Pandas_Talk_Viennese.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# The Apprentice's Enthusiastic Guide to pandas\n",
    "##     *or how to look at the world through* \n",
    "##     *the gentle eyes of one*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![title](img/panda_eyes.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The word Apprentice might describe the world of a \n",
    "# being who embarks on the wonderful\n",
    "# journey of learning. \n",
    "# Shall we?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Who is Ingrid and why does she love Pandas?\n",
    "\n",
    "![title](img/ingrid_panda.JPG)\n",
    "\n",
    "## P.S.: Ingrid is a developer at RadarServices and plays the flute on the meadows of the University of Arts in Graz. \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Pandas soon realized there's no way \n",
    "# they are going to survive\n",
    "\n",
    "# They needed a fresh view over the world \n",
    "# and its intrinsic mechanisms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# pandas\n",
    "\n",
    "\n",
    "\n",
    "##     a high performative library in the midst of the forest \n",
    "    \n",
    "##     a powerful toolkit for data structures \n",
    "   \n",
    "##     and data analysis\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas bite, you know? \n",
    "![title](img/pandas_bite.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let's import this long-dreamed library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd               # imports long-dreamed library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# shows pictures with colors\n",
    "%matplotlib inline\n",
    "\n",
    "# we don't want warnings in the presentation\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# we need this later\n",
    "import io\n",
    "from contextlib import redirect_stdout, contextmanager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Sets A.K.A. all the little mysteries and quests \n",
    "# they would like to solve. From Kaggle.com:\n",
    "\n",
    "# - Extinct and endangered languages in the world \n",
    "(it's the languages they had to master for world domination)\n",
    "\n",
    "# - Biodiversity in the US national parks\n",
    "(it's time to get to know our enemies, who are we going to share our prey with?)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas DataFrame - holds the precious data\n",
    "\n",
    "Think of an **Excel spreadsheet**, or forget about the **Excel spreadsheet**,\n",
    "# think of a rice paddy! \n",
    "# 田田田田"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![title](img/abstract_rice_paddy.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# A **row** is an instance, a finding or an observation.\n",
    "# **Columns** are the various attributes of the observation.\n",
    "\n",
    "For example pandas have a name, a literal Chinese name translation, their order and favorite food.\n",
    "\n",
    "- Giant Panda, Big Bear Cat, Carnivora, Bamboo\n",
    "- Red panda, Red Bear Cat, Carnivora, Bamboo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas allows us to read data from many formats like:\n",
    "\n",
    "- Excel spreadsheets\n",
    "- or CSV (comma separated values)\n",
    "- or SQL databases or many more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Enough talking, let's decide for a path in life,\n",
    "# let's make something meaningful out of it!\n",
    "\n",
    "idiomas = pd.read_csv('extinct_languages.csv')\n",
    "\n",
    "species = pd.read_csv('species.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "languages = pd.read_csv('extinct_languages.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Removing columns and from their genes their procrastinative behavioural patterns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# by name, axis=1 (we're removing a column), inplace=True => foreveeer\n",
    "languages.drop('ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name in English</th>\n",
       "      <th>Name in French</th>\n",
       "      <th>Name in Spanish</th>\n",
       "      <th>Countries</th>\n",
       "      <th>Country codes alpha 3</th>\n",
       "      <th>ISO639-3 codes</th>\n",
       "      <th>Degree of endangerment</th>\n",
       "      <th>Alternate names</th>\n",
       "      <th>Name in the language</th>\n",
       "      <th>Number of speakers</th>\n",
       "      <th>Sources</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Description of the location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South Italian</td>\n",
       "      <td>italien du sud</td>\n",
       "      <td>napolitano-calabrés</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "      <td>nap</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>Neapolitan; Neapolitan-Calabrese; неаполитанск...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7500000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.9798</td>\n",
       "      <td>15.249</td>\n",
       "      <td>Campania, Lucania (Basilicata), Abruzzi (Abruz...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name in English  Name in French      Name in Spanish Countries  \\\n",
       "0   South Italian  italien du sud  napolitano-calabrés     Italy   \n",
       "\n",
