Aula 2-Parte1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Python Libraries\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a python package that deals mostly with :\n",
    "- **Series**  (1d homogeneous array)\n",
    "- **DataFrame** (2d labeled heterogeneous array) \n",
    "- **Panel** (general 3d array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas *Series* is one-dimentional labeled array containing data of the same type (integers, strings, floating point numbers, Python objects, etc. ). The axis labels are often referred to as *index*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.203564\n",
      "1   -0.797999\n",
      "2    0.330457\n",
      "3   -0.542989\n",
      "4    0.901305\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Example of creating Pandas series :\n",
    "s1 = pd.Series( np.random.randn(5) )\n",
    "print(s1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We did not pass any index, so by default, it assigned the indexes ranging from 0 to len(data)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=5, step=1)\n"
     ]
    }
   ],
   "source": [
    "# View index values\n",
    "print(s1.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   -1.424659\n",
      "b   -0.277736\n",
      "c   -0.887227\n",
      "d   -0.731182\n",
      "e    0.139120\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Creating Pandas series with index:\n",
    "s2 = pd.Series( np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'] )\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# View index values\n",
    "print(s2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pi': 3.1415, 'e': 2.71828}\n",
      "pi    3.14150\n",
      "e     2.71828\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a Series from dictionary\n",
    "data = {'pi': 3.1415, 'e': 2.71828}  # dictionary\n",
    "print(data)\n",
    "s3 = pd.Series ( data )\n",
    "print(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e      2.71828\n",
      "pi     3.14150\n",
      "tau        NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# reordering the elements\n",
    "s4 = pd.Series ( data, index = ['e', 'pi', 'tau'])\n",
    "print(s4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NAN (non a number) - is used to specify a missing value in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "5    1\n",
      "6    1\n",
      "7    1\n",
      "8    1\n",
      "9    1\n",
      "Name: Ones, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creating a Pandas Series object from a single number:\n",
    "s5 = pd.Series( 1, index = range(10), name='Ones')\n",
    "print(s5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.203564\n",
       "1   -0.797999\n",
       "2    0.330457\n",
       "3   -0.542989\n",
       "4    0.901305\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.203564\n",
      "1   -0.797999\n",
      "2    0.330457\n",
      "3   -0.542989\n",
      "4    0.901305\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.5429891020151179"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Many ways to \"slice\" Pandas series (series have zero-based index by default):\n",
    "print(s1)\n",
    "s1[3]  # returns 4th element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.203564\n",
       "1   -0.797999\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[:2] # First 2 elements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    0.330457\n",
      "1   -0.797999\n",
      "0    1.203564\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print( s1[ [2,1,0]])  # Elements out of order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1415"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Slicing series using index label (access series like a dictionary)\n",
    "\n",
    "s4['pi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '_AXIS_ALIASES',\n",
       " '_AXIS_IALIASES',\n",
       " '_AXIS_LEN',\n",
       " '_AXIS_NAMES',\n",
       " '_AXIS_NUMBERS',\n",
       " '_AXIS_ORDERS',\n",
       " '_AXIS_REVERSED',\n",
       " '_AXIS_SLICEMAP',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__array__',\n",
       " '__array_prepare__',\n",
       " '__array_priority__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__bytes__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__div__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__finalize__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattr__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ipow__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__long__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__module__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__nonzero__',\n",
       " '__or__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdiv__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__round__',\n",
       " '__rpow__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__unicode__',\n",
       " '__weakref__',\n",
       " '__xor__',\n",
       " '_accessors',\n",
       " '_add_numeric_operations',\n",
       " '_add_series_only_operations',\n",
       " '_add_series_or_dataframe_operations',\n",
       " '_agg_by_level',\n",
       " '_agg_doc',\n",
       " '_aggregate',\n",
       " '_aggregate_multiple_funcs',\n",
       " '_align_frame',\n",
       " '_align_series',\n",
       " '_allow_index_ops',\n",
       " '_at',\n",
       " '_binop',\n",
       " '_box_item_values',\n",
       " '_builtin_table',\n",
       " '_can_hold_na',\n",
       " '_check_inplace_setting',\n",
       " '_check_is_chained_assignment_possible',\n",
       " '_check_percentile',\n",
       " '_check_setitem_copy',\n",
       " '_clear_item_cache',\n",
       " '_clip_with_scalar',\n",
       " '_consolidate',\n",
       " '_consolidate_inplace',\n",
       " '_construct_axes_dict',\n",
       " '_construct_axes_dict_for_slice',\n",
       " '_construct_axes_dict_from',\n",
       " '_construct_axes_from_arguments',\n",
       " '_constructor',\n",
       " '_constructor_expanddim',\n",
       " '_constructor_sliced',\n",
       " '_convert',\n",
       " '_create_indexer',\n",
       " '_cython_table',\n",
       " '_dir_additions',\n",
       " '_dir_deletions',\n",
       " '_expand_axes',\n",
       " '_from_axes',\n",
       " '_get_axis',\n",
       " '_get_axis_name',\n",
       " '_get_axis_number',\n",
       " '_get_axis_resolvers',\n",
       " '_get_block_manager_axis',\n",
       " '_get_bool_data',\n",
       " '_get_cacher',\n",
       " '_get_index_resolvers',\n",
       " '_get_item_cache',\n",
       " '_get_numeric_data',\n",
       " '_get_values',\n",
       " '_get_values_tuple',\n",
       " '_get_with',\n",
       " '_gotitem',\n",
       " '_iat',\n",
       " '_iget_item_cache',\n",
       " '_iloc',\n",
       " '_index',\n",
       " '_indexed_same',\n",
       " '_info_axis',\n",
       " '_info_axis_name',\n",
       " '_info_axis_number',\n",
       " '_init_mgr',\n",
       " '_internal_names',\n",
       " '_internal_names_set',\n",
       " '_is_builtin_func',\n",
       " '_is_cached',\n",
       " '_is_cython_func',\n",
       " '_is_datelike_mixed_type',\n",
       " '_is_mixed_type',\n",
       " '_is_numeric_mixed_type',\n",
       " '_is_view',\n",
       " '_ix',\n",
       " '_ixs',\n",
       " '_loc',\n",
       " '_make_cat_accessor',\n",
       " '_make_dt_accessor',\n",
       " '_make_str_accessor',\n",
       " '_maybe_cache_changed',\n",
       " '_maybe_update_cacher',\n",
       " '_metadata',\n",
