3-Series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apresentação do Pandas - Series\n",
    "\n",
    "O pandas trabalha com o conceito de dataframes e series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Séries é um vetor unidimensional rotulado capaz de armazenar qualquer tipo de dados (inteiros, strings, floats, objetos python, etc). Os rótulos (ou ids) são chamados de índice. O modo simples de criar uma Série é chamar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    D\n",
       "3    A\n",
       "4    E\n",
       "5    R\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['A', 'B', 'D', 'A', 'E', 'R'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos definir os índices de uma série para qualquer coisa. Vamos criar uma série de números aleatórios com índices de **a** a **e**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.610855\n",
       "b   -0.328086\n",
       "c   -1.762281\n",
       "d   -2.154608\n",
       "e    0.127399\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As séries também funcionam como os arrays do numpy, podendo ser segmentadas a partir de um valor, veja:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.610855\n",
       "e    0.127399\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positives = s[s>0]\n",
    "positives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valores também podem ser coisas complexas como a própria média da série:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.610855\n",
       "e    0.127399\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>s.median()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O acesso de uma série funciona como um dicionário:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12739922486940955"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('f', np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos também dar um nome para a série, fazendo com que ela possa ser referenciada por ele em outro momento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.691533\n",
       "1   -1.245737\n",
       "2   -0.838546\n",
       "3    0.808273\n",
       "4    0.173413\n",
       "Name: numeros, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(5), name='numeros')\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Como lidar com NaN's?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nannannannannannannannannannan Batman!'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(np.nan) * 10 + \" Batman!\""
   ]
  },  
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      NaN\n",
       "4      5.0\n",
       "5      6.0\n",
       "6      7.0\n",
       "7      8.0\n",
       "8      NaN\n",
       "9      9.0\n",
       "10    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3, np.nan, 5, 6, 7, 8, np.nan, 9, 10])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      4.0\n",
       "4      5.0\n",
       "5      6.0\n",
       "6      7.0\n",
       "7      8.0\n",
       "8      8.5\n",
       "9      9.0\n",
       "10    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.interpolate(method='linear')"
   ]
  }
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