Data Manipulation with pandas.ipynb

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     "text": [
      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n",
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "iris = datasets.load_iris()\n",
    "print(iris.keys())\n",
    "print(type(iris.data), type(iris.target))\n",
    "iris.data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "name": "stdout",
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     "text": [
      "C:\\Users\\T0230575\\Desktop\\Data Management\\PyCharmProjets_fm\\DataCamp\\Data Manipulation with Pandas\n",
      "   Unnamed: 0              region       state  individuals  family_members  \\\n",
      "0           0  East South Central     Alabama       2570.0           864.0   \n",
      "1           1             Pacific      Alaska       1434.0           582.0   \n",
      "2           2            Mountain     Arizona       7259.0          2606.0   \n",
      "3           3  West South Central    Arkansas       2280.0           432.0   \n",
      "4           4             Pacific  California     109008.0         20964.0   \n",
      "\n",
      "   state_pop  \n",
      "0    4887681  \n",
      "1     735139  \n",
      "2    7158024  \n",
      "3    3009733  \n",
      "4   39461588  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51 entries, 0 to 50\n",
      "Data columns (total 6 columns):\n",
      "Unnamed: 0        51 non-null int64\n",
      "region            51 non-null object\n",
      "state             51 non-null object\n",
      "individuals       51 non-null float64\n",
      "family_members    51 non-null float64\n",
      "state_pop         51 non-null int64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 2.5+ KB\n",
      "None\n",
      "(51, 6)\n",
      "       Unnamed: 0    individuals  family_members     state_pop\n",
      "count   51.000000      51.000000       51.000000  5.100000e+01\n",
      "mean    25.000000    7225.784314     3504.882353  6.405637e+06\n",
      "std     14.866069   15991.025083     7805.411811  7.327258e+06\n",
      "min      0.000000     434.000000       75.000000  5.776010e+05\n",
      "25%     12.500000    1446.500000      592.000000  1.777414e+06\n",
      "50%     25.000000    3082.000000     1482.000000  4.461153e+06\n",
      "75%     37.500000    6781.500000     3196.000000  7.340946e+06\n",
      "max     50.000000  109008.000000    52070.000000  3.946159e+07\n",
      "[[0 'East South Central' 'Alabama' 2570.0 864.0 4887681]\n",
      " [1 'Pacific' 'Alaska' 1434.0 582.0 735139]\n",
      " [2 'Mountain' 'Arizona' 7259.0 2606.0 7158024]\n",
      " [3 'West South Central' 'Arkansas' 2280.0 432.0 3009733]\n",
      " [4 'Pacific' 'California' 109008.0 20964.0 39461588]\n",
      " [5 'Mountain' 'Colorado' 7607.0 3250.0 5691287]\n",
      " [6 'New England' 'Connecticut' 2280.0 1696.0 3571520]\n",
      " [7 'South Atlantic' 'Delaware' 708.0 374.0 965479]\n",
      " [8 'South Atlantic' 'District of Columbia' 3770.0 3134.0 701547]\n",
      " [9 'South Atlantic' 'Florida' 21443.0 9587.0 21244317]\n",
      " [10 'South Atlantic' 'Georgia' 6943.0 2556.0 10511131]\n",
      " [11 'Pacific' 'Hawaii' 4131.0 2399.0 1420593]\n",
      " [12 'Mountain' 'Idaho' 1297.0 715.0 1750536]\n",
      " [13 'East North Central' 'Illinois' 6752.0 3891.0 12723071]\n",
      " [14 'East North Central' 'Indiana' 3776.0 1482.0 6695497]\n",
      " [15 'West North Central' 'Iowa' 1711.0 1038.0 3148618]\n",
      " [16 'West North Central' 'Kansas' 1443.0 773.0 2911359]\n",
      " [17 'East South Central' 'Kentucky' 2735.0 953.0 4461153]\n",
      " [18 'West South Central' 'Louisiana' 2540.0 519.0 4659690]\n",
      " [19 'New England' 'Maine' 1450.0 1066.0 1339057]\n",
      " [20 'South Atlantic' 'Maryland' 4914.0 2230.0 6035802]\n",
      " [21 'New England' 'Massachusetts' 6811.0 13257.0 6882635]\n",
      " [22 'East North Central' 'Michigan' 5209.0 3142.0 9984072]\n",
      " [23 'West North Central' 'Minnesota' 3993.0 3250.0 5606249]\n",
      " [24 'East South Central' 'Mississippi' 1024.0 328.0 2981020]\n",
      " [25 'West North Central' 'Missouri' 3776.0 2107.0 6121623]\n",
      " [26 'Mountain' 'Montana' 983.0 422.0 1060665]\n",
      " [27 'West North Central' 'Nebraska' 1745.0 676.0 1925614]\n",
      " [28 'Mountain' 'Nevada' 7058.0 486.0 3027341]\n",
      " [29 'New England' 'New Hampshire' 835.0 615.0 1353465]\n",
      " [30 'Mid-Atlantic' 'New Jersey' 6048.0 3350.0 8886025]\n",
      " [31 'Mountain' 'New Mexico' 1949.0 602.0 2092741]\n",
      " [32 'Mid-Atlantic' 'New York' 39827.0 52070.0 19530351]\n",
      " [33 'South Atlantic' 'North Carolina' 6451.0 2817.0 10381615]\n",
      " [34 'West North Central' 'North Dakota' 467.0 75.0 758080]\n",
      " [35 'East North Central' 'Ohio' 6929.0 3320.0 11676341]\n",
      " [36 'West South Central' 'Oklahoma' 2823.0 1048.0 3940235]\n",
      " [37 'Pacific' 'Oregon' 11139.0 3337.0 4181886]\n",
      " [38 'Mid-Atlantic' 'Pennsylvania' 8163.0 5349.0 12800922]\n",
      " [39 'New England' 'Rhode Island' 747.0 354.0 1058287]\n",
      " [40 'South Atlantic' 'South Carolina' 3082.0 851.0 5084156]\n",
      " [41 'West North Central' 'South Dakota' 836.0 323.0 878698]\n",
      " [42 'East South Central' 'Tennessee' 6139.0 1744.0 6771631]\n",
      " [43 'West South Central' 'Texas' 19199.0 6111.0 28628666]\n",
      " [44 'Mountain' 'Utah' 1904.0 972.0 3153550]\n",
      " [45 'New England' 'Vermont' 780.0 511.0 624358]\n",
      " [46 'South Atlantic' 'Virginia' 3928.0 2047.0 8501286]\n",
      " [47 'Pacific' 'Washington' 16424.0 5880.0 7523869]\n",
      " [48 'South Atlantic' 'West Virginia' 1021.0 222.0 1804291]\n",
      " [49 'East North Central' 'Wisconsin' 2740.0 2167.0 5807406]\n",
      " [50 'Mountain' 'Wyoming' 434.0 205.0 577601]]\n",
      "Index(['Unnamed: 0', 'region', 'state', 'individuals', 'family_members',\n",
      "       'state_pop'],\n",
      "      dtype='object')\n",
      "RangeIndex(start=0, stop=51, step=1)\n",
      "    Unnamed: 0              region         state  individuals  family_members  \\\n",
      "50          50            Mountain       Wyoming        434.0           205.0   \n",
      "34          34  West North Central  North Dakota        467.0            75.0   \n",
      "7            7      South Atlantic      Delaware        708.0           374.0   \n",
      "39          39         New England  Rhode Island        747.0           354.0   \n",
      "45          45         New England       Vermont        780.0           511.0   \n",
      "\n",
      "    state_pop  \n",
      "50     577601  \n",
      "34     758080  \n",
      "7      965479  \n",
      "39    1058287  \n",
      "45     624358  \n",
      "    Unnamed: 0              region          state  individuals  \\\n",
      "32          32        Mid-Atlantic       New York      39827.0   \n",
      "4            4             Pacific     California     109008.0   \n",
      "21          21         New England  Massachusetts       6811.0   \n",
      "9            9      South Atlantic        Florida      21443.0   \n",
      "43          43  West South Central          Texas      19199.0   \n",
      "\n",
      "    family_members  state_pop  \n",
      "32         52070.0   19530351  \n",
      "4          20964.0   39461588  \n",
      "21         13257.0    6882635  \n",
      "9           9587.0   21244317  \n",
      "43          6111.0   28628666  \n",
      "    Unnamed: 0              region      state  individuals  family_members  \\\n",
      "13          13  East North Central   Illinois       6752.0          3891.0   \n",
      "35          35  East North Central       Ohio       6929.0          3320.0   \n",
      "22          22  East North Central   Michigan       5209.0          3142.0   \n",
      "49          49  East North Central  Wisconsin       2740.0          2167.0   \n",
      "14          14  East North Central    Indiana       3776.0          1482.0   \n",
      "\n",
      "    state_pop  \n",
      "13   12723071  \n",
      "35   11676341  \n",
      "22    9984072  \n",
      "49    5807406  \n",
      "14    6695497  \n"
     ]
    }
   ],
   "source": [
    "# Data Manipulation with pandas\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "print(os.getcwd())\n",
    "os.chdir('C:/Users/T0230575/Desktop/Data Management/PyCharmProjets_fm/DataCamp/Data Manipulation with Pandas')\n",
    "homelessness = pd.read_csv('homelessness.csv')\n",
    "\n",
    "# Print the head of the homelessness data\n",
    "print(homelessness.head())\n",
    "\n",
    "# Print information about homelessness\n",
    "print(homelessness.info())\n",
    "\n",
    "# Print the shape of homelessness\n",
    "print(homelessness.shape)\n",
    "\n",
    "# Print a description of homelessness\n",
    "print(homelessness.describe())\n",
    "\n",
    "# Import pandas using the alias pd\n",
    "import pandas as pd\n",
    "\n",
    "# Print the values of homelessness\n",
    "print(homelessness.values)\n",
    "\n",
    "# Print the column index of homelessness\n",
    "print(homelessness.columns)\n",
    "\n",
    "# Print the row index of homelessness\n",
    "print(homelessness.index)\n",
    "\n",
    "\n",
    "\n",
    "# Sort homelessness by individual\n",
    "homelessness_ind = homelessness.sort_values('individuals').head()\n",
    "\n",
    "# Print the top few rows\n",
    "print(homelessness_ind)\n",
    "\n",
    "\n",
    "# Sort homelessness by descending family members\n",
    "homelessness_fam = homelessness.sort_values('family_members', ascending=False).head()\n",
    "\n",
    "# Print the top few rows\n",
    "print(homelessness_fam)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Sort homelessness by region, then descending family members\n",
    "homelessness_reg_fam = homelessness.sort_values(['region','family_members'], ascending=[True,False]).head()\n",
    "\n",
    "# Print the top few rows\n",
    "print(homelessness_reg_fam)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        state  family_members\n",
      "0     Alabama           864.0\n",
      "1      Alaska           582.0\n",
      "2     Arizona          2606.0\n",
      "3    Arkansas           432.0\n",
      "4  California         20964.0\n",
      "   individuals       state\n",
      "0       2570.0     Alabama\n",
      "1       1434.0      Alaska\n",
      "2       7259.0     Arizona\n",
      "3       2280.0    Arkansas\n",
      "4     109008.0  California\n",
      "    Unnamed: 0              region       state  individuals  family_members  \\\n",
      "4            4             Pacific  California     109008.0         20964.0   \n",
      "9            9      South Atlantic     Florida      21443.0          9587.0   \n",
      "32          32        Mid-Atlantic    New York      39827.0         52070.0   \n",
      "37          37             Pacific      Oregon      11139.0          3337.0   \n",
      "43          43  West South Central       Texas      19199.0          6111.0   \n",
      "47          47             Pacific  Washington      16424.0          5880.0   \n",
      "\n",
      "    state_pop     total  p_individuals  indiv_per_10k  \n",
      "4    39461588  129972.0       0.838704      27.623825  \n",
      "9    21244317   31030.0       0.691041      10.093523  \n",
      "32   19530351   91897.0       0.433387      20.392363  \n",
      "37    4181886   14476.0       0.769481      26.636307  \n",
      "43   28628666   25310.0       0.758554       6.706215  \n",
      "47    7523869   22304.0       0.736370      21.829195  \n",
      "    Unnamed: 0    region       state  individuals  family_members  state_pop  \\\n",
      "2            2  Mountain     Arizona       7259.0          2606.0    7158024   \n",
      "5            5  Mountain    Colorado       7607.0          3250.0    5691287   \n",
      "12          12  Mountain       Idaho       1297.0           715.0    1750536   \n",
      "26          26  Mountain     Montana        983.0           422.0    1060665   \n",
      "28          28  Mountain      Nevada       7058.0           486.0    3027341   \n",
      "31          31  Mountain  New Mexico       1949.0           602.0    2092741   \n",
      "44          44  Mountain        Utah       1904.0           972.0    3153550   \n",
      "50          50  Mountain     Wyoming        434.0           205.0     577601   \n",
      "\n",
      "      total  p_individuals  indiv_per_10k  \n",
      "2    9865.0       0.735834      10.141067  \n",
      "5   10857.0       0.700654      13.366045  \n",
      "12   2012.0       0.644632       7.409159  \n",
      "26   1405.0       0.699644       9.267771  \n",
      "28   7544.0       0.935578      23.314189  \n",
      "31   2551.0       0.764014       9.313145  \n",
      "44   2876.0       0.662031       6.037640  \n",
      "50    639.0       0.679186       7.513837  \n",
      "   Unnamed: 0   region   state  individuals  family_members  state_pop  \\\n",
      "1           1  Pacific  Alaska       1434.0           582.0     735139   \n",
      "\n",
      "    total  p_individuals  indiv_per_10k  \n",
      "1  2016.0        0.71131      19.506515  \n",
      "    Unnamed: 0          region                 state  individuals  \\\n",
      "7            7  South Atlantic              Delaware        708.0   \n",
      "8            8  South Atlantic  District of Columbia       3770.0   \n",
      "9            9  South Atlantic               Florida      21443.0   \n",
      "10          10  South Atlantic               Georgia       6943.0   \n",
      "20          20  South Atlantic              Maryland       4914.0   \n",
      "30          30    Mid-Atlantic            New Jersey       6048.0   \n",
      "32          32    Mid-Atlantic              New York      39827.0   \n",
      "33          33  South Atlantic        North Carolina       6451.0   \n",
      "38          38    Mid-Atlantic          Pennsylvania       8163.0   \n",
      "40          40  South Atlantic        South Carolina       3082.0   \n",
      "46          46  South Atlantic              Virginia       3928.0   \n",
      "48          48  South Atlantic         West Virginia       1021.0   \n",
      "\n",
      "    family_members  state_pop    total  p_individuals  indiv_per_10k  \n",
      "7            374.0     965479   1082.0       0.654344       7.333148  \n",
      "8           3134.0     701547   6904.0       0.546060      53.738381  \n",
      "9           9587.0   21244317  31030.0       0.691041      10.093523  \n",
      "10          2556.0   10511131   9499.0       0.730919       6.605379  \n",
      "20          2230.0    6035802   7144.0       0.687850       8.141420  \n",
      "30          3350.0    8886025   9398.0       0.643541       6.806193  \n",
      "32         52070.0   19530351  91897.0       0.433387      20.392363  \n",
      "33          2817.0   10381615   9268.0       0.696051       6.213869  \n",
      "38          5349.0   12800922  13512.0       0.604130       6.376884  \n",
      "40           851.0    5084156   3933.0       0.783626       6.061970  \n",
      "46          2047.0    8501286   5975.0       0.657406       4.620477  \n",
      "48           222.0    1804291   1243.0       0.821400       5.658732  \n",
      "    Unnamed: 0    region       state  individuals  family_members  state_pop  \\\n",
      "2            2  Mountain     Arizona       7259.0          2606.0    7158024   \n",
      "4            4   Pacific  California     109008.0         20964.0   39461588   \n",
      "28          28  Mountain      Nevada       7058.0           486.0    3027341   \n",
      "44          44  Mountain        Utah       1904.0           972.0    3153550   \n",
      "\n",
      "       total  p_individuals  indiv_per_10k  \n",
      "2     9865.0       0.735834      10.141067  \n",
      "4   129972.0       0.838704      27.623825  \n",
      "28    7544.0       0.935578      23.314189  \n",
      "44    2876.0       0.662031       6.037640  \n",
      "    Unnamed: 0              region                 state  individuals  \\\n",
      "0            0  East South Central               Alabama       2570.0   \n",
      "1            1             Pacific                Alaska       1434.0   \n",
      "2            2            Mountain               Arizona       7259.0   \n",
      "3            3  West South Central              Arkansas       2280.0   \n",
      "4            4             Pacific            California     109008.0   \n",
      "5            5            Mountain              Colorado       7607.0   \n",
      "6            6         New England           Connecticut       2280.0   \n",
      "7            7      South Atlantic              Delaware        708.0   \n",
      "8            8      South Atlantic  District of Columbia       3770.0   \n",
      "9            9      South Atlantic               Florida      21443.0   \n",
      "10          10      South Atlantic               Georgia       6943.0   \n",
      "11          11             Pacific                Hawaii       4131.0   \n",
      "12          12            Mountain                 Idaho       1297.0   \n",
      "13          13  East North Central              Illinois       6752.0   \n",
      "14          14  East North Central               Indiana       3776.0   \n",
      "15          15  West North Central                  Iowa       1711.0   \n",
