C1_5_ManejoDataFrames.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9201e0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc27f0a",
   "metadata": {},
   "source": [
    "# 5. Estructura de datos en Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2edb73",
   "metadata": {},
   "source": [
    "* Pandas is a Python library used for working with data sets.\n",
    "* It has functions for analyzing, cleaning, exploring, and manipulating data.\n",
    "* The name \"Pandas\" has a reference to both \"Panel Data\", and \"Python Data Analysis\" and was created by Wes McKinney in 2008."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f670bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\1-Python_para_principiantes'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9308b904",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/jpeg": {
       "height": 750,
       "width": 750
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='pandas.jpg', width=750, height=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7412ca25",
   "metadata": {},
   "source": [
    "## Series\n",
    "A Pandas *Series* is a one-dimensional array of indexed data.\n",
    "\n",
    "A series can be created using various inputs like: Array, Dict, Scalar value or constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5262ee3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2.4\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "851bee46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.50\n",
       "2    0.75\n",
       "3    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
    "data\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "097b9677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index\n",
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55bc1597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modificando indices\n",
    "\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                 index=['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ddc1d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.Series({'California': 38332521, 'Texas': 26448193, 'New York': 19651127,\n",
    "                   'Florida': 19552860, 'Illinois': 12882135})\n",
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd88e74e",
   "metadata": {},
   "source": [
    "## DataFrame\n",
    "is an analog of a two-dimensional array with both flexible row indices and flexible column names.\n",
    "\n",
    "A pandas DataFrame can be created using various inputs like: Lists, dict, Series, Numpy ndarrays, Another DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "49732cfb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   0\n",
       "0  1\n",
       "1  2\n",
       "2  3\n",
       "3  4\n",
       "4  5"
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
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      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = [1,2,3,4,5]\n",
    "data ; type(data)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df ; type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f3c60c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.index\n",
    "df.columns"
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3dfeb548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "(5, 1)"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.ndim\n",
    "df.shape\n",
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56871d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Alex', 10], ['Bob', 12], ['Clarke', 13]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
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     "execution_count": 13,
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       "     Name  Age\n",
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    "\n",
    "df = pd.DataFrame(data, columns=['Name','Age'])\n",
    "df ; type(df)"
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   "execution_count": 14,
   "id": "be4e32fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
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     "execution_count": 14,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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    "df.columns"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db90cf9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "(3, 2)"
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     "execution_count": 15,
     "metadata": {},
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    {
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     "execution_count": 15,
     "metadata": {},
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    "df.ndim\n",
    "df.shape\n",
    "df.size"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8cb423ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Name': ['Tom', 'Jack', 'Steve', 'Ricky'], 'Age': [28, 34, 29, 42]}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dict"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
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       "</table>\n",
       "</div>"
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       "    Name  Age\n",
       "0    Tom   28\n",
       "1   Jack   34\n",
       "2  Steve   29\n",
       "3  Ricky   42"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'], 'Age':[28,34,29,42]}\n",
    "data ; type(data)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a9ce0a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>rank3</th>\n",
       "      <td>Steve</td>\n",
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       "      <th>rank4</th>\n",
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       "        Name  Age\n",
       "rank1    Tom   28\n",
       "rank2   Jack   34\n",
       "rank3  Steve   29\n",
       "rank4  Ricky   42"
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     "metadata": {},
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   "source": [
    "# cambiar nombre de indices de filas\n",
    "\n",
    "df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5cd97954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank1', 'rank2', 'rank3', 'rank4'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "daa28cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "list"
