{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f99860f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45c65a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('rentingMedia.xlsx',sheet_name='rent')\n",
    "df1=df.set_index('price').T.reset_index()\n",
    "df1['date']=pd.to_datetime(df1['index'], format='%d%m%Y').dt.date\n",
    "df1=df1.set_index(['date']).drop('index',axis=1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbcdf24",
   "metadata": {},
   "source": [
    "## Pandas\n",
    "\n",
    "* Pandas as wrapper instead of usin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ce97dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=df1[['Ajuda', 'Alvalade','Areeiro']].plot(subplots=True,color='blue',figsize=(10, 20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e43212a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=df1[['Ajuda', 'Alvalade','Parque das Nações']].plot(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780adb87",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=df1[['Ajuda', 'Alvalade','Parque das Nações']].plot.bar(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68104597",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=df1.plot.scatter('Alvalade','Parque das Nações')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd13bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df1[['Ajuda', 'Alvalade','Areeiro']].reset_index().astype('str').set_index('date').T[['2022-01-01']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4da05ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['invesments']=df2[['2022-01-01']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87fc154d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=df2.plot.pie(y='invesments', figsize=(5, 5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e55f094c",
   "metadata": {},
   "source": [
    "## matplotlib\n",
    "\n",
    "* https://matplotlib.org/stable/\n",
    "* one of the best-known Python packages for plotting 2D charts. \n",
    "* the package works around the classes Figure, Subplot and Axes. \n",
    "* the most popular python plotting library. \n",
    "* Usually, it is imported as plt.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2926429f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dead39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pf=plt.plot(df1['Olivais'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a07bc50",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplots()\n",
    "plt.plot(df1['Olivais'])\n",
    "plt.plot(df1['Parque das Nações'])\n",
    "pf=plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dabee06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df1['Olivais'], label=\"Olivais\")\n",
    "ax.plot(df1['Parque das Nações'], label=\"Parque das Nações\")\n",
    "ax.plot(df1['Benfica'], label=\"Benfica\")\n",
    "ax.plot(df1['Beato'], label=\"Beato\")\n",
    "ax.plot(df1['Marvila'], label=\"Marvila\")\n",
    "ax.set_title('Renting Price m2')\n",
    "ax.legend()\n",
    "pf=ax.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6d6ce4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ts = pd.Series(np.random.randn(100),index=pd.date_range('1/1/2022', periods=100))\n",
    "df = pd.DataFrame(np.random.randn(100, 4), index=ts.index, columns=list('ABCD'))\n",
    "df = df.cumsum()\n",
    "pf=df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d62679cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(2, 2,figsize=(15, 10))\n",
    "axs[0, 0].plot(df1['Parque das Nações'])\n",
    "axs[0, 0].set_title('Axis [0, 0]:Parque das Nações')\n",
    "axs[0, 1].plot(df1['Beato'], 'tab:orange')\n",
    "axs[0, 1].set_title('Axis [0, 1]:Beato')\n",
    "axs[1, 0].plot(df1['Benfica'], 'tab:pink')\n",
    "axs[1, 0].set_title('Axis [1, 0]: Benfica')\n",
    "axs[1, 1].plot(df1['Misericórdia'], 'tab:red')\n",
    "axs[1, 1].set_title('Axis [1, 1]: Misericórdia')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad55759",
   "metadata": {},
   "outputs": [],
   "source": [
    "c=[2,0,0,3,2,2,2,4,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a412e506",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "ax = plt.figure().add_subplot(projection='3d')\n",
    "\n",
    "\n",
    "X=df1['Parque das Nações']\n",
    "Y =df1['Beato']\n",
    "Z =df1['Belém']\n",
    "ax.scatter(X, Y,Z,c=c)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80dda20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "ax1.plot(df1['Olivais'])\n",
    "ax1.set_title('Sharing Y axis')\n",
    "ax2.scatter(df1['Parque das Nações'], df1['Olivais'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710189d2",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "\n",
    "* is a Python data visualization library based on Matplotlib.\n",
    "* provides a high-level interface for creating attractive charts.\n",
    "* less lines than Matplotlib. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d2ca6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(15,15)) \n",
    "ft=sns.heatmap(df1.corr(numeric_only=True), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dee789e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df1[['Ajuda', 'Alvalade','Areeiro', 'Estrela','Avenidas Novas']]\n",
    "pf=sns.pairplot(df2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b60522f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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