      "16          16  West North Central                Kansas       1443.0   \n",
      "17          17  East South Central              Kentucky       2735.0   \n",
      "18          18  West South Central             Louisiana       2540.0   \n",
      "19          19         New England                 Maine       1450.0   \n",
      "20          20      South Atlantic              Maryland       4914.0   \n",
      "21          21         New England         Massachusetts       6811.0   \n",
      "22          22  East North Central              Michigan       5209.0   \n",
      "23          23  West North Central             Minnesota       3993.0   \n",
      "24          24  East South Central           Mississippi       1024.0   \n",
      "25          25  West North Central              Missouri       3776.0   \n",
      "26          26            Mountain               Montana        983.0   \n",
      "27          27  West North Central              Nebraska       1745.0   \n",
      "28          28            Mountain                Nevada       7058.0   \n",
      "29          29         New England         New Hampshire        835.0   \n",
      "30          30        Mid-Atlantic            New Jersey       6048.0   \n",
      "31          31            Mountain            New Mexico       1949.0   \n",
      "32          32        Mid-Atlantic              New York      39827.0   \n",
      "33          33      South Atlantic        North Carolina       6451.0   \n",
      "34          34  West North Central          North Dakota        467.0   \n",
      "35          35  East North Central                  Ohio       6929.0   \n",
      "36          36  West South Central              Oklahoma       2823.0   \n",
      "37          37             Pacific                Oregon      11139.0   \n",
      "38          38        Mid-Atlantic          Pennsylvania       8163.0   \n",
      "39          39         New England          Rhode Island        747.0   \n",
      "40          40      South Atlantic        South Carolina       3082.0   \n",
      "41          41  West North Central          South Dakota        836.0   \n",
      "42          42  East South Central             Tennessee       6139.0   \n",
      "43          43  West South Central                 Texas      19199.0   \n",
      "44          44            Mountain                  Utah       1904.0   \n",
      "45          45         New England               Vermont        780.0   \n",
      "46          46      South Atlantic              Virginia       3928.0   \n",
      "47          47             Pacific            Washington      16424.0   \n",
      "48          48      South Atlantic         West Virginia       1021.0   \n",
      "49          49  East North Central             Wisconsin       2740.0   \n",
      "50          50            Mountain               Wyoming        434.0   \n",
      "\n",
      "    family_members  state_pop     total  p_individuals  indiv_per_10k  \n",
      "0            864.0    4887681    3434.0       0.748398       5.258117  \n",
      "1            582.0     735139    2016.0       0.711310      19.506515  \n",
      "2           2606.0    7158024    9865.0       0.735834      10.141067  \n",
      "3            432.0    3009733    2712.0       0.840708       7.575423  \n",
      "4          20964.0   39461588  129972.0       0.838704      27.623825  \n",
      "5           3250.0    5691287   10857.0       0.700654      13.366045  \n",
      "6           1696.0    3571520    3976.0       0.573441       6.383837  \n",
      "7            374.0     965479    1082.0       0.654344       7.333148  \n",
      "8           3134.0     701547    6904.0       0.546060      53.738381  \n",
      "9           9587.0   21244317   31030.0       0.691041      10.093523  \n",
      "10          2556.0   10511131    9499.0       0.730919       6.605379  \n",
      "11          2399.0    1420593    6530.0       0.632619      29.079406  \n",
      "12           715.0    1750536    2012.0       0.644632       7.409159  \n",
      "13          3891.0   12723071   10643.0       0.634408       5.306895  \n",
      "14          1482.0    6695497    5258.0       0.718144       5.639611  \n",
      "15          1038.0    3148618    2749.0       0.622408       5.434130  \n",
      "16           773.0    2911359    2216.0       0.651173       4.956448  \n",
      "17           953.0    4461153    3688.0       0.741594       6.130702  \n",
      "18           519.0    4659690    3059.0       0.830337       5.451006  \n",
      "19          1066.0    1339057    2516.0       0.576312      10.828516  \n",
      "20          2230.0    6035802    7144.0       0.687850       8.141420  \n",
      "21         13257.0    6882635   20068.0       0.339396       9.895919  \n",
      "22          3142.0    9984072    8351.0       0.623758       5.217310  \n",
      "23          3250.0    5606249    7243.0       0.551291       7.122409  \n",
      "24           328.0    2981020    1352.0       0.757396       3.435066  \n",
      "25          2107.0    6121623    5883.0       0.641849       6.168299  \n",
      "26           422.0    1060665    1405.0       0.699644       9.267771  \n",
      "27           676.0    1925614    2421.0       0.720777       9.062045  \n",
      "28           486.0    3027341    7544.0       0.935578      23.314189  \n",
      "29           615.0    1353465    1450.0       0.575862       6.169351  \n",
      "30          3350.0    8886025    9398.0       0.643541       6.806193  \n",
      "31           602.0    2092741    2551.0       0.764014       9.313145  \n",
      "32         52070.0   19530351   91897.0       0.433387      20.392363  \n",
      "33          2817.0   10381615    9268.0       0.696051       6.213869  \n",
      "34            75.0     758080     542.0       0.861624       6.160300  \n",
      "35          3320.0   11676341   10249.0       0.676066       5.934222  \n",
      "36          1048.0    3940235    3871.0       0.729269       7.164547  \n",
      "37          3337.0    4181886   14476.0       0.769481      26.636307  \n",
      "38          5349.0   12800922   13512.0       0.604130       6.376884  \n",
      "39           354.0    1058287    1101.0       0.678474       7.058577  \n",
      "40           851.0    5084156    3933.0       0.783626       6.061970  \n",
      "41           323.0     878698    1159.0       0.721311       9.514077  \n",
      "42          1744.0    6771631    7883.0       0.778764       9.065763  \n",
      "43          6111.0   28628666   25310.0       0.758554       6.706215  \n",
      "44           972.0    3153550    2876.0       0.662031       6.037640  \n",
      "45           511.0     624358    1291.0       0.604183      12.492833  \n",
      "46          2047.0    8501286    5975.0       0.657406       4.620477  \n",
      "47          5880.0    7523869   22304.0       0.736370      21.829195  \n",
      "48           222.0    1804291    1243.0       0.821400       5.658732  \n",
      "49          2167.0    5807406    4907.0       0.558386       4.718113  \n",
      "50           205.0     577601     639.0       0.679186       7.513837  \n",
      "                   state  indiv_per_10k\n",
      "8   District of Columbia      53.738381\n",
      "11                Hawaii      29.079406\n",
      "4             California      27.623825\n",
      "37                Oregon      26.636307\n",
      "28                Nevada      23.314189\n",
      "47            Washington      21.829195\n",
      "32              New York      20.392363\n"
     ]
    }
   ],
   "source": [
    "# Select the state and family_members columns\n",
    "state_fam = homelessness[['state','family_members']]\n",
    "\n",
    "# Print the head of the result\n",
    "print(state_fam.head())\n",
    "\n",
    "# Select only the individuals and state columns, in that order\n",
    "ind_state = homelessness[['individuals','state']]\n",
    "\n",
    "# Print the head of the result\n",
    "print(ind_state.head())\n",
    "\n",
    "# Filter for rows where individuals is greater than 10000\n",
    "ind_gt_10k = homelessness[homelessness['individuals'] > 10000]\n",
    "\n",
    "# See the result\n",
    "print(ind_gt_10k)\n",
    "\n",
    "# Filter for rows where region is Mountain\n",
    "mountain_reg = homelessness[homelessness['region'] == 'Mountain']\n",
    "\n",
    "# See the result\n",
    "print(mountain_reg)\n",
    "\n",
    "# Filter for rows where family_members is less than 1000 \n",
    "# and region is Pacific\n",
    "fam_lt_1k_pac = homelessness[(homelessness['family_members'] < 1000) & (homelessness['region'] == 'Pacific')]\n",
    "\n",
    "# See the result\n",
    "print(fam_lt_1k_pac)\n",
    "\n",
    "\n",
    "\n",
    "# Subset for rows in South Atlantic or Mid-Atlantic regions\n",
    "#crit = ['South Atlantic','Mid-Atlantic']\n",
    "#south_mid_atlantic = homelessness['region'].isin(crit)\n",
    "\n",
    "south_mid_atlantic = homelessness[(homelessness['region'] == 'South Atlantic') | (homelessness['region'] == 'Mid-Atlantic')]\n",
    "\n",
    "\n",
    "# See the result\n",
    "print(south_mid_atlantic)\n",
    "\n",
    "\n",
    "# The Mojave Desert states\n",
    "canu = [\"California\", \"Arizona\", \"Nevada\", \"Utah\"]\n",
    "\n",
    "# Filter for rows in the Mojave Desert states\n",
    "mojave_homelessness = homelessness[homelessness[\"state\"].isin(canu)]\n",
    "\n",
    "# See the result\n",
    "print(mojave_homelessness)\n",
    "\n",
    "\n",
    "\n",
    "# Add total col as sum of individuals and family_members\n",
    "homelessness[\"total\"] = homelessness[\"individuals\"] + homelessness[\"family_members\"]\n",
    "\n",
    "# Add p_individuals col as proportion of individuals\n",
    "homelessness[\"p_individuals\"] = homelessness[\"individuals\"] / homelessness[\"total\"]\n",
    "\n",
    "# See the result\n",
    "print(homelessness)\n",
    "\n",
    "\n",
    "\n",
    "# Create indiv_per_10k col as homeless individuals per 10k state pop\n",
    "homelessness[\"indiv_per_10k\"] = 10000 * homelessness[\"individuals\"] / homelessness[\"state_pop\"] \n",
    "\n",
    "# Subset rows for indiv_per_10k greater than 20\n",
    "high_homelessness = homelessness[homelessness[\"indiv_per_10k\"] > 20]\n",
    "\n",
    "# Sort high_homelessness by descending indiv_per_10k\n",
    "high_homelessness_srt = high_homelessness.sort_values(\"indiv_per_10k\", ascending=False)\n",
    "\n",
    "# From high_homelessness_srt, select the state and indiv_per_10k cols\n",
    "result = high_homelessness_srt[[\"state\", \"indiv_per_10k\"]]\n",
    "\n",
    "# See the result\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  store type  department        date  weekly_sales  is_holiday  \\\n",
      "0           0      1    A           1  2010-02-05      24924.50       False   \n",
      "1           1      1    A           1  2010-03-05      21827.90       False   \n",
      "2           2      1    A           1  2010-04-02      57258.43       False   \n",
      "3           3      1    A           1  2010-05-07      17413.94       False   \n",
      "4           4      1    A           1  2010-06-04      17558.09       False   \n",
      "\n",
      "   temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0       5.727778              0.679451         8.106  \n",
      "1       8.055556              0.693452         8.106  \n",
      "2      16.816667              0.718284         7.808  \n",
      "3      22.527778              0.748928         7.808  \n",
      "4      27.050000              0.714586         7.808  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10774 entries, 0 to 10773\n",
      "Data columns (total 10 columns):\n",
      "Unnamed: 0              10774 non-null int64\n",
      "store                   10774 non-null int64\n",
      "type                    10774 non-null object\n",
      "department              10774 non-null int64\n",
      "date                    10774 non-null object\n",
      "weekly_sales            10774 non-null float64\n",
      "is_holiday              10774 non-null bool\n",
      "temperature_c           10774 non-null float64\n",
      "fuel_price_usd_per_l    10774 non-null float64\n",
      "unemployment            10774 non-null float64\n",
      "dtypes: bool(1), float64(4), int64(3), object(2)\n",
      "memory usage: 768.1+ KB\n",
      "None\n",
      "23843.950148505668\n",
      "12049.064999999999\n",
      "2012-10-26\n",
      "2010-02-05\n",
      "16.58333333333334\n",
      "temperature_c           16.583333\n",
      "fuel_price_usd_per_l     0.073176\n",
      "unemployment             0.565000\n",
      "dtype: float64\n",
      "        temperature_c  fuel_price_usd_per_l  unemployment\n",
      "iqr         16.583333              0.073176         0.565\n",
      "median      16.966667              0.743381         8.099\n",
      "             date  weekly_sales  cum_weekly_sales  cum_max_sales\n",
      "0      2010-02-05      24924.50      2.492450e+04       24924.50\n",
      "6437   2010-02-05      38597.52      6.352202e+04       38597.52\n",
      "1249   2010-02-05       3840.21      6.736223e+04       38597.52\n",
      "6449   2010-02-05      17590.59      8.495282e+04       38597.52\n",
      "6461   2010-02-05       4929.87      8.988269e+04       38597.52\n",
      "6473   2010-02-05      20578.79      1.104615e+05       38597.52\n",
      "6485   2010-02-05       3953.70      1.144152e+05       38597.52\n",
      "1237   2010-02-05       6916.00      1.213312e+05       38597.52\n",
      "6425   2010-02-05       5089.54      1.264207e+05       38597.52\n",
      "6506   2010-02-05       6261.35      1.326821e+05       38597.52\n",
      "1225   2010-02-05      10322.43      1.430045e+05       38597.52\n",
      "6530   2010-02-05      12478.00      1.554825e+05       38597.52\n",
      "6542   2010-02-05      25160.36      1.806429e+05       38597.52\n",
      "6566   2010-02-05      10577.70      1.912206e+05       38597.52\n",
      "1213   2010-02-05       1592.41      1.928130e+05       38597.52\n",
      "6578   2010-02-05       7043.39      1.998564e+05       38597.52\n",
      "6590   2010-02-05       3303.98      2.031603e+05       38597.52\n",
      "6518   2010-02-05       7638.42      2.107988e+05       38597.52\n",
      "6602   2010-02-05       1232.07      2.120308e+05       38597.52\n",
      "6413   2010-02-05      21710.43      2.337413e+05       38597.52\n",
      "6401   2010-02-05      24525.54      2.582668e+05       38597.52\n",
      "1309   2010-02-05        975.50      2.592423e+05       38597.52\n",
      "6237   2010-02-05     213042.66      4.722850e+05      213042.66\n",
      "6258   2010-02-05      45017.96      5.173029e+05      213042.66\n",
      "6270   2010-02-05      24273.74      5.415767e+05      213042.66\n",
      "1297   2010-02-05       4216.00      5.457927e+05      213042.66\n",
      "6293   2010-02-05      21500.58      5.672932e+05      213042.66\n",
      "6305   2010-02-05      52144.50      6.194377e+05      213042.66\n",
      "1261   2010-02-05      17212.26      6.366500e+05      213042.66\n",
      "6317   2010-02-05      18215.03      6.548650e+05      213042.66\n",
      "...           ...           ...               ...            ...\n",
      "3591   2012-07-13          0.07      2.568869e+08      293966.05\n",
      "9007   2012-07-13          0.06      2.568869e+08      293966.05\n",
      "8071   2012-07-20         80.96      2.568870e+08      293966.05\n",
      "1594   2012-07-27          6.00      2.568870e+08      293966.05\n",
      "5016   2012-08-10        -98.00      2.568869e+08      293966.05\n",
      "6256   2012-08-10          6.39      2.568869e+08      293966.05\n",
      "7905   2012-08-17       -175.00      2.568867e+08      293966.05\n",
      "7725   2012-08-24        -34.00      2.568867e+08      293966.05\n",
      "8630   2012-08-24          0.00      2.568867e+08      293966.05\n",
      "6734   2012-08-31          4.47      2.568867e+08      293966.05\n",
      "10568  2012-08-31         14.96      2.568867e+08      293966.05\n",
      "8072   2012-09-07         -8.97      2.568867e+08      293966.05\n",
      "6735   2012-09-07         13.41      2.568867e+08      293966.05\n",
      "3223   2012-09-21        389.00      2.568871e+08      293966.05\n",
      "525    2012-09-28        224.00      2.568873e+08      293966.05\n",
      "8073   2012-10-05         18.89      2.568874e+08      293966.05\n",
      "2698   2012-10-05        490.00      2.568878e+08      293966.05\n",
      "900    2012-10-05        635.00      2.568885e+08      293966.05\n",
      "7198   2012-10-05        870.00      2.568894e+08      293966.05\n",
      "6816   2012-10-05        448.00      2.568898e+08      293966.05\n",
      "6736   2012-10-05         13.41      2.568898e+08      293966.05\n",
      "9008   2012-10-05        435.00      2.568902e+08      293966.05\n",
      "6292   2012-10-05        750.00      2.568910e+08      293966.05\n",
      "5407   2012-10-05       1130.00      2.568921e+08      293966.05\n",
      "1797   2012-10-05        595.00      2.568927e+08      293966.05\n",
      "3592   2012-10-05        440.00      2.568932e+08      293966.05\n",
      "8108   2012-10-05        660.00      2.568938e+08      293966.05\n",
      "10773  2012-10-05        915.00      2.568947e+08      293966.05\n",