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     "execution_count": 19,
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    "data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]\n",
    "data ; type(data)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
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   "cell_type": "code",
   "execution_count": 20,
   "id": "f2340a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "(2, 3)"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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    {
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      "text/plain": [
       "6"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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    "df.shape\n",
    "df.size"
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   "cell_type": "code",
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   "id": "e9148800",
   "metadata": {},
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    {
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      "text/plain": [
       "{'one': a    1\n",
       " b    2\n",
       " c    3\n",
       " dtype: int64,\n",
       " 'two': a    1\n",
       " b    2\n",
       " c    3\n",
       " d    4\n",
       " dtype: int64}"
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dict"
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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    "d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),\n",
    "   'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}\n",
    "d ; type(d)\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea760309",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "     a  b    c\n",
       "0  1.0  2  NaN\n",
       "1  NaN  3  4.0"
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     "execution_count": 22,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 23,
   "id": "66d3c044",
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      "text/plain": [
       "array([[ 1.48860905,  0.67601087],\n",
       "       [-0.41845137, -0.80652081],\n",
       "       [ 0.55587583, -0.70550429]])"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[11,  6],\n",
       "       [ 5, 18],\n",
       "       [ 5, 11]])"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "array([[ 1.48860905,  0.67601087, 11.        ,  6.        ],\n",
       "       [-0.41845137, -0.80652081,  5.        , 18.        ],\n",
       "       [ 0.55587583, -0.70550429,  5.        , 11.        ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(2021)\n",
    "x = np.random.standard_normal((3,2))\n",
    "x\n",
    "\n",
    "np.random.seed(2021)\n",
    "y = np.random.poisson(10, (3,2))\n",
    "y\n",
    "\n",
    "arr = np.concatenate([x, y], axis=1)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "999b2154",
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>v4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.488609</td>\n",
       "      <td>0.676011</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.418451</td>\n",
       "      <td>-0.806521</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.555876</td>\n",
       "      <td>-0.705504</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         v1        v2    v3    v4\n",
       "0  1.488609  0.676011  11.0   6.0\n",
       "1 -0.418451 -0.806521   5.0  18.0\n",
       "2  0.555876 -0.705504   5.0  11.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arr, columns = ['v1', 'v2', 'v3', 'v4'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db115b50",
   "metadata": {},
   "source": [
    "## Importacion de archivos de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7597f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\1-Python_para_principiantes'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6458978f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\1-Python_para_principiantes\\\\datasets'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\1-Python_para_principiantes\\\\datasets')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ebd2e634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>ID</th>\n",
       "      <th>CUENTA</th>\n",
       "      <th>MONTO_SOLES</th>\n",
       "      <th>FECHA_TRANSAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID12796</td>\n",
       "      <td>CORRIENTE</td>\n",
       "      <td>150.32</td>\n",
       "      <td>07/02/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID12797</td>\n",
       "      <td>AHORRO</td>\n",
       "      <td>1211.89</td>\n",
       "      <td>30/07/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID12797</td>\n",
       "      <td>CORRIENTE</td>\n",
       "      <td>512.92</td>\n",
       "      <td>09/08/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID12798</td>\n",
       "      <td>AHORRO</td>\n",
       "      <td>541.13</td>\n",
       "      <td>21/12/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID12798</td>\n",
       "      <td>CORRIENTE</td>\n",
       "      <td>735.48</td>\n",
       "      <td>21/05/2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID     CUENTA  MONTO_SOLES FECHA_TRANSAX\n",
       "0  ID12796  CORRIENTE       150.32    07/02/2016\n",
       "1  ID12797     AHORRO      1211.89    30/07/2016\n",
       "2  ID12797  CORRIENTE       512.92    09/08/2016\n",
       "3  ID12798     AHORRO       541.13    21/12/2016\n",
       "4  ID12798  CORRIENTE       735.48    21/05/2016"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CUENTA</th>\n",
       "      <th>MONTO_SOLES</th>\n",
       "      <th>FECHA_TRANSAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>ID12899</td>\n",
       "      <td>AHORRO</td>\n",
       "      <td>230.0</td>\n",
       "      <td>22/07/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>ID12899</td>\n",
       "      <td>CORRIENTE</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13/07/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>ID12900</td>\n",
       "      <td>AHORRO</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>16/04/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>ID12900</td>\n",