       "  Country codes alpha 3 ISO639-3 codes Degree of endangerment  \\\n",
       "0                   ITA            nap             Vulnerable   \n",
       "\n",
       "                                     Alternate names Name in the language  \\\n",
       "0  Neapolitan; Neapolitan-Calabrese; неаполитанск...                  NaN   \n",
       "\n",
       "   Number of speakers Sources  Latitude  Longitude  \\\n",
       "0           7500000.0     NaN   40.9798     15.249   \n",
       "\n",
       "                         Description of the location  \n",
       "0  Campania, Lucania (Basilicata), Abruzzi (Abruz...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "languages.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Enough joking around, off with their heads!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# dropping by column number\n",
    "idiomas = languages.drop(languages.columns[[1, 2, 4, 5, 7, 8, 10, 13]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Name in English</th>\n",
       "      <th>Countries</th>\n",
       "      <th>Degree of endangerment</th>\n",
       "      <th>Number of speakers</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2720</th>\n",
       "      <td>Zangskari</td>\n",
       "      <td>India, Pakistan</td>\n",
       "      <td>Definitely endangered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0833</td>\n",
       "      <td>76.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2721</th>\n",
       "      <td>Zidgali</td>\n",
       "      <td>Oman</td>\n",
       "      <td>Critically endangered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4027</td>\n",
       "      <td>58.0957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name in English        Countries Degree of endangerment  \\\n",
       "2720       Zangskari  India, Pakistan  Definitely endangered   \n",
       "2721         Zidgali             Oman  Critically endangered   \n",
       "\n",
       "      Number of speakers  Latitude  Longitude  \n",
       "2720                 NaN   34.0833    76.8500  \n",
       "2721                 NaN   23.4027    58.0957  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Renaming the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "idiomas.rename(columns = {'Name in English': 'Name', \n",
    "                          'Degree of endangerment': 'Endangerment', \n",
    "                          'Number of speakers': 'Speakers'}, \n",
    "               inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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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>Countries</th>\n",
       "      <th>Endangerment</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South Italian</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>7500000.0</td>\n",
       "      <td>40.9798</td>\n",
       "      <td>15.2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sicilian</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>37.4399</td>\n",
       "      <td>14.5019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name Countries Endangerment   Speakers  Latitude  Longitude\n",
       "0  South Italian     Italy   Vulnerable  7500000.0   40.9798    15.2490\n",
       "1       Sicilian     Italy   Vulnerable  5000000.0   37.4399    14.5019"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Types\n",
    "\n",
    "- `float`, `int`, `bool`, \n",
    "- `datetime64[ns]` and `datetime64[ns, tz]`, `timedelta[ns]`, \n",
    "- `category` and `object`.\n",
    "\n",
    "**Attention, Attention:** \n",
    "`object` might contain various data types like \n",
    "- float(42.1 or NaN)\n",
    "- string (\"Snape\").\n",
    "\n",
    "#  Pandas uses the `object` `dtype` for storing strings!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             object\n",
       "Countries        object\n",
       "Endangerment     object\n",
       "Speakers        float64\n",
       "Latitude        float64\n",
       "Longitude       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What are categories?\n",
    "\n",
    "\n",
    "   Categoricals are a pandas data type, which correspond to categorical variables in statistics: a variable, \n",
    "    which can take on only a limited, and usually fixed, number of possible values (categories, levels in R).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Yes, but \n",
    "\n",
    "## What are categories after all?\n",
    "\n",
    "Houses (categories) in Harry Potter:\n",
    "\n",
    "- Gryffindor \n",
    "- Slytherin\n",
    "- Hufflepuff\n",
    "- Ravenclaw\n",
    "\n",
    "In the `idiomas` dataset the degree of endangerment would make a good\n",
    "category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Switching to categorical data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "endangerments = idiomas['Endangerment'].astype('category')\n",
    "idiomas['Endangerment_category'] = endangerments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['Critically endangered', 'Definitely endangered', 'Extinct',\n",
       "                  'Severely endangered', 'Vulnerable'],\n",
       "                 ordered=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.Endangerment_category.dtype    # oh noooo, ordered=False !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# let's clean up the unordered category\n",