       " '_needs_reindex_multi',\n",
       " '_obj_with_exclusions',\n",
       " '_protect_consolidate',\n",
       " '_reduce',\n",
       " '_reindex_axes',\n",
       " '_reindex_axis',\n",
       " '_reindex_indexer',\n",
       " '_reindex_multi',\n",
       " '_reindex_with_indexers',\n",
       " '_repr_data_resource_',\n",
       " '_repr_latex_',\n",
       " '_reset_cache',\n",
       " '_reset_cacher',\n",
       " '_selected_obj',\n",
       " '_selection',\n",
       " '_selection_list',\n",
       " '_selection_name',\n",
       " '_set_as_cached',\n",
       " '_set_axis',\n",
       " '_set_axis_name',\n",
       " '_set_is_copy',\n",
       " '_set_item',\n",
       " '_set_labels',\n",
       " '_set_name',\n",
       " '_set_subtyp',\n",
       " '_set_values',\n",
       " '_set_with',\n",
       " '_set_with_engine',\n",
       " '_setup_axes',\n",
       " '_shallow_copy',\n",
       " '_slice',\n",
       " '_stat_axis',\n",
       " '_stat_axis_name',\n",
       " '_stat_axis_number',\n",
       " '_try_aggregate_string_function',\n",
       " '_typ',\n",
       " '_unpickle_series_compat',\n",
       " '_update_inplace',\n",
       " '_validate_dtype',\n",
       " '_values',\n",
       " '_where',\n",
       " '_xs',\n",
       " 'abs',\n",
       " 'add',\n",
       " 'add_prefix',\n",
       " 'add_suffix',\n",
       " 'agg',\n",
       " 'aggregate',\n",
       " 'align',\n",
       " 'all',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argsort',\n",
       " 'as_blocks',\n",
       " 'as_matrix',\n",
       " 'asfreq',\n",
       " 'asobject',\n",
       " 'asof',\n",
       " 'astype',\n",
       " 'at',\n",
       " 'at_time',\n",
       " 'autocorr',\n",
       " 'axes',\n",
       " 'base',\n",
       " 'between',\n",
       " 'between_time',\n",
       " 'bfill',\n",
       " 'blocks',\n",
       " 'bool',\n",
       " 'clip',\n",
       " 'clip_lower',\n",
       " 'clip_upper',\n",
       " 'combine',\n",
       " 'combine_first',\n",
       " 'compound',\n",
       " 'compress',\n",
       " 'consolidate',\n",
       " 'convert_objects',\n",
       " 'copy',\n",
       " 'corr',\n",
       " 'count',\n",
       " 'cov',\n",
       " 'cummax',\n",
       " 'cummin',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'describe',\n",
       " 'diff',\n",
       " 'div',\n",
       " 'divide',\n",
       " 'dot',\n",
       " 'drop',\n",
       " 'drop_duplicates',\n",
       " 'dropna',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'duplicated',\n",
       " 'empty',\n",
       " 'eq',\n",
       " 'equals',\n",
       " 'ewm',\n",
       " 'expanding',\n",
       " 'factorize',\n",
       " 'ffill',\n",
       " 'fillna',\n",
       " 'filter',\n",
       " 'first',\n",
       " 'first_valid_index',\n",
       " 'flags',\n",
       " 'floordiv',\n",
       " 'from_array',\n",
       " 'from_csv',\n",
       " 'ftype',\n",
       " 'ftypes',\n",
       " 'ge',\n",
       " 'get',\n",
       " 'get_dtype_counts',\n",
       " 'get_ftype_counts',\n",
       " 'get_value',\n",
       " 'get_values',\n",
       " 'groupby',\n",
       " 'gt',\n",
       " 'hasnans',\n",
       " 'head',\n",
       " 'hist',\n",
       " 'iat',\n",
       " 'idxmax',\n",
       " 'idxmin',\n",
       " 'iloc',\n",
       " 'imag',\n",
       " 'index',\n",
       " 'interpolate',\n",
       " 'is_copy',\n",
       " 'is_monotonic',\n",
       " 'is_monotonic_decreasing',\n",
       " 'is_monotonic_increasing',\n",
       " 'is_unique',\n",
       " 'isin',\n",
       " 'isnull',\n",
       " 'item',\n",
       " 'items',\n",
       " 'itemsize',\n",
       " 'iteritems',\n",
       " 'ix',\n",
       " 'keys',\n",
       " 'kurt',\n",
       " 'kurtosis',\n",
       " 'last',\n",
       " 'last_valid_index',\n",
       " 'le',\n",
       " 'loc',\n",
       " 'lt',\n",
       " 'mad',\n",
       " 'map',\n",
       " 'mask',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memory_usage',\n",
       " 'min',\n",
       " 'mod',\n",
       " 'mode',\n",
       " 'mul',\n",
       " 'multiply',\n",
       " 'name',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'ne',\n",
       " 'nlargest',\n",
       " 'nonzero',\n",
       " 'notnull',\n",
       " 'nsmallest',\n",
       " 'nunique',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'plot',\n",
       " 'pop',\n",
       " 'pow',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'quantile',\n",
       " 'radd',\n",
       " 'rank',\n",
       " 'ravel',\n",
       " 'rdiv',\n",
       " 'real',\n",
       " 'reindex',\n",
       " 'reindex_axis',\n",
       " 'reindex_like',\n",
       " 'rename',\n",
       " 'rename_axis',\n",
       " 'reorder_levels',\n",
       " 'repeat',\n",
       " 'replace',\n",
       " 'resample',\n",
       " 'reset_index',\n",
       " 'reshape',\n",
       " 'rfloordiv',\n",
       " 'rmod',\n",
       " 'rmul',\n",
       " 'rolling',\n",
       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
       " 'rtruediv',\n",
       " 'sample',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'sem',\n",
       " 'set_axis',\n",
       " 'set_value',\n",
       " 'shape',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'slice_shift',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'sortlevel',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'strides',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dense',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_frame',\n",
       " 'to_hdf',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_msgpack',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_sparse',\n",
       " 'to_sql',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'tolist',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tshift',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unique',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'valid',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'view',\n",
       " 'where',\n",
       " 'xs']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(s4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median: 2.9298900000000003\n"
     ]
    }
   ],
   "source": [
    "# Series can be used as ndarray:\n",
    "print(\"Median:\" , s4.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.203564\n",
       "2    0.330457\n",
       "4    0.901305\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[s1 > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pi    3.1415\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy functions can be used on series as usual:\n",
    "s4[s4 > s4.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.331972\n",
       "1    0.450229\n",
       "2    1.391604\n",
       "3    0.581009\n",
       "4    2.462814\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vector operations:\n",
    "np.exp(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    1\n",
      "2    2\n",
      "3    3\n",
      "4    4\n",
      "5    5\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    2.0\n",
       "2    4.0\n",
       "3    6.0\n",
       "4    8.0\n",
       "5    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unlike ndarray Series automatically allign the data based on label:\n",
    "s5 = pd.Series (range(6))\n",
    "print(s5)\n",
    "s5[1:] + s5[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Popular Attributes and Methods:\n",
    "\n",
    "|  Attribute/Method | Description |\n",
    "|-----|-----|\n",
    "| dtype | data type of values in series |\n",
