3. Introducción Rápida a Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducción Rápida a Pandas\n",
    "\n",
    "Es un paquete que provee estructuras de datos rapidas, flexibles y expresivas, diseñadas para trabajar con datos \"relacionados\" o \"etiquetados\" de forma facil e intruitiva. Tiene como objetivo de ser la base de construcción de alto nivel para hacer analisis de datos practico y del mundo real usando python.\n",
    "\n",
    "### Incluyendo la libreria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargando Datos con Pandas\n",
    "\n",
    "Regularmente pandas se usa de las siguientes formas:\n",
    "\n",
    "- Convertir una lista, diccionario o arrelo de numpy al Dataframe de pandas\n",
    "- Abrir un archivo local usando Pandas, usualmente un archivo CSV, etc\n",
    "- Abrir un archivo remoto o base de datos, CSV o JSONon consumiendo de una URL o consulta SQL\n",
    "\n",
    "Como ejemplo podemos cargar datos desde un archivo csv desde google:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",
    "#el conjunto de datos es de pasajeros del titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viendo e Inspeccionando Datos\n",
    "\n",
    "Los siguientes metodos permiten manejar el conjunto de datos cargados:\n",
    "\n",
    "|   |   |\n",
    "|---|---|\n",
    "|df.head(n)| Retorna las primeras n filas|\n",
    "|df.tail(n)| Retorna las ultimas n filas|\n",
    "|df.shape| Retorna el numero de filas y columnas|\n",
    "|df.info() | Retorna el indice, datos no nulos y tipo de datos por columna|\n",
    "|df.describe()| Ofrece un resumen de estadisticas para columnas numericas y es posible conseguir estadisticas para todo el conjunto de datos o solo una parte|\n",
    "|df.mean()|  Retorna el significado de todas las columnas|\n",
    "|df.corr()|  Retorna la correlación entre columnas en el conjunto de datos|\n",
    "|df.count()| Retorna el numero de valores no nulos en cada columna del conjunto de datos|\n",
    "|df.max()|   Retorna el valor máximo de cada columna|\n",
    "|df.min()|   Retorna el valor mínimo de cada columna|\n",
    "|df.median()|Retorna la mediana de cada columna|\n",
    "|df.std()|   Retorna la desviación estandar de cada columna|\n",
    "\n",
    "De acuerdo a los datos cargados anteriormente podemos ejecutar algunos de los métodos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>y</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>Second</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>Third</td>\n",
       "      <td>G</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   survived     sex   age  n_siblings_spouses  parch     fare   class  \\\n",
       "0         0    male  22.0                   1      0   7.2500   Third   \n",
       "1         1  female  38.0                   1      0  71.2833   First   \n",
       "2         1  female  26.0                   0      0   7.9250   Third   \n",
       "3         1  female  35.0                   1      0  53.1000   First   \n",
       "4         0    male  28.0                   0      0   8.4583   Third   \n",
       "5         0    male   2.0                   3      1  21.0750   Third   \n",
       "6         1  female  27.0                   0      2  11.1333   Third   \n",
       "7         1  female  14.0                   1      0  30.0708  Second   \n",
       "8         1  female   4.0                   1      1  16.7000   Third   \n",
       "9         0    male  20.0                   0      0   8.0500   Third   \n",
       "\n",
       "      deck  embark_town alone  \n",
       "0  unknown  Southampton     n  \n",
       "1        C    Cherbourg     n  \n",
       "2  unknown  Southampton     y  \n",
       "3        C  Southampton     n  \n",
       "4  unknown   Queenstown     y  \n",
       "5  unknown  Southampton     n  \n",
       "6  unknown  Southampton     n  \n",
       "7  unknown    Cherbourg     n  \n",
       "8        G  Southampton     n  \n",
       "9  unknown  Southampton     y  "
      ]
     },
     "execution_count": 3,
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   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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    {
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>627.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>627.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.387560</td>\n",
       "      <td>29.631308</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.379585</td>\n",
       "      <td>34.385399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.487582</td>\n",
       "      <td>12.511818</td>\n",
       "      <td>1.151090</td>\n",
       "      <td>0.792999</td>\n",
       "      <td>54.597730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.045800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.387500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         survived         age  n_siblings_spouses       parch        fare\n",