    "del idiomas['Endangerment_category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2722 entries, 0 to 2721\n",
      "Data columns (total 6 columns):\n",
      "Name            2722 non-null object\n",
      "Countries       2721 non-null object\n",
      "Endangerment    2722 non-null object\n",
      "Speakers        2539 non-null float64\n",
      "Latitude        2719 non-null float64\n",
      "Longitude       2719 non-null float64\n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 626.6 KB\n"
     ]
    }
   ],
   "source": [
    "# We use memory_usage='deep' for a deep inspection\n",
    "# of all object dtypes\n",
    "\n",
    "idiomas.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Switching to categorical data with CategoricalDTypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "258 µs ± 4.07 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit idiomas.Endangerment == 'Definitely endangered'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from pandas.api.types import CategoricalDtype\n",
    "categories = ['Vulnerable', 'Definitely endangered', \n",
    "              'Severely endangered', 'Critically endangered', 'Extinct']\n",
    "\n",
    "# However, in Hogwarts - we could not order the houses.\n",
    "degree_of_endangerment = CategoricalDtype(categories=categories, ordered=True)\n",
    "\n",
    "dangers = idiomas['Endangerment'].astype(degree_of_endangerment)\n",
    "idiomas['Endangerment'] = dangers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115 µs ± 6.27 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit \n",
    "idiomas.Endangerment == 'Definitely endangered'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The `dtype` of Endangerment is now `category`\n",
    "\n",
    "# But what type does pandas use for categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Vulnerable',\n",
       " 1: 'Definitely endangered',\n",
       " 2: 'Severely endangered',\n",
       " 3: 'Critically endangered',\n",
       " 4: 'Extinct'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we ask ourselves how the category works -> it is stored as a list / array and pandas saves the index.\n",
    "\n",
    "dict(enumerate(idiomas['Endangerment'].cat.categories))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "# We save a `0` instead of 'Vulnerable' or a `4` instead of 'Extinct'\n",
    "So instead of cluttering our minds with many many strings, \n",
    "we only have to remember a few numbers! \n",
    "\n",
    "Our memory will say thanks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let's check the `DataFrame.info()` again!\n",
    "\n",
    "Is the category type in there? Without a `category` we needed **626.6 KB** of memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2722 entries, 0 to 2721\n",
      "Data columns (total 6 columns):\n",
      "Name            2722 non-null object\n",
      "Countries       2721 non-null object\n",
      "Endangerment    2722 non-null category\n",
      "Speakers        2539 non-null float64\n",
      "Latitude        2719 non-null float64\n",
      "Longitude       2719 non-null float64\n",
      "dtypes: category(1), float64(3), object(2)\n",
      "memory usage: 433.7 KB\n"
     ]
    }
   ],
   "source": [
    "idiomas.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Laborious efforts of dealing with missing data\n",
    "\n",
    "Because data is often missing, malformed or empty.\n",
    "\n",
    "The method `DataFrame.isnull` can help us to\n",
    "find those entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# There are languages which hide their Number of Speakers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name              0\n",
       "Countries         1\n",
       "Endangerment      0\n",
       "Speakers        183\n",
       "Latitude          3\n",
       "Longitude         3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            0\n",
       "Countries       1\n",
       "Endangerment    0\n",
       "Speakers        0\n",
       "Latitude        3\n",
       "Longitude       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can be sneaky and fill the speakers information with .fillna\n",
    "\n",
    "idiomas.Speakers = idiomas.Speakers.fillna(0).astype('int')\n",
    "idiomas.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Species dataset: We found a lot of nulls in the column `Unnamed: 13`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119248, 14)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species ID                  0\n",
       "Park Name                   0\n",
       "Category                    0\n",
       "Order                    1472\n",
       "Family                   1512\n",
       "Scientific Name             0\n",
       "Common Names                0\n",
       "Record Status               0\n",
       "Occurrence              20142\n",
       "Nativeness              25045\n",
       "Abundance               42942\n",
       "Seasonality             99091\n",
       "Conservation Status    114530\n",
       "Unnamed: 13            119243\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# There are some rows where `Unnamed: 13` is not null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "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>Species ID</th>\n",