       "      <td>CORRIENTE</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31/05/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>ID12900</td>\n",
       "      <td>CORRIENTE</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>07/11/2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID     CUENTA  MONTO_SOLES FECHA_TRANSAX\n",
       "188  ID12899     AHORRO        230.0    22/07/2016\n",
       "189  ID12899  CORRIENTE         50.0    13/07/2016\n",
       "190  ID12900     AHORRO       1125.0    16/04/2016\n",
       "191  ID12900  CORRIENTE         50.0    31/05/2016\n",
       "192  ID12900  CORRIENTE       2100.0    07/11/2016"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importar un archivo .csv\n",
    "\n",
    "dep2016 = pd.read_csv('Depositos_2016.csv')\n",
    "dep2016.head()\n",
    "dep2016.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b3618a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID12701</td>\n",
       "      <td>23</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>18766</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID12702</td>\n",
       "      <td>30</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>9915</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID12703</td>\n",
       "      <td>45</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>21881</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID12704</td>\n",
       "      <td>50</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>46794</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID12705</td>\n",
       "      <td>41</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>20721</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "0  ID12701    23  MASCULINO           LIMA_ESTE     18766   SI          0   SI\n",
       "1  ID12702    30  MASCULINO    LIMA_CENTRO           9915  NO           1  NO \n",
       "2  ID12703    45     FEMENINO  LIMA_CENTRO          21881  NO           0   SI\n",
       "3  ID12704    50  MASCULINO    LIMA_NORTE           46794   SI          2  NO \n",
       "4  ID12705    41     FEMENINO         LIMA_ESTE     20721   SI          0   SI"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importar archivo .xlsx\n",
    "\n",
    "clientes = pd.read_excel('Clientes_2016_2017.xlsx')\n",
    "clientes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c313aa05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importar archivo .sav (IBM SPSS)\n",
    "\n",
    "# !pip install pyreadstat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a3cd5431",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyreadstat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1984536a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>id</th>\n",
       "      <th>sexo</th>\n",
       "      <th>fechnac</th>\n",
       "      <th>educ</th>\n",
       "      <th>catlab</th>\n",
       "      <th>salario</th>\n",
       "      <th>salini</th>\n",
       "      <th>tiempemp</th>\n",
       "      <th>expprev</th>\n",
       "      <th>minoría</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>1952-02-03</td>\n",
       "      <td>15</td>\n",
       "      <td>Directivo</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>1958-05-23</td>\n",
       "      <td>16</td>\n",
       "      <td>Administrativo</td>\n",
       "      <td>40200.0</td>\n",
       "      <td>18750.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>1929-07-26</td>\n",
       "      <td>12</td>\n",
       "      <td>Administrativo</td>\n",
       "      <td>21450.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>1947-04-15</td>\n",
       "      <td>8</td>\n",
       "      <td>Administrativo</td>\n",
       "      <td>21900.0</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>1955-02-09</td>\n",
       "      <td>15</td>\n",
       "      <td>Administrativo</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id    sexo     fechnac educ          catlab  salario   salini tiempemp  \\\n",
       "0  1.0  Hombre  1952-02-03   15       Directivo  57000.0  27000.0     98.0   \n",
       "1  2.0  Hombre  1958-05-23   16  Administrativo  40200.0  18750.0     98.0   \n",
       "2  3.0   Mujer  1929-07-26   12  Administrativo  21450.0  12000.0     98.0   \n",
       "3  4.0   Mujer  1947-04-15    8  Administrativo  21900.0  13200.0     98.0   \n",
       "4  5.0  Hombre  1955-02-09   15  Administrativo  45000.0  21000.0     98.0   \n",
       "\n",
       "  expprev minoría  \n",
       "0   144.0      No  \n",
       "1    36.0      No  \n",
       "2   381.0      No  \n",
       "3   190.0      No  \n",
       "4   138.0      No  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed = pd.read_spss('Employee data.sav')\n",
    "ed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd6e6c1",
   "metadata": {},
   "source": [
    "## Selección de campos y registros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2162d287",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID12701</td>\n",
       "      <td>23</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>18766</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID12702</td>\n",
       "      <td>30</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>9915</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID12703</td>\n",
       "      <td>45</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>21881</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID12704</td>\n",
       "      <td>50</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>46794</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID12705</td>\n",
       "      <td>41</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>20721</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "0  ID12701    23  MASCULINO           LIMA_ESTE     18766   SI          0   SI\n",
       "1  ID12702    30  MASCULINO    LIMA_CENTRO           9915  NO           1  NO \n",
       "2  ID12703    45     FEMENINO  LIMA_CENTRO          21881  NO           0   SI\n",
       "3  ID12704    50  MASCULINO    LIMA_NORTE           46794   SI          2  NO \n",
       "4  ID12705    41     FEMENINO         LIMA_ESTE     20721   SI          0   SI"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
