{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SysArmy Surveys: Análisis por género de los resultados de todas las encuestas\n",
    "\n",
    "Datos actualizados al 2do período de 2017\n",
    "\n",
    "NOTA: Los primeros dos datasets no contienen información sobre género.\n",
    "\n",
    "NOTA2: Los sueldos indicados son llevados a bruto, es decir, algunos encuestados optaron por informar sueldos netos,  estos valores netos fueron llevados a bruto mediante un cálculo simple pero a la vez impreciso.  Es importante aclarar que la imprecisión de la conversión tiende a la baja del salario, ya que no se tuvieron en cuenta descuentos por ganancias u otros beneficios.\n",
    "\n",
    "### Datasets de sueldos\n",
    "\n",
    "| Period  | Path             | URL                                                                                                         |\n",
    "|---------|------------------|-------------------------------------------------------------------------------------------------------------|\n",
    "| ~~2014.02~~ | data/2014.02.csv | https://drive.google.com/open?id=0B7UapTwn9AahWkdEWnltRjUwVjQ                                               |\n",
    "| ~~2015.01~~ | data/2015.01.csv | https://drive.google.com/open?id=0B7UapTwn9AahSHl1aGNfTlNrYWc                                               |\n",
    "| 2016.01 | data/2016.01.csv | https://drive.google.com/open?id=0B7UapTwn9AahLTJmSE5zOUQ0aGc                                               |\n",
    "| 2016.02 | data/2016.02/    | https://drive.google.com/open?id=0B7UapTwn9AahUGtySFBjdHBCcWs                                               |\n",
    "| 2017.01 | data/2017.01/    | https://docs.google.com/spreadsheets/d/1diZG7ruHJ4OgO2-tbnO-ZnhjxjOnYjMVSxl7I-4LT54/edit#gid=1424918437     |\n",
    "| 2017.02 | data/2017.02/    | https://docs.google.com/spreadsheets/d/1f1CAmzbVtoL66IwXJWEf0Mn-QbMNGZIoCQ_b1HB-91E                         |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Géneros censandos en los datasets"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Hombre (16899 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Mujer (1420 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Otros (19 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from matplotlib import cm\n",
    "from functools import reduce\n",
    "from IPython.display import display, Markdown as md\n",
    "\n",
    "def getPeriodDF(period, publish_date, csv_path, columns_src, columns_dst, transforms = []):\n",
    "    # given a csv_path, extract src columns into dst columns by applying a transform function for a certain period name\n",
    "    df_src = pd.read_csv(csv_path)\n",
    "    df_dst = pd.DataFrame(columns=['period', 'publish_date']+columns_dst)\n",
    "    df_dst[columns_dst] = df_src[columns_src]\n",
    "    df_dst['period'] = period\n",
    "    df_dst['publish_date'] = pd.to_datetime(publish_date)\n",
    "    # apply custom transformations\n",
    "    for transform in transforms:\n",
    "        df_src, df_dst = transform(df_src, df_dst)\n",
    "    return df_dst\n",
    "\n",
    "def transformNormalize(df_src, df_dst):\n",
    "    # cast to float the salary column, some datasets gets wrongly parsed due to crappy user input\n",
    "    df_dst['salary'] = df_dst['salary'].astype(float)\n",
    "    df_dst['age'] = df_dst['age'].astype(str)\n",
    "    # normalize stack column\n",
    "    re_separator = ' *, *| *; *| *\\. *| *\\| *| *\\/ *| *- *'\n",
    "    re_garbage = '\"\"\"|\\*|nan|='\n",
    "    df_dst['stack'] = df_dst['stack'].apply(lambda s: re.split(re_separator, re.sub(re_garbage, '', str(s)).strip().lower()))\n",
    "    # munge the dataset, removing entries that we consider not realistic for our analysis\n",
    "    munging_mask = (df_dst.salary > 5000) & (df_dst.salary < 300000)\n",
    "    df_src = df_src.loc[munging_mask]\n",
    "    df_dst = df_dst.loc[munging_mask]\n",
    "    return df_src, df_dst\n",
    "\n",
    "def transformNet2Brute(df_src, df_dst):\n",
    "    # filter wether net or brute income\n",
    "    mask = np.array(df_src['Bruto o neto?'].str.contains('Neto'), dtype = bool)\n",
    "    net_factor = np.array([1.0] * len(df_dst));\n",
    "    # scales up net salary into brute, given Argentina's social security contributions\n",
    "    net_factor[ mask ] = 1/0.83\n",
    "    df_dst['salary'] = net_factor * df_dst['salary']\n",
    "    return df_src, df_dst\n",
    "\n",
    "def transformNet2Brute201702(df_src, df_dst):\n",
    "    # filter wether net or brute income\n",