       "      <th>Park Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Order</th>\n",
       "      <th>Family</th>\n",
       "      <th>Scientific Name</th>\n",
       "      <th>Common Names</th>\n",
       "      <th>Record Status</th>\n",
       "      <th>Occurrence</th>\n",
       "      <th>Nativeness</th>\n",
       "      <th>Abundance</th>\n",
       "      <th>Seasonality</th>\n",
       "      <th>Conservation Status</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6441</th>\n",
       "      <td>BISC-1026</td>\n",
       "      <td>Biscayne National Park</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Sirenia</td>\n",
       "      <td>Trichechidae</td>\n",
       "      <td>Trichechus manatus</td>\n",
       "      <td>Manatee</td>\n",
       "      <td>Manati</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Present</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Endangered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31786</th>\n",
       "      <td>EVER-1414</td>\n",
       "      <td>Everglades National Park</td>\n",
       "      <td>Reptile</td>\n",
       "      <td>Crocodilia</td>\n",
       "      <td>Crocodylidae</td>\n",
       "      <td>Crocodylus acutus</td>\n",
       "      <td>American Crocodile</td>\n",
       "      <td>Cocodrilo De Tumbes</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Present</td>\n",
       "      <td>Native</td>\n",
       "      <td>Uncommon</td>\n",
       "      <td>Resident</td>\n",
       "      <td>Threatened</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Species ID                 Park Name Category       Order        Family  \\\n",
       "6441   BISC-1026    Biscayne National Park   Mammal     Sirenia  Trichechidae   \n",
       "31786  EVER-1414  Everglades National Park  Reptile  Crocodilia  Crocodylidae   \n",
       "\n",
       "          Scientific Name        Common Names         Record Status  \\\n",
       "6441   Trichechus manatus             Manatee                Manati   \n",
       "31786   Crocodylus acutus  American Crocodile   Cocodrilo De Tumbes   \n",
       "\n",
       "      Occurrence Nativeness Abundance Seasonality Conservation Status  \\\n",
       "6441    Approved    Present   Unknown     Unknown                 NaN   \n",
       "31786   Approved    Present    Native    Uncommon            Resident   \n",
       "\n",
       "      Unnamed: 13  \n",
       "6441   Endangered  \n",
       "31786  Threatened  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on 5 rows the columns shifted to the right.\n",
    "species[species['Unnamed: 13'].notnull()][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# it is only shifted on certain rows & certain columns!\n",
    "\n",
    "shifted_rows = species['Unnamed: 13'].notnull()\n",
    "shifted_columns = ['Record Status', 'Occurrence', 'Nativeness',\n",
    "                   'Abundance', 'Seasonality', 'Conservation Status',\n",
    "                   'Unnamed: 13']\n",
    "\n",
    "indexes = species[shifted_rows].index\n",
    "shifted = species[shifted_rows][shifted_columns]\n",
    "\n",
    "species.loc[indexes, shifted_columns] = shifted.shift(-1, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Species ID</th>\n",
       "      <th>Park Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Order</th>\n",
       "      <th>Family</th>\n",
       "      <th>Scientific Name</th>\n",
       "      <th>Common Names</th>\n",
       "      <th>Record Status</th>\n",
       "      <th>Occurrence</th>\n",
       "      <th>Nativeness</th>\n",
       "      <th>Abundance</th>\n",
       "      <th>Seasonality</th>\n",
       "      <th>Conservation Status</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6441</th>\n",
       "      <td>BISC-1026</td>\n",
       "      <td>Biscayne National Park</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Sirenia</td>\n",
       "      <td>Trichechidae</td>\n",
       "      <td>Trichechus manatus</td>\n",
       "      <td>Manatee</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Present</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Endangered</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31786</th>\n",
       "      <td>EVER-1414</td>\n",
       "      <td>Everglades National Park</td>\n",
       "      <td>Reptile</td>\n",
       "      <td>Crocodilia</td>\n",
       "      <td>Crocodylidae</td>\n",
       "      <td>Crocodylus acutus</td>\n",
       "      <td>American Crocodile</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Present</td>\n",
       "      <td>Native</td>\n",
       "      <td>Uncommon</td>\n",
       "      <td>Resident</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31826</th>\n",
       "      <td>EVER-1454</td>\n",
       "      <td>Everglades National Park</td>\n",
       "      <td>Reptile</td>\n",
       "      <td>Testudines</td>\n",
       "      <td>Cheloniidae</td>\n",
       "      <td>Caretta caretta</td>\n",
       "      <td>Loggerhead</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Present</td>\n",
       "      <td>Native</td>\n",
       "      <td>Rare</td>\n",
       "      <td>Breeder</td>\n",
       "      <td>Threatened</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Species ID                 Park Name Category       Order        Family  \\\n",