      "6257   2012-10-12          3.00      2.568947e+08      293966.05\n",
      "3384   2012-10-26        -21.63      2.568947e+08      293966.05\n",
      "\n",
      "[10774 rows x 4 columns]\n",
      "      Unnamed: 0  store type  department        date  weekly_sales  \\\n",
      "0              0      1    A           1  2010-02-05      24924.50   \n",
      "901          901      2    A           1  2010-02-05      35034.06   \n",
      "1798        1798      4    A           1  2010-02-05      38724.42   \n",
      "2699        2699      6    A           1  2010-02-05      25619.00   \n",
      "3593        3593     10    B           1  2010-02-05      40212.84   \n",
      "\n",
      "      is_holiday  temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0          False       5.727778              0.679451         8.106  \n",
      "901        False       4.550000              0.679451         8.324  \n",
      "1798       False       6.533333              0.686319         8.623  \n",
      "2699       False       4.683333              0.679451         7.259  \n",
      "3593       False      12.411111              0.782478         9.765  \n",
      "    Unnamed: 0  store type  department        date  weekly_sales  is_holiday  \\\n",
      "0            0      1    A           1  2010-02-05      24924.50       False   \n",
      "12          12      1    A           2  2010-02-05      50605.27       False   \n",
      "24          24      1    A           3  2010-02-05      13740.12       False   \n",
      "36          36      1    A           4  2010-02-05      39954.04       False   \n",
      "48          48      1    A           5  2010-02-05      32229.38       False   \n",
      "\n",
      "    temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0        5.727778              0.679451         8.106  \n",
      "12       5.727778              0.679451         8.106  \n",
      "24       5.727778              0.679451         8.106  \n",
      "36       5.727778              0.679451         8.106  \n",
      "48       5.727778              0.679451         8.106  \n",
      "498     2010-09-10\n",
      "691     2011-11-25\n",
      "2315    2010-02-12\n",
      "6735    2012-09-07\n",
      "6810    2010-12-31\n",
      "6815    2012-02-10\n",
      "6820    2011-09-09\n",
      "Name: date, dtype: object\n",
      "A    11\n",
      "B     1\n",
      "Name: type, dtype: int64\n",
      "A    0.916667\n",
      "B    0.083333\n",
      "Name: type, dtype: float64\n",
      "41    12\n",
      "30    12\n",
      "23    12\n",
      "24    12\n",
      "25    12\n",
      "26    12\n",
      "27    12\n",
      "28    12\n",
      "29    12\n",
      "31    12\n",
      "21    12\n",
      "32    12\n",
      "33    12\n",
      "34    12\n",
      "35    12\n",
      "36    12\n",
      "38    12\n",
      "40    12\n",
      "22    12\n",
      "20    12\n",
      "42    12\n",
      "9     12\n",
      "2     12\n",
      "3     12\n",
      "4     12\n",
      "5     12\n",
      "6     12\n",
      "7     12\n",
      "8     12\n",
      "10    12\n",
      "      ..\n",
      "82    12\n",
      "83    12\n",
      "85    12\n",
      "87    12\n",
      "90    12\n",
      "91    12\n",
      "67    12\n",
      "60    12\n",
      "59    12\n",
      "58    12\n",
      "56    12\n",
      "55    12\n",
      "54    12\n",
      "52    12\n",
      "51    12\n",
      "94    12\n",
      "49    12\n",
      "95    12\n",
      "47    12\n",
      "46    12\n",
      "96    12\n",
      "45    12\n",
      "97    12\n",
      "92    12\n",
      "99    11\n",
      "37    10\n",
      "48     8\n",
      "50     6\n",
      "39     4\n",
      "43     2\n",
      "Name: department, Length: 80, dtype: int64\n",
      "41    0.012917\n",
      "30    0.012917\n",
      "23    0.012917\n",
      "24    0.012917\n",
      "25    0.012917\n",
      "26    0.012917\n",
      "27    0.012917\n",
      "28    0.012917\n",
      "29    0.012917\n",
      "31    0.012917\n",
      "21    0.012917\n",
      "32    0.012917\n",
      "33    0.012917\n",
      "34    0.012917\n",
      "35    0.012917\n",
      "36    0.012917\n",
      "38    0.012917\n",
      "40    0.012917\n",
      "22    0.012917\n",
      "20    0.012917\n",
      "42    0.012917\n",
      "9     0.012917\n",
      "2     0.012917\n",
      "3     0.012917\n",
      "4     0.012917\n",
      "5     0.012917\n",
      "6     0.012917\n",
      "7     0.012917\n",
      "8     0.012917\n",
      "10    0.012917\n",
      "        ...   \n",
      "82    0.012917\n",
      "83    0.012917\n",
      "85    0.012917\n",
      "87    0.012917\n",
      "90    0.012917\n",
      "91    0.012917\n",
      "67    0.012917\n",
      "60    0.012917\n",
      "59    0.012917\n",
      "58    0.012917\n",
      "56    0.012917\n",
      "55    0.012917\n",
      "54    0.012917\n",
      "52    0.012917\n",
      "51    0.012917\n",
      "94    0.012917\n",
      "49    0.012917\n",
      "95    0.012917\n",
      "47    0.012917\n",
      "46    0.012917\n",
      "96    0.012917\n",
      "45    0.012917\n",
      "97    0.012917\n",
      "92    0.012917\n",
      "99    0.011841\n",
      "37    0.010764\n",
      "48    0.008611\n",
      "50    0.006459\n",
      "39    0.004306\n",
      "43    0.002153\n",
      "Name: department, Length: 80, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales = pd.read_csv('sales_subset.csv')\n",
    "sales_1_1 = sales\n",
    "\n",
    "# Print the head of the sales DataFrame\n",
    "print(sales.head())\n",
    "\n",
    "# Print the info about the sales DataFrame\n",
    "print(sales.info())\n",
    "\n",
    "# Print the mean of weekly_sales\n",
    "print(sales.weekly_sales.mean())\n",
    "\n",
    "# Print the median of weekly_sales\n",
    "print(sales.weekly_sales.median())\n",
    "\n",
    "# Print the maximum of the date column\n",
    "print(sales.date.max())\n",
    "\n",
    "# Print the minimum of the date column\n",
    "print(sales.date.min())\n",
    "\n",
    "# A custom IQR function\n",
    "def iqr(column):\n",
    "    return column.quantile(0.75) - column.quantile(0.25)\n",
    "    \n",
    "# Print IQR of the temperature_c column\n",
    "print(sales.temperature_c.agg(iqr))\n",
    "\n",
    "\n",
    "\n",
    "# A custom IQR function\n",
    "def iqr(column):\n",
    "    return column.quantile(0.75) - column.quantile(0.25)\n",
    "\n",
    "# Update to print IQR of temperature_c, fuel_price_usd_per_l, & unemployment\n",
    "print(sales[[\"temperature_c\", \"fuel_price_usd_per_l\", \"unemployment\"]].agg(iqr))\n",
    "\n",
    "\n",
    "# Import NumPy and create custom IQR function\n",
    "import numpy as np\n",
    "def iqr(column):\n",
    "    return column.quantile(0.75) - column.quantile(0.25)\n",
    "\n",
    "# Update to print IQR and median of temperature_c, fuel_price_usd_per_l, & unemployment\n",
    "print(sales[[\"temperature_c\", \"fuel_price_usd_per_l\", \"unemployment\"]].agg([iqr, np.median]))\n",
    "\n",
    "\n",
    "\n",
    "# Sort sales_1_1 by date\n",
    "sales_1_1 = sales_1_1.sort_values('date', ascending=True)\n",
    "\n",
    "# Get the cumulative sum of weekly_sales, add as cum_weekly_sales col\n",
    "sales_1_1['cum_weekly_sales'] = sales_1_1['weekly_sales'].cumsum()\n",
    "\n",
    "# Get the cumulative max of weekly_sales, add as cum_max_sales col\n",
    "sales_1_1['cum_max_sales'] = sales_1_1['weekly_sales'].cummax()\n",
    "\n",
    "# See the columns you calculated\n",
    "print(sales_1_1[[\"date\", \"weekly_sales\", \"cum_weekly_sales\", \"cum_max_sales\"]])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Drop duplicate store/type combinations\n",
    "store_types = sales.drop_duplicates(subset=['store','type'])\n",
    "print(store_types.head())\n",
    "\n",
    "# Drop duplicate store/department combinations\n",
    "store_depts = sales.drop_duplicates(subset=[\"store\",\"department\"])\n",
    "print(store_depts.head())\n",
    "\n",
    "# Subset the rows where is_holiday is True and drop duplicate dates\n",
    "holiday_dates = sales[sales['is_holiday']].drop_duplicates(subset=\"date\")\n",
    "\n",
    "# Print date col of holiday_dates\n",
    "print(holiday_dates['date'])\n",
    "\n",
    "\n",
    "\n",
    "# Count the number of stores of each type\n",
    "store_counts = store_types['type'].value_counts()\n",
    "print(store_counts)\n",
    "\n",
    "# Get the proportion of stores of each type\n",
    "store_props = store_types['type'].value_counts(normalize=True)\n",
    "print(store_props)\n",
    "\n",
    "# Count the number of each department number and sort\n",
    "dept_counts_sorted = store_depts['department'].value_counts(sort=True)\n",
    "print(dept_counts_sorted)\n",
    "\n",
    "# Get the proportion of departments of each number and sort\n",
    "dept_props_sorted = store_depts['department'].value_counts(sort=True, normalize=True)\n",
    "print(dept_props_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9097747 0.0902253 0.       ]\n",
      "type\n",
      "A    0.909775\n",
      "B    0.090225\n",
      "Name: weekly_sales, dtype: float64\n",
      "type  is_holiday\n",
      "A     False         2.336927e+08\n",
      "      True          2.360181e+04\n",
      "B     False         2.317678e+07\n",
      "      True          1.621410e+03\n",
      "Name: weekly_sales, dtype: float64\n",
      "type  is_holiday\n",
      "A     True          23601.81\n",
      "B     True           1621.41\n",
      "Name: weekly_sales, dtype: float64\n",
      "type  is_holiday\n",
      "A     True          23601.81\n",
      "Name: weekly_sales, dtype: float64\n",
      "        amin       amax    median          mean\n",
      "type                                           \n",
      "A    -1098.0  293966.05  11943.92  23674.667242\n",
      "B     -798.0  232558.51  13336.08  25696.678370\n",
      "     unemployment                         fuel_price_usd_per_l            \\\n",
      "             amin   amax median      mean                 amin      amax   \n",
      "type                                                                       \n",
      "A           3.879  8.992  8.067  7.972611             0.664129  1.107410   \n",
      "B           7.170  9.765  9.199  9.279323             0.760023  1.107674   \n",
      "\n",
      "                          \n",
      "        median      mean  \n",
      "type                      \n",
      "A     0.735455  0.744619  \n",
      "B     0.803348  0.805858  \n",
      "        amin       amax    median          mean\n",
      "type                                           \n",
      "A    -1098.0  293966.05  11943.92  23674.667242\n",
      "B     -798.0  232558.51  13336.08  25696.678370\n",
      "                unemployment                          fuel_price_usd_per_l  \\\n",
      "                        amin   amax  median      mean                 amin   \n",
      "type is_holiday                                                              \n",
      "A    False             3.879  8.992  8.0670  7.973170             0.664129   \n",
      "     True              5.143  8.992  7.9245  7.835100             0.673111   \n",
      "B    False             7.170  9.765  9.1990  9.280973             0.760023   \n",
      "     True              7.874  9.199  8.5365  8.536500             0.782214   \n",
      "\n",
      "                                               \n",
      "                     amax    median      mean  \n",
      "type is_holiday                                \n",
      "A    False       1.107410  0.735455  0.744332  \n",
      "     True        1.076766  0.814311  0.815169  \n",
      "B    False       1.107674  0.803348  0.805676  \n",
      "     True        0.993287  0.887751  0.887751  \n"
     ]
    }
   ],
   "source": [
    "# Calc total weekly sales\n",
    "sales_all = sales[\"weekly_sales\"].sum()\n",
    "\n",
    "# Subset for type A stores, calc total weekly sales\n",
    "sales_A = sales[sales[\"type\"] == \"A\"][\"weekly_sales\"].sum()\n",
    "\n",
    "# Subset for type B stores, calc total weekly sales\n",
    "sales_B = sales[sales[\"type\"] == \"B\"][\"weekly_sales\"].sum()\n",
    "\n",
    "# Subset for type C stores, calc total weekly sales\n",
    "sales_C = sales[sales[\"type\"] == \"C\"][\"weekly_sales\"].sum()\n",
    "\n",
    "# Get proportion for each type\n",
    "sales_propn_by_type = [sales_A, sales_B, sales_C] / sales_all\n",
    "print(sales_propn_by_type)\n",
    "\n",
    "\n",
    "# Group by type; calc total weekly sales\n",
    "sales_by_type = sales.groupby(\"type\")[\"weekly_sales\"].sum()\n",
    "\n",
    "# Get proportion for each type\n",
    "sales_propn_by_type = sales_by_type / sum(sales_by_type)\n",
    "print(sales_propn_by_type)\n",
    "\n",
    "\n",
    "# From previous step\n",
    "sales_by_type = sales.groupby(\"type\")[\"weekly_sales\"].sum()\n",
    "\n",
    "# Group by type and is_holiday; calc total weekly sales\n",
    "sales_by_type_is_holiday = sales.groupby(['type','is_holiday'])['weekly_sales'].sum()\n",
    "print(sales_by_type_is_holiday)\n",
    "\n",
    "\n",
    "\n",
    "# From previous step\n",
    "sales_by_type = sales.groupby(\"type\")[\"weekly_sales\"].sum()\n",
    "\n",
    "# Group by type and is_holiday; calc total weekly sales\n",
    "sales_by_type_is_holiday = sales[sales['is_holiday'] == True].groupby(['type','is_holiday'])['weekly_sales'].sum()\n",
    "print(sales_by_type_is_holiday)\n",
    "\n",
    "\n",
    "\n",
    "# From previous step\n",
    "sales_by_type = sales.groupby(\"type\")[\"weekly_sales\"].sum()\n",
    "\n",
    "# Group by type and is_holiday; calc total weekly sales\n",
    "sales_by_type_is_holiday = sales[(sales['is_holiday'] == True) & (sales['type'] == \"A\")].groupby(['type','is_holiday'])['weekly_sales'].sum()\n",
    "print(sales_by_type_is_holiday)\n",
    "\n",
    "\n",
    "\n",
    "# Import numpy with the alias np\n",
    "import numpy as np\n",
    "\n",
    "# For each store type, aggregate weekly_sales: get min, max, mean, and median\n",
    "sales_stats = sales.groupby('type')['weekly_sales'].agg([np.min, np.max, np.median, np.mean])\n",
    "\n",
    "# Print sales_stats\n",
    "print(sales_stats)\n",
    "\n",
    "# For each store type, aggregate unemployment and fuel_price_usd_per_l: get min, max, mean, and median\n",
    "unemp_fuel_stats = sales.groupby('type')[['unemployment','fuel_price_usd_per_l']].agg([np.min, np.max, np.median, np.mean])\n",
    "\n",
    "# Print unemp_fuel_stats\n",
    "print(unemp_fuel_stats)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Import numpy with the alias np\n",
    "import numpy as np\n",
    "\n",
    "# For each store type, aggregate weekly_sales: get min, max, mean, and median\n",
    "sales_stats = sales.groupby('type')['weekly_sales'].agg([np.min,np.max,np.median, np.mean])\n",
    "\n",
    "# Print sales_stats\n",
    "print(sales_stats)\n",
    "\n",
    "# For each store type, aggregate unemployment and fuel_price_usd_per_l: get min, max, mean, and median\n",
    "unemp_fuel_stats = sales.groupby(['type','is_holiday'])[['unemployment','fuel_price_usd_per_l']].agg([np.min,np.max,np.median,np.mean])\n",
    "\n",
    "# Print unemp_fuel_stats\n",
    "print(unemp_fuel_stats)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Pivot for mean weekly_sales for each store type\n",
    "mean_sales_by_type = sales.pivot_table(values=\"weekly_sales\", index=\"type\")\n",
    "\n",
    "# Print mean_sales_by_type\n",
    "print(mean_sales_by_type)\n",
    "\n",
    "# Import NumPy as np\n",
    "import numpy as np\n",
    "\n",
    "# Pivot for mean and median weekly_sales for each store type\n",
    "mean_med_sales_by_type = sales.pivot_table(values=\"weekly_sales\", index='type', aggfunc=[np.mean, np.median])\n",
    "\n",
    "# Print mean_med_sales_by_type\n",
    "print(mean_med_sales_by_type)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Pivot for mean weekly_sales by store type and holiday \n",
    "mean_sales_by_type_holiday = sales.pivot_table(values=\"weekly_sales\", columns=\"is_holiday\", index=\"type\")\n",
    "\n",
    "# Print mean_sales_by_type_holiday\n",
    "print(mean_sales_by_type_holiday)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Print mean weekly_sales by department and type; fill missing values with 0\n",
    "print(sales.pivot_table(values=\"weekly_sales\", index=\"type\", columns=\"department\", fill_value=0))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Print the mean weekly_sales by department and type; fill missing values with 0s; sum all rows and cols\n",
    "print(sales.pivot_table(values=\"weekly_sales\", index=\"department\", columns=\"type\", fill_value=0, margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16500 entries, 0 to 16499\n",
      "Data columns (total 5 columns):\n",
      "Unnamed: 0    16500 non-null int64\n",
      "date          16500 non-null object\n",
      "city          16500 non-null object\n",
      "country       16500 non-null object\n",
      "avg_temp_c    16407 non-null float64\n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 644.6+ KB\n",
      "None\n",
      "       Unnamed: 0        date     city        country  avg_temp_c\n",