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       "      <th>AUTO</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>ID12896</td>\n",
       "      <td>66</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>58792</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>ID12897</td>\n",
       "      <td>19</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>17906</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
       "      <td>SI</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>ID12898</td>\n",
       "      <td>54</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>29348</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>ID12899</td>\n",
       "      <td>42</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>20552</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>ID12900</td>\n",
       "      <td>34</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>25843</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS  \\\n",
       "193  ID12896    66  MASCULINO           LIMA_ESTE     58792  NO           1   \n",
       "194  ID12897    19  MASCULINO           LIMA_ESTE     17906  NO           2   \n",
       "195  ID12898    54  MASCULINO    LIMA_CENTRO          29348  NO           0   \n",
       "196  ID12899    42     FEMENINO  LIMA_NORTE           20552   SI          0   \n",
       "197  ID12900    34  MASCULINO           LIMA_ESTE     25843  NO           2   \n",
       "\n",
       "    AUTO  \n",
       "193   SI  \n",
       "194   SI  \n",
       "195  NO   \n",
       "196   SI  \n",
       "197  NO   "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes.head()\n",
    "clientes.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3682556f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    30\n",
       "2    45\n",
       "3    50\n",
       "4    41\n",
       "Name: EDAD, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    30\n",
       "2    45\n",
       "3    50\n",
       "4    41\n",
       "Name: EDAD, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar solo el campo EDAD\n",
    "\n",
    "clientes['EDAD'].head()\n",
    "\n",
    "clientes.iloc[:, 1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2c47e515",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>MASCULINO</td>\n",
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       "      <td>NO</td>\n",
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       "      <td>NO</td>\n",
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       "      <td>SI</td>\n",
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      "text/plain": [
       "          SEXO            REGION  INGRESOS   CASADO\n",
       "0  MASCULINO           LIMA_ESTE     18766   SI    \n",
       "1  MASCULINO    LIMA_CENTRO           9915  NO     \n",
       "2     FEMENINO  LIMA_CENTRO          21881  NO     \n",
       "3  MASCULINO    LIMA_NORTE           46794   SI    \n",
       "4     FEMENINO         LIMA_ESTE     20721   SI    "
      ]
     },
     "execution_count": 34,
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     "output_type": "execute_result"
    },
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       "      <td>FEMENINO</td>\n",
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       "      <td>21881</td>\n",
       "      <td>NO</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
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       "      <td>FEMENINO</td>\n",
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       "      <td>20721</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SEXO            REGION  INGRESOS   CASADO\n",
       "0  MASCULINO           LIMA_ESTE     18766   SI    \n",
       "1  MASCULINO    LIMA_CENTRO           9915  NO     \n",
       "2     FEMENINO  LIMA_CENTRO          21881  NO     \n",
       "3  MASCULINO    LIMA_NORTE           46794   SI    \n",
       "4     FEMENINO         LIMA_ESTE     20721   SI    "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar campos desde SEXO hasta CASADO\n",
    "\n",
    "clientes.loc[:, 'SEXO':'CASADO'].head()\n",
    "\n",
    "clientes.iloc[:, 2:6].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bbb6fd9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
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      ],
      "text/plain": [
       "   EDAD            REGION   CASADO\n",
       "0    23         LIMA_ESTE   SI    \n",
       "1    30  LIMA_CENTRO       NO     \n",
       "2    45  LIMA_CENTRO       NO     \n",
       "3    50  LIMA_NORTE         SI    \n",
       "4    41         LIMA_ESTE   SI    "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EDAD</th>\n",
       "      <th>REGION</th>\n",
       "      <th>CASADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>NO</td>\n",
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       "      <td>50</td>\n",
       "      <td>LIMA_NORTE</td>\n",
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       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EDAD            REGION   CASADO\n",
       "0    23         LIMA_ESTE   SI    \n",
       "1    30  LIMA_CENTRO       NO     \n",
       "2    45  LIMA_CENTRO       NO     \n",
       "3    50  LIMA_NORTE         SI    \n",
       "4    41         LIMA_ESTE   SI    "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar campos EDAD, REGION y CASADO\n",
    "\n",
    "clientes[['EDAD', 'REGION', 'CASADO']].head()\n",
    "\n",
    "clientes.iloc[:, [1,3,5]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d3c0552",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>ID12706</td>\n",
       "      <td>20</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>16688</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ID12707</td>\n",
       "      <td>46</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>39068</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ID12708</td>\n",
       "      <td>50</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>27740</td>\n",
       "      <td>SI</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ID12709</td>\n",
       "      <td>42</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>33584</td>\n",