       "6441   BISC-1026    Biscayne National Park   Mammal     Sirenia  Trichechidae   \n",
       "31786  EVER-1414  Everglades National Park  Reptile  Crocodilia  Crocodylidae   \n",
       "31826  EVER-1454  Everglades National Park  Reptile  Testudines   Cheloniidae   \n",
       "\n",
       "          Scientific Name        Common Names Record Status Occurrence  \\\n",
       "6441   Trichechus manatus             Manatee      Approved    Present   \n",
       "31786   Crocodylus acutus  American Crocodile      Approved    Present   \n",
       "31826     Caretta caretta          Loggerhead      Approved    Present   \n",
       "\n",
       "      Nativeness Abundance Seasonality Conservation Status Unnamed: 13  \n",
       "6441     Unknown   Unknown         NaN          Endangered         NaN  \n",
       "31786     Native  Uncommon    Resident          Threatened         NaN  \n",
       "31826     Native      Rare     Breeder          Threatened         NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.loc[indexes].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "especies = species.drop(species.columns[[0, 3, 4, 7, 9, 11,13]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "especies.rename(columns = {'Park Name': 'Park_Name', \n",
    "                           'Category': 'Class', \n",
    "                           'Scientific Name': 'Scientific_Name', \n",
    "                           'Common Names': 'Common_Names', \n",
    "                           'Conservation Status': 'Conservation'},\n",
    "               inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Park_Name</th>\n",
       "      <th>Class</th>\n",
       "      <th>Scientific_Name</th>\n",
       "      <th>Common_Names</th>\n",
       "      <th>Occurrence</th>\n",
       "      <th>Abundance</th>\n",
       "      <th>Conservation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Acadia National Park</td>\n",
       "      <td>Bird</td>\n",
       "      <td>Calidris minutilla</td>\n",
       "      <td>Least Sandpiper</td>\n",
       "      <td>Present</td>\n",
       "      <td>Common</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Acadia National Park</td>\n",
       "      <td>Bird</td>\n",
       "      <td>Calidris pusilla</td>\n",
       "      <td>Semipalmated Sandpiper</td>\n",
       "      <td>Present</td>\n",
       "      <td>Abundant</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Acadia National Park</td>\n",
       "      <td>Bird</td>\n",
       "      <td>Catoptrophorus semipalmatus</td>\n",
       "      <td>Eastern Willet, Willet</td>\n",
       "      <td>Not Confirmed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Acadia National Park</td>\n",
       "      <td>Bird</td>\n",
       "      <td>Gallinago gallinago</td>\n",
       "      <td>Common Snipe, Wilson's Common Snipe, Wilson's ...</td>\n",
       "      <td>Present</td>\n",
       "      <td>Rare</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Park_Name Class              Scientific_Name  \\\n",
       "158  Acadia National Park  Bird           Calidris minutilla   \n",
       "159  Acadia National Park  Bird             Calidris pusilla   \n",
       "160  Acadia National Park  Bird  Catoptrophorus semipalmatus   \n",
       "161  Acadia National Park  Bird          Gallinago gallinago   \n",
       "\n",
       "                                          Common_Names     Occurrence  \\\n",
       "158                                    Least Sandpiper        Present   \n",
       "159                             Semipalmated Sandpiper        Present   \n",
       "160                             Eastern Willet, Willet  Not Confirmed   \n",
       "161  Common Snipe, Wilson's Common Snipe, Wilson's ...        Present   \n",
       "\n",
       "    Abundance Conservation  \n",
       "158    Common          NaN  \n",
       "159  Abundant          NaN  \n",
       "160       NaN          NaN  \n",
       "161      Rare          NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "especies.loc[158:161]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Which countries have the most endangered languages?\n",
    "\n",
    "`Series.value_counts` counts how often it spots a value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    216\n",
       "Brazil                      186\n",
       "India                       163\n",
       "Indonesia                   149\n",
       "Mexico                      143\n",
       "China                       122\n",
       "Russian Federation          119\n",
       "Australia                   108\n",
       "Papua New Guinea             98\n",
       "Canada                       82\n",
       "Name: Countries, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.Countries.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Another option to group is to `groupby`.\n",
    "\n",
    "# How many people are, in total, in each group?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Endangerment\n",
       "Vulnerable               90995144\n",
       "Definitely endangered    39503297\n",
       "Severely endangered       5226486\n",
       "Critically endangered      501388\n",
       "Extinct                     13642\n",
       "Name: Speakers, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endangerment_groups = idiomas.groupby('Endangerment')\n",