      "0               0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1               1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2               2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3               3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4               4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "5               5  2000-06-01  Abidjan  Côte D'Ivoire      25.812\n",
      "6               6  2000-07-01  Abidjan  Côte D'Ivoire      24.870\n",
      "7               7  2000-08-01  Abidjan  Côte D'Ivoire      24.884\n",
      "8               8  2000-09-01  Abidjan  Côte D'Ivoire      25.405\n",
      "9               9  2000-10-01  Abidjan  Côte D'Ivoire      26.074\n",
      "10             10  2000-11-01  Abidjan  Côte D'Ivoire      27.315\n",
      "11             11  2000-12-01  Abidjan  Côte D'Ivoire      26.929\n",
      "12             12  2001-01-01  Abidjan  Côte D'Ivoire      26.920\n",
      "13             13  2001-02-01  Abidjan  Côte D'Ivoire      28.234\n",
      "14             14  2001-03-01  Abidjan  Côte D'Ivoire      28.706\n",
      "15             15  2001-04-01  Abidjan  Côte D'Ivoire      27.702\n",
      "16             16  2001-05-01  Abidjan  Côte D'Ivoire      27.653\n",
      "17             17  2001-06-01  Abidjan  Côte D'Ivoire      25.940\n",
      "18             18  2001-07-01  Abidjan  Côte D'Ivoire      24.841\n",
      "19             19  2001-08-01  Abidjan  Côte D'Ivoire      24.280\n",
      "20             20  2001-09-01  Abidjan  Côte D'Ivoire      24.797\n",
      "21             21  2001-10-01  Abidjan  Côte D'Ivoire      26.278\n",
      "22             22  2001-11-01  Abidjan  Côte D'Ivoire      27.374\n",
      "23             23  2001-12-01  Abidjan  Côte D'Ivoire      27.598\n",
      "24             24  2002-01-01  Abidjan  Côte D'Ivoire      27.192\n",
      "25             25  2002-02-01  Abidjan  Côte D'Ivoire      28.796\n",
      "26             26  2002-03-01  Abidjan  Côte D'Ivoire      28.564\n",
      "27             27  2002-04-01  Abidjan  Côte D'Ivoire      28.508\n",
      "28             28  2002-05-01  Abidjan  Côte D'Ivoire      27.721\n",
      "29             29  2002-06-01  Abidjan  Côte D'Ivoire      25.972\n",
      "...           ...         ...      ...            ...         ...\n",
      "16470       16470  2011-04-01     Xian          China      14.876\n",
      "16471       16471  2011-05-01     Xian          China      18.225\n",
      "16472       16472  2011-06-01     Xian          China      23.879\n",
      "16473       16473  2011-07-01     Xian          China      24.895\n",
      "16474       16474  2011-08-01     Xian          China      23.069\n",
      "16475       16475  2011-09-01     Xian          China      16.775\n",
      "16476       16476  2011-10-01     Xian          China      12.587\n",
      "16477       16477  2011-11-01     Xian          China       7.543\n",
      "16478       16478  2011-12-01     Xian          China      -0.490\n",
      "16479       16479  2012-01-01     Xian          China      -2.416\n",
      "16480       16480  2012-02-01     Xian          China       0.162\n",
      "16481       16481  2012-03-01     Xian          China       6.430\n",
      "16482       16482  2012-04-01     Xian          China      14.908\n",
      "16483       16483  2012-05-01     Xian          China      19.041\n",
      "16484       16484  2012-06-01     Xian          China      22.781\n",
      "16485       16485  2012-07-01     Xian          China      24.646\n",
      "16486       16486  2012-08-01     Xian          China      23.885\n",
      "16487       16487  2012-09-01     Xian          China      17.028\n",
      "16488       16488  2012-10-01     Xian          China      12.399\n",
      "16489       16489  2012-11-01     Xian          China       4.736\n",
      "16490       16490  2012-12-01     Xian          China      -0.822\n",
      "16491       16491  2013-01-01     Xian          China      -1.635\n",
      "16492       16492  2013-02-01     Xian          China       2.362\n",
      "16493       16493  2013-03-01     Xian          China      10.110\n",
      "16494       16494  2013-04-01     Xian          China      12.563\n",
      "16495       16495  2013-05-01     Xian          China      18.979\n",
      "16496       16496  2013-06-01     Xian          China      23.522\n",
      "16497       16497  2013-07-01     Xian          China      25.251\n",
      "16498       16498  2013-08-01     Xian          China      24.528\n",
      "16499       16499  2013-09-01     Xian          China         NaN\n",
      "\n",
      "[16500 rows x 5 columns]\n",
      "         Unnamed: 0        date        country  avg_temp_c\n",
      "city                                                      \n",
      "Abidjan           0  2000-01-01  Côte D'Ivoire      27.293\n",
      "Abidjan           1  2000-02-01  Côte D'Ivoire      27.685\n",
      "Abidjan           2  2000-03-01  Côte D'Ivoire      29.061\n",
      "Abidjan           3  2000-04-01  Côte D'Ivoire      28.162\n",
      "Abidjan           4  2000-05-01  Côte D'Ivoire      27.547\n",
      "Abidjan           5  2000-06-01  Côte D'Ivoire      25.812\n",
      "Abidjan           6  2000-07-01  Côte D'Ivoire      24.870\n",
      "Abidjan           7  2000-08-01  Côte D'Ivoire      24.884\n",
      "Abidjan           8  2000-09-01  Côte D'Ivoire      25.405\n",
      "Abidjan           9  2000-10-01  Côte D'Ivoire      26.074\n",
      "Abidjan          10  2000-11-01  Côte D'Ivoire      27.315\n",
      "Abidjan          11  2000-12-01  Côte D'Ivoire      26.929\n",
      "Abidjan          12  2001-01-01  Côte D'Ivoire      26.920\n",
      "Abidjan          13  2001-02-01  Côte D'Ivoire      28.234\n",
      "Abidjan          14  2001-03-01  Côte D'Ivoire      28.706\n",
      "Abidjan          15  2001-04-01  Côte D'Ivoire      27.702\n",
      "Abidjan          16  2001-05-01  Côte D'Ivoire      27.653\n",
      "Abidjan          17  2001-06-01  Côte D'Ivoire      25.940\n",
      "Abidjan          18  2001-07-01  Côte D'Ivoire      24.841\n",
      "Abidjan          19  2001-08-01  Côte D'Ivoire      24.280\n",
      "Abidjan          20  2001-09-01  Côte D'Ivoire      24.797\n",
      "Abidjan          21  2001-10-01  Côte D'Ivoire      26.278\n",
      "Abidjan          22  2001-11-01  Côte D'Ivoire      27.374\n",
      "Abidjan          23  2001-12-01  Côte D'Ivoire      27.598\n",
      "Abidjan          24  2002-01-01  Côte D'Ivoire      27.192\n",
      "Abidjan          25  2002-02-01  Côte D'Ivoire      28.796\n",
      "Abidjan          26  2002-03-01  Côte D'Ivoire      28.564\n",
      "Abidjan          27  2002-04-01  Côte D'Ivoire      28.508\n",
      "Abidjan          28  2002-05-01  Côte D'Ivoire      27.721\n",
      "Abidjan          29  2002-06-01  Côte D'Ivoire      25.972\n",
      "...             ...         ...            ...         ...\n",
      "Xian          16470  2011-04-01          China      14.876\n",
      "Xian          16471  2011-05-01          China      18.225\n",
      "Xian          16472  2011-06-01          China      23.879\n",
      "Xian          16473  2011-07-01          China      24.895\n",
      "Xian          16474  2011-08-01          China      23.069\n",
      "Xian          16475  2011-09-01          China      16.775\n",
      "Xian          16476  2011-10-01          China      12.587\n",
      "Xian          16477  2011-11-01          China       7.543\n",
      "Xian          16478  2011-12-01          China      -0.490\n",
      "Xian          16479  2012-01-01          China      -2.416\n",
      "Xian          16480  2012-02-01          China       0.162\n",
      "Xian          16481  2012-03-01          China       6.430\n",
      "Xian          16482  2012-04-01          China      14.908\n",
      "Xian          16483  2012-05-01          China      19.041\n",
      "Xian          16484  2012-06-01          China      22.781\n",
      "Xian          16485  2012-07-01          China      24.646\n",
      "Xian          16486  2012-08-01          China      23.885\n",
      "Xian          16487  2012-09-01          China      17.028\n",
      "Xian          16488  2012-10-01          China      12.399\n",
      "Xian          16489  2012-11-01          China       4.736\n",
      "Xian          16490  2012-12-01          China      -0.822\n",
      "Xian          16491  2013-01-01          China      -1.635\n",
      "Xian          16492  2013-02-01          China       2.362\n",
      "Xian          16493  2013-03-01          China      10.110\n",
      "Xian          16494  2013-04-01          China      12.563\n",
      "Xian          16495  2013-05-01          China      18.979\n",
      "Xian          16496  2013-06-01          China      23.522\n",
      "Xian          16497  2013-07-01          China      25.251\n",
      "Xian          16498  2013-08-01          China      24.528\n",
      "Xian          16499  2013-09-01          China         NaN\n",
      "\n",
      "[16500 rows x 4 columns]\n",
      "          city  Unnamed: 0        date        country  avg_temp_c\n",
      "0      Abidjan           0  2000-01-01  Côte D'Ivoire      27.293\n",
      "1      Abidjan           1  2000-02-01  Côte D'Ivoire      27.685\n",
      "2      Abidjan           2  2000-03-01  Côte D'Ivoire      29.061\n",
      "3      Abidjan           3  2000-04-01  Côte D'Ivoire      28.162\n",
      "4      Abidjan           4  2000-05-01  Côte D'Ivoire      27.547\n",
      "5      Abidjan           5  2000-06-01  Côte D'Ivoire      25.812\n",
      "6      Abidjan           6  2000-07-01  Côte D'Ivoire      24.870\n",
      "7      Abidjan           7  2000-08-01  Côte D'Ivoire      24.884\n",
      "8      Abidjan           8  2000-09-01  Côte D'Ivoire      25.405\n",
      "9      Abidjan           9  2000-10-01  Côte D'Ivoire      26.074\n",
      "10     Abidjan          10  2000-11-01  Côte D'Ivoire      27.315\n",
      "11     Abidjan          11  2000-12-01  Côte D'Ivoire      26.929\n",
      "12     Abidjan          12  2001-01-01  Côte D'Ivoire      26.920\n",
      "13     Abidjan          13  2001-02-01  Côte D'Ivoire      28.234\n",
      "14     Abidjan          14  2001-03-01  Côte D'Ivoire      28.706\n",
      "15     Abidjan          15  2001-04-01  Côte D'Ivoire      27.702\n",
      "16     Abidjan          16  2001-05-01  Côte D'Ivoire      27.653\n",
      "17     Abidjan          17  2001-06-01  Côte D'Ivoire      25.940\n",
      "18     Abidjan          18  2001-07-01  Côte D'Ivoire      24.841\n",
      "19     Abidjan          19  2001-08-01  Côte D'Ivoire      24.280\n",
      "20     Abidjan          20  2001-09-01  Côte D'Ivoire      24.797\n",
      "21     Abidjan          21  2001-10-01  Côte D'Ivoire      26.278\n",
      "22     Abidjan          22  2001-11-01  Côte D'Ivoire      27.374\n",
      "23     Abidjan          23  2001-12-01  Côte D'Ivoire      27.598\n",
      "24     Abidjan          24  2002-01-01  Côte D'Ivoire      27.192\n",
      "25     Abidjan          25  2002-02-01  Côte D'Ivoire      28.796\n",
      "26     Abidjan          26  2002-03-01  Côte D'Ivoire      28.564\n",
      "27     Abidjan          27  2002-04-01  Côte D'Ivoire      28.508\n",
      "28     Abidjan          28  2002-05-01  Côte D'Ivoire      27.721\n",
      "29     Abidjan          29  2002-06-01  Côte D'Ivoire      25.972\n",
      "...        ...         ...         ...            ...         ...\n",
      "16470     Xian       16470  2011-04-01          China      14.876\n",
      "16471     Xian       16471  2011-05-01          China      18.225\n",
      "16472     Xian       16472  2011-06-01          China      23.879\n",
      "16473     Xian       16473  2011-07-01          China      24.895\n",
      "16474     Xian       16474  2011-08-01          China      23.069\n",
      "16475     Xian       16475  2011-09-01          China      16.775\n",
      "16476     Xian       16476  2011-10-01          China      12.587\n",
      "16477     Xian       16477  2011-11-01          China       7.543\n",
      "16478     Xian       16478  2011-12-01          China      -0.490\n",
      "16479     Xian       16479  2012-01-01          China      -2.416\n",
      "16480     Xian       16480  2012-02-01          China       0.162\n",
      "16481     Xian       16481  2012-03-01          China       6.430\n",
      "16482     Xian       16482  2012-04-01          China      14.908\n",
      "16483     Xian       16483  2012-05-01          China      19.041\n",
      "16484     Xian       16484  2012-06-01          China      22.781\n",
      "16485     Xian       16485  2012-07-01          China      24.646\n",
      "16486     Xian       16486  2012-08-01          China      23.885\n",
      "16487     Xian       16487  2012-09-01          China      17.028\n",
      "16488     Xian       16488  2012-10-01          China      12.399\n",
      "16489     Xian       16489  2012-11-01          China       4.736\n",
      "16490     Xian       16490  2012-12-01          China      -0.822\n",
      "16491     Xian       16491  2013-01-01          China      -1.635\n",
      "16492     Xian       16492  2013-02-01          China       2.362\n",
      "16493     Xian       16493  2013-03-01          China      10.110\n",
      "16494     Xian       16494  2013-04-01          China      12.563\n",
      "16495     Xian       16495  2013-05-01          China      18.979\n",
      "16496     Xian       16496  2013-06-01          China      23.522\n",
      "16497     Xian       16497  2013-07-01          China      25.251\n",
      "16498     Xian       16498  2013-08-01          China      24.528\n",
      "16499     Xian       16499  2013-09-01          China         NaN\n",
      "\n",
      "[16500 rows x 5 columns]\n",
      "       Unnamed: 0        date        country  avg_temp_c\n",
      "0               0  2000-01-01  Côte D'Ivoire      27.293\n",
      "1               1  2000-02-01  Côte D'Ivoire      27.685\n",
      "2               2  2000-03-01  Côte D'Ivoire      29.061\n",
      "3               3  2000-04-01  Côte D'Ivoire      28.162\n",
      "4               4  2000-05-01  Côte D'Ivoire      27.547\n",
      "5               5  2000-06-01  Côte D'Ivoire      25.812\n",
      "6               6  2000-07-01  Côte D'Ivoire      24.870\n",
      "7               7  2000-08-01  Côte D'Ivoire      24.884\n",
      "8               8  2000-09-01  Côte D'Ivoire      25.405\n",
      "9               9  2000-10-01  Côte D'Ivoire      26.074\n",
      "10             10  2000-11-01  Côte D'Ivoire      27.315\n",
      "11             11  2000-12-01  Côte D'Ivoire      26.929\n",
      "12             12  2001-01-01  Côte D'Ivoire      26.920\n",
      "13             13  2001-02-01  Côte D'Ivoire      28.234\n",
      "14             14  2001-03-01  Côte D'Ivoire      28.706\n",
      "15             15  2001-04-01  Côte D'Ivoire      27.702\n",
      "16             16  2001-05-01  Côte D'Ivoire      27.653\n",
      "17             17  2001-06-01  Côte D'Ivoire      25.940\n",
      "18             18  2001-07-01  Côte D'Ivoire      24.841\n",
      "19             19  2001-08-01  Côte D'Ivoire      24.280\n",
      "20             20  2001-09-01  Côte D'Ivoire      24.797\n",
      "21             21  2001-10-01  Côte D'Ivoire      26.278\n",
      "22             22  2001-11-01  Côte D'Ivoire      27.374\n",
      "23             23  2001-12-01  Côte D'Ivoire      27.598\n",
      "24             24  2002-01-01  Côte D'Ivoire      27.192\n",
      "25             25  2002-02-01  Côte D'Ivoire      28.796\n",
      "26             26  2002-03-01  Côte D'Ivoire      28.564\n",
      "27             27  2002-04-01  Côte D'Ivoire      28.508\n",
      "28             28  2002-05-01  Côte D'Ivoire      27.721\n",
      "29             29  2002-06-01  Côte D'Ivoire      25.972\n",
      "...           ...         ...            ...         ...\n",
      "16470       16470  2011-04-01          China      14.876\n",
      "16471       16471  2011-05-01          China      18.225\n",
      "16472       16472  2011-06-01          China      23.879\n",
      "16473       16473  2011-07-01          China      24.895\n",
      "16474       16474  2011-08-01          China      23.069\n",
      "16475       16475  2011-09-01          China      16.775\n",
      "16476       16476  2011-10-01          China      12.587\n",
      "16477       16477  2011-11-01          China       7.543\n",
      "16478       16478  2011-12-01          China      -0.490\n",
      "16479       16479  2012-01-01          China      -2.416\n",
      "16480       16480  2012-02-01          China       0.162\n",
      "16481       16481  2012-03-01          China       6.430\n",
      "16482       16482  2012-04-01          China      14.908\n",
      "16483       16483  2012-05-01          China      19.041\n",
      "16484       16484  2012-06-01          China      22.781\n",
      "16485       16485  2012-07-01          China      24.646\n",
      "16486       16486  2012-08-01          China      23.885\n",
      "16487       16487  2012-09-01          China      17.028\n",
      "16488       16488  2012-10-01          China      12.399\n",
      "16489       16489  2012-11-01          China       4.736\n",
      "16490       16490  2012-12-01          China      -0.822\n",
      "16491       16491  2013-01-01          China      -1.635\n",
      "16492       16492  2013-02-01          China       2.362\n",