    "| empty | True if series is empty |\n",
    "| size | number of elements |\n",
    "| values | Returns values as ndarray |\n",
    "| head() | First n elements |\n",
    "| tail() | Last n elements |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Exercise* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    -0.142166\n",
      "1     0.205725\n",
      "2    -0.922897\n",
      "3    -0.333762\n",
      "4     0.576225\n",
      "5     0.167272\n",
      "6    -0.980457\n",
      "7    -1.436905\n",
      "8     0.365407\n",
      "9    -1.178882\n",
      "10    0.014384\n",
      "11   -0.581560\n",
      "12   -0.404659\n",
      "13   -0.571142\n",
      "14    0.624461\n",
      "15    0.884035\n",
      "16    0.882488\n",
      "17    0.660616\n",
      "18    1.064503\n",
      "19    1.436744\n",
      "20   -0.297113\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a series of your choice and explore it\n",
    "# <your code goes here >\n",
    "mys = pd.Series( np.random.randn(21))\n",
    "print(mys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.142166\n",
       "1    0.205725\n",
       "2   -0.922897\n",
       "3   -0.333762\n",
       "4    0.576225\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mys.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mys.empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas *DataFrame* is two-dimensional, size-mutable, heterogeneous tabular data structure with labeled rows and columns ( axes ). Can be thought of a dictionary-like container to store python Series objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Name  Age\n",
      "0  Alice   21\n",
      "1    Bob   25\n",
      "2  Chris   23\n"
     ]
    }
   ],
   "source": [
    "d =  pd.DataFrame({ 'Name': pd.Series(['Alice','Bob','Chris']), \n",
    "                  'Age': pd.Series([ 21,25,23]) } )\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>21</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>25</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chris</td>\n",
       "      <td>23</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  height\n",
       "0  Alice   21     5.2\n",
       "1    Bob   25     6.0\n",
       "2  Chris   23     5.6"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a new column:\n",
    "d['height'] = pd.Series([5.2,6.0,5.6])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read csv file\n",
    "df = pd.read_csv(\"Salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>81285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service   sex  salary\n",
       "0       Prof          B   56       49  Male  186960\n",
       "1       Prof          A   12        6  Male   93000\n",
       "2       Prof          A   23       20  Male  110515\n",
       "3       Prof          A   40       31  Male  131205\n",
       "4       Prof          B   20       18  Male  104800\n",
       "5       Prof          A   20       20  Male  122400\n",
       "6  AssocProf          A   20       17  Male   81285\n",
       "7       Prof          A   18       18  Male  126300\n",
       "8       Prof          A   29       19  Male   94350\n",
       "9       Prof          A   51       51  Male   57800"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display a few first records\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Exercise*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Display first 10 records\n",
    "# <your code goes here>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Display first 20 records\n",
    "# <your code goes here>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>71065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "71  AssocProf          B   12        9  Female   71065\n",
       "72       Prof          B   24       15  Female  161101\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display the last 5 records\n",
    "# <your code goes here>\n",
    "df.tail(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify the type of df object\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the type of a column \"salary\"\n",
    "df['salary'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          object\n",
       "discipline    object\n",
       "phd            int64\n",
       "service        int64\n",
       "sex           object\n",
       "salary         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List the types of all columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List the column names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=78, step=1),\n",
       " Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List the row labels and the column names\n",
    "df.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of dimensions\n",
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "468"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total number of elements in the Data Frame\n",
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78, 6)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.705128</td>\n",
       "      <td>15.051282</td>\n",
       "      <td>108023.782051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.498425</td>\n",
       "      <td>12.139768</td>\n",
       "      <td>28293.661022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.250000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>88612.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>104671.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.750000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>126774.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>186960.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             phd    service         salary\n",
       "count  78.000000  78.000000      78.000000\n",
       "mean   19.705128  15.051282  108023.782051\n",
       "std    12.498425  12.139768   28293.661022\n",
       "min     1.000000   0.000000   57800.000000\n",
       "25%    10.250000   5.250000   88612.500000\n",
       "50%    18.500000  14.500000  104671.000000\n",
       "75%    27.750000  20.750000  126774.750000\n",
       "max    56.000000  51.000000  186960.000000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Output basic statistics for the numeric columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd            19.705128\n",
       "service        15.051282\n",
       "salary     108023.782051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate mean for all numeric columns\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Exercise*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd           12.498425\n",
       "service       12.139768\n",
       "salary     28293.661022\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the standard deviation (std() method) for all numeric columns\n",
    "# <your code goes here>\n",
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate average of the columns in the first 50 rows\n",
    "# <your code goes here>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "### Data slicing and grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Male\n",
       "1    Male\n",
       "2    Male\n",
       "3    Male\n",
       "4    Male\n",
       "Name: sex, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract a column by name (method 1)\n",
    "df['sex'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract a column name (method 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Exercise*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the basic statistics for the salary column (used describe() method)\n",