    "endangerment_groups.Speakers.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Can you show me this as a picture, Mr. Pandas?\n",
    "\n",
    "![title](img/quinlingpandabearrbrown.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Ay, Ay, with a plot pie!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# we first remove the languages which are 'extinct' \n",
    "# because there are no speakers there unfortunately\n",
    "not_extinct = idiomas.Endangerment != 'Extinct'\n",
    "endangerment_groups = idiomas[not_extinct].groupby('Endangerment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "endangerment_groups.Speakers.sum().plot.pie(figsize=(16, 12), fontsize=22, \n",
    "                                            legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Filtering the nonsensical superstitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "# Quest: filter! find me specific rows which fulfill \n",
    "# certain requirements!\n",
    "\n",
    "**Filters** are just lists containing `True` or `False`\n",
    "\n",
    "with the same length as the **number of rows** in the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# When you apply a filter directly to the dataset...\n",
    "\n",
    "# you can go fully berserk!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Countries</th>\n",
       "      <th>Endangerment</th>\n",
       "      <th>Speakers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Nomatsiguenga</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Severely endangered</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Quechua of Cajatambo, Pasco and northern Junín</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Severely endangered</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Wanka Quechua</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Severely endangered</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>Quechua of Yauyos</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Critically endangered</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name Countries  \\\n",
       "753                                    Nomatsiguenga      Peru   \n",
       "277   Quechua of Cajatambo, Pasco and northern Junín      Peru   \n",
       "299                                    Wanka Quechua      Peru   \n",
       "1034                               Quechua of Yauyos      Peru   \n",
       "\n",
       "               Endangerment  Speakers  \n",
       "753     Severely endangered      4500  \n",
       "277     Severely endangered     40000  \n",
       "299     Severely endangered     35000  \n",
       "1034  Critically endangered      1700  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas.loc[(idiomas.Speakers > 1000) &\n",
    "        (idiomas.Countries == 'Peru') & \n",
    "        (idiomas.Endangerment >= 'Severely endangered'), :'Speakers'].sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Let's find languages which are **only** in Romania!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Countries</th>\n",
       "      <th>Endangerment</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Transylvanian Saxon</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Severely endangered</td>\n",
       "      <td>50000</td>\n",
       "      <td>46.2140</td>\n",
       "      <td>24.2413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>Csángó Hungarian</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Severely endangered</td>\n",
       "      <td>25000</td>\n",
       "      <td>46.7925</td>\n",
       "      <td>27.1911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2643</th>\n",
       "      <td>Nogay (Dobruja)</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Severely endangered</td>\n",
       "      <td>0</td>\n",
       "      <td>44.6491</td>\n",
       "      <td>27.8173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name Countries         Endangerment  Speakers  Latitude  \\\n",
       "244   Transylvanian Saxon   Romania  Severely endangered     50000   46.2140   \n",
       "353      Csángó Hungarian   Romania  Severely endangered     25000   46.7925   \n",
       "2643      Nogay (Dobruja)   Romania  Severely endangered         0   44.6491   \n",
       "\n",
       "      Longitude  \n",
       "244     24.2413  \n",
       "353     27.1911  \n",
       "2643    27.8173  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiomas[idiomas.Countries.isin(['Romania'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Creating a filter with a dictionary or a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def contains_moose(animal):\n",
    "    \"\"\"\n",
    "    I check if an animal is a mooooose...\n",
    "    Yes, we can build amazing filtering functions! \n",
    "    Hoooooray!\n",
    "    \"\"\"\n",
    "    animal = str(animal)\n",
    "    if 'moose' in animal.lower():\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Where's Mr. `moose` at?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "mooooooses = especies['Common_Names'].map(contains_moose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Oops, my moose! \n",
    "\n",
    "**American Cranberrybush, Mooseberry** or **Yellow Moosedung Moss**?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Moose                                      14\n",
