Exploring Pandas and Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Snacks</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sour cream dip</td>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ice cream, rich</td>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese (cheddar, Swiss)</td>\n",
       "      <td>113.8475</td>\n",
       "      <td>5.95793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>160.3800</td>\n",
       "      <td>4.56840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milk shake</td>\n",
       "      <td>393.3700</td>\n",
       "      <td>8.32020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Snacks  Calories      Fat\n",
       "0           Sour cream dip  133.6500  7.36898\n",
       "1          Ice cream, rich  133.6500  7.36898\n",
       "2  Cheese (cheddar, Swiss)  113.8475  5.95793\n",
       "3               Cappuccino  160.3800  4.56840\n",
       "4               Milk shake  393.3700  8.32020"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=pd.read_csv(\"food.csv\")\n",
    "p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1            Ice cream, rich\n",
       "2    Cheese (cheddar, Swiss)\n",
       "3                 Cappuccino\n",
       "4                 Milk shake\n",
       "Name: Snacks, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p['Snacks'][1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Snacks</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ice cream, rich</td>\n",
       "      <td>133.65000</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese (cheddar, Swiss)</td>\n",
       "      <td>113.84750</td>\n",
       "      <td>5.95793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>160.38000</td>\n",
       "      <td>4.56840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milk shake</td>\n",
       "      <td>393.37000</td>\n",
       "      <td>8.32020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chocolate flavored drink</td>\n",
       "      <td>128.78000</td>\n",
       "      <td>0.84940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Whipped cream, sweetened</td>\n",
       "      <td>103.53000</td>\n",
       "      <td>6.44385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rum &amp; coke</td>\n",
       "      <td>273.60000</td>\n",
       "      <td>0.01440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Flavored yogurt, whole milk</td>\n",
       "      <td>28.65875</td>\n",
       "      <td>0.56750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Snacks   Calories      Fat\n",
       "1              Ice cream, rich  133.65000  7.36898\n",
       "2      Cheese (cheddar, Swiss)  113.84750  5.95793\n",
       "3                   Cappuccino  160.38000  4.56840\n",
       "4                   Milk shake  393.37000  8.32020\n",
       "5     Chocolate flavored drink  128.78000  0.84940\n",
       "6     Whipped cream, sweetened  103.53000  6.44385\n",
       "7                   Rum & coke  273.60000  0.01440\n",
       "8  Flavored yogurt, whole milk   28.65875  0.56750"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.iloc[1:9,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 3 columns):\n",
      "Snacks      25 non-null object\n",
      "Calories    25 non-null float64\n",
      "Fat         25 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 680.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "p.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Calories</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167.964630</td>\n",
