Skewness.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics can provide a good sensing of the data. For instance, if the median of your data differs by the mean by a lot, you can infer that your data is skewed. On the hand, if you detect that your dataset has high variance or standard deviation, you can expect that you data probably either has quite a lot of outliers or have a large spread of values. However, one can only be sure of the distribution by plotting the data out. For example, it would be difficult to know that the distribution is bimodal just looking at descriptive statistics.\n",
    "\n",
    "What is distribution? Distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It represents all possible values for the data and how often those values occur. For instance, the image below depicts the probability distribution for the sum of counts from two dice. The outcomes created a histogram, where the distribution is depicted by the shape of the histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the normal distribution is defined by the mean and the standard deviation. In a normal distribution, 68% of the data lies within one standard deviation of the mean, and 95% lies within two standard deviations. It is often important to ascertain whether a distribution follows a normal distribution, as the statistical methods used will differ, depending whether the data is normally distributed or skewed."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, both histogram and Kernel Density Estimation, can be plotted. This allows direct visualisation of the distribution of observations in a dataset.\n",
    "\n",
    "To understand how to use Python to plot histogram and KDE, let’s use the iris example data from plotly express. You can upload the data by using the commands below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>species_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species  species_id\n",
       "0           5.1          3.5           1.4          0.2  setosa           1\n",
       "1           4.9          3.0           1.4          0.2  setosa           1\n",
       "2           4.7          3.2           1.3          0.2  setosa           1\n",
       "3           4.6          3.1           1.5          0.2  setosa           1\n",
       "4           5.0          3.6           1.4          0.2  setosa           1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "df = px.data.iris()\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s visualise the distribution of the sepal length of all the species in the dataset. To create the histogram, simply use sns.histplot(). To add KDE, we set KDE parameter equal to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram and KDE of sepal length')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "sns.histplot(data=df, x= \"sepal_length\", kde=True)\n",
    "ax.set_title(\"Histogram and KDE of sepal length\")"
   ]
  },
  {
   "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": "3.10.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}