    "# <your code goes here>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate how many values in the salary column (use count() method)\n",
    "# <your code goes here>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the average salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group data using rank\n",
    "df_rank = df.groupby('rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AssocProf</th>\n",
       "      <td>15.076923</td>\n",
       "      <td>11.307692</td>\n",
       "      <td>91786.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsstProf</th>\n",
       "      <td>5.052632</td>\n",
       "      <td>2.210526</td>\n",
       "      <td>81362.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof</th>\n",
       "      <td>27.065217</td>\n",
       "      <td>21.413043</td>\n",
       "      <td>123624.804348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 phd    service         salary\n",
       "rank                                          \n",
       "AssocProf  15.076923  11.307692   91786.230769\n",
       "AsstProf    5.052632   2.210526   81362.789474\n",
       "Prof       27.065217  21.413043  123624.804348"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate mean of all numeric columns for the grouped object\n",
    "df_rank.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>16.512821</td>\n",
       "      <td>11.564103</td>\n",
       "      <td>101002.410256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>22.897436</td>\n",
       "      <td>18.538462</td>\n",
       "      <td>115045.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              phd    service         salary\n",
       "sex                                        \n",
       "Female  16.512821  11.564103  101002.410256\n",
       "Male    22.897436  18.538462  115045.153846"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('sex').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Female    101002.410256\n",
       "Male      115045.153846\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the mean salary for men and women. The following produce Pandas Series (single brackets around salary)\n",
    "df.groupby('sex')['salary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>101002.410256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>115045.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               salary\n",
       "sex                  \n",
       "Female  101002.410256\n",
       "Male    115045.153846"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we use double brackets Pandas will produce a DataFrame\n",
    "df.groupby('sex')[['salary']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AssocProf</th>\n",
       "      <th>Female</th>\n",
       "      <td>88512.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>102697.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AsstProf</th>\n",
       "      <th>Female</th>\n",
       "      <td>78049.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>85918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Prof</th>\n",
       "      <th>Female</th>\n",
       "      <td>121967.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>124690.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         salary\n",
       "rank      sex                  \n",
       "AssocProf Female   88512.800000\n",
       "          Male    102697.666667\n",
       "AsstProf  Female   78049.909091\n",
       "          Male     85918.000000\n",
       "Prof      Female  121967.611111\n",
       "          Male    124690.142857"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group using 2 variables - sex and rank:\n",
    "df.groupby(['rank','sex'], sort=True)[['salary']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Excersize*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group data by the discipline and find the average salary for each group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service   sex  salary\n",
       "0   Prof          B   56       49  Male  186960\n",
       "3   Prof          A   40       31  Male  131205\n",
       "5   Prof          A   20       20  Male  122400\n",
       "7   Prof          A   18       18  Male  126300\n",
       "10  Prof          B   39       33  Male  128250"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select observation with the value in the salary column > 120K\n",
    "df_sub = df[ df['salary'] > 120000]\n",
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Int64Index([ 0,  3,  5,  7, 10, 11, 13, 14, 15, 19, 26, 27, 29, 31, 35, 36, 39,\n",
       "             40, 44, 45, 49, 51, 58, 72, 75],\n",
       "            dtype='int64'),\n",
       " Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>74830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>80225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>77000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "39       Prof          B   18       18  Female  129000\n",
       "40       Prof          A   39       36  Female  137000\n",
       "41  AssocProf          A   13        8  Female   74830\n",
       "42   AsstProf          B    4        2  Female   80225\n",
       "43   AsstProf          B    5        0  Female   77000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select data for female professors\n",
    "df_w = df[ df['sex'] == 'Female']\n",
    "df_w.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Exercise*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd           21.53\n",
       "service       15.72\n",
       "salary     98331.11\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using filtering, find the mean value of the salary for the discipline A\n",
    "df[ df['discipline'] =='A'].mean().round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Challange:\n",
    "# Extract (filter) only observations with high salary ( > 100K) and find how many female and male professors in each group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### More on slicing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Select column salary\n",
    "df1 = df['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check data type of the result\n",
    "type(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    186960\n",
       "1     93000\n",
       "2    110515\n",
       "3    131205\n",
       "4    104800\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the first few elements of the output\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select column salary and make the output to be a data frame\n",
    "df2 = df[['salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the type\n",
    "type(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
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       "      <th>0</th>\n",
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       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
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       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
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       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>81285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service   sex  salary\n",
       "0       Prof          B   56       49  Male  186960\n",
       "1       Prof          A   12        6  Male   93000\n",
       "2       Prof          A   23       20  Male  110515\n",