       "      <td>NO</td>\n",
       "      <td>3</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ID12710</td>\n",
       "      <td>57</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>19621</td>\n",
       "      <td>SI</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "5  ID12706    20  MASCULINO           LIMA_ESTE     16688  NO           1  NO \n",
       "6  ID12707    46     FEMENINO  LIMA_CENTRO          39068   SI          0   SI\n",
       "7  ID12708    50     FEMENINO         LIMA_ESTE     27740   SI          1   SI\n",
       "8  ID12709    42  MASCULINO           LIMA_ESTE     33584  NO           3   SI\n",
       "9  ID12710    57     FEMENINO  LIMA_NORTE           19621   SI          1   SI"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar filas desde el indice 5\n",
    "\n",
    "clientes[5:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d1846e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID12701</td>\n",
       "      <td>23</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>18766</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID12702</td>\n",
       "      <td>30</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>9915</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID12703</td>\n",
       "      <td>45</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>21881</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID12704</td>\n",
       "      <td>50</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>46794</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "0  ID12701    23  MASCULINO           LIMA_ESTE     18766   SI          0   SI\n",
       "1  ID12702    30  MASCULINO    LIMA_CENTRO           9915  NO           1  NO \n",
       "2  ID12703    45     FEMENINO  LIMA_CENTRO          21881  NO           0   SI\n",
       "3  ID12704    50  MASCULINO    LIMA_NORTE           46794   SI          2  NO "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar filas hasta el indice 3\n",
    "\n",
    "clientes[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9036cd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID12704</td>\n",
       "      <td>50</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>46794</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID12705</td>\n",
       "      <td>41</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>20721</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ID12706</td>\n",
       "      <td>20</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>16688</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ID12707</td>\n",
       "      <td>46</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>39068</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ID12708</td>\n",
       "      <td>50</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>27740</td>\n",
       "      <td>SI</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "3  ID12704    50  MASCULINO    LIMA_NORTE           46794   SI          2  NO \n",
       "4  ID12705    41     FEMENINO         LIMA_ESTE     20721   SI          0   SI\n",
       "5  ID12706    20  MASCULINO           LIMA_ESTE     16688  NO           1  NO \n",
       "6  ID12707    46     FEMENINO  LIMA_CENTRO          39068   SI          0   SI\n",
       "7  ID12708    50     FEMENINO         LIMA_ESTE     27740   SI          1   SI"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar filas desde el indice 3 al 7\n",
    "\n",
    "clientes[3:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3370aebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEXO</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>NO</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SEXO   CASADO AUTO\n",
       "10     FEMENINO   SI      NO \n",
       "11     FEMENINO  NO        SI\n",
       "12     FEMENINO   SI       SI\n",
       "13     FEMENINO   SI       SI\n",
       "14  MASCULINO     SI      NO "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEXO</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FEMENINO</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FEMENINO</td>\n",
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       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SEXO   CASADO AUTO\n",
       "10     FEMENINO   SI      NO \n",
       "11     FEMENINO  NO        SI\n",
       "12     FEMENINO   SI       SI\n",
       "13     FEMENINO   SI       SI\n",
       "14  MASCULINO     SI      NO "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar filas desde el indice 10 al 14 y los campos SEXO, CASADO y AUTO\n",
    "\n",
    "clientes.iloc[10:15, [2,5,7]]\n",
    "\n",
    "clientes.loc[10:14, ['SEXO', 'CASADO', 'AUTO']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56484bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "      <th>IE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID12701</td>\n",
       "      <td>23</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>18766</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "      <td>815.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID12702</td>\n",
       "      <td>30</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>9915</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "      <td>330.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID12703</td>\n",
       "      <td>45</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>21881</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "      <td>486.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID12704</td>\n",
       "      <td>50</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>46794</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "      <td>935.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID12705</td>\n",
       "      <td>41</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>20721</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "      <td>505.390244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS  \\\n",
       "0  ID12701    23  MASCULINO           LIMA_ESTE     18766   SI          0   \n",
       "1  ID12702    30  MASCULINO    LIMA_CENTRO           9915  NO           1   \n",
       "2  ID12703    45     FEMENINO  LIMA_CENTRO          21881  NO           0   \n",