       "count  627.000000  627.000000          627.000000  627.000000  627.000000\n",
       "mean     0.387560   29.631308            0.545455    0.379585   34.385399\n",
       "std      0.487582   12.511818            1.151090    0.792999   54.597730\n",
       "min      0.000000    0.750000            0.000000    0.000000    0.000000\n",
       "25%      0.000000   23.000000            0.000000    0.000000    7.895800\n",
       "50%      0.000000   28.000000            0.000000    0.000000   15.045800\n",
       "75%      1.000000   35.000000            1.000000    0.000000   31.387500\n",
       "max      1.000000   80.000000            8.000000    5.000000  512.329200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seleccionando Datos\n",
    "\n",
    "Seleccionar datos en un set de datos es tan sencillo como acceder a un arreglo normal en python indicando inicialmente la o las columnas y luego el indice del elemento, si se desea acceder a todos los datos de una fila se debe usar `iloc`. Además se pueden hacer muchas otras operaciones para indexar y filtrar los datos\n",
    "\n",
    "#### Ejemplos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived                        0\n",
       "sex                          male\n",
       "age                            22\n",
       "n_siblings_spouses              1\n",
       "parch                           0\n",
       "fare                         7.25\n",
       "class                       Third\n",
       "deck                      unknown\n",
       "embark_town           Southampton\n",
       "alone                           n\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'male'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sexo del registro 0\n",
    "df.iloc[0].sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>n</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>Third</td>\n",
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      "text/plain": [
       "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
       "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
       "1         1  female  38.0                   1      0  71.2833  First        C   \n",
       "2         1  female  26.0                   0      0   7.9250  Third  unknown   \n",
       "\n",
       "   embark_town alone  \n",
       "0  Southampton     n  \n",
       "1    Cherbourg     n  \n",
       "2  Southampton     y  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>Second</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Third</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived   sex   age  n_siblings_spouses  parch     fare   class  \\\n",
       "0           0  male  22.0                   1      0   7.2500   Third   \n",
       "4           0  male  28.0                   0      0   8.4583   Third   \n",
       "5           0  male   2.0                   3      1  21.0750   Third   \n",
       "9           0  male  20.0                   0      0   8.0500   Third   \n",
       "10          0  male  39.0                   1      5  31.2750   Third   \n",
       "..        ...   ...   ...                 ...    ...      ...     ...   \n",
       "619         0  male  19.0                   0      0   7.8958   Third   \n",
       "620         0  male  28.0                   0      0   7.8958   Third   \n",
       "622         0  male  28.0                   0      0  10.5000  Second   \n",
       "623         0  male  25.0                   0      0   7.0500   Third   \n",
       "626         0  male  32.0                   0      0   7.7500   Third   \n",
       "\n",
       "        deck  embark_town alone  \n",
       "0    unknown  Southampton     n  \n",
       "4    unknown   Queenstown     y  \n",
       "5    unknown  Southampton     n  \n",
       "9    unknown  Southampton     y  \n",
       "10   unknown  Southampton     n  \n",
       "..       ...          ...   ...  \n",
       "619  unknown  Southampton     y  \n",
       "620  unknown  Southampton     y  \n",
       "622  unknown  Southampton     y  \n",
       "623  unknown  Southampton     y  \n",
       "626  unknown   Queenstown     y  \n",
       "\n",
       "[410 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtrando la columna 'sex' y seleccionando los valores 'male'\n",
    "df[df['sex'].isin(['male'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Referencias\n",
    "\n",
    "- [A Quick Introduction to Pandas](https://towardsdatascience.com/a-quick-introduction-to-the-pandas-python-library-f1b678f34673)\n",
    "- [Pandas Overview](https://pandas.pydata.org/docs/getting_started/overview.html)\n",
    "- [10 minutes to pandas](https://pandas.pydata.org/docs/getting_started/10min.html)\n",
    "- McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. \" O'Reilly Media, Inc.\"."
   ]
  }
 ],
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