    "    mask = np.array(df_src['¿Porcentaje, bruto o neto?'].str.contains('Neto'), dtype = bool)\n",
    "    net_factor = np.array([1.0] * len(df_dst));\n",
    "    # scales up net salary into brute, given Argentina's social security contributions\n",
    "    net_factor[ mask ] = 1/0.83\n",
    "    df_dst['salary'] = net_factor * df_dst['salary']\n",
    "    return df_src, df_dst\n",
    "\n",
    "src_common_cols = [\n",
    "    'Tengo',\n",
    "    'Años de experiencia',\n",
    "    'Años en el puesto actual',\n",
    "    'Trabajo de',\n",
    "    'Tecnologías que utilizás']\n",
    "\n",
    "dst_cols = [\n",
    "    'salary',\n",
    "    'location',\n",
    "    'gender',\n",
    "    'age',\n",
    "    'yoe',\n",
    "    'yip',\n",
    "    'role',\n",
    "    'stack']\n",
    "\n",
    "df3 = getPeriodDF('2016.01', '2016-02-01', '../data/2016.01.csv', \n",
    "                  ['Salario mensual (AR$)', 'Trabajo en', 'Soy'] + src_common_cols, dst_cols, \n",
    "                  [transformNormalize, transformNet2Brute])\n",
    "\n",
    "df4 = getPeriodDF('2016.02', '2016-08-01', '../data/2016.02/argentina.csv', \n",
    "                  ['Salario mensual (en tu moneda local)', 'Argentina', 'Soy'] + src_common_cols, dst_cols, \n",
    "                  [transformNormalize, transformNet2Brute])\n",
    "\n",
    "df5 = getPeriodDF('2017.01', '2017-02-01', '../data/2017.01/argentina.csv', \n",
    "                  ['Salario mensual (en tu moneda local)', 'Argentina', 'Soy'] + src_common_cols, dst_cols, \n",
    "                  [transformNormalize, transformNet2Brute])\n",
    "\n",
    "df6 = getPeriodDF('2017.02', '2017-09-01', '../data/2017.02/argentina.csv', \n",
    "                  ['Salario mensual (en tu moneda local)', 'Argentina', 'Me identifico'] + src_common_cols, dst_cols, \n",
    "                  [transformNormalize, transformNet2Brute201702])\n",
    "\n",
    "# compute the union of all the datasets as a signe pandas dataframe\n",
    "df = pd.concat([df3, df4, df5, df6])\n",
    "\n",
    "# extract the list of periods\n",
    "periods = df.period.unique()\n",
    "\n",
    "# extract the list of genders\n",
    "genders = df.gender.unique()\n",
    "\n",
    "# extract the list of yoes\n",
    "yoes = df.yoe.unique()\n",
    "\n",
    "display(md('## Géneros censandos en los datasets'))\n",
    "[display(md('- %s (%d total)' % (g, len(df.query('gender == \"%s\"' % g))))) for g in genders]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## Años de experiencia\n",
       "\n",
       "El seniority fue simplificado en tres niveles, de los cuales se seleccionaron arbitrariamente los rangos\n",
       "que conforman estos niveles.  Para evitar discrepancias, se sobrelaparon rangos en los diferentes niveles.\n",
       "Por ejemplo, un desarrollador con 3 años de experiencia puede considerarse Junior ó Semi-Senior a la vez, eso\n",
       "dependerá de cada caso.\n",
       "\n",
       "### Sobre la categoría \"Otros\"\n",
       "\n",
       "Al parecer, solo se registra el grupo \"Otros\" en el último dataset, donde se lo habría incluído.  Por la \n",
       "cantidad baja de resultados, y la ausencia de dimensión tiempo en los datasets, resulta imposible visualilzar dichos\n",
       "resultados.\n",
       "\n",
       "A continuación se detalla las cantidades por segmento y período de la muestra.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Hombre"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Juniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 1 - 2 año(s) (1288 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Menos de un año año(s) (831 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 1 año(s) (332 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 2 año(s) (527 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 año(s) (562 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Semi-Seniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 - 5 año(s) (2203 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 año(s) (562 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 4 año(s) (451 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 año(s) (680 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 6 año(s) (520 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Seniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 - 7 año(s) (1718 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 8 - 10 año(s) (1602 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 