AntonPythonData Types.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('download.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Data Types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has five standard Data Types:\n",
    "    1. Numbers \n",
    "    2. String\n",
    "    3. List\n",
    "    4. Tuple\n",
    "    5. Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Python on Jupyter\n"
     ]
    }
   ],
   "source": [
    "a = 'this is Python on Jupyter'\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n",
      "['a', 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "list_example = [1, 2, 3, 4, 5]\n",
    "list_example_2 = ['a', 'b', 'c']\n",
    "\n",
    "print(list_example)\n",
    "print(list_example_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Tuple\n",
    "\n",
    "Tuples are a group of values like a list and are manipulated in similar ways. But, tuples are fixed in size once they are assigned. In Python the fixed size is considered immutable as compared to a list that is dynamic and mutable. Tuples are defined by parenthesis ()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 'three')\n"
     ]
    }
   ],
   "source": [
    "tuple_example = (1, 2, 'three')\n",
    "\n",
    "print(tuple_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionaries in Python are lists of Key:Value pairs. This is a very powerful datatype to hold a lot of related information that can be associated through keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'key1': 'value1', 'key2': 2345}\n"
     ]
    }
   ],
   "source": [
    "dictionary_example = {}\n",
    "\n",
    "dictionary_example['key1'] = 'value1'\n",
    "dictionary_example['key2'] = 2345\n",
    "\n",
    "print(dictionary_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.7.1 in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (1.15.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (2.7.3)\n",
      "Requirement already satisfied: pytz in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (2018.5)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (1.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\wiranata\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib) (40.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the file \n",
    "dataframe = pd.read_csv('data_for_classification_training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 3)\n"
     ]
    }
   ],
   "source": [
    "print(dataframe.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2  col3  target\n",
       "0   3.5   1.4       0\n",
       "1   3.0   1.4       0\n",
       "2   3.2   1.3       0\n",
       "3   3.1   1.5       0\n",
       "4   3.6   1.4       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "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>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col2  col3  target\n",
       "145   3.0   5.2       2\n",
       "146   2.5   5.0       2\n",
       "147   3.0   5.2       2\n",
       "148   3.4   5.4       2\n",
       "149   3.0   5.1       2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean value of col2 is  3.0540000000000007\n",
      "the mean value of col3 is  3.7586666666666693\n"
     ]
    }
   ],
   "source": [
    "mean_value_col2 = np.mean(dataframe['col2'])\n",
    "mean_value_col3 = np.mean(dataframe['col3'])\n",
    "\n",
    "print('the mean value of col2 is ', mean_value_col2)\n",
    "print('the mean value of col3 is ', mean_value_col3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.4 2.0\n"
     ]
    }
   ],
   "source": [
    "max_value = np.max(dataframe['col2'])\n",
    "min_value = np.min(dataframe['col2'])\n",
    "\n",
    "print(max_value, min_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_statistics_of_acolumn(df_input, col_name):\n",
    "    \n",
    "    mean_val = np.mean(df_input[col_name])\n",
    "    median_val = np.median(df_input[col_name])\n",
    "    std_val = np.std(df_input[col_name])\n",
    "    \n",
    "    return mean_val, median_val, std_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.7586666666666693, 4.35, 1.7585291834055201)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_statistics_of_acolumn(dataframe, 'col3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    50\n",
       "1    50\n",
       "0    50\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "\n",
    "plt.scatter(dataframe['col2'], dataframe['col3'], c=[kk for kk in dataframe['target']], s=40, cmap='viridis')\n",
    "plt.xlabel('col2')\n",
    "plt.ylabel('col3')\n",
    "plt.title('dataframe vizualization')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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