       "3       Prof          A   40       31  Male  131205\n",
       "4       Prof          B   20       18  Male  104800\n",
       "5       Prof          A   20       20  Male  122400\n",
       "6  AssocProf          A   20       17  Male   81285\n",
       "7       Prof          A   18       18  Male  126300\n",
       "8       Prof          A   29       19  Male   94350\n",
       "9       Prof          A   51       51  Male   57800"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select a subset of rows (based on their position):\n",
    "# Note 1: The location of the first row is 0\n",
    "# Note 2: The last value in the range is not included\n",
    "df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>Male</td>\n",
       "      <td>88000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>Male</td>\n",
       "      <td>75044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>107300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank   sex  salary\n",
       "10      Prof  Male  128250\n",
       "11      Prof  Male  134778\n",
       "12  AsstProf  Male   88000\n",
       "13      Prof  Male  162200\n",
       "14      Prof  Male  153750\n",
       "15      Prof  Male  150480\n",
       "16  AsstProf  Male   75044\n",
       "17  AsstProf  Male   92000\n",
       "18      Prof  Male  107300\n",
       "19      Prof  Male  150500\n",
       "20  AsstProf  Male   92000"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If we want to select both rows and columns we can use method .loc\n",
    "df.loc[10:20,['rank', 'sex','salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <th>5</th>\n",
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       "      <td>Male</td>\n",
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       "      <td>33</td>\n",
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       "      <th>11</th>\n",
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       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service   sex  salary\n",
       "0   Prof          B   56       49  Male  186960\n",
       "3   Prof          A   40       31  Male  131205\n",
       "5   Prof          A   20       20  Male  122400\n",
       "7   Prof          A   18       18  Male  126300\n",
       "10  Prof          B   39       33  Male  128250\n",
       "11  Prof          B   23       23  Male  134778\n",
       "13  Prof          B   35       33  Male  162200\n",
       "14  Prof          B   25       19  Male  153750\n",
       "15  Prof          B   17        3  Male  150480\n",
       "19  Prof          A   29       27  Male  150500\n",
       "26  Prof          A   38       19  Male  148750\n",
       "27  Prof          A   45       43  Male  155865\n",
       "29  Prof          B   21       20  Male  123683\n",
       "31  Prof          B   22       21  Male  155750\n",
       "35  Prof          B   28       23  Male  126933"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank   sex  salary\n",
       "10  Prof  Male  128250\n",
       "11  Prof  Male  134778\n",
       "13  Prof  Male  162200\n",
       "14  Prof  Male  153750\n",
       "15  Prof  Male  150480\n",
       "19  Prof  Male  150500"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see what we get for our df_sub data frame\n",
    "# Method .loc subset the data frame based on the labels:\n",
    "df_sub.loc[10:20,['rank','sex','salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank  service     sex  salary\n",
       "26  Prof       19    Male  148750\n",
       "27  Prof       43    Male  155865\n",
       "29  Prof       20    Male  123683\n",
       "31  Prof       21    Male  155750\n",
       "35  Prof       23    Male  126933\n",
       "36  Prof       45    Male  146856\n",
       "39  Prof       18  Female  129000\n",
       "40  Prof       36  Female  137000\n",
       "44  Prof       19  Female  151768\n",
       "45  Prof       25  Female  140096"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Unlike method .loc, method iloc selects rows (and columns) by poistion:\n",
    "df_sub.iloc[10:20, [0,3,4,5]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>77000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>88000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service     sex  salary\n",
       "55  AsstProf          A    2        0  Female   72500\n",
       "23  AsstProf          A    2        0    Male   85000\n",
       "43  AsstProf          B    5        0  Female   77000\n",
       "17  AsstProf          B    4        0    Male   92000\n",
       "12  AsstProf          B    1        0    Male   88000"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort the data frame by yrs.service and create a new data frame\n",
    "df_sorted = df.sort_values(by = 'service')\n",
    "df_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "9   Prof          A   51       51    Male   57800\n",
       "0   Prof          B   56       49    Male  186960\n",
       "36  Prof          B   45       45    Male  146856\n",
       "27  Prof          A   45       43    Male  155865\n",
       "40  Prof          A   39       36  Female  137000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort the data frame by yrs.service and overwrite the original dataset\n",
    "df.sort_values(by = 'service', ascending = False, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Restore the original order (by sorting using index)\n",
    "df.sort_index(axis=0, ascending = True, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Excersize*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort data frame by the salary (in descending order) and display the first few records of the output (head)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>88000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>77000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>91300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>80225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>77500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service     sex  salary\n",
       "52      Prof          A   12        0  Female  105000\n",
       "17  AsstProf          B    4        0    Male   92000\n",
       "12  AsstProf          B    1        0    Male   88000\n",
       "23  AsstProf          A    2        0    Male   85000\n",
       "43  AsstProf          B    5        0  Female   77000\n",
       "55  AsstProf          A    2        0  Female   72500\n",
       "57  AsstProf          A    3        1  Female   72500\n",
       "28  AsstProf          B    7        2    Male   91300\n",
       "42  AsstProf          B    4        2  Female   80225\n",
       "68  AsstProf          A    4        2  Female   77500"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort the data frame using 2 or more columns:\n",
    "df_sorted = df.sort_values(by = ['service', 'salary'], ascending = [True,False])\n",
    "df_sorted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>flight</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>517.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N14228</td>\n",
       "      <td>1545</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>533.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24211</td>\n",
       "      <td>1714</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1416</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>542.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>N619AA</td>\n",
       "      <td>1141</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1089</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>N668DN</td>\n",
       "      <td>461</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.0</td>\n",
       "      <td>762</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N39463</td>\n",
       "      <td>1696</td>\n",
       "      <td>EWR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>150.0</td>\n",
       "      <td>719</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  dep_time  dep_delay  arr_time  arr_delay carrier tailnum  \\\n",
       "0  2013      1    1     517.0        2.0     830.0       11.0      UA  N14228   \n",
       "1  2013      1    1     533.0        4.0     850.0       20.0      UA  N24211   \n",
       "2  2013      1    1     542.0        2.0     923.0       33.0      AA  N619AA   \n",
       "3  2013      1    1     554.0       -6.0     812.0      -25.0      DL  N668DN   \n",
       "4  2013      1    1     554.0       -4.0     740.0       12.0      UA  N39463   \n",
       "\n",
       "   flight origin dest  air_time  distance  hour  minute  \n",
       "0    1545    EWR  IAH     227.0      1400   5.0    17.0  \n",
       "1    1714    LGA  IAH     227.0      1416   5.0    33.0  \n",
       "2    1141    JFK  MIA     160.0      1089   5.0    42.0  \n",
       "3     461    LGA  ATL     116.0       762   5.0    54.0  \n",
       "4    1696    EWR  ORD     150.0       719   5.0    54.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read a dataset with missing values\n",
    "import pandas as pd\n",
    "flights = pd.read_csv(\"flights.csv\")\n",
    "flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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",
       "        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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>flight</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1807.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UA</td>\n",
       "      <td>N31412</td>\n",
       "      <td>1228</td>\n",
       "      <td>EWR</td>\n",
       "      <td>SAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2425</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AA</td>\n",
       "      <td>N3EHAA</td>\n",
       "      <td>791</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AA</td>\n",
       "      <td>N3EVAA</td>\n",
       "      <td>1925</td>\n",
       "      <td>LGA</td>\n",
       "      <td>MIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UA</td>\n",
       "      <td>N12221</td>\n",
       "      <td>1299</td>\n",
       "      <td>EWR</td>\n",
       "      <td>RSW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1068</td>\n",
       "      <td>21.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month  day  dep_time  dep_delay  arr_time  arr_delay carrier  \\\n",
       "330  2013      1    1    1807.0       29.0    2251.0        NaN      UA   \n",
       "403  2013      1    1       NaN        NaN       NaN        NaN      AA   \n",
       "404  2013      1    1       NaN        NaN       NaN        NaN      AA   \n",
       "855  2013      1    2    2145.0       16.0       NaN        NaN      UA   \n",
       "858  2013      1    2       NaN        NaN       NaN        NaN      AA   \n",
       "\n",
       "    tailnum  flight origin dest  air_time  distance  hour  minute  \n",
       "330  N31412    1228    EWR  SAN       NaN      2425  18.0     7.0  \n",
       "403  N3EHAA     791    LGA  DFW       NaN      1389   NaN     NaN  \n",
       "404  N3EVAA    1925    LGA  MIA       NaN      1096   NaN     NaN  \n",
       "855  N12221    1299    EWR  RSW       NaN      1068  21.0    45.0  \n",
       "858     NaN     133    JFK  LAX       NaN      2475   NaN     NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows that have at least one missing value\n",
    "flights[flights.isnull().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>flight</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>517.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N14228</td>\n",
       "      <td>1545</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>533.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24211</td>\n",
       "      <td>1714</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1416</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>542.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>N619AA</td>\n",
       "      <td>1141</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1089</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>N668DN</td>\n",
       "      <td>461</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.0</td>\n",
       "      <td>762</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N39463</td>\n",
       "      <td>1696</td>\n",
       "      <td>EWR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>150.0</td>\n",
       "      <td>719</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  dep_time  dep_delay  arr_time  arr_delay carrier tailnum  \\\n",
       "0  2013      1    1     517.0        2.0     830.0       11.0      UA  N14228   \n",
       "1  2013      1    1     533.0        4.0     850.0       20.0      UA  N24211   \n",
       "2  2013      1    1     542.0        2.0     923.0       33.0      AA  N619AA   \n",
       "3  2013      1    1     554.0       -6.0     812.0      -25.0      DL  N668DN   \n",
       "4  2013      1    1     554.0       -4.0     740.0       12.0      UA  N39463   \n",
       "\n",
       "   flight origin dest  air_time  distance  hour  minute  \n",
       "0    1545    EWR  IAH     227.0      1400   5.0    17.0  \n",
       "1    1714    LGA  IAH     227.0      1416   5.0    33.0  \n",
       "2    1141    JFK  MIA     160.0      1089   5.0    42.0  \n",
       "3     461    LGA  ATL     116.0       762   5.0    54.0  \n",
       "4    1696    EWR  ORD     150.0       719   5.0    54.0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter all the rows where arr_delay value is missing:\n",
    "flights1 = flights[ flights['arr_delay'].notnull( )]\n",
    "flights1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all the observations with missing values\n",
    "flights2 = flights.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values with zeros\n",
    "nomiss =flights['dep_delay'].fillna(0)\n",
    "nomiss.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Exercise*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count how many missing data are in dep_delay and arr_delay columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Common Aggregation Functions:\n",
    "\n",
    "|Function|Description\n",
    "|-------|--------\n",
    "|min   | minimum\n",
    "|max   | maximum\n",
    "|count   | number of non-null observations\n",
    "|sum   | sum of values\n",
    "|mean  | arithmetic mean of values\n",
    "|median | median\n",
    "|mad | mean absolute deviation\n",
    "|mode | mode\n",
    "|prod   | product of values\n",
    "|std  | standard deviation\n",
    "|var | unbiased variance\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>flight</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>160754.0</td>\n",
       "      <td>160754.000000</td>\n",
       "      <td>160754.000000</td>\n",
       "      <td>158418.000000</td>\n",
       "      <td>158418.000000</td>\n",
       "      <td>158275.000000</td>\n",
       "      <td>157927.000000</td>\n",
       "      <td>160754.000000</td>\n",
       "      <td>157927.000000</td>\n",
       "      <td>160754.000000</td>\n",