año(s) (680 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 6 año(s) (520 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 7 año(s) (460 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 8 año(s) (461 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 9 año(s) (431 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 10 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 10+ año(s) (4833 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Mujer"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Juniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 1 - 2 año(s) (115 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Menos de un año año(s) (98 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 1 año(s) (40 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 2 año(s) (61 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 año(s) (62 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Semi-Seniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 - 5 año(s) (171 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 año(s) (62 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 4 año(s) (64 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 año(s) (79 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 6 año(s) (51 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Seniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 - 7 año(s) (94 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 8 - 10 año(s) (101 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 año(s) (79 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 6 año(s) (51 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 7 año(s) (51 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 8 año(s) (47 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 9 año(s) (52 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 10 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 10+ año(s) (334 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Otros"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Juniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 1 - 2 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- Menos de un año año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 1 año(s) (2 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 2 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 año(s) (3 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Semi-Seniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 - 5 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 3 año(s) (3 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 4 año(s) (1 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 6 año(s) (3 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Seniors"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 - 7 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 8 - 10 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 5 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 6 año(s) (3 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 7 año(s) (1 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 8 año(s) (1 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 9 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 10 año(s) (0 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "- 10+ año(s) (8 total)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define arbitrary ranges for seniority\n",
    "yoes_jr = ['1 - 2', 'Menos de un año', '1', '2', '3']\n",
    "yoes_ssr = ['3 - 5', '3', '4', '5', '6']\n",
    "yoes_sr = ['5 - 7', '8 - 10', '5', '6', '7', '8', '9', '10', '10+']\n",
    "\n",
    "yoes_ranges = [yoes_jr, yoes_ssr, yoes_sr]\n",
    "yoes_ranges_names = ['Juniors', 'Semi-Seniors', 'Seniors']\n",
    "\n",
    "display(md(\"\"\"\n",
    "## Años de experiencia\n",
    "\n",
    "El seniority fue simplificado en tres niveles, de los cuales se seleccionaron arbitrariamente los rangos\n",
    "que conforman estos niveles.  Para evitar discrepancias, se sobrelaparon rangos en los diferentes niveles.\n",
    "Por ejemplo, un desarrollador con 3 años de experiencia puede considerarse Junior ó Semi-Senior a la vez, eso\n",
    "dependerá de cada caso.\n",
    "\n",
    "### Sobre la categoría \"Otros\"\n",
    "\n",
    "Al parecer, solo se registra el grupo \"Otros\" en el último dataset, donde se lo habría incluído.  Por la \n",
    "cantidad baja de resultados, y la ausencia de dimensión tiempo en los datasets, resulta imposible visualilzar dichos\n",
    "resultados.\n",
    "\n",
