAAMQfAAADEDwAQAwAMEHAMAABB8AAAMQfAAADEDwAQAwAMEHAMAABB8AAAMQfAAADEDwAQAwAMEHAMAABB8AAAMQfAAADOCyewAs5rw8rVUTx+0eA7gljuQVSZKVv8rmSYBPznl5WtKddo+xbAj+beSee8rsHgFYFu+88zdJ0j13323zJMCtuHNF/XvZYVmWZfcQmZJMpjQzc9nuMQDjBALPS5I6OjptngQwT0mJ95rrvIcPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABCD4AAAYg+AAAGIDgAwBgAIIPAIABuFsejDI0dEInTvzJ7jFWvPTtcVfQrUVvRw899LAefPAhu8fAbeZ6d8tzZXkOAAbw+YrtHgHA/+CEDwDACnK9Ez7v4QMAYACCDwCAAQg+AAAGyKlf2ltYWNBPfvITvf322yooKND+/fv1+c9/3u6xAAC47eXUCf+1115TIpHQsWPHtHPnTv3sZz+zeyQAAHJCTgU/Eomorq5OkvS1r31Nf/3rX22eCACA3JBTl/RjsZg8Hk/6sdPp1Pz8vFyua78Mp9Mhn291tsYDAOC2lVPB93g8isfj6ccLCwvXjb0kpVIWf4cPADDKivg7/MrKSg0PD0uSzpw5I7/fb/NEAADkhpw64T/88MN6/fXXtWnTJlmWpZdeesnukQAAyAl8tC4AACvIirikDwAAPhmCDwCAAVb0JX0AAPAfnPABADAAwQcAwAAEHwAAAxB8AAAMQPABADAAwQcAwAD/Dw3H1PL6r2NkAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "IQR = diamond_data[\"price\"].quantile(0.75) - diamond_data[\"price\"].quantile(0.25)\n", "\n", "lower_price_limit = diamond_data[\"price\"].quantile(0.25) - (IQR * 1.5)\n", "upper_price_limit = diamond_data[\"price\"].quantile(0.75) + (IQR * 1.5)\n", "\n", "print(lower_fare_limit)\n", "print(upper_fare_limit)\n", "\n", "diamond_data[\"price\"]= np.where(diamond_data[\"price\"] > upper_price_limit , upper_price_limit ,\n", " np.where(diamond_data[\"price\"] < lower_price_limit , lower_price_limit , diamond_data[\"price\"]))\n", "\n", "sns.boxplot( y='price', data=diamond_data)" ] }, { "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }