{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Using cached pandas-0.20.3-cp36-cp36m-manylinux1_x86_64.whl\n",
      "Collecting matplotlib\n",
      "  Using cached matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl\n",
      "Collecting numpy\n",
      "  Using cached numpy-1.13.1-cp36-cp36m-manylinux1_x86_64.whl\n",
      "Collecting pytz>=2011k (from pandas)\n",
      "  Using cached pytz-2017.2-py2.py3-none-any.whl\n",
      "Requirement already satisfied: python-dateutil>=2 in ./lib/python3.6/site-packages (from pandas)\n",
      "Collecting pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 (from matplotlib)\n",
      "  Using cached pyparsing-2.2.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: six>=1.10 in ./lib/python3.6/site-packages (from matplotlib)\n",
      "Collecting cycler>=0.10 (from matplotlib)\n",
      "  Using cached cycler-0.10.0-py2.py3-none-any.whl\n",
      "Installing collected packages: pytz, numpy, pandas, pyparsing, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 matplotlib-2.0.2 numpy-1.13.1 pandas-0.20.3 pyparsing-2.2.0 pytz-2017.2\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas matplotlib numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = [[1,2,3], [4,5,6], [7,8,9]]\n",
    "print(grid)\n",
    "\n",
    "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2\n",
      "0  1  2  3\n",
      "1  4  5  6\n",
      "2  7  8  9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(grid)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  two  three\n",
      "0    1    2      3\n",
      "1    4    5      6\n",
      "2    7    8      9\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(grid, columns=[\"one\", \"two\", \"three\"] )\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2\n",
      "1    5\n",
      "2    8\n",
      "Name: two, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"two\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2, 5, 8]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print([x[1] for x in grid])\n",
    "[2, 5, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "5\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for x in df[\"two\"]:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  three\n",
      "0    1      3\n",
      "1    4      6\n",
      "2    7      9\n"
     ]
    }
   ],
   "source": [
    "edges = df[[\"one\", \"three\"]]\n",
    "print(edges)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  three\n",
      "0    3      5\n",
      "1    6      8\n",
      "2    9     11\n"
     ]
    }
   ],
   "source": [
    "print(edges.add(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "5    1\n",
       "8    1\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['two'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   report\n",
      "abs_humidity                         None\n",
      "atmo_opacity                        Sunny\n",
      "ls                                    296\n",
      "max_temp                               -1\n",
      "max_temp_fahrenheit                  30.2\n",
      "min_temp                              -72\n",
      "min_temp_fahrenheit                 -97.6\n",
      "pressure                              869\n",
      "pressure_string                    Higher\n",
      "season                           Month 10\n",
      "sol                                  1576\n",
      "sunrise              2017-01-11T12:31:00Z\n",
      "sunset               2017-01-12T00:46:00Z\n",
      "terrestrial_date               2017-01-11\n",
      "wind_direction                         --\n",
      "wind_speed                           None\n"
     ]
    }
   ],
   "source": [
    "mars = pd.read_json(\"mars_data_01.json\")\n",
    "print(mars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = pd.read_csv(\"temp_data_01.csv\", header=0, names=range(18), usecols=range(4,18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           4      5    6     7     8      9    10    11    12       13   14  \\\n",
      "0  1979/01/01  17.48  994   6.0  30.5   2.89  994 -13.6  15.8  Missing    0   \n",
      "1  1979/01/02   4.64  994  -6.4  15.8  -9.03  994 -23.6   6.6  Missing    0   \n",
      "2  1979/01/03  11.05  994  -0.7  24.7  -2.17  994 -18.3  12.9  Missing    0   \n",
      "3  1979/01/04   9.51  994   0.2  27.6  -0.43  994 -16.3  16.3  Missing    0   \n",
      "4  1979/05/15  68.42  994  61.0  75.1  51.30  994  43.3  57.0  Missing    0   \n",
      "5  1979/05/16  70.29  994  63.4  73.5  48.09  994  41.1  53.0  Missing    0   \n",
      "6  1979/05/17  75.34  994  64.0  80.5  50.84  994  44.3  55.7    82.60    2   \n",
      "7  1979/05/18  79.13  994  75.5  82.1  55.68  994  50.0  61.1    81.42  349   \n",
      "8  1979/05/19  74.94  994  66.9  83.1  58.59  994  50.9  63.2    82.87   78   \n",