    "A continuación se detalla las cantidades por segmento y período de la muestra.\n",
    "\"\"\"))\n",
    "\n",
    "for i, gender in enumerate(genders):\n",
    "    display(md('### %s' % gender))\n",
    "    for j, yoes_range in enumerate(yoes_ranges):\n",
    "        display(md('#### %s' % yoes_ranges_names[j]))\n",
    "        [display(md('- %s año(s) (%d total)' % (y, len(df.query('gender == @gender & yoe in @y'))))) for y in yoes_range]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Participación en las encuestas por género"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1113b1f98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAAIdCAYAAAAtT27BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1100ff2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use(['classic', 'ggplot'])\n",
    "plt.rcParams.update({'figure.figsize': (10.0, 6.0), 'font.size': 8})\n",
    "\n",
    "genders_colors = cm.rainbow(np.linspace(0, 1, len(genders)))\n",
    "publish_dates = np.unique(df['publish_date'])\n",
    "\n",
    "y = []\n",
    "for i, gender in enumerate(genders):\n",
    "    gender_y = []\n",
    "    for j, date in enumerate(publish_dates):\n",
    "        count = df.query('gender == @gender & publish_date == @date').count()[0]\n",
    "        gender_y.append(count)\n",
    "    y.append(gender_y)\n",
    "\n",
    "# define data points for stackplot\n",
    "x = publish_dates\n",
    "y = np.row_stack(y)\n",
    "percent = np.divide(y, y.sum(axis=0).astype(float)*0.01)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.stackplot(x, percent, colors=genders_colors)\n",
    "ax.set_title('Nivel de participación en las encuestas por género')\n",
    "ax.set_ylabel('Porcentaje (%)')\n",
    "\n",
    "display(md('## Participación en las encuestas por género'))\n",
    "\n",
    "# creating the legend manually\n",
    "plt.legend([mpatches.Patch(color=genders_colors[i]) for i, gender in enumerate(genders)],\n",
    "           genders, \n",
    "           bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAV4CAYAAAC0LYv3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112a3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def getPlotSalarySeries(currencySeries):\n",
    "    s = currencySeries.apply(lambda x: float('{0:.2f}'.format(round(x/1000, 1))))\n",
    "    mu, sigma = np.mean(s), np.std(s)\n",
    "    median = np.median(s)\n",
    "    t = np.linspace(mu - 3*sigma, mu + 3*sigma, 1000)\n",
    "    d = sigma**2/mu\n",
    "    return mu, sigma, t, d, median, s\n",
    "\n",
    "plt.rcParams.update({'figure.figsize': (10.0, 24.0), 'font.size': 8})\n",
    "yoes_colors = cm.rainbow(np.linspace(0, 1, 2*len(yoes_ranges)))\n",
    "\n",
    "dfnd = []\n",
    "\n",
    "fig, ax = plt.subplots(len(yoes_ranges), 1)\n",
    "for i, yoe_range in enumerate(yoes_ranges):\n",
    "    for j, gender in enumerate(genders):\n",
    "        mus, sigmas, dates = [], [], []\n",
    "        for period in periods:\n",
    "            dft = df.query('gender == @gender & period == @period & yoe in @yoe_range')\n",
    "            if len(dft) == 0: continue\n",
    "            mu, sigma, t, d, m, s = getPlotSalarySeries(dft.salary)\n",
    "            mus.append(mu)\n",
    "            sigmas.append(sigma)\n",
    "            dates.append(np.max(dft['publish_date']))\n",
    "            dfnd += [[gender, yoes_ranges_names[i], period, mu, sigma]]\n",
    "\n",
    "        ax[i].plot(dates, mus, '+', label='$\\mu$ %s' % gender, ls='-', c=yoes_colors[j])\n",
    "        ax[i].plot(dates, sigmas, '+', label='$\\sigma$ %s' % gender, ls=':', c=yoes_colors[j])\n",
    "        ax[i].set_title('%s %s' % (yoes_ranges_names[i], yoe_range))\n",
    "\n",
    "    ax[i].set_xlabel('date'); ax[i].set_ylabel('ingresos brutos [1000 AR$/mo]')\n",
    "    ax[i].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusión\n",
    "\n",
    "Como es de público conocimiento, en la Industria del Software la desigualdad de género es muy remarcada.  Se puede observar sin embargo, una leve tendencia a revertir esta situación (ver primer gráfico).  Aunque cabe aclarar, como ya se presentó en el [análisis previo](https://github.com/gerardobort/sysarmy-data/blob/master/notebooks/intra_period_argentina.ipynb), existe una variación del espacio muestral en las encuestas de `SysArmy` a través del tiempo, lo cual puede interferir en los resultados.\n",
    "\n",