       "      <td>3.175368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>124.681266</td>\n",
       "      <td>3.078660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.250000</td>\n",
       "      <td>0.001270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>97.402500</td>\n",
       "      <td>0.567500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>133.650000</td>\n",
       "      <td>2.586400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>210.210000</td>\n",
       "      <td>5.957930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456.940000</td>\n",
       "      <td>8.835000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Calories        Fat\n",
       "count   25.000000  25.000000\n",
       "mean   167.964630   3.175368\n",
       "std    124.681266   3.078660\n",
       "min     14.250000   0.001270\n",
       "25%     97.402500   0.567500\n",
       "50%    133.650000   2.586400\n",
       "75%    210.210000   5.957930\n",
       "max    456.940000   8.835000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Snacks</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sour cream dip</td>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ice cream, rich</td>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese (cheddar, Swiss)</td>\n",
       "      <td>113.8475</td>\n",
       "      <td>5.95793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milk shake</td>\n",
       "      <td>393.3700</td>\n",
       "      <td>8.32020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Whipped cream, sweetened</td>\n",
       "      <td>103.5300</td>\n",
       "      <td>6.44385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Candy</td>\n",
       "      <td>331.7000</td>\n",
       "      <td>8.83500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chocolate chips</td>\n",
       "      <td>201.1800</td>\n",
       "      <td>7.45500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>French fries, deep-fried</td>\n",
       "      <td>456.9400</td>\n",
       "      <td>5.25280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Snacks  Calories      Fat\n",
       "0             Sour cream dip  133.6500  7.36898\n",
       "1            Ice cream, rich  133.6500  7.36898\n",
       "2    Cheese (cheddar, Swiss)  113.8475  5.95793\n",
       "4                 Milk shake  393.3700  8.32020\n",
       "6   Whipped cream, sweetened  103.5300  6.44385\n",
       "11                    Candy   331.7000  8.83500\n",
       "13           Chocolate chips  201.1800  7.45500\n",
       "17  French fries, deep-fried  456.9400  5.25280"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to do desired task\n",
    "p[p['Fat']>5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Calories</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113.8475</td>\n",
       "      <td>5.95793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160.3800</td>\n",
       "      <td>4.56840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>393.3700</td>\n",
       "      <td>8.32020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Calories      Fat\n",
       "0  133.6500  7.36898\n",
       "1  133.6500  7.36898\n",
       "2  113.8475  5.95793\n",
       "3  160.3800  4.56840\n",
       "4  393.3700  8.32020"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to eleminate any row\n",
