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Central tendency for skewed distribution
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import the necessary packages\n",
"import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns, warnings\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1172612e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set Matplotlib figure size\n",
"plt.figure(figsize=(16,3))\n",
"\n",
"# Positive Skew Distribution\n",
"dia = pd.read_csv('../data/diabetes.csv')\n",
"dia.columns = ['preg', 'glu', 'bp', 'skin', 'insu', 'bmi', 'pedi', 'age', 'class']\n",
"ax = plt.subplot(1,3, 2)\n",
"sns.distplot(dia['age']);\n",
"plt.gca().set_title(\"mean > median > mode\",fontsize=16)\n",
"plt.xlabel('Positive Skew Distribution', fontsize=12)\n",
"plt.axvline(dia['age'].mean(), color='red',label='mean')\n",
"plt.axvline(dia['age'].median(), color='green',label='median')\n",
"plt.axvline((dia['age'].mode())[0], color='black',label='mode')\n",
"ax.legend()\n",
"\n",
"# Negative Skew Distribution\n",
"income = pd.read_csv('../data/us.income.csv')\n",
"ax = plt.subplot(1,3, 3)\n",
"sns.distplot(income['distance']);\n",
"plt.axvline(income['distance'].mean(), color='red',label='mean')\n",
"plt.axvline(income['distance'].median(), color='green',label='median')\n",
"plt.axvline((income['distance'].mode())[0], color='black',label='mode')\n",
"plt.gca().set_title(\"mean < median < mode\",fontsize=16)\n",
"plt.xlabel('Negative Skew Distribution', fontsize=12)\n",
"ax.legend()\n",
"\n",
"plt.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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