       "      <td>158418.000000</td>\n",
       "      <td>158418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>6.547395</td>\n",
       "      <td>15.716567</td>\n",
       "      <td>1316.146006</td>\n",
       "      <td>9.463773</td>\n",
       "      <td>1517.471161</td>\n",
       "      <td>2.094537</td>\n",
       "      <td>1156.344987</td>\n",
       "      <td>180.685158</td>\n",
       "      <td>1282.445420</td>\n",
       "      <td>12.837582</td>\n",
       "      <td>32.387847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.410001</td>\n",
       "      <td>8.762794</td>\n",
       "      <td>470.823715</td>\n",
       "      <td>36.545109</td>\n",
       "      <td>510.695413</td>\n",
       "      <td>41.479349</td>\n",
       "      <td>695.884283</td>\n",
       "      <td>97.507866</td>\n",
       "      <td>765.895383</td>\n",
       "      <td>4.725552</td>\n",
       "      <td>18.687423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-75.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>855.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>1112.000000</td>\n",
       "      <td>-19.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>733.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1345.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1541.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1076.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1944.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1715.000000</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>1728.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1007.000000</td>\n",
       "      <td>2599.000000</td>\n",
       "      <td>695.000000</td>\n",
       "      <td>4963.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year          month            day       dep_time      dep_delay  \\\n",
       "count  160754.0  160754.000000  160754.000000  158418.000000  158418.000000   \n",
       "mean     2013.0       6.547395      15.716567    1316.146006       9.463773   \n",
       "std         0.0       3.410001       8.762794     470.823715      36.545109   \n",
       "min      2013.0       1.000000       1.000000       1.000000     -33.000000   \n",
       "25%      2013.0       4.000000       8.000000     855.000000      -5.000000   \n",
       "50%      2013.0       7.000000      16.000000    1345.000000      -2.000000   \n",
       "75%      2013.0      10.000000      23.000000    1725.000000       7.000000   \n",
       "max      2013.0      12.000000      31.000000    2400.000000    1014.000000   \n",
       "\n",
       "            arr_time      arr_delay         flight       air_time  \\\n",
       "count  158275.000000  157927.000000  160754.000000  157927.000000   \n",
       "mean     1517.471161       2.094537    1156.344987     180.685158   \n",
       "std       510.695413      41.479349     695.884283      97.507866   \n",
       "min         1.000000     -75.000000       1.000000      21.000000   \n",
       "25%      1112.000000     -19.000000     504.000000     111.000000   \n",
       "50%      1541.000000      -7.000000    1157.000000     153.000000   \n",
       "75%      1944.000000       9.000000    1715.000000     258.000000   \n",
       "max      2400.000000    1007.000000    2599.000000     695.000000   \n",
       "\n",
       "            distance           hour         minute  \n",
       "count  160754.000000  158418.000000  158418.000000  \n",
       "mean     1282.445420      12.837582      32.387847  \n",
       "std       765.895383       4.725552      18.687423  \n",
       "min        17.000000       0.000000       0.000000  \n",
       "25%       733.000000       8.000000      16.000000  \n",
       "50%      1076.000000      13.000000      32.000000  \n",
       "75%      1728.000000      17.000000      51.000000  \n",
       "max      4963.000000      24.000000      59.000000  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the number of non-missing values in each column\n",
    "flights.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         2013\n",
       "month           1\n",
       "day             1\n",
       "dep_time        1\n",
       "dep_delay     -33\n",
       "arr_time        1\n",
       "arr_delay     -75\n",
       "carrier        AA\n",
       "flight          1\n",
       "origin        EWR\n",
       "dest          ANC\n",
       "air_time       21\n",
       "distance       17\n",
       "hour            0\n",
       "minute          0\n",
       "dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find mean value for all the columns in the dataset\n",
    "flights.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carrier\n",
       "AA     8.586016\n",
       "AS     5.804775\n",
       "DL     9.264505\n",
       "UA    12.106073\n",
       "US     3.782418\n",
       "Name: dep_delay, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's compute summary statistic per a group':\n",
    "flights.groupby('carrier')['dep_delay'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-33.000000</td>\n",
       "      <td>-75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.463773</td>\n",
       "      <td>2.094537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1007.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dep_delay    arr_delay\n",
       "min    -33.000000   -75.000000\n",
       "mean     9.463773     2.094537\n",
       "max   1014.000000  1007.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use agg() methods for aggregation:\n",
    "flights[['dep_delay','arr_delay']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>carrier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.463773</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-33.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dep_delay  carrier\n",
       "max      1014.000000      NaN\n",
       "mean        9.463773      NaN\n",
       "min       -33.000000      NaN\n",
       "nunique          NaN      5.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An example of computing different statistics for different columns\n",
    "flights.agg({'dep_delay':['min','mean',max], 'carrier':['nunique']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic descriptive statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Function|Description\n",
    "|-------|--------\n",
    "|min   | minimum\n",
    "|max   | maximum\n",
    "|mean  | arithmetic mean of values\n",
    "|median | median\n",
    "|mad | mean absolute deviation\n",
    "|mode | mode\n",
    "|std  | standard deviation\n",
    "|var | unbiased variance\n",
    "|sem | standard error of the mean\n",
    "|skew| sample skewness\n",
    "|kurt|kurtosis\n",
    "|quantile| value at %\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    158418.000000\n",
       "mean          9.463773\n",
       "std          36.545109\n",
       "min         -33.000000\n",
       "25%          -5.000000\n",
       "50%          -2.000000\n",
       "75%           7.000000\n",
       "max        1014.000000\n",
       "Name: dep_delay, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convinient describe() function computes a veriety of statistics\n",
    "flights.dep_delay.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54111"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the index of the maximum or minimum value\n",
    "# if there are multiple values matching idxmin() and idxmax() will return the first match\n",
