Matplotlib Assignment (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the min and max temperatures in a city In India for each months of the year.\n",
    "We would like to find a function to describe this and show it graphically, the dataset\n",
    "given below.\n",
    "\n",
    "Task:\n",
    "1.fitting it to the periodic function\n",
    "2.plot the fit\n",
    "\n",
    "Data\n",
    "\n",
    "Max = 39, 41, 43, 47, 49, 51, 45, 38, 37, 29, 27, 25\n",
    "Min = 21, 23, 27, 28, 32, 35, 31, 28, 21, 19, 17, 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have been given the data for temperatures for each month\n",
    "#Lets create a function which will make a df of this with the respective months\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_t = np.array([39, 41, 43, 47, 49, 51, 45, 38, 37, 29, 27, 25]) \n",
    "min_t = np.array([21, 23, 27, 28, 32, 35, 31, 28, 21, 19, 17, 18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = np.arange(0,12)\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temperature (C)')"
      ]
     },
     "execution_count": 5,
     "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(months,max_t,'ro-')\n",
    "plt.plot(months,min_t,'bo-')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Temperature (C)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n",
    "def yearly_temps(times, avg, ampl, time_offset):\n",
    "    return (avg + ampl * np.cos((times + time_offset) * 2 * np.pi / times.max()))\n",
    "\n",
    "res_max, cov_max = optimize.curve_fit(yearly_temps, months,\n",
    "                                      max_t, [20, 10, 0])\n",
    "res_min, cov_min = optimize.curve_fit(yearly_temps, months,\n",
    "                                      min_t, [-40, 20, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temperature ($^\\\\circ$C)')"
      ]
     },
     "execution_count": 7,
     "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": [
    "days = np.linspace(0, 12, num=365)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(months, max_t, 'ro')\n",
    "plt.plot(days, yearly_temps(days, *res_max), 'r-')\n",
    "plt.plot(months, min_t, 'bo')\n",
    "plt.plot(days, yearly_temps(days, *res_min), 'b-')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Temperature ($^\\circ$C)')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib:\n",
    "\n",
    "This assignment is for visualization using matplotlib:\n",
    "data to use:\n",
    "\n",
    "url=https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_original.csv\n",
    "\n",
    "titanic = pd.read_csv(url)\n",
    "\n",
    "Charts to plot:\n",
    "\n",
    "1. Create a pie chart presenting the male/female proportion\n",
    "2. Create a scatterplot with the Fare paid and the Age, differ the plot color by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_original.csv\"\n",
    "\n",
    "titanic = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fbc0e8ff08>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "titanic['survived'].groupby(titanic['sex']).sum().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = []\n",
    "for i in titanic.sex:\n",
    "    if i == 'female':\n",
    "        gender.append(1)\n",
    "    else:\n",
    "        gender.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['gender'] = gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  gender  \n",
       "0                     St Louis, MO       1  \n",
       "1  Montreal, PQ / Chesterville, ON       0  \n",
       "2  Montreal, PQ / Chesterville, ON       1  \n",
       "3  Montreal, PQ / Chesterville, ON       0  \n",
       "4  Montreal, PQ / Chesterville, ON       1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fbc132d2c8>"
      ]
     },
     "execution_count": 51,
     "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": [
    "titanic.plot(x = 'age', y = 'fare', kind = 'scatter', c = 'gender')"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}