      "16493       16493  2013-03-01          China      10.110\n",
      "16494       16494  2013-04-01          China      12.563\n",
      "16495       16495  2013-05-01          China      18.979\n",
      "16496       16496  2013-06-01          China      23.522\n",
      "16497       16497  2013-07-01          China      25.251\n",
      "16498       16498  2013-08-01          China      24.528\n",
      "16499       16499  2013-09-01          China         NaN\n",
      "\n",
      "[16500 rows x 4 columns]\n",
      "       Unnamed: 0        date              city country  avg_temp_c\n",
      "10725       10725  2000-01-01            Moscow  Russia      -7.313\n",
      "10726       10726  2000-02-01            Moscow  Russia      -3.551\n",
      "10727       10727  2000-03-01            Moscow  Russia      -1.661\n",
      "10728       10728  2000-04-01            Moscow  Russia      10.096\n",
      "10729       10729  2000-05-01            Moscow  Russia      10.357\n",
      "10730       10730  2000-06-01            Moscow  Russia      15.243\n",
      "10731       10731  2000-07-01            Moscow  Russia      18.676\n",
      "10732       10732  2000-08-01            Moscow  Russia      16.420\n",
      "10733       10733  2000-09-01            Moscow  Russia       9.775\n",
      "10734       10734  2000-10-01            Moscow  Russia       6.611\n",
      "10735       10735  2000-11-01            Moscow  Russia      -0.168\n",
      "10736       10736  2000-12-01            Moscow  Russia      -2.954\n",
      "10737       10737  2001-01-01            Moscow  Russia      -4.914\n",
      "10738       10738  2001-02-01            Moscow  Russia      -7.761\n",
      "10739       10739  2001-03-01            Moscow  Russia      -2.833\n",
      "10740       10740  2001-04-01            Moscow  Russia      10.055\n",
      "10741       10741  2001-05-01            Moscow  Russia      11.012\n",
      "10742       10742  2001-06-01            Moscow  Russia      15.733\n",
      "10743       10743  2001-07-01            Moscow  Russia      22.306\n",
      "10744       10744  2001-08-01            Moscow  Russia      16.576\n",
      "10745       10745  2001-09-01            Moscow  Russia      11.631\n",
      "10746       10746  2001-10-01            Moscow  Russia       4.778\n",
      "10747       10747  2001-11-01            Moscow  Russia      -0.865\n",
      "10748       10748  2001-12-01            Moscow  Russia     -11.924\n",
      "10749       10749  2002-01-01            Moscow  Russia      -5.558\n",
      "10750       10750  2002-02-01            Moscow  Russia      -1.074\n",
      "10751       10751  2002-03-01            Moscow  Russia       1.883\n",
      "10752       10752  2002-04-01            Moscow  Russia       6.496\n",
      "10753       10753  2002-05-01            Moscow  Russia      12.188\n",
      "10754       10754  2002-06-01            Moscow  Russia      16.590\n",
      "...           ...         ...               ...     ...         ...\n",
      "13335       13335  2011-04-01  Saint Petersburg  Russia       4.146\n",
      "13336       13336  2011-05-01  Saint Petersburg  Russia       9.442\n",
      "13337       13337  2011-06-01  Saint Petersburg  Russia      15.823\n",
      "13338       13338  2011-07-01  Saint Petersburg  Russia      20.389\n",
      "13339       13339  2011-08-01  Saint Petersburg  Russia      16.426\n",
      "13340       13340  2011-09-01  Saint Petersburg  Russia      12.704\n",
      "13341       13341  2011-10-01  Saint Petersburg  Russia       7.691\n",
      "13342       13342  2011-11-01  Saint Petersburg  Russia       4.454\n",
      "13343       13343  2011-12-01  Saint Petersburg  Russia       2.647\n",
      "13344       13344  2012-01-01  Saint Petersburg  Russia      -5.211\n",
      "13345       13345  2012-02-01  Saint Petersburg  Russia     -10.957\n",
      "13346       13346  2012-03-01  Saint Petersburg  Russia      -1.624\n",
      "13347       13347  2012-04-01  Saint Petersburg  Russia       2.877\n",
      "13348       13348  2012-05-01  Saint Petersburg  Russia      10.349\n",
      "13349       13349  2012-06-01  Saint Petersburg  Russia      12.955\n",
      "13350       13350  2012-07-01  Saint Petersburg  Russia      17.733\n",
      "13351       13351  2012-08-01  Saint Petersburg  Russia      15.219\n",
      "13352       13352  2012-09-01  Saint Petersburg  Russia      12.251\n",
      "13353       13353  2012-10-01  Saint Petersburg  Russia       6.208\n",
      "13354       13354  2012-11-01  Saint Petersburg  Russia       3.448\n",
      "13355       13355  2012-12-01  Saint Petersburg  Russia      -7.316\n",
      "13356       13356  2013-01-01  Saint Petersburg  Russia      -6.218\n",
      "13357       13357  2013-02-01  Saint Petersburg  Russia      -3.228\n",
      "13358       13358  2013-03-01  Saint Petersburg  Russia      -8.178\n",
      "13359       13359  2013-04-01  Saint Petersburg  Russia       2.167\n",
      "13360       13360  2013-05-01  Saint Petersburg  Russia      12.355\n",
      "13361       13361  2013-06-01  Saint Petersburg  Russia      17.185\n",
      "13362       13362  2013-07-01  Saint Petersburg  Russia      17.234\n",
      "13363       13363  2013-08-01  Saint Petersburg  Russia      17.153\n",
      "13364       13364  2013-09-01  Saint Petersburg  Russia         NaN\n",
      "\n",
      "[330 rows x 5 columns]\n",
      "                  Unnamed: 0        date country  avg_temp_c\n",
      "city                                                        \n",
      "Moscow                 10725  2000-01-01  Russia      -7.313\n",
      "Moscow                 10726  2000-02-01  Russia      -3.551\n",
      "Moscow                 10727  2000-03-01  Russia      -1.661\n",
      "Moscow                 10728  2000-04-01  Russia      10.096\n",
      "Moscow                 10729  2000-05-01  Russia      10.357\n",
      "Moscow                 10730  2000-06-01  Russia      15.243\n",
      "Moscow                 10731  2000-07-01  Russia      18.676\n",
      "Moscow                 10732  2000-08-01  Russia      16.420\n",
      "Moscow                 10733  2000-09-01  Russia       9.775\n",
      "Moscow                 10734  2000-10-01  Russia       6.611\n",
      "Moscow                 10735  2000-11-01  Russia      -0.168\n",
      "Moscow                 10736  2000-12-01  Russia      -2.954\n",
      "Moscow                 10737  2001-01-01  Russia      -4.914\n",
      "Moscow                 10738  2001-02-01  Russia      -7.761\n",
      "Moscow                 10739  2001-03-01  Russia      -2.833\n",
      "Moscow                 10740  2001-04-01  Russia      10.055\n",
      "Moscow                 10741  2001-05-01  Russia      11.012\n",
      "Moscow                 10742  2001-06-01  Russia      15.733\n",
      "Moscow                 10743  2001-07-01  Russia      22.306\n",
      "Moscow                 10744  2001-08-01  Russia      16.576\n",
      "Moscow                 10745  2001-09-01  Russia      11.631\n",
      "Moscow                 10746  2001-10-01  Russia       4.778\n",
      "Moscow                 10747  2001-11-01  Russia      -0.865\n",
      "Moscow                 10748  2001-12-01  Russia     -11.924\n",
      "Moscow                 10749  2002-01-01  Russia      -5.558\n",
      "Moscow                 10750  2002-02-01  Russia      -1.074\n",
      "Moscow                 10751  2002-03-01  Russia       1.883\n",
      "Moscow                 10752  2002-04-01  Russia       6.496\n",
      "Moscow                 10753  2002-05-01  Russia      12.188\n",
      "Moscow                 10754  2002-06-01  Russia      16.590\n",
      "...                      ...         ...     ...         ...\n",
      "Saint Petersburg       13335  2011-04-01  Russia       4.146\n",
      "Saint Petersburg       13336  2011-05-01  Russia       9.442\n",
      "Saint Petersburg       13337  2011-06-01  Russia      15.823\n",
      "Saint Petersburg       13338  2011-07-01  Russia      20.389\n",
      "Saint Petersburg       13339  2011-08-01  Russia      16.426\n",
      "Saint Petersburg       13340  2011-09-01  Russia      12.704\n",
      "Saint Petersburg       13341  2011-10-01  Russia       7.691\n",
      "Saint Petersburg       13342  2011-11-01  Russia       4.454\n",
      "Saint Petersburg       13343  2011-12-01  Russia       2.647\n",
      "Saint Petersburg       13344  2012-01-01  Russia      -5.211\n",
      "Saint Petersburg       13345  2012-02-01  Russia     -10.957\n",
      "Saint Petersburg       13346  2012-03-01  Russia      -1.624\n",
      "Saint Petersburg       13347  2012-04-01  Russia       2.877\n",
      "Saint Petersburg       13348  2012-05-01  Russia      10.349\n",
      "Saint Petersburg       13349  2012-06-01  Russia      12.955\n",
      "Saint Petersburg       13350  2012-07-01  Russia      17.733\n",
      "Saint Petersburg       13351  2012-08-01  Russia      15.219\n",
      "Saint Petersburg       13352  2012-09-01  Russia      12.251\n",
      "Saint Petersburg       13353  2012-10-01  Russia       6.208\n",
      "Saint Petersburg       13354  2012-11-01  Russia       3.448\n",
      "Saint Petersburg       13355  2012-12-01  Russia      -7.316\n",
      "Saint Petersburg       13356  2013-01-01  Russia      -6.218\n",
      "Saint Petersburg       13357  2013-02-01  Russia      -3.228\n",
      "Saint Petersburg       13358  2013-03-01  Russia      -8.178\n",
      "Saint Petersburg       13359  2013-04-01  Russia       2.167\n",
      "Saint Petersburg       13360  2013-05-01  Russia      12.355\n",
      "Saint Petersburg       13361  2013-06-01  Russia      17.185\n",
      "Saint Petersburg       13362  2013-07-01  Russia      17.234\n",
      "Saint Petersburg       13363  2013-08-01  Russia      17.153\n",
      "Saint Petersburg       13364  2013-09-01  Russia         NaN\n",
      "\n",
      "[330 rows x 4 columns]\n",
      "                  Unnamed: 0        date country  avg_temp_c\n",
      "city                                                        \n",
      "Moscow                 10725  2000-01-01  Russia      -7.313\n",
      "Moscow                 10726  2000-02-01  Russia      -3.551\n",
      "Moscow                 10727  2000-03-01  Russia      -1.661\n",
      "Moscow                 10728  2000-04-01  Russia      10.096\n",
      "Moscow                 10729  2000-05-01  Russia      10.357\n",
      "Moscow                 10730  2000-06-01  Russia      15.243\n",
      "Moscow                 10731  2000-07-01  Russia      18.676\n",
      "Moscow                 10732  2000-08-01  Russia      16.420\n",
      "Moscow                 10733  2000-09-01  Russia       9.775\n",
      "Moscow                 10734  2000-10-01  Russia       6.611\n",
      "Moscow                 10735  2000-11-01  Russia      -0.168\n",
      "Moscow                 10736  2000-12-01  Russia      -2.954\n",
      "Moscow                 10737  2001-01-01  Russia      -4.914\n",
      "Moscow                 10738  2001-02-01  Russia      -7.761\n",
      "Moscow                 10739  2001-03-01  Russia      -2.833\n",
      "Moscow                 10740  2001-04-01  Russia      10.055\n",
      "Moscow                 10741  2001-05-01  Russia      11.012\n",
      "Moscow                 10742  2001-06-01  Russia      15.733\n",
      "Moscow                 10743  2001-07-01  Russia      22.306\n",
      "Moscow                 10744  2001-08-01  Russia      16.576\n",
      "Moscow                 10745  2001-09-01  Russia      11.631\n",
      "Moscow                 10746  2001-10-01  Russia       4.778\n",
      "Moscow                 10747  2001-11-01  Russia      -0.865\n",
      "Moscow                 10748  2001-12-01  Russia     -11.924\n",
      "Moscow                 10749  2002-01-01  Russia      -5.558\n",
      "Moscow                 10750  2002-02-01  Russia      -1.074\n",
      "Moscow                 10751  2002-03-01  Russia       1.883\n",
      "Moscow                 10752  2002-04-01  Russia       6.496\n",
      "Moscow                 10753  2002-05-01  Russia      12.188\n",
      "Moscow                 10754  2002-06-01  Russia      16.590\n",
      "...                      ...         ...     ...         ...\n",
      "Saint Petersburg       13335  2011-04-01  Russia       4.146\n",
      "Saint Petersburg       13336  2011-05-01  Russia       9.442\n",
      "Saint Petersburg       13337  2011-06-01  Russia      15.823\n",
      "Saint Petersburg       13338  2011-07-01  Russia      20.389\n",
      "Saint Petersburg       13339  2011-08-01  Russia      16.426\n",
      "Saint Petersburg       13340  2011-09-01  Russia      12.704\n",
      "Saint Petersburg       13341  2011-10-01  Russia       7.691\n",
      "Saint Petersburg       13342  2011-11-01  Russia       4.454\n",
      "Saint Petersburg       13343  2011-12-01  Russia       2.647\n",
      "Saint Petersburg       13344  2012-01-01  Russia      -5.211\n",
      "Saint Petersburg       13345  2012-02-01  Russia     -10.957\n",
      "Saint Petersburg       13346  2012-03-01  Russia      -1.624\n",
      "Saint Petersburg       13347  2012-04-01  Russia       2.877\n",
      "Saint Petersburg       13348  2012-05-01  Russia      10.349\n",
      "Saint Petersburg       13349  2012-06-01  Russia      12.955\n",
      "Saint Petersburg       13350  2012-07-01  Russia      17.733\n",
      "Saint Petersburg       13351  2012-08-01  Russia      15.219\n",
      "Saint Petersburg       13352  2012-09-01  Russia      12.251\n",
      "Saint Petersburg       13353  2012-10-01  Russia       6.208\n",
      "Saint Petersburg       13354  2012-11-01  Russia       3.448\n",
      "Saint Petersburg       13355  2012-12-01  Russia      -7.316\n",
      "Saint Petersburg       13356  2013-01-01  Russia      -6.218\n",
      "Saint Petersburg       13357  2013-02-01  Russia      -3.228\n",
      "Saint Petersburg       13358  2013-03-01  Russia      -8.178\n",
      "Saint Petersburg       13359  2013-04-01  Russia       2.167\n",
      "Saint Petersburg       13360  2013-05-01  Russia      12.355\n",
      "Saint Petersburg       13361  2013-06-01  Russia      17.185\n",
      "Saint Petersburg       13362  2013-07-01  Russia      17.234\n",
      "Saint Petersburg       13363  2013-08-01  Russia      17.153\n",
      "Saint Petersburg       13364  2013-09-01  Russia         NaN\n",
      "\n",
      "[330 rows x 4 columns]\n",
      "                         Unnamed: 0        date  avg_temp_c\n",
      "country  city                                              \n",
      "Brazil   Rio De Janeiro       12540  2000-01-01      25.974\n",
      "         Rio De Janeiro       12541  2000-02-01      26.699\n",
      "         Rio De Janeiro       12542  2000-03-01      26.270\n",
      "         Rio De Janeiro       12543  2000-04-01      25.750\n",
      "         Rio De Janeiro       12544  2000-05-01      24.356\n",
      "         Rio De Janeiro       12545  2000-06-01      24.059\n",
      "         Rio De Janeiro       12546  2000-07-01      22.049\n",
      "         Rio De Janeiro       12547  2000-08-01      22.447\n",
      "         Rio De Janeiro       12548  2000-09-01      22.099\n",
      "         Rio De Janeiro       12549  2000-10-01      23.844\n",
      "         Rio De Janeiro       12550  2000-11-01      23.601\n",
      "         Rio De Janeiro       12551  2000-12-01      25.209\n",
      "         Rio De Janeiro       12552  2001-01-01      26.852\n",
      "         Rio De Janeiro       12553  2001-02-01      27.992\n",
      "         Rio De Janeiro       12554  2001-03-01      27.559\n",
      "         Rio De Janeiro       12555  2001-04-01      27.281\n",
      "         Rio De Janeiro       12556  2001-05-01      24.784\n",
      "         Rio De Janeiro       12557  2001-06-01      24.773\n",
      "         Rio De Janeiro       12558  2001-07-01      23.822\n",
      "         Rio De Janeiro       12559  2001-08-01      23.598\n",
      "         Rio De Janeiro       12560  2001-09-01      22.412\n",
      "         Rio De Janeiro       12561  2001-10-01      22.259\n",
      "         Rio De Janeiro       12562  2001-11-01      23.689\n",
      "         Rio De Janeiro       12563  2001-12-01      24.848\n",
      "         Rio De Janeiro       12564  2002-01-01      26.016\n",
      "         Rio De Janeiro       12565  2002-02-01      26.140\n",
      "         Rio De Janeiro       12566  2002-03-01      27.786\n",
      "         Rio De Janeiro       12567  2002-04-01      26.730\n",
      "         Rio De Janeiro       12568  2002-05-01      25.657\n",
      "         Rio De Janeiro       12569  2002-06-01      25.293\n",
      "...                             ...         ...         ...\n",
      "Pakistan Lahore                8550  2011-04-01      26.235\n",
      "         Lahore                8551  2011-05-01      34.134\n",
      "         Lahore                8552  2011-06-01      34.013\n",
      "         Lahore                8553  2011-07-01      32.325\n",
      "         Lahore                8554  2011-08-01      31.273\n",
      "         Lahore                8555  2011-09-01      29.856\n",
      "         Lahore                8556  2011-10-01      26.483\n",
      "         Lahore                8557  2011-11-01      21.799\n",
      "         Lahore                8558  2011-12-01      14.259\n",