      "\n",
      "        15       16      17  \n",
      "0  Missing  Missing   0.00%  \n",
      "1  Missing  Missing   0.00%  \n",
      "2  Missing  Missing   0.00%  \n",
      "3  Missing  Missing   0.00%  \n",
      "4  Missing  Missing   0.00%  \n",
      "5  Missing  Missing   0.00%  \n",
      "6    82.40    82.80   0.20%  \n",
      "7    80.20    83.40  35.11%  \n",
      "8    81.60    85.20   7.85%  \n"
     ]
    }
   ],
   "source": [
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           4      5    6     7     8      9    10    11    12     13   14  \\\n",
      "0  1979/01/01  17.48  994   6.0  30.5   2.89  994 -13.6  15.8    NaN    0   \n",
      "1  1979/01/02   4.64  994  -6.4  15.8  -9.03  994 -23.6   6.6    NaN    0   \n",
      "2  1979/01/03  11.05  994  -0.7  24.7  -2.17  994 -18.3  12.9    NaN    0   \n",
      "3  1979/01/04   9.51  994   0.2  27.6  -0.43  994 -16.3  16.3    NaN    0   \n",
      "4  1979/05/15  68.42  994  61.0  75.1  51.30  994  43.3  57.0    NaN    0   \n",
      "5  1979/05/16  70.29  994  63.4  73.5  48.09  994  41.1  53.0    NaN    0   \n",
      "6  1979/05/17  75.34  994  64.0  80.5  50.84  994  44.3  55.7  82.60    2   \n",
      "7  1979/05/18  79.13  994  75.5  82.1  55.68  994  50.0  61.1  81.42  349   \n",
      "8  1979/05/19  74.94  994  66.9  83.1  58.59  994  50.9  63.2  82.87   78   \n",
      "\n",
      "     15    16      17  \n",
      "0   NaN   NaN   0.00%  \n",
      "1   NaN   NaN   0.00%  \n",
      "2   NaN   NaN   0.00%  \n",
      "3   NaN   NaN   0.00%  \n",
      "4   NaN   NaN   0.00%  \n",
      "5   NaN   NaN   0.00%  \n",
      "6  82.4  82.8   0.20%  \n",
      "7  80.2  83.4  35.11%  \n",
      "8  81.6  85.2   7.85%  \n"
     ]
    }
   ],
   "source": [
    "temp = pd.read_csv(\"temp_data_01.csv\", na_values=['Missing'], header=0, names=range(18), usecols=range(4,18))\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.00%'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[17][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.00'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[17]=temp[17].str.strip(\"%\")\n",
    "temp[17][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.00'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[17][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[17] = pd.to_numeric(temp[17])\n",
    "temp[17][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0000\n",
       "1    0.0000\n",
       "2    0.0000\n",
       "3    0.0000\n",
       "4    0.0000\n",
       "5    0.0000\n",
       "6    0.0020\n",
       "7    0.3511\n",
       "8    0.0785\n",
       "Name: 17, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[17] = temp[17].div(100)\n",
    "temp[17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Team member  Territory  Month  Calls\n",
      "0        Jorge          3      1    107\n",
      "1        Jorge          3      2     88\n",
      "2        Jorge          3      3     84\n",
      "3        Jorge          3      4    113\n",
      "4          Ana          1      1     91\n",
      "5          Ana          1      2    129\n",
      "6          Ana          1      3     96\n",
      "7          Ana          1      4    128\n",
      "8          Ali          2      1    120\n",
      "9          Ali          2      2     85\n",
      "10         Ali          2      3     87\n",
      "11         Ali          2      4     87\n"
     ]
    }
   ],
   "source": [
    "calls = pd.read_csv(\"sales_calls.csv\")\n",
    "print(calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Territory  Month  Amount\n",
      "0           1      1   54228\n",
      "1           1      2   61640\n",
      "2           1      3   43491\n",
      "3           1      4   52173\n",
      "4           2      1   36061\n",
      "5           2      2   44957\n",
      "6           2      3   35058\n",
      "7           2      4   33855\n",
      "8           3      1   50876\n",
      "9           3      2   57682\n",
      "10          3      3   53689\n",
      "11          3      4   49173\n"
     ]
    }
   ],
   "source": [
    "revenue = pd.read_csv(\"sales_revenue.csv\")\n",
    "print(revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Team member  Territory  Month  Calls  Amount\n",
      "0        Jorge          3      1    107   50876\n",
      "1        Jorge          3      2     88   57682\n",
      "2        Jorge          3      3     84   53689\n",
      "3        Jorge          3      4    113   49173\n",
      "4          Ana          1      1     91   54228\n",
      "5          Ana          1      2    129   61640\n",
      "6          Ana          1      3     96   43491\n",
      "7          Ana          1      4    128   52173\n",