       "3  ID12704    50  MASCULINO    LIMA_NORTE           46794   SI          2   \n",
       "4  ID12705    41     FEMENINO         LIMA_ESTE     20721   SI          0   \n",
       "\n",
       "  AUTO          IE  \n",
       "0   SI  815.913043  \n",
       "1  NO   330.500000  \n",
       "2   SI  486.244444  \n",
       "3  NO   935.880000  \n",
       "4   SI  505.390244  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear la variable IE = INGRESOS/EDAD\n",
    "\n",
    "clientes2 = clientes.copy()\n",
    "\n",
    "clientes2['IE'] = clientes['INGRESOS'] / clientes['EDAD']\n",
    "clientes2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed479119",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>101</th>\n",
       "      <td>ID12803</td>\n",
       "      <td>66</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>61476</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>ID12884</td>\n",
       "      <td>66</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>60635</td>\n",
       "      <td>SI</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ID12761</td>\n",
       "      <td>64</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>60176</td>\n",
       "      <td>SI</td>\n",
       "      <td>3</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>ID12896</td>\n",
       "      <td>66</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>58792</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ID12747</td>\n",
       "      <td>65</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>58002</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS  \\\n",
       "101  ID12803    66  MASCULINO    LIMA_NORTE           61476   SI          0   \n",
       "181  ID12884    66     FEMENINO         LIMA_ESTE     60635   SI          1   \n",
       "59   ID12761    64  MASCULINO    LIMA_CENTRO          60176   SI          3   \n",
       "193  ID12896    66  MASCULINO           LIMA_ESTE     58792  NO           1   \n",
       "45   ID12747    65     FEMENINO         LIMA_ESTE     58002  NO           0   \n",
       "\n",
       "    AUTO  \n",
       "101  NO   \n",
       "181   SI  \n",
       "59   NO   \n",
       "193   SI  \n",
       "45   NO   "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>ID12835</td>\n",
       "      <td>44</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>39740</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ID12779</td>\n",
       "      <td>57</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>39576</td>\n",
       "      <td>NO</td>\n",
       "      <td>3</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ID12727</td>\n",
       "      <td>48</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_NORTE</td>\n",
       "      <td>39465</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ID12728</td>\n",
       "      <td>52</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>39387</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>ID12782</td>\n",
       "      <td>44</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>39202</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  EDAD         SEXO            REGION  INGRESOS   CASADO  HIJOS  \\\n",
       "133  ID12835    44     FEMENINO         LIMA_ESTE     39740   SI          2   \n",
       "77   ID12779    57  MASCULINO           LIMA_ESTE     39576  NO           3   \n",
       "26   ID12727    48     FEMENINO  LIMA_NORTE           39465   SI          0   \n",
       "27   ID12728    52     FEMENINO  LIMA_CENTRO          39387   SI          0   \n",
       "80   ID12782    44  MASCULINO    LIMA_CENTRO          39202  NO           1   \n",
       "\n",
       "    AUTO  \n",
       "133   SI  \n",
       "77    SI  \n",
       "26    SI  \n",
       "27   NO   \n",
       "80    SI  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear un subconjunto de datos, donde el INGRESO es menor a 40000\n",
    "\n",
    "clientes.sort_values('INGRESOS', ascending = False).head()\n",
    "\n",
    "clientesS1 = clientes[clientes['INGRESOS'] < 40000]\n",
    "\n",
    "clientesS1.sort_values('INGRESOS', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "284b6318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID12703</td>\n",
       "      <td>45</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>21881</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>ID12810</td>\n",
       "      <td>20</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>18824</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>ID12851</td>\n",
       "      <td>41</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_SUR</td>\n",
       "      <td>18093</td>\n",
       "      <td>SI</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>ID12850</td>\n",
       "      <td>52</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>42415</td>\n",
       "      <td>SI</td>\n",
       "      <td>0</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ID12849</td>\n",
       "      <td>56</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_CENTRO</td>\n",
       "      <td>37699</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  EDAD      SEXO            REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "2    ID12703    45  FEMENINO  LIMA_CENTRO          21881  NO           0   SI\n",
       "108  ID12810    20  FEMENINO         LIMA_ESTE     18824  NO           0  NO \n",
       "149  ID12851    41  FEMENINO        LIMA_SUR       18093   SI          1  NO \n",
       "148  ID12850    52  FEMENINO         LIMA_ESTE     42415   SI          0  NO \n",
       "147  ID12849    56  FEMENINO  LIMA_CENTRO          37699  NO           1  NO "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear un subconj. de datos, considerando solamente las mujeres\n",
    "\n",
    "clientesS2 = clientes[clientes['SEXO'] == 'FEMENINO']\n",
    "\n",
    "clientesS2.sort_values('SEXO', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "44c6c081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>INGRESOS</th>\n",
       "      <th>CASADO</th>\n",
       "      <th>HIJOS</th>\n",
       "      <th>AUTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>ID12882</td>\n",