      "         Lahore                8559  2012-01-01      12.013\n",
      "         Lahore                8560  2012-02-01      14.180\n",
      "         Lahore                8561  2012-03-01      21.212\n",
      "         Lahore                8562  2012-04-01      26.697\n",
      "         Lahore                8563  2012-05-01      33.013\n",
      "         Lahore                8564  2012-06-01      35.649\n",
      "         Lahore                8565  2012-07-01      34.235\n",
      "         Lahore                8566  2012-08-01      31.900\n",
      "         Lahore                8567  2012-09-01      29.956\n",
      "         Lahore                8568  2012-10-01      25.480\n",
      "         Lahore                8569  2012-11-01      19.835\n",
      "         Lahore                8570  2012-12-01      14.263\n",
      "         Lahore                8571  2013-01-01      12.219\n",
      "         Lahore                8572  2013-02-01      15.414\n",
      "         Lahore                8573  2013-03-01      20.229\n",
      "         Lahore                8574  2013-04-01      27.056\n",
      "         Lahore                8575  2013-05-01      33.457\n",
      "         Lahore                8576  2013-06-01      34.456\n",
      "         Lahore                8577  2013-07-01      33.279\n",
      "         Lahore                8578  2013-08-01      31.511\n",
      "         Lahore                8579  2013-09-01         NaN\n",
      "\n",
      "[330 rows x 3 columns]\n",
      "                    Unnamed: 0        date  avg_temp_c\n",
      "country     city                                      \n",
      "Afghanistan Kabul         7260  2000-01-01       3.326\n",
      "            Kabul         7261  2000-02-01       3.454\n",
      "            Kabul         7262  2000-03-01       9.612\n",
      "            Kabul         7263  2000-04-01      17.925\n",
      "            Kabul         7264  2000-05-01      24.658\n",
      "            Kabul         7265  2000-06-01      25.582\n",
      "            Kabul         7266  2000-07-01      26.107\n",
      "            Kabul         7267  2000-08-01      25.459\n",
      "            Kabul         7268  2000-09-01      22.116\n",
      "            Kabul         7269  2000-10-01      16.806\n",
      "            Kabul         7270  2000-11-01       9.720\n",
      "            Kabul         7271  2000-12-01       5.107\n",
      "            Kabul         7272  2001-01-01       2.208\n",
      "            Kabul         7273  2001-02-01       5.567\n",
      "            Kabul         7274  2001-03-01      10.807\n",
      "            Kabul         7275  2001-04-01      16.587\n",
      "            Kabul         7276  2001-05-01      23.782\n",
      "            Kabul         7277  2001-06-01      25.905\n",
      "            Kabul         7278  2001-07-01      26.065\n",
      "            Kabul         7279  2001-08-01      25.149\n",
      "            Kabul         7280  2001-09-01      21.178\n",
      "            Kabul         7281  2001-10-01      16.552\n",
      "            Kabul         7282  2001-11-01      10.731\n",
      "            Kabul         7283  2001-12-01       5.644\n",
      "            Kabul         7284  2002-01-01       3.432\n",
      "            Kabul         7285  2002-02-01       4.735\n",
      "            Kabul         7286  2002-03-01      11.382\n",
      "            Kabul         7287  2002-04-01      15.668\n",
      "            Kabul         7288  2002-05-01      21.992\n",
      "            Kabul         7289  2002-06-01      25.957\n",
      "...                        ...         ...         ...\n",
      "Zimbabwe    Harare        5580  2011-04-01      21.151\n",
      "            Harare        5581  2011-05-01      19.022\n",
      "            Harare        5582  2011-06-01      16.512\n",
      "            Harare        5583  2011-07-01      15.315\n",
      "            Harare        5584  2011-08-01      17.287\n",
      "            Harare        5585  2011-09-01      22.377\n",
      "            Harare        5586  2011-10-01      24.353\n",
      "            Harare        5587  2011-11-01      24.380\n",
      "            Harare        5588  2011-12-01      22.857\n",
      "            Harare        5589  2012-01-01      22.334\n",
      "            Harare        5590  2012-02-01      22.943\n",
      "            Harare        5591  2012-03-01      22.254\n",
      "            Harare        5592  2012-04-01      19.129\n",
      "            Harare        5593  2012-05-01      18.853\n",
      "            Harare        5594  2012-06-01      16.119\n",
      "            Harare        5595  2012-07-01      15.406\n",
      "            Harare        5596  2012-08-01      18.191\n",
      "            Harare        5597  2012-09-01      22.206\n",
      "            Harare        5598  2012-10-01      22.692\n",
      "            Harare        5599  2012-11-01      23.474\n",
      "            Harare        5600  2012-12-01      22.679\n",
      "            Harare        5601  2013-01-01      22.235\n",
      "            Harare        5602  2013-02-01      22.737\n",
      "            Harare        5603  2013-03-01      22.051\n",
      "            Harare        5604  2013-04-01      20.180\n",
      "            Harare        5605  2013-05-01      18.298\n",
      "            Harare        5606  2013-06-01      17.020\n",
      "            Harare        5607  2013-07-01      16.299\n",
      "            Harare        5608  2013-08-01      19.232\n",
      "            Harare        5609  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 3 columns]\n",
      "                       Unnamed: 0        date  avg_temp_c\n",
      "country       city                                       \n",
      "Côte D'Ivoire Abidjan           0  2000-01-01      27.293\n",
      "              Abidjan           1  2000-02-01      27.685\n",
      "              Abidjan           2  2000-03-01      29.061\n",
      "              Abidjan           3  2000-04-01      28.162\n",
      "              Abidjan           4  2000-05-01      27.547\n",
      "              Abidjan           5  2000-06-01      25.812\n",
      "              Abidjan           6  2000-07-01      24.870\n",
      "              Abidjan           7  2000-08-01      24.884\n",
      "              Abidjan           8  2000-09-01      25.405\n",
      "              Abidjan           9  2000-10-01      26.074\n",
      "              Abidjan          10  2000-11-01      27.315\n",
      "              Abidjan          11  2000-12-01      26.929\n",
      "              Abidjan          12  2001-01-01      26.920\n",
      "              Abidjan          13  2001-02-01      28.234\n",
      "              Abidjan          14  2001-03-01      28.706\n",
      "              Abidjan          15  2001-04-01      27.702\n",
      "              Abidjan          16  2001-05-01      27.653\n",
      "              Abidjan          17  2001-06-01      25.940\n",
      "              Abidjan          18  2001-07-01      24.841\n",
      "              Abidjan          19  2001-08-01      24.280\n",
      "              Abidjan          20  2001-09-01      24.797\n",
      "              Abidjan          21  2001-10-01      26.278\n",
      "              Abidjan          22  2001-11-01      27.374\n",
      "              Abidjan          23  2001-12-01      27.598\n",
      "              Abidjan          24  2002-01-01      27.192\n",
      "              Abidjan          25  2002-02-01      28.796\n",
      "              Abidjan          26  2002-03-01      28.564\n",
      "              Abidjan          27  2002-04-01      28.508\n",
      "              Abidjan          28  2002-05-01      27.721\n",
      "              Abidjan          29  2002-06-01      25.972\n",
      "...                           ...         ...         ...\n",
      "China         Xian          16470  2011-04-01      14.876\n",
      "              Xian          16471  2011-05-01      18.225\n",
      "              Xian          16472  2011-06-01      23.879\n",
      "              Xian          16473  2011-07-01      24.895\n",
      "              Xian          16474  2011-08-01      23.069\n",
      "              Xian          16475  2011-09-01      16.775\n",
      "              Xian          16476  2011-10-01      12.587\n",
      "              Xian          16477  2011-11-01       7.543\n",
      "              Xian          16478  2011-12-01      -0.490\n",
      "              Xian          16479  2012-01-01      -2.416\n",
      "              Xian          16480  2012-02-01       0.162\n",
      "              Xian          16481  2012-03-01       6.430\n",
      "              Xian          16482  2012-04-01      14.908\n",
      "              Xian          16483  2012-05-01      19.041\n",
      "              Xian          16484  2012-06-01      22.781\n",
      "              Xian          16485  2012-07-01      24.646\n",
      "              Xian          16486  2012-08-01      23.885\n",
      "              Xian          16487  2012-09-01      17.028\n",
      "              Xian          16488  2012-10-01      12.399\n",
      "              Xian          16489  2012-11-01       4.736\n",
      "              Xian          16490  2012-12-01      -0.822\n",
      "              Xian          16491  2013-01-01      -1.635\n",
      "              Xian          16492  2013-02-01       2.362\n",
      "              Xian          16493  2013-03-01      10.110\n",
      "              Xian          16494  2013-04-01      12.563\n",
      "              Xian          16495  2013-05-01      18.979\n",
      "              Xian          16496  2013-06-01      23.522\n",
      "              Xian          16497  2013-07-01      25.251\n",
      "              Xian          16498  2013-08-01      24.528\n",
      "              Xian          16499  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 3 columns]\n",
      "                    Unnamed: 0        date  avg_temp_c\n",
      "country     city                                      \n",
      "Afghanistan Kabul         7260  2000-01-01       3.326\n",
      "            Kabul         7261  2000-02-01       3.454\n",
      "            Kabul         7262  2000-03-01       9.612\n",
      "            Kabul         7263  2000-04-01      17.925\n",
      "            Kabul         7264  2000-05-01      24.658\n",
      "            Kabul         7265  2000-06-01      25.582\n",
      "            Kabul         7266  2000-07-01      26.107\n",
      "            Kabul         7267  2000-08-01      25.459\n",
      "            Kabul         7268  2000-09-01      22.116\n",
      "            Kabul         7269  2000-10-01      16.806\n",
      "            Kabul         7270  2000-11-01       9.720\n",
      "            Kabul         7271  2000-12-01       5.107\n",
      "            Kabul         7272  2001-01-01       2.208\n",
      "            Kabul         7273  2001-02-01       5.567\n",
      "            Kabul         7274  2001-03-01      10.807\n",
      "            Kabul         7275  2001-04-01      16.587\n",
      "            Kabul         7276  2001-05-01      23.782\n",
      "            Kabul         7277  2001-06-01      25.905\n",
      "            Kabul         7278  2001-07-01      26.065\n",
      "            Kabul         7279  2001-08-01      25.149\n",
      "            Kabul         7280  2001-09-01      21.178\n",
      "            Kabul         7281  2001-10-01      16.552\n",
      "            Kabul         7282  2001-11-01      10.731\n",
      "            Kabul         7283  2001-12-01       5.644\n",
      "            Kabul         7284  2002-01-01       3.432\n",
      "            Kabul         7285  2002-02-01       4.735\n",
      "            Kabul         7286  2002-03-01      11.382\n",
      "            Kabul         7287  2002-04-01      15.668\n",
      "            Kabul         7288  2002-05-01      21.992\n",
      "            Kabul         7289  2002-06-01      25.957\n",
      "...                        ...         ...         ...\n",
      "Zimbabwe    Harare        5580  2011-04-01      21.151\n",
      "            Harare        5581  2011-05-01      19.022\n",
      "            Harare        5582  2011-06-01      16.512\n",
      "            Harare        5583  2011-07-01      15.315\n",
      "            Harare        5584  2011-08-01      17.287\n",
      "            Harare        5585  2011-09-01      22.377\n",
      "            Harare        5586  2011-10-01      24.353\n",
      "            Harare        5587  2011-11-01      24.380\n",
      "            Harare        5588  2011-12-01      22.857\n",
      "            Harare        5589  2012-01-01      22.334\n",
      "            Harare        5590  2012-02-01      22.943\n",
      "            Harare        5591  2012-03-01      22.254\n",
      "            Harare        5592  2012-04-01      19.129\n",
      "            Harare        5593  2012-05-01      18.853\n",
      "            Harare        5594  2012-06-01      16.119\n",
      "            Harare        5595  2012-07-01      15.406\n",
      "            Harare        5596  2012-08-01      18.191\n",
      "            Harare        5597  2012-09-01      22.206\n",
      "            Harare        5598  2012-10-01      22.692\n",
      "            Harare        5599  2012-11-01      23.474\n",
      "            Harare        5600  2012-12-01      22.679\n",
      "            Harare        5601  2013-01-01      22.235\n",
      "            Harare        5602  2013-02-01      22.737\n",
      "            Harare        5603  2013-03-01      22.051\n",
      "            Harare        5604  2013-04-01      20.180\n",
      "            Harare        5605  2013-05-01      18.298\n",
      "            Harare        5606  2013-06-01      17.020\n",
      "            Harare        5607  2013-07-01      16.299\n",
      "            Harare        5608  2013-08-01      19.232\n",
      "            Harare        5609  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 3 columns]\n",
      "                           Unnamed: 0        date  avg_temp_c\n",
      "country  city                                                \n",
      "Pakistan Faisalabad              4785  2000-01-01      12.792\n",
      "         Faisalabad              4786  2000-02-01      14.339\n",
      "         Faisalabad              4787  2000-03-01      20.309\n",
      "         Faisalabad              4788  2000-04-01      29.072\n",
      "         Faisalabad              4789  2000-05-01      34.845\n",
      "         Faisalabad              4790  2000-06-01      34.299\n",
      "         Faisalabad              4791  2000-07-01      32.302\n",
      "         Faisalabad              4792  2000-08-01      32.255\n",
      "         Faisalabad              4793  2000-09-01      30.438\n",
      "         Faisalabad              4794  2000-10-01      27.395\n",
      "         Faisalabad              4795  2000-11-01      20.640\n",
      "         Faisalabad              4796  2000-12-01      15.195\n",
      "         Faisalabad              4797  2001-01-01      11.853\n",
      "         Faisalabad              4798  2001-02-01      16.701\n",
      "         Faisalabad              4799  2001-03-01      21.885\n",
      "         Faisalabad              4800  2001-04-01      26.814\n",
      "         Faisalabad              4801  2001-05-01      33.924\n",
      "         Faisalabad              4802  2001-06-01      32.882\n",
      "         Faisalabad              4803  2001-07-01      32.091\n",
      "         Faisalabad              4804  2001-08-01      32.562\n",
      "         Faisalabad              4805  2001-09-01      30.615\n",
      "         Faisalabad              4806  2001-10-01      27.015\n",
      "         Faisalabad              4807  2001-11-01      20.501\n",
      "         Faisalabad              4808  2001-12-01      15.306\n",
      "         Faisalabad              4809  2002-01-01      12.844\n",
      "         Faisalabad              4810  2002-02-01      15.569\n",
      "         Faisalabad              4811  2002-03-01      22.269\n",
      "         Faisalabad              4812  2002-04-01      28.641\n",
      "         Faisalabad              4813  2002-05-01      34.396\n",
      "         Faisalabad              4814  2002-06-01      34.613\n",
      "...                               ...         ...         ...\n",
      "Russia   Saint Petersburg       13335  2011-04-01       4.146\n",
      "         Saint Petersburg       13336  2011-05-01       9.442\n",
      "         Saint Petersburg       13337  2011-06-01      15.823\n",
      "         Saint Petersburg       13338  2011-07-01      20.389\n",
      "         Saint Petersburg       13339  2011-08-01      16.426\n",
      "         Saint Petersburg       13340  2011-09-01      12.704\n",
      "         Saint Petersburg       13341  2011-10-01       7.691\n",
      "         Saint Petersburg       13342  2011-11-01       4.454\n",
      "         Saint Petersburg       13343  2011-12-01       2.647\n",
      "         Saint Petersburg       13344  2012-01-01      -5.211\n",
      "         Saint Petersburg       13345  2012-02-01     -10.957\n",
      "         Saint Petersburg       13346  2012-03-01      -1.624\n",