    "p1=p.drop('Snacks',axis=1)\n",
    "p1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Calories</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133.6500</td>\n",
       "      <td>7.36898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113.8475</td>\n",
       "      <td>5.95793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160.3800</td>\n",
       "      <td>4.56840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>393.3700</td>\n",
       "      <td>8.32020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>128.7800</td>\n",
       "      <td>0.84940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Calories      Fat\n",
       "0  133.6500  7.36898\n",
       "2  113.8475  5.95793\n",
       "3  160.3800  4.56840\n",
       "4  393.3700  8.32020\n",
       "5  128.7800  0.84940"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove duplicate items in rows if any\n",
    "p2=p1.drop_duplicates(keep='first')\n",
    "p2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fat vs Cal')"
      ]
     },
     "execution_count": 42,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Scatter plot\n",
    "plt.scatter(x,y,c='r')\n",
    "plt.xlabel(\"Fat\")\n",
    "plt.ylabel(\"Calories\")\n",
    "plt.xticks(np.arange(0,18,step=2))\n",
    "plt.yticks(np.arange(0,600,step=100))\n",
    "plt.title(\"Fat vs Cal\")\n",
    "#to save the graph      -->plt.savefig('name.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fat vs Cal')"
      ]
     },
     "execution_count": 51,
     "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": [
    "# plt plot\n",
    "x1=np.arange(0,20)\n",
    "y1=x1*x1\n",
    "plt.plot(x1,y1,'r*-',linewidth=1,markersize=8) \n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Fat vs Cal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc3bbcbc9e8>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#creating subpllots\n",
    "\n",
    "plt.subplot(2,2,1) #2*2\n",
    "plt.scatter(x,y,c='g')\n",
    "plt.subplot(2,2,4) #2*2\n",
    "plt.plot(x1,y1,'r--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc3bbfda5c0>]"
      ]
     },
     "execution_count": 60,
     "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": [
    "#sin\n",
    "\n",
    "x2=np.arange(0,4*3.14,0.1)\n",
    "y2=np.sin(x2)\n",
    "plt.plot(x2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 65,
     "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": [
    "#bar plot\n",
    "\n",
    "x3=[3,7,10]\n",
    "y3=[52,44,78]\n",
    "\n",
    "x4=[5,6,9,8]\n",
    "y4=[19,34,23,78]\n",
    "plt.bar(x3,y3)\n",
    "plt.bar(x4,y4,color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 12, 11, 2, 0, 3, 6, 10, 10, 8, 6, 5, 2, 10, 11, 13, 5, 0, 1, 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([3., 3., 1., 2., 2., 1., 1., 3., 2., 2.]),\n",
       " array([ 0. ,  1.3,  2.6,  3.9,  5.2,  6.5,  7.8,  9.1, 10.4, 11.7, 13. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAATdUlEQVR4nO3de5BkZXnH8e+wyw4IQXLBAHIRCvcBmSDQK5QC5W1DSkpAoogFISqyQAIGEy6lXASxjAm6YFFSkoAEIiGkdEVFFoPcRAKItlwyCA9UCqgga2lKY5TL7AKbP84Z7B1nd073nNlh3v1+qraYc3vOM6f7/Ob0292HodWrVyNJKsdGs92AJKldBrskFcZgl6TCGOySVBiDXZIKM382d97tdoeBNwArgBdmsxdJmkPmAdsA3+90OmMTF85qsFOF+ndnuQdJmqsOAO6YOHO2g30FwMKFC1mwYEHfG4+OjjIyMtJ6U+ujvr2v/9ozXd/e13/tma7/cu195cqVPPLII1Bn6ESzHewvACxYsIDh4eGBCgy63cuhvr2v/9ozXd/e13/tma7/Mu990iFs3zyVpMIY7JJUGINdkgpjsEtSYRq9eRoR5wHvAVYDX8zMCyYs3xO4DNgCuB04ITOfb7lXSVIDU16xR8SbgbcBewCLgA9HRExY7SrgpMxcCAwBS9puVJLUzJTBnpnfAd5aX4G/iuoq/+nx5RGxI7BpZt5dz7oCOLz9ViVJTQw1/R9tRMQngFOBLwMfzMzV9fw3Ap/JzP3r6V2A5fXV+zp1u93XAI8N1jrsutvubPaKTQbdfFrGVj7P8IL1/zWAp595jocfenC971cbhtk8p3xuD2SnTqfz+MSZjZMpM8+JiL8HrqMaavnHetFGVGPv44aAF/vpbGRkZOAP6R98ytcH2m66rlt66Kzs+7qlh9LpdKZcr9vtNlpvEHO19kzXL6X32TynBvn9NsTHdGxsjNHR0bUubzLGvmv95iiZ+QzwVarx9nFPUt2MZtzWwFN9dypJakWTjzvuDFwaEcMRsQA4lJ6bzmTmE8BzEbFfPeto4IbWO5UkNdLkzdPlwPXAvUAXuDMzr4mI5RGxqF7tKODCiHgY2By4aKYaliStW6Mx9sw8Fzh3wryDen6+H9inzcYkSYPxm6eSVBiDXZIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklQYg12SCmOwS1JhDHZJKozBLkmFMdglqTAGuyQVxmCXpMIY7JJUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFcZgl6TCGOySVJj5TVaKiHOA99aT12fm6ZMsPwb4RT3r0sy8uLUuJUmNTRnsEbEYOBDYC1gNfCsiDsvMa3tWWwS8LzPvmpk2JUlNNbliXwGckpkrASLiIWCHCessAs6IiB2B24FTM/O5VjuVJDUy5Rh7Zj6YmXcDRMRrqYZklo8vj4jNgXuB04C9gS2Bs2ekW0nSlBqNsQNExO7A9cBpmfno+PzM/DVwUM96S4HLgTOb1h4dHW266ho6nc5A28113W631fVmsoeXW+2Zrj/Xe5/tc2rQ38/HdE1N3zzdD1gGfCQzr5mwbAdgcWZeXs8aAlb108TIyAjDw8P9bLJBa3LyzeRJOldrz3R9e5++QXqYy8dl0PpjY2PrvCBu8ubp9sDXgCMy85ZJVnkWOD8ibgUeB04Erp1kPUnSetDkiv1UYBPggogYn3cJcAjw8cz8QUQcD1wHLADuAJbOQK+SpAamDPbMPBk4eZJFl/Sss4xqqEaSNMv85qkkFcZgl6TCGOySVBiDXZIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklQYg12SCmOwS1JhDHZJKozBLkmFMdglqTAGuyQVxmCXpMIY7JJUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFcZgl6TCzG+yUkScA7y3nrw+M0+fsHxP4DJgC+B24ITMfL7NRiVJzUx5xR4Ri4EDgb2APYFORBw2YbWrgJMycyEwBCxpu1FJUjNNhmJWAKdk5srMXAU8BOwwvjAidgQ2zcy761lXAIe33agkqZkph2Iy88HxnyPitVRDMvv1rLItVfiPWwFs108To6Oj/az+kk6nM9B2c1232211vaZ23W13NnvFJjN63CerPbbyeYYXNBo1HKj+2jz9zHM8/NCDU6/Yo+1jvj7rd7vdWT2nVq56gQUbz+t7u+n2vK7n10wfj113231GHtPGZ0tE7A5cD5yWmY/2LNoIWN0zPQS82E8TIyMjDA8P97PJBq3Jk22mTtKDT/l66zWnct3SQ2dtv/0cw5kOxpmsP9uhDrBg43kb1PNrfN+DHPexsbF1XhA3+lRMROwH3Ax8NDOvnLD4SWCbnumtgaf67FOS1JImb55uD3wNODIzr5m4PDOfAJ6rwx/gaOCGVruUJDXWZCjmVGAT4IKIGJ93CXAI8PHM/AFwFHBpRGwB/BC4aAZ6lSQ10OTN05OBkydZdEnPOvcD+7TYlyRpQH7zVJIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklQYg12SCmOwS1JhDHZJKozBLkmFMdglqTAGuyQVxmCXpMIY7JJUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFcZgl6TCGOySVBiDXZIKM7/pihGxBXAn8M7MfHzCsnOAY4Bf1LMuzcyL22pSktRco2CPiH2BS4GFa1llEfC+zLyrrcYkSYNpOhSzBDgReGotyxcBZ0TEAxHx+YjYpJXuJEl9axTsmXlsZn53smURsTlwL3AasDewJXB2ax1KkvrSeIx9bTLz18BB49MRsRS4HDizaY3R0dGB9t3pdAbabq7rdrutrtfUhni8+z2GbR/z9Vm/2+1ukI/xbJuJx3TawR4ROwCLM/PyetYQsKqfGiMjIwwPD0+3lQ1Gk5PPk7Qd/RzDmT7mM1nf58vsGeS4j42NrfOCeNrBDjwLnB8RtwKPU43FX9tCXUnSAAb+HHtELI+IRZn5M+B44Dogqa7Yl7bUnySpT31dsWfma3p+Pqjn52XAsvbakiQNym+eSlJhDHZJKozBLkmFMdglqTAGuyQVxmCXpMIY7JJUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFcZgl6TCGOySVBiDXZIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklQYg12SCmOwS1JhDHZJKsz8JitFxBbAncA7M/PxCcv2BC4DtgBuB07IzOdb7lOS1NCUV+wRsS9wB7BwLatcBZyUmQuBIWBJe+1JkvrVZChmCXAi8NTEBRGxI7BpZt5dz7oCOLy17iRJfZtyKCYzjwWIiMkWbwus6JleAWzXbxOjo6P9bgJAp9MZaLu5bOWqF1iw8bwp19sQj03bmh7rcW0e87GVzzO8YM3TcyYfU58vs6fb7bZes9EY+zpsBKzumR4CXuy3yMjICMPDw9NsZcOwYON5HHzK12dl39ctPXRW9jtbZvtYz8a+N7TH+OVgkD+qY2Nj67wgnu6nYp4EtumZ3ppJhmwkSevPtII9M58AnouI/epZRwM3TLsrSdLABgr2iFgeEYvqyaOACyPiYWBz4KK2mpMk9a/xGHtmvqbn54N6fr4f2KfdtiRJg/Kbp5JUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFcZgl6TCGOySVBiDXZIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklQYg12SCmOwS1JhDHZJKozBLkmFMdglqTAGuyQVxmCXpMIY7JJUmPlNVoqII4GzgI2Bz2XmxROWnwMcA/yinnXpxHUkSevHlMEeEa8GPgV0gDHgzoi4NTN/1LPaIuB9mXnXzLQpSWqqyVDMYuCWzPx5Zj4NfAV4z4R1FgFnRMQDEfH5iNik7UYlSc00CfZtgRU90yuA7cYnImJz4F7gNGBvYEvg7BZ7lCT1ockY+0bA6p7pIeDF8YnM/DVw0Ph0RCwFLgfObNrE6Oho01XX0Ol0BtpOkl4uut1u6zWbBPuTwAE901sDT41PRMQOwOLMvLyeNQSs6qeJkZERhoeH+9lEkoowyAXq2NjYOi+ImwT7TcC5EbEV8DTwbuC4nuXPAudHxK3A48CJwLV9dypJasWUY+yZ+WOqYZVbgfuAqzPznohYHhGLMvNnwPHAdUBSXbEvncGeJUnr0Ohz7Jl5NXD1hHkH9fy8DFjWbmuSpEH4zVNJKozBLkmFMdglqTAGuyQVxmCXpMIY7JJUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFcZgl6TCGOySVBiDXZIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklQYg12SCmOwS1JhDHZJKozBLkmFmd9kpYg4EjgL2Bj4XGZePGH5nsBlwBbA7cAJmfl8y71KkhqY8oo9Il4NfArYH9gTOC4iXjdhtauAkzJzITAELGm7UUlSM02u2BcDt2TmzwEi4ivAe4Dz6ukdgU0z8+56/SuATwBfaFB7HsDKlSv767rHlpvNG3jb6RgbG5uVfc/Wfmdz3xvafmdz3/7O63/fg+jJzEkbH1q9evU6C0TEx4DNMvOsevpYYJ/MPK6efiPwmczcv57eBVheX72vU7fb3R/4brNfRZI0wQGdTueOiTObXLFvBPSm/xDwYh/L1+X7wAHACuCFhttI0oZuHrANVYb+libB/iRV+I7bGnhqwvJt1rF8rTqdzhjwW39tJElT+q+1LWjyccebgLdHxFYR8Qrg3cC3xhdm5hPAcxGxXz3raOCGaTQrSZqGKYM9M38MnAncCtwHXJ2Z90TE8ohYVK92FHBhRDwMbA5cNFMNS5LWbco3TyVJc4vfPJWkwhjsklQYg12SCmOwS1JhGt0E7OVoqhuTtVB/C+BO4J2Z+XiLdc8B3ltPXp+Zp7dVu65/HtUtH1YDX8zMC9qsX+/js8AfZOYHWq57K/AqYFU96/jM/F5LtQ8GzgE2A27MzJPbqFvXPhY4qWfWTsCXMvOktWzSb/0/Az5WT96Qmae2Ubeu/VHgg8AY8G+Z+akWaq5x7kTEYuACYNN6H2e1Wb+etzHVx7A/mZm3tdj7ccBfUZ1PP6B6Tg58D5RJ6v8F1XNnCLgeOD0zp/2Jljl5xd7wxmTTqb8v1RenprwtQp91FwMHAntR9d2JiMNarP9m4G3AHsAi4MMREW3Vr/fxduD9bdas6w5RHe/XZ+ae9b+2Qn1n4BLgXVTHZu+IeEcbtQEy87Lxnqk++vtT4Nw2atffHbkIeDPweuCA+nnURu3FwJHAG6iek/tGxJ9Os+Ya505EbApcDhwK7Aa8YTrHfrJzs36O3wa8aeDGJ6kdEQuB0+q6e1Dl5Ykt1t8J+BtgH+CP6v388eC/wW/MyWCn58Zkmfk0MH5jsrYsoXoAG32Dtg8rgFMyc2VmrgIeAnZoq3hmfgd4a33L5FdRvSJ7uq36EfF7VH9Q/7atmr3l6//eGBH3R0QrV7u1w6iuFJ+sj/sRQCt/NCbxBeCMzPyflurNozpPN6N6dbox8GxLtfcC/j0z/y8zX6C64n3XNGtOPHf2AR7NzMfq5+VVwOEt1gf4EPAZpv+YTqw9BvxlfXxWA//J9M7XNepn5mPA6+oM2xJ4JfC/06j/krk6FLMtVUiOW0H1BGpFZh4L0PLFLpn54PjPEfFaqiGZ/da+xUD7WBURnwBOBb4M/LjF8v9A9WW17VusOe53gZuBD1OF120RkZn57RZq7wKsjIhvUJ2Y3wTObqHuGuor4E0z88tt1czMX0XE2cDDwDPAd6heyrfhh1RfLPx0XfsQpnmxN8m5M9m5ul2L9RkfzoyIjwxad7La9bfqn6jnbUU1ZPKBturX81ZFxBLgs8A9VF8Cnba5esU+nRuPzbqI2B34NnBaZj7adv3MPAfYiiqAW7k3fj2O/N+ZeXMb9SbKzLsy888z85f11e4XgYNaKj+f6lXeh4A3AvsyA8NJwPFUY8mtiYg9gGOAHalC8gWqP9rTVj+WV1ANY3yLaphg8HtoT25On6vw0tDvzVTvWd3Wdv3MvBT4feAntDSEN1eDfeAbj822+p46NwMfzcwrW669a/1/syIznwG+SjU22IYjgAMj4j6qe/EfEhEXtlSbiNi/Hr8fN8Rv3kSdrp8AN2XmzzLzWeBaWnyFBxARC6jGwb/RZl3gT4CbM/OnmTlGFcRvaaNwRPwOsCwz98jMt1ANPaz1xlIDmrPnKlTnFNUrpCsz85Mt195+/B5b9TDVNbR0vs7VoZibgHPrl0dPU92Y7LjZbWlqEbE98DXgiMy8ZQZ2sTPwiYjYn+oq6VCqN66mLTNfelMnIj4AvCUz/7qN2rUtgfMi4k1UQzHvB05oqfY3gSsjYkvgV8A7qB6HNu0BPFKPl7bpfuD8iNiMarjkYNZyq9YB7AT8c33Pp82oXtF8qKXa475H9f7mLsBjVG/WtvKcnGn1H74bgTMz80szsItXAv9SX4z9kup9wlbudjsnr9jXdmOy2e2qkVOBTYALIuK++l9b4UVmLqf6yNS9QBe4MzOvaav+TMrMb7Jm75dn5l0t1f4ecD7VSfMjqnHTf2qjdo+dqa5OW5WZNwL/SnVMHqD6o/d3LdV+AFhW172H6mPD/9FG7Z59PEc1Lr2M6tg/TPVhh7ngWOAPgVN6ztfz2iqemaPAp6leEdxP9Yd7aRu1vQmYJBVmTl6xS5LWzmCXpMIY7JJUGINdkgpjsEtSYQx2SSqMwS5JhTHYJakw/w8QUevuL1OiIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram\n",
    "\n",
    "a=[np.random.randint(0,15) for i in range(20)]\n",
    "print(a)\n",
    "plt.xticks(np.arange(0,15,step=1))\n",
    "plt.hist(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[83, 91, 56, 1, 36]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b=[np.random.randint(0,100) for i in range(5)]\n",
    "l=['a','b','c','d','e']\n",
    "print(b)\n",
    "plt.pie(b,labels=l,autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}