      "8          Ali          2      1    120   36061\n",
      "9          Ali          2      2     85   44957\n",
      "10         Ali          2      3     87   35058\n",
      "11         Ali          2      4     87   33855\n"
     ]
    }
   ],
   "source": [
    "calls_revenue = pd.merge(calls, revenue, on=['Territory', 'Month'])\n",
    "print(calls_revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Team member  Territory  Month  Calls  Amount\n",
      "0       Jorge          3      1    107   50876\n",
      "1       Jorge          3      2     88   57682\n",
      "2       Jorge          3      3     84   53689\n",
      "3       Jorge          3      4    113   49173\n"
     ]
    }
   ],
   "source": [
    "print(calls_revenue[calls_revenue.Territory==3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Team member  Territory  Month  Calls  Amount\n",
      "1       Jorge          3      2     88   57682\n",
      "2       Jorge          3      3     84   53689\n",
      "4         Ana          1      1     91   54228\n",
      "9         Ali          2      2     85   44957\n"
     ]
    }
   ],
   "source": [
    "print(calls_revenue[calls_revenue.Amount/calls_revenue.Calls>500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Team member  Territory  Month  Calls  Amount  Call_Amount\n",
      "0        Jorge          3      1    107   50876   475.476636\n",
      "1        Jorge          3      2     88   57682   655.477273\n",
      "2        Jorge          3      3     84   53689   639.154762\n",
      "3        Jorge          3      4    113   49173   435.159292\n",
      "4          Ana          1      1     91   54228   595.912088\n",
      "5          Ana          1      2    129   61640   477.829457\n",
      "6          Ana          1      3     96   43491   453.031250\n",
      "7          Ana          1      4    128   52173   407.601562\n",
      "8          Ali          2      1    120   36061   300.508333\n",
      "9          Ali          2      2     85   44957   528.905882\n",
      "10         Ali          2      3     87   35058   402.965517\n",
      "11         Ali          2      4     87   33855   389.137931\n"
     ]
    }
   ],
   "source": [
    "calls_revenue['Call_Amount'] = calls_revenue.Amount/calls_revenue.Calls\n",
    "print(calls_revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1215\n",
      "101.25\n",
      "93.5\n",
      "129\n",
      "84\n"
     ]
    }
   ],
   "source": [
    "print(calls_revenue.Calls.sum())\n",
    "print(calls_revenue.Calls.mean())\n",
    "print(calls_revenue.Calls.median())\n",
    "print(calls_revenue.Calls.max())\n",
    "print(calls_revenue.Calls.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Team member  Territory  Month  Calls  Amount  Call_Amount\n",
      "0       Jorge          3      1    107   50876   475.476636\n",
      "1       Jorge          3      2     88   57682   655.477273\n",
      "2       Jorge          3      3     84   53689   639.154762\n",
      "4         Ana          1      1     91   54228   595.912088\n",
      "5         Ana          1      2    129   61640   477.829457\n",
      "9         Ali          2      2     85   44957   528.905882\n",
      "464.2539427570093\n"
     ]
    }
   ],
   "source": [
    "print(calls_revenue[calls_revenue.Call_Amount >= calls_revenue.Call_Amount.median()])\n",
    "print(calls_revenue.Call_Amount.median())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Calls  Amount\n",
      "Month               \n",
      "1        318  141165\n",
      "2        302  164279\n",
      "3        267  132238\n",
      "4        328  135201\n"
     ]
    }
   ],
   "source": [
    "print(calls_revenue[['Month', 'Calls', 'Amount']].groupby(['Month']).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Calls  Amount\n",
      "Territory               \n",
      "1            444  211532\n",
      "2            379  149931\n",
      "3            392  211420\n"
     ]
    }
   ],
   "source": [
    "print(calls_revenue[['Territory', 'Calls', 'Amount']].groupby(['Territory']).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3576c9390>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q5wumJKlyhl6SKmfoVY2ImB4R68vX9ojYNmj9yBEe846I+GRZ/qOIaG/urKWx5zl6VSki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\nVDlDL0mVM/SSVDlDL0mV+392RSHJcm35ZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3578ceda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calls_revenue[['Territory', 'Calls']].groupby(['Territory']).sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdee6e07cf8>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdee75d2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calls_revenue[['Month', 'Call_Amount']].groupby(['Month']).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