    "Por otra parte, los niveles de ingresos censados presentan marcadas diferencias, sobre todo para puestos Seniors.  Este dato es también conocido en Argentina y el resto del mundo, y pudo corroborarse.  Se puede observar también, que la brecha salarial entre distíntos géneros no tiende a cerrarse en el caso de Seniors, sino por el contrario se ensancha.  Finalmente, para puestos de nivel medio, sí se observa una mejora en la reducción de la brecha salarial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## Bonus track\n",
       "\n",
       "Detalle de sueldos por período, seniority, y género.\n",
       "Véase también [CESSI dio a conocer el salario de los Programadores en Argentina](http://www.cessi.org.ar/ver-noticias-cessi-dio-a-conocer-el-salario-de-los-programadores-en-argentina-2129)\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### 2017.02"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Género</th>\n",
       "      <th>Seniority</th>\n",
       "      <th>period</th>\n",
       "      <th>Salario Bruto mensual</th>\n",
       "      <th>Desviación estándar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>22.257480</td>\n",
       "      <td>11.832849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>20.246903</td>\n",
       "      <td>7.922758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Otros</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>26.320000</td>\n",
       "      <td>19.660051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>30.186012</td>\n",
       "      <td>16.461985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>30.402632</td>\n",
       "      <td>15.933381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Otros</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>32.485714</td>\n",
       "      <td>21.407571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>42.138753</td>\n",
       "      <td>25.585862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>37.127532</td>\n",
       "      <td>17.357438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Otros</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2017.02</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>22.502581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Género     Seniority   period  Salario Bruto mensual  Desviación estándar\n",
       "3   Hombre       Juniors  2017.02              22.257480            11.832849\n",
       "7    Mujer       Juniors  2017.02              20.246903             7.922758\n",
       "8    Otros       Juniors  2017.02              26.320000            19.660051\n",
       "12  Hombre  Semi-Seniors  2017.02              30.186012            16.461985\n",
       "16   Mujer  Semi-Seniors  2017.02              30.402632            15.933381\n",
       "17   Otros  Semi-Seniors  2017.02              32.485714            21.407571\n",
       "21  Hombre       Seniors  2017.02              42.138753            25.585862\n",
       "25   Mujer       Seniors  2017.02              37.127532            17.357438\n",
       "26   Otros       Seniors  2017.02              46.300000            22.502581"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### 2017.01"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Género</th>\n",
       "      <th>Seniority</th>\n",
       "      <th>period</th>\n",
       "      <th>Salario Bruto mensual</th>\n",
       "      <th>Desviación estándar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2017.01</td>\n",
       "      <td>17.773200</td>\n",
       "      <td>8.071967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2017.01</td>\n",
       "      <td>17.087379</td>\n",
       "      <td>8.367328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2017.01</td>\n",
       "      <td>23.722034</td>\n",
       "      <td>10.861093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2017.01</td>\n",
       "      <td>22.267308</td>\n",
       "      <td>10.409393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2017.01</td>\n",
       "      <td>31.226782</td>\n",
       "      <td>16.490655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2017.01</td>\n",
       "      <td>27.227861</td>\n",
       "      <td>11.869105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Género     Seniority   period  Salario Bruto mensual  Desviación estándar\n",
       "2   Hombre       Juniors  2017.01              17.773200             8.071967\n",
       "6    Mujer       Juniors  2017.01              17.087379             8.367328\n",
       "11  Hombre  Semi-Seniors  2017.01              23.722034            10.861093\n",
       "15   Mujer  Semi-Seniors  2017.01              22.267308            10.409393\n",