       "Eurasian Elk, Moose                         4\n",
       "Eastern Leatherwood, Moosewood              3\n",
       "Yellowstone Moose                           1\n",
       "Caribou, Cervids, Deer, Moose, Wapiti       1\n",
       "Yellow Moosedung Moss                       1\n",
       "Striped Maple, Striped Maple, Moosewood     1\n",
       "Moosebrush                                  1\n",
       "American Cranberrybush, Mooseberry          1\n",
       "Striped Maple, Moosewood                    1\n",
       "Moose Maple, Mountain Maple                 1\n",
       "Name: Common_Names, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "especies[mooooooses]['Common_Names'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![title](img/yellow_moose.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Precision it's what we need when we're talking about `Moose`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Moose                                    14\n",
       "Eurasian Elk, Moose                       4\n",
       "Caribou, Cervids, Deer, Moose, Wapiti     1\n",
       "Yellowstone Moose                         1\n",
       "Name: Common_Names, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_mammal = especies.Class == 'Mammal'\n",
    "especies[mooooooses & is_mammal]['Common_Names'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![title](img/alaskan_moose_pair.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Bar Chart horizontal: The number of species per class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_vasculars = especies.Class != 'Vascular Plant'\n",
    "no_vascular_plants = especies[filter_vasculars].Class.value_counts()\n",
    "no_vascular_plants.plot.barh(figsize=(10, 6), fontsize=20, color='orchid'); \n",
    "# barh barh!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# We enter a new Land:\n",
    "\n",
    "# The Mystifying Land of Accessors!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# But what are accessors?\n",
    "\n",
    "# Properties that serve as an interface to additional methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cat', 'dt', 'sparse', 'str'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series._accessors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"['capitalize', 'cat', 'center', 'contains', 'count', 'decode', 'encode', 'endswith', 'extract', 'extractall', 'find', 'findall', 'get', 'get_dummies', 'index', 'isalnum', 'isalpha', 'isdecimal', 'isdigit', 'islower', 'isnumeric', 'isspace', 'istitle', 'isupper', 'join', 'len', 'ljust', 'lower', 'lstrip', 'match', 'normalize', 'pad', 'partition', 'repeat', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'slice', 'slice_replace', 'split', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'wrap', 'zfill']\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessors provide a huge number of useful methods  \n",
    "# depending on the type a column has, for example:\n",
    "\n",
    "str([name for name in dir(especies.Class.str) if not name.startswith('_')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    " # Let's try it out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Acadia National Park'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove this unnecessary information from each National Park name\n",
    "\n",
    "especies['Park_Name'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Acadia'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The .str accessor method contains a huge amount of useful string manipulation functions!\n",
    "\n",
    "especies['Park_Name'] = especies['Park_Name'].str.replace('National Park', '')\n",
    "especies['Park_Name'] = especies['Park_Name'].str.strip()\n",
    "especies['Park_Name'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Countries</th>\n",
       "      <th>Endangerment</th>\n",
       "      <th>Speakers</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>Yiddish (Europe)</td>\n",
       "      <td>Germany, Austria, Belarus, Belgium, Denmark, E...</td>\n",
       "      <td>Definitely endangered</td>\n",
       "      <td>0</td>\n",
       "      <td>53.3571</td>\n",
       "      <td>25.2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>Bavarian</td>\n",
       "      <td>Germany, Austria, Hungary, Italy, Switzerland,...</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>0</td>\n",
       "      <td>48.4146</td>\n",
       "      <td>12.6123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>Burgenland Croatian</td>\n",
       "      <td>Austria, Hungary, Slovakia</td>\n",
       "      <td>Definitely endangered</td>\n",
       "      <td>30000</td>\n",
       "      <td>47.8435</td>\n",
       "      <td>16.5179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>Alemannic</td>\n",
       "      <td>Germany, Austria, France, Italy, Liechtenstein...</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>0</td>\n",
       "      <td>47.2792</td>\n",
       "      <td>7.6904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name                                          Countries  \\\n",
       "2716     Yiddish (Europe)  Germany, Austria, Belarus, Belgium, Denmark, E...   \n",
       "2551             Bavarian  Germany, Austria, Hungary, Italy, Switzerland,...   \n",
       "316   Burgenland Croatian                         Austria, Hungary, Slovakia   \n",