       "      <td>35</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>15850</td>\n",
       "      <td>SI</td>\n",
       "      <td>1</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>ID12815</td>\n",
       "      <td>35</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>19918</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>ID12798</td>\n",
       "      <td>37</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>31979</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ID12757</td>\n",
       "      <td>38</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>30943</td>\n",
       "      <td>NO</td>\n",
       "      <td>3</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ID12852</td>\n",
       "      <td>39</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>LIMA_ESTE</td>\n",
       "      <td>14413</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  EDAD         SEXO     REGION  INGRESOS   CASADO  HIJOS AUTO\n",
       "179  ID12882    35  MASCULINO    LIMA_ESTE     15850   SI          1   SI\n",
       "113  ID12815    35  MASCULINO    LIMA_ESTE     19918   SI          2   SI\n",
       "96   ID12798    37  MASCULINO    LIMA_ESTE     31979  NO           1  NO \n",
       "55   ID12757    38  MASCULINO    LIMA_ESTE     30943  NO           3   SI\n",
       "150  ID12852    39     FEMENINO  LIMA_ESTE     14413  NO           1  NO "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear un subconj. de datos considerando clientes mayores a 35 años, con hijos y viven en Lima este\n",
    "\n",
    "clientesS3 = clientes[((clientes['EDAD'] >= 35) & (clientes['HIJOS'] > 0)) & (clientes['REGION'] == 'LIMA_ESTE')]\n",
    "\n",
    "clientesS3.sort_values(['EDAD','HIJOS','REGION']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14275991",
   "metadata": {},
   "source": [
    "## Combinacion de datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8e330374",
   "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>name</th>\n",
       "      <th>band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mick</td>\n",
       "      <td>Stones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>Beatles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Beatles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name     band\n",
       "0  Mick   Stones\n",
       "1  John  Beatles\n",
       "2  Paul  Beatles"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>plays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>guitar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>bass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Keith</td>\n",
       "      <td>guitar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name   plays\n",
       "0   John  guitar\n",
       "1   Paul    bass\n",
       "2  Keith  guitar"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band = pd.read_csv('band.csv')\n",
    "instrum = pd.read_csv('instrument.csv')\n",
    "band ; instrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7622db94",
   "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>name</th>\n",
       "      <th>band</th>\n",
       "      <th>plays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mick</td>\n",
       "      <td>Stones</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>Beatles</td>\n",
       "      <td>guitar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Beatles</td>\n",
       "      <td>bass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name     band   plays\n",
       "0  Mick   Stones     NaN\n",
       "1  John  Beatles  guitar\n",
       "2  Paul  Beatles    bass"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LEFT JOIN\n",
    "\n",
    "pd.merge(band, instrum, how = 'left', on = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "be9d27ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mick</td>\n",
       "      <td>Stones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>Beatles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Beatles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "   name     band\n",
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       "2  Paul  Beatles"
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     "execution_count": 47,
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    "# RIGHT JOIN\n",
    "\n",
    "band ; instrum\n",
    "pd.merge(band, instrum, how = 'right', on = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "57f3362f",
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   "outputs": [
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       "0   John  guitar\n",
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       "2  Keith  guitar"
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   "source": [
    "# INNER JOIN\n",
    "\n",
    "band ; instrum\n",
    "pd.merge(band, instrum, how = 'inner', on = 'name')"
   ]
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  {
   "cell_type": "code",
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   "id": "e2949d66",
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       "2  Paul  Beatles"
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       "2  Keith  guitar"
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       "    name     band   plays\n",
       "0   Mick   Stones     NaN\n",
       "1   John  Beatles  guitar\n",
       "2   Paul  Beatles    bass\n",
       "3  Keith      NaN  guitar"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OUTER JOIN\n",
    "\n",
    "band ; instrum\n",
    "pd.merge(band, instrum, how = 'outer', on = 'name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4e6dbfe7",
   "metadata": {},
   "outputs": [
    {
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       "   V1  V2\n",
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       "   V1  V2\n",
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    "df1 = pd.DataFrame({'V1':range(3,5), 'V2':range(3,5)})\n",
    "df2 = pd.DataFrame({'V1':range(5,7), 'V2':range(5,7)})\n",
    "\n",
    "df1 ; df2\n",
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "39a1f2ce",
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   "id": "bda56cc4",
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