       "20  Hombre       Seniors  2017.01              31.226782            16.490655\n",
       "24   Mujer       Seniors  2017.01              27.227861            11.869105"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### 2016.02"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Género</th>\n",
       "      <th>Seniority</th>\n",
       "      <th>period</th>\n",
       "      <th>Salario Bruto mensual</th>\n",
       "      <th>Desviación estándar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2016.02</td>\n",
       "      <td>16.047674</td>\n",
       "      <td>16.167943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2016.02</td>\n",
       "      <td>15.288732</td>\n",
       "      <td>10.758312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2016.02</td>\n",
       "      <td>20.026973</td>\n",
       "      <td>11.060387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2016.02</td>\n",
       "      <td>19.489873</td>\n",
       "      <td>8.208017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2016.02</td>\n",
       "      <td>27.762592</td>\n",
       "      <td>14.072298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2016.02</td>\n",
       "      <td>27.952239</td>\n",
       "      <td>23.824735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Género     Seniority   period  Salario Bruto mensual  Desviación estándar\n",
       "1   Hombre       Juniors  2016.02              16.047674            16.167943\n",
       "5    Mujer       Juniors  2016.02              15.288732            10.758312\n",
       "10  Hombre  Semi-Seniors  2016.02              20.026973            11.060387\n",
       "14   Mujer  Semi-Seniors  2016.02              19.489873             8.208017\n",
       "19  Hombre       Seniors  2016.02              27.762592            14.072298\n",
       "23   Mujer       Seniors  2016.02              27.952239            23.824735"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### 2016.01"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Género</th>\n",
       "      <th>Seniority</th>\n",
       "      <th>period</th>\n",
       "      <th>Salario Bruto mensual</th>\n",
       "      <th>Desviación estándar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2016.01</td>\n",
       "      <td>14.183154</td>\n",
       "      <td>15.205461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Juniors</td>\n",
       "      <td>2016.01</td>\n",
       "      <td>13.808989</td>\n",
       "      <td>10.260655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2016.01</td>\n",
       "      <td>17.260857</td>\n",
       "      <td>14.086386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Semi-Seniors</td>\n",
       "      <td>2016.01</td>\n",
       "      <td>15.630435</td>\n",
       "      <td>7.932456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2016.01</td>\n",
       "      <td>22.315252</td>\n",
       "      <td>14.905378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Seniors</td>\n",
       "      <td>2016.01</td>\n",
       "      <td>20.658861</td>\n",
       "      <td>10.821493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Género     Seniority   period  Salario Bruto mensual  Desviación estándar\n",
       "0   Hombre       Juniors  2016.01              14.183154            15.205461\n",
       "4    Mujer       Juniors  2016.01              13.808989            10.260655\n",
       "9   Hombre  Semi-Seniors  2016.01              17.260857            14.086386\n",
       "13   Mujer  Semi-Seniors  2016.01              15.630435             7.932456\n",
       "18  Hombre       Seniors  2016.01              22.315252            14.905378\n",
       "22   Mujer       Seniors  2016.01              20.658861            10.821493"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(md(\"\"\"\n",
    "## Bonus track\n",
    "\n",
    "Detalle de sueldos por período, seniority, y género.\n",
    "Véase también [CESSI dio a conocer el salario de los Programadores en Argentina](http://www.cessi.org.ar/ver-noticias-cessi-dio-a-conocer-el-salario-de-los-programadores-en-argentina-2129)\n",
    "\"\"\"))\n",
    "\n",
    "\n",
    "dfn = pd.DataFrame(dfnd, columns=['Género', 'Seniority', 'period', 'Salario Bruto mensual', 'Desviación estándar'])\n",
    "dfn\n",
    "\n",
    "for i, period in enumerate(reversed(periods)):\n",
    "    display(md('### %s' % period))\n",
    "    display(dfn.query('period == @period'))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}