      "         Saint Petersburg       13347  2012-04-01       2.877\n",
      "         Saint Petersburg       13348  2012-05-01      10.349\n",
      "         Saint Petersburg       13349  2012-06-01      12.955\n",
      "         Saint Petersburg       13350  2012-07-01      17.733\n",
      "         Saint Petersburg       13351  2012-08-01      15.219\n",
      "         Saint Petersburg       13352  2012-09-01      12.251\n",
      "         Saint Petersburg       13353  2012-10-01       6.208\n",
      "         Saint Petersburg       13354  2012-11-01       3.448\n",
      "         Saint Petersburg       13355  2012-12-01      -7.316\n",
      "         Saint Petersburg       13356  2013-01-01      -6.218\n",
      "         Saint Petersburg       13357  2013-02-01      -3.228\n",
      "         Saint Petersburg       13358  2013-03-01      -8.178\n",
      "         Saint Petersburg       13359  2013-04-01       2.167\n",
      "         Saint Petersburg       13360  2013-05-01      12.355\n",
      "         Saint Petersburg       13361  2013-06-01      17.185\n",
      "         Saint Petersburg       13362  2013-07-01      17.234\n",
      "         Saint Petersburg       13363  2013-08-01      17.153\n",
      "         Saint Petersburg       13364  2013-09-01         NaN\n",
      "\n",
      "[1155 rows x 3 columns]\n",
      "                    Unnamed: 0        date  avg_temp_c\n",
      "country city                                          \n",
      "Mexico  Mexico           10230  2000-01-01      12.694\n",
      "        Mexico           10231  2000-02-01      14.677\n",
      "        Mexico           10232  2000-03-01      17.376\n",
      "        Mexico           10233  2000-04-01      18.294\n",
      "        Mexico           10234  2000-05-01      18.562\n",
      "        Mexico           10235  2000-06-01      17.270\n",
      "        Mexico           10236  2000-07-01      17.723\n",
      "        Mexico           10237  2000-08-01      16.967\n",
      "        Mexico           10238  2000-09-01      16.996\n",
      "        Mexico           10239  2000-10-01      15.605\n",
      "        Mexico           10240  2000-11-01      15.237\n",
      "        Mexico           10241  2000-12-01      11.966\n",
      "        Mexico           10242  2001-01-01      12.733\n",
      "        Mexico           10243  2001-02-01      14.461\n",
      "        Mexico           10244  2001-03-01      15.926\n",
      "        Mexico           10245  2001-04-01      18.156\n",
      "        Mexico           10246  2001-05-01      18.121\n",
      "        Mexico           10247  2001-06-01      17.809\n",
      "        Mexico           10248  2001-07-01      17.571\n",
      "        Mexico           10249  2001-08-01      17.710\n",
      "        Mexico           10250  2001-09-01      16.960\n",
      "        Mexico           10251  2001-10-01      15.281\n",
      "        Mexico           10252  2001-11-01      13.861\n",
      "        Mexico           10253  2001-12-01      13.157\n",
      "        Mexico           10254  2002-01-01      12.965\n",
      "        Mexico           10255  2002-02-01      13.622\n",
      "        Mexico           10256  2002-03-01      17.116\n",
      "        Mexico           10257  2002-04-01      18.396\n",
      "        Mexico           10258  2002-05-01      19.608\n",
      "        Mexico           10259  2002-06-01      18.479\n",
      "...                        ...         ...         ...\n",
      "Morocco Casablanca        3105  2011-04-01      17.895\n",
      "        Casablanca        3106  2011-05-01      21.258\n",
      "        Casablanca        3107  2011-06-01      25.375\n",
      "        Casablanca        3108  2011-07-01      26.377\n",
      "        Casablanca        3109  2011-08-01      26.832\n",
      "        Casablanca        3110  2011-09-01      24.017\n",
      "        Casablanca        3111  2011-10-01      20.498\n",
      "        Casablanca        3112  2011-11-01      14.384\n",
      "        Casablanca        3113  2011-12-01      10.716\n",
      "        Casablanca        3114  2012-01-01       9.267\n",
      "        Casablanca        3115  2012-02-01       9.012\n",
      "        Casablanca        3116  2012-03-01      15.032\n",
      "        Casablanca        3117  2012-04-01      14.457\n",
      "        Casablanca        3118  2012-05-01      21.708\n",
      "        Casablanca        3119  2012-06-01      24.682\n",
      "        Casablanca        3120  2012-07-01      28.219\n",
      "        Casablanca        3121  2012-08-01      27.487\n",
      "        Casablanca        3122  2012-09-01      23.689\n",
      "        Casablanca        3123  2012-10-01      19.567\n",
      "        Casablanca        3124  2012-11-01      14.821\n",
      "        Casablanca        3125  2012-12-01      10.995\n",
      "        Casablanca        3126  2013-01-01      10.686\n",
      "        Casablanca        3127  2013-02-01      11.204\n",
      "        Casablanca        3128  2013-03-01      14.071\n",
      "        Casablanca        3129  2013-04-01      16.050\n",
      "        Casablanca        3130  2013-05-01      19.217\n",
      "        Casablanca        3131  2013-06-01      23.649\n",
      "        Casablanca        3132  2013-07-01      27.488\n",
      "        Casablanca        3133  2013-08-01      27.952\n",
      "        Casablanca        3134  2013-09-01         NaN\n",
      "\n",
      "[330 rows x 3 columns]\n",
      "                 Unnamed: 0        date  avg_temp_c\n",
      "country  city                                      \n",
      "Pakistan Lahore        8415  2000-01-01      12.792\n",
      "         Lahore        8416  2000-02-01      14.339\n",
      "         Lahore        8417  2000-03-01      20.309\n",
      "         Lahore        8418  2000-04-01      29.072\n",
      "         Lahore        8419  2000-05-01      34.845\n",
      "         Lahore        8420  2000-06-01      34.299\n",
      "         Lahore        8421  2000-07-01      32.302\n",
      "         Lahore        8422  2000-08-01      32.255\n",
      "         Lahore        8423  2000-09-01      30.438\n",
      "         Lahore        8424  2000-10-01      27.395\n",
      "         Lahore        8425  2000-11-01      20.640\n",
      "         Lahore        8426  2000-12-01      15.195\n",
      "         Lahore        8427  2001-01-01      11.853\n",
      "         Lahore        8428  2001-02-01      16.701\n",
      "         Lahore        8429  2001-03-01      21.885\n",
      "         Lahore        8430  2001-04-01      26.814\n",
      "         Lahore        8431  2001-05-01      33.924\n",
      "         Lahore        8432  2001-06-01      32.882\n",
      "         Lahore        8433  2001-07-01      32.091\n",
      "         Lahore        8434  2001-08-01      32.562\n",
      "         Lahore        8435  2001-09-01      30.615\n",
      "         Lahore        8436  2001-10-01      27.015\n",
      "         Lahore        8437  2001-11-01      20.501\n",
      "         Lahore        8438  2001-12-01      15.306\n",
      "         Lahore        8439  2002-01-01      12.844\n",
      "         Lahore        8440  2002-02-01      15.569\n",
      "         Lahore        8441  2002-03-01      22.269\n",
      "         Lahore        8442  2002-04-01      28.641\n",
      "         Lahore        8443  2002-05-01      34.396\n",
      "         Lahore        8444  2002-06-01      34.613\n",
      "...                     ...         ...         ...\n",
      "Russia   Moscow       10860  2011-04-01       5.729\n",
      "         Moscow       10861  2011-05-01      13.982\n",
      "         Moscow       10862  2011-06-01      18.437\n",
      "         Moscow       10863  2011-07-01      22.434\n",
      "         Moscow       10864  2011-08-01      18.149\n",
      "         Moscow       10865  2011-09-01      11.812\n",
      "         Moscow       10866  2011-10-01       5.926\n",
      "         Moscow       10867  2011-11-01      -0.344\n",
      "         Moscow       10868  2011-12-01      -0.868\n",
      "         Moscow       10869  2012-01-01      -7.713\n",
      "         Moscow       10870  2012-02-01     -12.533\n",
      "         Moscow       10871  2012-03-01      -3.467\n",
      "         Moscow       10872  2012-04-01       7.247\n",
      "         Moscow       10873  2012-05-01      14.592\n",
      "         Moscow       10874  2012-06-01      16.333\n",
      "         Moscow       10875  2012-07-01      20.001\n",
      "         Moscow       10876  2012-08-01      17.395\n",
      "         Moscow       10877  2012-09-01      12.509\n",
      "         Moscow       10878  2012-10-01       6.061\n",
      "         Moscow       10879  2012-11-01       1.076\n",
      "         Moscow       10880  2012-12-01      -9.090\n",
      "         Moscow       10881  2013-01-01      -9.230\n",
      "         Moscow       10882  2013-02-01      -4.726\n",
      "         Moscow       10883  2013-03-01      -7.497\n",
      "         Moscow       10884  2013-04-01       5.353\n",
      "         Moscow       10885  2013-05-01      16.152\n",
      "         Moscow       10886  2013-06-01      18.718\n",
      "         Moscow       10887  2013-07-01      18.136\n",
      "         Moscow       10888  2013-08-01      17.485\n",
      "         Moscow       10889  2013-09-01         NaN\n",
      "\n",
      "[660 rows x 3 columns]\n",
      "                   Unnamed: 0        date  avg_temp_c\n",
      "country city                                         \n",
      "India   Hyderabad        5940  2000-01-01      23.779\n",
      "        Hyderabad        5941  2000-02-01      25.826\n",
      "        Hyderabad        5942  2000-03-01      28.821\n",
      "        Hyderabad        5943  2000-04-01      32.698\n",
      "        Hyderabad        5944  2000-05-01      32.438\n",
      "        Hyderabad        5945  2000-06-01      28.422\n",
      "        Hyderabad        5946  2000-07-01      27.137\n",
      "        Hyderabad        5947  2000-08-01      26.576\n",
      "        Hyderabad        5948  2000-09-01      27.433\n",
      "        Hyderabad        5949  2000-10-01      26.927\n",
      "        Hyderabad        5950  2000-11-01      24.776\n",
      "        Hyderabad        5951  2000-12-01      21.949\n",
      "        Hyderabad        5952  2001-01-01      23.406\n",
      "        Hyderabad        5953  2001-02-01      26.677\n",
      "        Hyderabad        5954  2001-03-01      29.393\n",
      "        Hyderabad        5955  2001-04-01      31.289\n",
      "        Hyderabad        5956  2001-05-01      34.030\n",
      "        Hyderabad        5957  2001-06-01      29.432\n",
      "        Hyderabad        5958  2001-07-01      28.309\n",
      "        Hyderabad        5959  2001-08-01      26.670\n",
      "        Hyderabad        5960  2001-09-01      27.698\n",
      "        Hyderabad        5961  2001-10-01      25.955\n",
      "        Hyderabad        5962  2001-11-01      25.182\n",
      "        Hyderabad        5963  2001-12-01      22.621\n",
      "        Hyderabad        5964  2002-01-01      23.253\n",
      "        Hyderabad        5965  2002-02-01      25.942\n",
      "        Hyderabad        5966  2002-03-01      29.713\n",
      "        Hyderabad        5967  2002-04-01      32.571\n",
      "        Hyderabad        5968  2002-05-01      33.501\n",
      "        Hyderabad        5969  2002-06-01      29.717\n",
      "...                       ...         ...         ...\n",
      "Iraq    Baghdad          1125  2011-04-01      23.178\n",
      "        Baghdad          1126  2011-05-01      29.493\n",
      "        Baghdad          1127  2011-06-01      34.736\n",
      "        Baghdad          1128  2011-07-01      37.184\n",
      "        Baghdad          1129  2011-08-01      36.151\n",
      "        Baghdad          1130  2011-09-01      32.055\n",
      "        Baghdad          1131  2011-10-01      24.179\n",
      "        Baghdad          1132  2011-11-01      14.241\n",
      "        Baghdad          1133  2011-12-01      10.369\n",
      "        Baghdad          1134  2012-01-01       9.664\n",
      "        Baghdad          1135  2012-02-01      11.571\n",
      "        Baghdad          1136  2012-03-01      15.376\n",
      "        Baghdad          1137  2012-04-01      24.659\n",
      "        Baghdad          1138  2012-05-01      30.878\n",
      "        Baghdad          1139  2012-06-01      35.726\n",
      "        Baghdad          1140  2012-07-01      37.371\n",
      "        Baghdad          1141  2012-08-01      36.559\n",
      "        Baghdad          1142  2012-09-01      32.574\n",
      "        Baghdad          1143  2012-10-01      26.319\n",
      "        Baghdad          1144  2012-11-01      18.590\n",
      "        Baghdad          1145  2012-12-01      12.851\n",
      "        Baghdad          1146  2013-01-01      11.518\n",
      "        Baghdad          1147  2013-02-01      14.889\n",
      "        Baghdad          1148  2013-03-01      17.636\n",
      "        Baghdad          1149  2013-04-01      22.769\n",
      "        Baghdad          1150  2013-05-01      28.673\n",
      "        Baghdad          1151  2013-06-01      33.803\n",
      "        Baghdad          1152  2013-07-01      36.392\n",
      "        Baghdad          1153  2013-08-01      35.463\n",
      "        Baghdad          1154  2013-09-01         NaN\n",
      "\n",
      "[2145 rows x 3 columns]\n",
      "                          date  avg_temp_c\n",
      "country     city                          \n",
      "Afghanistan Kabul   2000-01-01       3.326\n",
      "            Kabul   2000-02-01       3.454\n",
      "            Kabul   2000-03-01       9.612\n",
      "            Kabul   2000-04-01      17.925\n",
      "            Kabul   2000-05-01      24.658\n",
      "            Kabul   2000-06-01      25.582\n",
      "            Kabul   2000-07-01      26.107\n",
      "            Kabul   2000-08-01      25.459\n",
      "            Kabul   2000-09-01      22.116\n",
      "            Kabul   2000-10-01      16.806\n",
      "            Kabul   2000-11-01       9.720\n",
      "            Kabul   2000-12-01       5.107\n",
      "            Kabul   2001-01-01       2.208\n",
      "            Kabul   2001-02-01       5.567\n",
      "            Kabul   2001-03-01      10.807\n",
      "            Kabul   2001-04-01      16.587\n",
      "            Kabul   2001-05-01      23.782\n",
      "            Kabul   2001-06-01      25.905\n",
      "            Kabul   2001-07-01      26.065\n",
      "            Kabul   2001-08-01      25.149\n",
      "            Kabul   2001-09-01      21.178\n",
      "            Kabul   2001-10-01      16.552\n",
      "            Kabul   2001-11-01      10.731\n",
      "            Kabul   2001-12-01       5.644\n",
      "            Kabul   2002-01-01       3.432\n",
      "            Kabul   2002-02-01       4.735\n",
      "            Kabul   2002-03-01      11.382\n",
      "            Kabul   2002-04-01      15.668\n",
      "            Kabul   2002-05-01      21.992\n",
      "            Kabul   2002-06-01      25.957\n",
      "...                        ...         ...\n",
      "Zimbabwe    Harare  2011-04-01      21.151\n",
      "            Harare  2011-05-01      19.022\n",
      "            Harare  2011-06-01      16.512\n",
      "            Harare  2011-07-01      15.315\n",
      "            Harare  2011-08-01      17.287\n",
      "            Harare  2011-09-01      22.377\n",
      "            Harare  2011-10-01      24.353\n",
      "            Harare  2011-11-01      24.380\n",
      "            Harare  2011-12-01      22.857\n",
      "            Harare  2012-01-01      22.334\n",
      "            Harare  2012-02-01      22.943\n",
      "            Harare  2012-03-01      22.254\n",
      "            Harare  2012-04-01      19.129\n",
      "            Harare  2012-05-01      18.853\n",
      "            Harare  2012-06-01      16.119\n",
      "            Harare  2012-07-01      15.406\n",
      "            Harare  2012-08-01      18.191\n",
      "            Harare  2012-09-01      22.206\n",
      "            Harare  2012-10-01      22.692\n",
      "            Harare  2012-11-01      23.474\n",
      "            Harare  2012-12-01      22.679\n",
      "            Harare  2013-01-01      22.235\n",
      "            Harare  2013-02-01      22.737\n",
      "            Harare  2013-03-01      22.051\n",
      "            Harare  2013-04-01      20.180\n",
      "            Harare  2013-05-01      18.298\n",
      "            Harare  2013-06-01      17.020\n",
      "            Harare  2013-07-01      16.299\n",
      "            Harare  2013-08-01      19.232\n",
      "            Harare  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 2 columns]\n",
      "                         date  avg_temp_c\n",
      "country city                             \n",
      "India   Hyderabad  2000-01-01      23.779\n",
      "        Hyderabad  2000-02-01      25.826\n",
      "        Hyderabad  2000-03-01      28.821\n",
      "        Hyderabad  2000-04-01      32.698\n",
      "        Hyderabad  2000-05-01      32.438\n",
      "        Hyderabad  2000-06-01      28.422\n",