    "flights['dep_delay'].idxmin()  #minimum value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UA    58665\n",
       "DL    48110\n",
       "AA    32729\n",
       "US    20536\n",
       "AS      714\n",
       "Name: carrier, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of records for each different value in a vector\n",
    "flights['carrier'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore data using graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Show graphs withint Python notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bruno/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6510: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([7.14677085e-06, 8.73494215e-06, 1.74698843e-05, 8.73494215e-06,\n",
       "        9.52902780e-06, 6.35268520e-06, 3.17634260e-06, 7.94085650e-07]),\n",
       " array([ 57800.,  73945.,  90090., 106235., 122380., 138525., 154670.,\n",
       "        170815., 186960.]),\n",
       " <a list of 8 Patch objects>)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use matplotlib to draw a histogram of a salary data\n",
    "plt.hist(df['salary'],bins=8, normed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bruno/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use seaborn package to draw a histogram\n",
    "sns.distplot(df['salary']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7cde99b390>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8EriiO6buhXaaagZ9Mq/u9spfwODTi14JXNDvSBrRy4AfAL9RVbcDzwEe0e9I0mTcI5nMj5zL7IddTIequr17b/RfSfJPwNeBd/Y8ljQRgz6Z3ecyHwW8wXOZl74kTwRe0X3dCXyIwWtJP9frYNI88EXRCXgu8/RJ8gDwOeDMqtrerbupqp7Q72TS5DyGPhnPZZ4+vwTcBlyZ5B+TnIyX/KsR7qFPwHOZp1eSRwCnMTj0chJwCfDxqvpUr4NJE3APfTI7a/Av4mnA31bVu4BH9TyTRlBV91XVB6rqFxl82MVWBm9/LE0t99AnkOQzwL8Crwaey+AzRa+pquN6HUzSsuQe+mQ8l1nSkuFpixPwXGZJS4lBH4PnMktaijyGPgbPZZa0FHkMfTyeyyxpyXEPfQKeyyxpKTHo8yTJo4FfBl5WVSf3PY+k5cegS1IjPIYuSY0w6JLUCIMuPYQkb05yTt9zSKMw6Fo2MuDfeTXLv9xqWpLVSW5McgnwZWB9kk1Jrk9y/qztvpHk/O6DvrcledJe/qzfSvKJJAcu5n+DNCov/ddycAywrqq+mOTQqroryQpgY5LjZn3C1J1V9ZQkvwucA/zm7j8gyWuB5wNrq+oHi/5fII3APXQtBzdX1Re722ck2cLg/c9/Gjh21nYf675vBlbPWv/rwCnA6cZcS5lB13JwH0CSoxjseZ/cvWf9FcABs7bbHetd/PD/vW5jEPhVCz6pNAGDruXkIAZxvyfJ4Qz2ukexFfht4LIkj12o4aRJGXQtG1V1LYM4fxX4APAfc/jZzzPYu78iyWELM6E0GS/9l6RGuIcuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY34P1dSMoAr9VfYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use regular matplotlib function to display a barplot\n",
    "df.groupby(['rank'])['salary'].count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use seaborn package to display a barplot\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "ax = sns.barplot(x='rank',y ='salary', data=df, estimator=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split into 2 groups:\n",
    "ax = sns.barplot(x='rank',y ='salary', hue='sex', data=df, estimator=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7cda2e1a20>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Violinplot\n",
    "sns.violinplot(x = \"salary\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f7cda2527b8>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot in seaborn\n",
    "sns.jointplot(x='service', y='salary', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7cda16a400>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#If we are interested in linear regression plot for 2 numeric variables we can use regplot\n",
    "sns.regplot(x='service', y='salary', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7cda12b2e8>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot\n",
    "sns.boxplot(x='rank',y='salary', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7cda0c98d0>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# side-by-side box plot\n",
    "sns.boxplot(x='rank',y='salary', data=df, hue='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7cd9ffd438>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# swarm plot\n",
    "sns.swarmplot(x='rank',y='salary', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bruno/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7cd9fe49e8>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#factorplot\n",
    "sns.factorplot(x='carrier',y='dep_delay', data=flights, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f7cd9f99358>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pairplot \n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Exercise*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using seaborn package explore the dependency of arr_delay on dep_delay (scatterplot or regplot) using flights dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Basic statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Statsmodel functions:\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 salary   R-squared:                       0.283\n",
      "Model:                            OLS   Adj. R-squared:                  0.274\n",
      "Method:                 Least Squares   F-statistic:                     30.03\n",
      "Date:                Mon, 05 Aug 2019   Prob (F-statistic):           5.31e-07\n",
      "Time:                        15:14:07   Log-Likelihood:                -896.72\n",
      "No. Observations:                  78   AIC:                             1797.\n",
      "Df Residuals:                      76   BIC:                             1802.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8.935e+04   4365.651     20.468      0.000    8.07e+04     9.8e+04\n",
      "service     1240.3567    226.341      5.480      0.000     789.560    1691.153\n",
      "==============================================================================\n",
      "Omnibus:                       12.741   Durbin-Watson:                   1.630\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               21.944\n",
      "Skew:                          -0.576   Prob(JB):                     1.72e-05\n",
      "Kurtosis:                       5.329   Cond. No.                         30.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# create a fitted model\n",
    "lm = smf.ols(formula='salary ~ service', data=df).fit()\n",
    "\n",
    "#print model summary\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    89354.824215\n",
       "service       1240.356654\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the coefficients\n",
    "lm.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef: [[1240.3566535]] \n",
      "Intercept: [89354.82421525]\n"
     ]
    }
   ],
   "source": [
    "#using scikit-learn:\n",
    "from sklearn import linear_model\n",
    "est = linear_model.LinearRegression(fit_intercept = True)   # create estimator object\n",
    "est.fit(df[['service']], df[['salary']])\n",
    "\n",
    "#print result\n",
    "print(\"Coef:\", est.coef_, \"\\nIntercept:\", est.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*Excersize*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a linear model for arr_delay ~ dep_delay\n",
    "\n",
    "\n",
    "#print model summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "### Student T-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.2486865976699053, pvalue=0.027429778657910103)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using scipy package:\n",
    "from scipy import stats\n",
    "df_w = df[ df['sex'] == 'Female']['salary']\n",
    "df_m = df[ df['sex'] == 'Male']['salary']\n",
    "stats.ttest_ind(df_w, df_m)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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