       "2543            Alemannic  Germany, Austria, France, Italy, Liechtenstein...   \n",
       "\n",
       "               Endangerment  Speakers  Latitude  Longitude  \n",
       "2716  Definitely endangered         0   53.3571    25.2465  \n",
       "2551             Vulnerable         0   48.4146    12.6123  \n",
       "316   Definitely endangered     30000   47.8435    16.5179  \n",
       "2543             Vulnerable         0   47.2792     7.6904  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which languages are flying away from Austria? \n",
    "# We can try str.contains for that!\n",
    "\n",
    "idiomas.Countries = idiomas.Countries.astype('str')\n",
    "idiomas[idiomas.Countries.str.contains(\"Austria\")].sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Oh, but sorry, I need a different type of accessor!\n",
    "# well, no prob lad - you can define your own proper accessor!\n",
    "\n",
    "@pd.api.extensions.register_series_accessor(\"panda_likes_bamboo\")\n",
    "class PandasRevolutionBambooAccessor:\n",
    "    def __init__(self, series):\n",
    "        self._validate(series)\n",
    "        self._series = series\n",
    "    \n",
    "    @staticmethod\n",
    "    def _validate(series):\n",
    "        # I am compulsory - do not ignore me :) !!!!!!!\n",
    "        pass\n",
    "\n",
    "    @property\n",
    "    def is_bamboo(self):\n",
    "        return self._series.str.lower().str.contains('bamboo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Park_Name</th>\n",
       "      <th>Class</th>\n",
       "      <th>Scientific_Name</th>\n",
       "      <th>Common_Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7832</th>\n",
       "      <td>Biscayne</td>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Smilax auriculata</td>\n",
       "      <td>Earleaf Greenbrier, Wild-Bamboo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98659</th>\n",
       "      <td>Saguaro</td>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Muhlenbergia dumosa</td>\n",
       "      <td>Bamboo Muhly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63510</th>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Nandina domestica</td>\n",
       "      <td>Sacred-Bamboo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47915</th>\n",
       "      <td>Great Smoky Mountains</td>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Sasa palmata</td>\n",
       "      <td>Broadleaf Bamboo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Park_Name           Class      Scientific_Name  \\\n",
       "7832                Biscayne  Vascular Plant    Smilax auriculata   \n",
       "98659                Saguaro  Vascular Plant  Muhlenbergia dumosa   \n",
       "63510            Hot Springs  Vascular Plant    Nandina domestica   \n",
       "47915  Great Smoky Mountains  Vascular Plant         Sasa palmata   \n",
       "\n",
       "                          Common_Names  \n",
       "7832   Earleaf Greenbrier, Wild-Bamboo  \n",
       "98659                     Bamboo Muhly  \n",
       "63510                    Sacred-Bamboo  \n",
       "47915                 Broadleaf Bamboo  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What a treat! So much bamboo!\n",
    "\n",
    "contains_bamboo = especies.Common_Names.panda_likes_bamboo.is_bamboo\n",
    "especies.loc[contains_bamboo, 'Park_Name':'Common_Names'].sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![title](img/Giant_Pandas_having_a_snack.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function pandas.core.accessor.register_series_accessor(name)>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# But until now we only spoke about the series accessors! \n",
    "# We want diversity!\n",
    "# no problem, plenty of them to choose from! Register on!\n",
    "\n",
    "pd.api.extensions.register_dataframe_accessor # dataframe\n",
    "pd.api.extensions.register_index_accessor     # index\n",
    "pd.api.extensions.register_series_accessor    # series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# because PANDAS NEVER REPEAT THEMSELVES \n",
    "\n",
    "@pd.api.extensions.register_dataframe_accessor(\"panda_revolution\")\n",
    "class PandasDonTRepeatThemselves(object):\n",
    "    def __init__(self, pandas_obj):\n",
    "        self._validate(pandas_obj)\n",
    "        self._obj = pandas_obj\n",
    "\n",
    "    @staticmethod\n",
    "    def _validate(obj):\n",
    "        # You MUST IMPLEMENT ME!\n",
    "        if 'Latitude' not in obj.columns or 'Longitude' not in obj.columns:\n",
    "            raise AttributeError(\"Must have 'Latitude' and 'Longitude'.\")\n",
    "\n",
    "    def world_map(self):\n",
    "        self._obj.plot.scatter('Longitude', 'Latitude', figsize=(28, 18), \n",
    "                               title='The Blue Planet', label='Language')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2016x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# They made it! World domination is what they reached! \n",
    "idiomas.panda_revolution.world_map()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Pandas thank you for your attention :)\n",
    "# You can find the talk later at: https://github.com/twigleingrid/pydaysvienna2019"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}