      "        Hyderabad  2000-07-01      27.137\n",
      "        Hyderabad  2000-08-01      26.576\n",
      "        Hyderabad  2000-09-01      27.433\n",
      "        Hyderabad  2000-10-01      26.927\n",
      "        Hyderabad  2000-11-01      24.776\n",
      "        Hyderabad  2000-12-01      21.949\n",
      "        Hyderabad  2001-01-01      23.406\n",
      "        Hyderabad  2001-02-01      26.677\n",
      "        Hyderabad  2001-03-01      29.393\n",
      "        Hyderabad  2001-04-01      31.289\n",
      "        Hyderabad  2001-05-01      34.030\n",
      "        Hyderabad  2001-06-01      29.432\n",
      "        Hyderabad  2001-07-01      28.309\n",
      "        Hyderabad  2001-08-01      26.670\n",
      "        Hyderabad  2001-09-01      27.698\n",
      "        Hyderabad  2001-10-01      25.955\n",
      "        Hyderabad  2001-11-01      25.182\n",
      "        Hyderabad  2001-12-01      22.621\n",
      "        Hyderabad  2002-01-01      23.253\n",
      "        Hyderabad  2002-02-01      25.942\n",
      "        Hyderabad  2002-03-01      29.713\n",
      "        Hyderabad  2002-04-01      32.571\n",
      "        Hyderabad  2002-05-01      33.501\n",
      "        Hyderabad  2002-06-01      29.717\n",
      "...                       ...         ...\n",
      "Iraq    Baghdad    2011-04-01      23.178\n",
      "        Baghdad    2011-05-01      29.493\n",
      "        Baghdad    2011-06-01      34.736\n",
      "        Baghdad    2011-07-01      37.184\n",
      "        Baghdad    2011-08-01      36.151\n",
      "        Baghdad    2011-09-01      32.055\n",
      "        Baghdad    2011-10-01      24.179\n",
      "        Baghdad    2011-11-01      14.241\n",
      "        Baghdad    2011-12-01      10.369\n",
      "        Baghdad    2012-01-01       9.664\n",
      "        Baghdad    2012-02-01      11.571\n",
      "        Baghdad    2012-03-01      15.376\n",
      "        Baghdad    2012-04-01      24.659\n",
      "        Baghdad    2012-05-01      30.878\n",
      "        Baghdad    2012-06-01      35.726\n",
      "        Baghdad    2012-07-01      37.371\n",
      "        Baghdad    2012-08-01      36.559\n",
      "        Baghdad    2012-09-01      32.574\n",
      "        Baghdad    2012-10-01      26.319\n",
      "        Baghdad    2012-11-01      18.590\n",
      "        Baghdad    2012-12-01      12.851\n",
      "        Baghdad    2013-01-01      11.518\n",
      "        Baghdad    2013-02-01      14.889\n",
      "        Baghdad    2013-03-01      17.636\n",
      "        Baghdad    2013-04-01      22.769\n",
      "        Baghdad    2013-05-01      28.673\n",
      "        Baghdad    2013-06-01      33.803\n",
      "        Baghdad    2013-07-01      36.392\n",
      "        Baghdad    2013-08-01      35.463\n",
      "        Baghdad    2013-09-01         NaN\n",
      "\n",
      "[2145 rows x 2 columns]\n",
      "       Unnamed: 0        date         city        country  avg_temp_c\n",
      "120           120  2010-01-01      Abidjan  Côte D'Ivoire      28.270\n",
      "121           121  2010-02-01      Abidjan  Côte D'Ivoire      29.262\n",
      "122           122  2010-03-01      Abidjan  Côte D'Ivoire      29.596\n",
      "123           123  2010-04-01      Abidjan  Côte D'Ivoire      29.068\n",
      "124           124  2010-05-01      Abidjan  Côte D'Ivoire      28.258\n",
      "125           125  2010-06-01      Abidjan  Côte D'Ivoire      26.683\n",
      "126           126  2010-07-01      Abidjan  Côte D'Ivoire      25.589\n",
      "127           127  2010-08-01      Abidjan  Côte D'Ivoire      25.400\n",
      "128           128  2010-09-01      Abidjan  Côte D'Ivoire      25.710\n",
      "129           129  2010-10-01      Abidjan  Côte D'Ivoire      26.397\n",
      "130           130  2010-11-01      Abidjan  Côte D'Ivoire      27.446\n",
      "131           131  2010-12-01      Abidjan  Côte D'Ivoire      27.666\n",
      "132           132  2011-01-01      Abidjan  Côte D'Ivoire      27.360\n",
      "133           133  2011-02-01      Abidjan  Côte D'Ivoire      28.295\n",
      "134           134  2011-03-01      Abidjan  Côte D'Ivoire      28.922\n",
      "135           135  2011-04-01      Abidjan  Côte D'Ivoire      28.754\n",
      "136           136  2011-05-01      Abidjan  Côte D'Ivoire      27.809\n",
      "137           137  2011-06-01      Abidjan  Côte D'Ivoire      26.427\n",
      "138           138  2011-07-01      Abidjan  Côte D'Ivoire      25.302\n",
      "139           139  2011-08-01      Abidjan  Côte D'Ivoire      24.824\n",
      "140           140  2011-09-01      Abidjan  Côte D'Ivoire      25.405\n",
      "141           141  2011-10-01      Abidjan  Côte D'Ivoire      26.388\n",
      "142           142  2011-11-01      Abidjan  Côte D'Ivoire      27.531\n",
      "143           143  2011-12-01      Abidjan  Côte D'Ivoire      27.194\n",
      "285           285  2010-01-01  Addis Abeba       Ethiopia      18.390\n",
      "286           286  2010-02-01  Addis Abeba       Ethiopia      18.931\n",
      "287           287  2010-03-01  Addis Abeba       Ethiopia      20.102\n",
      "288           288  2010-04-01  Addis Abeba       Ethiopia      19.876\n",
      "289           289  2010-05-01  Addis Abeba       Ethiopia      19.646\n",
      "290           290  2010-06-01  Addis Abeba       Ethiopia      18.397\n",
      "...           ...         ...          ...            ...         ...\n",
      "16308       16308  2011-07-01        Wuhan          China      29.765\n",
      "16309       16309  2011-08-01        Wuhan          China      28.297\n",
      "16310       16310  2011-09-01        Wuhan          China      23.784\n",
      "16311       16311  2011-10-01        Wuhan          China      18.326\n",
      "16312       16312  2011-11-01        Wuhan          China      15.259\n",
      "16313       16313  2011-12-01        Wuhan          China       5.811\n",
      "16455       16455  2010-01-01         Xian          China      -0.237\n",
      "16456       16456  2010-02-01         Xian          China       2.581\n",
      "16457       16457  2010-03-01         Xian          China       6.969\n",
      "16458       16458  2010-04-01         Xian          China      11.377\n",
      "16459       16459  2010-05-01         Xian          China      18.365\n",
      "16460       16460  2010-06-01         Xian          China      23.357\n",
      "16461       16461  2010-07-01         Xian          China      25.722\n",
      "16462       16462  2010-08-01         Xian          China      23.434\n",
      "16463       16463  2010-09-01         Xian          China      19.630\n",
      "16464       16464  2010-10-01         Xian          China      12.292\n",
      "16465       16465  2010-11-01         Xian          China       6.742\n",
      "16466       16466  2010-12-01         Xian          China       0.845\n",
      "16467       16467  2011-01-01         Xian          China      -4.811\n",
      "16468       16468  2011-02-01         Xian          China       2.430\n",
      "16469       16469  2011-03-01         Xian          China       5.924\n",
      "16470       16470  2011-04-01         Xian          China      14.876\n",
      "16471       16471  2011-05-01         Xian          China      18.225\n",
      "16472       16472  2011-06-01         Xian          China      23.879\n",
      "16473       16473  2011-07-01         Xian          China      24.895\n",
      "16474       16474  2011-08-01         Xian          China      23.069\n",
      "16475       16475  2011-09-01         Xian          China      16.775\n",
      "16476       16476  2011-10-01         Xian          China      12.587\n",
      "16477       16477  2011-11-01         Xian          China       7.543\n",
      "16478       16478  2011-12-01         Xian          China      -0.490\n",
      "\n",
      "[2400 rows x 5 columns]\n",
      "            Unnamed: 0            city       country  avg_temp_c\n",
      "date                                                            \n",
      "2010-01-01        4905      Faisalabad      Pakistan      11.810\n",
      "2010-01-01       10185       Melbourne     Australia      20.016\n",
      "2010-01-01        3750       Chongqing         China       7.921\n",
      "2010-01-01       13155       São Paulo        Brazil      23.738\n",
      "2010-01-01        5400       Guangzhou         China      14.136\n",
      "2010-01-01        8370           Lagos       Nigeria      27.830\n",
      "2010-01-01        4410           Delhi         India      14.014\n",
      "2010-01-01        8865            Lima          Peru      20.450\n",
      "2010-01-01       10020         Mashhad          Iran       4.746\n",
      "2010-01-01        3915           Dakar       Senegal      23.188\n",
      "2010-01-01        4575           Dhaka    Bangladesh      17.354\n",
      "2010-01-01        1605  Belo Horizonte        Brazil      24.349\n",
      "2010-01-01        2265        Brasília        Brazil      23.417\n",
      "2010-01-01       10515       Mogadishu       Somalia      27.520\n",
      "2010-01-01        1770          Berlin       Germany      -4.702\n",
      "2010-01-01        2595        Calcutta         India      17.503\n",
      "2010-01-01        8040            Kiev       Ukraine      -9.968\n",
      "2010-01-01       10845          Moscow        Russia     -15.846\n",
      "2010-01-01       10350          Mexico        Mexico      11.607\n",
      "2010-01-01        2430           Cairo         Egypt      15.835\n",
      "2010-01-01       14805           Surat         India      21.461\n",
      "2010-01-01        2100          Bombay         India      26.120\n",
      "2010-01-01        4245   Dar Es Salaam      Tanzania      28.256\n",
      "2010-01-01       12825          Riyadh  Saudi Arabia      16.055\n",
      "2010-01-01       10680        Montreal        Canada      -7.611\n",
      "2010-01-01        9690          Madrid         Spain       3.443\n",
      "2010-01-01        8700         Lakhnau         India      13.147\n",
      "2010-01-01        4080          Dalian         China      -5.459\n",
      "2010-01-01       13980           Seoul   South Korea      -4.935\n",
      "2010-01-01        6390        Istanbul        Turkey       6.212\n",
      "...                ...             ...           ...         ...\n",
      "2010-12-01       10691        Montreal        Canada      -6.906\n",
      "2010-12-01       10361          Mexico        Mexico      11.375\n",
      "2010-12-01        2771            Cali      Colombia      21.238\n",
      "2010-12-01        6071       Hyderabad         India      22.234\n",
      "2010-12-01       15971         Toronto        Canada      -5.594\n",
      "2010-12-01       14651        Surabaya     Indonesia      26.735\n",
      "2010-12-01        8546          Lahore      Pakistan      13.539\n",
      "2010-12-01       15806           Tokyo         Japan       5.719\n",
      "2010-12-01        1781          Berlin       Germany      -4.412\n",
      "2010-12-01       12176          Peking         China      -1.778\n",
      "2010-12-01        3431         Chengdu         China       1.550\n",
      "2010-12-01       12341            Pune         India      20.091\n",
      "2010-12-01        9701          Madrid         Spain       4.022\n",
      "2010-12-01       11351         Nairobi         Kenya      17.069\n",
      "2010-12-01         956          Ankara        Turkey       4.582\n",
      "2010-12-01        3101      Casablanca       Morocco      13.236\n",
      "2010-12-01        1286       Bangalore         India      22.719\n",
      "2010-12-01        9371          Luanda        Angola      24.044\n",
      "2010-12-01       16466            Xian         China       0.845\n",
      "2010-12-01       11681       New Delhi         India      15.393\n",
      "2010-12-01        6731          Jaipur         India      16.214\n",
      "2010-12-01        5081       Fortaleza        Brazil      28.277\n",
      "2010-12-01         296     Addis Abeba      Ethiopia      17.080\n",
      "2010-12-01       14321        Shenyang         China      -8.517\n",
      "2010-12-01        3761       Chongqing         China       7.941\n",
      "2010-12-01        6896         Jakarta     Indonesia      26.602\n",
      "2010-12-01        5246           Gizeh         Egypt      16.530\n",
      "2010-12-01       11186          Nagpur         India      19.120\n",
      "2010-12-01       14981          Sydney     Australia      19.559\n",
      "2010-12-01       13496        Salvador        Brazil      26.265\n",
      "\n",
      "[1200 rows x 4 columns]\n",
      "            Unnamed: 0              city        country  avg_temp_c\n",
      "date                                                               \n",
      "2010-08-01        2602          Calcutta          India      30.226\n",
      "2010-08-01       12337              Pune          India      24.941\n",
      "2010-08-01        6562             Izmir         Turkey      28.352\n",
      "2010-08-01       15637           Tianjin          China      25.543\n",
      "2010-08-01        9862            Manila    Philippines      27.101\n",
      "2010-08-01       14977            Sydney      Australia      14.817\n",
      "2010-08-01        8542            Lahore       Pakistan      31.180\n",
      "2010-08-01        9367            Luanda         Angola      21.680\n",
      "2010-08-01       12997              Rome          Italy      22.289\n",
      "2010-08-01        1777            Berlin        Germany      18.692\n",
      "2010-08-01         952            Ankara         Turkey      26.044\n",
      "2010-08-01         292       Addis Abeba       Ethiopia      17.037\n",
      "2010-08-01       16297             Wuhan          China      29.582\n",
      "2010-08-01        7882           Karachi       Pakistan      30.043\n",
      "2010-08-01        3262         Changchun          China      22.173\n",
      "2010-08-01        7057            Jiddah   Saudi Arabia      34.133\n",
      "2010-08-01       14317          Shenyang          China      23.230\n",
      "2010-08-01       10357            Mexico         Mexico      17.996\n",
      "2010-08-01        9697            Madrid          Spain      22.934\n",
      "2010-08-01        4087            Dalian          China      23.929\n",
      "2010-08-01        3427           Chengdu          China      19.915\n",
      "2010-08-01        2272          Brasília         Brazil      21.876\n",
      "2010-08-01       16132        Umm Durman          Sudan      30.628\n",
      "2010-08-01       10522         Mogadishu        Somalia      26.784\n",
      "2010-08-01       14812             Surat          India      27.870\n",
      "2010-08-01        6892           Jakarta      Indonesia      27.093\n",
      "2010-08-01        2932         Cape Town   South Africa      13.461\n",
      "2010-08-01        9202       Los Angeles  United States      23.545\n",
      "2010-08-01        3922             Dakar        Senegal      29.053\n",
      "2010-08-01       13657          Santiago          Chile       0.320\n",
      "...                ...               ...            ...         ...\n",
      "2011-01-01       11187            Nagpur          India      18.337\n",
      "2011-01-01        6897           Jakarta      Indonesia      26.558\n",
      "2011-01-01        3267         Changchun          China     -19.623\n",
      "2011-01-01         132           Abidjan  Côte D'Ivoire      27.360\n",
      "2011-01-01       15477          Tangshan          China      -5.277\n",
      "2011-01-01       12837            Riyadh   Saudi Arabia      16.124\n",
      "2011-01-01       16137        Umm Durman          Sudan      22.254\n",
      "2011-01-01        6567             Izmir         Turkey      10.652\n",
      "2011-01-01        6237            Ibadan        Nigeria      27.290\n",
      "2011-01-01       13002              Rome          Italy       3.928\n",
      "2011-01-01        9702            Madrid          Spain       4.232\n",
      "2011-01-01       16302             Wuhan          China       1.496\n",
      "2011-01-01        3432           Chengdu          China      -1.447\n",
      "2011-01-01        4092            Dalian          China      -6.268\n",
      "2011-01-01        2442             Cairo          Egypt      14.397\n",
      "2011-01-01        6402          Istanbul         Turkey       5.689\n",
      "2011-01-01        6732            Jaipur          India      14.783\n",
      "2011-01-01       15807             Tokyo          Japan       0.817\n",
      "2011-01-01       11682         New Delhi          India      13.376\n",
      "2011-01-01       15972           Toronto         Canada      -9.348\n",
      "2011-01-01        5907  Ho Chi Minh City        Vietnam      25.985\n",
      "2011-01-01       15642           Tianjin          China      -5.667\n",
      "2011-01-01       13167         São Paulo         Brazil      23.878\n",
      "2011-01-01         627            Aleppo          Syria       6.789\n",
      "2011-01-01        2277          Brasília         Brazil      22.892\n",
      "2011-01-01        4257     Dar Es Salaam       Tanzania      28.541\n",
      "2011-01-01       11352           Nairobi          Kenya      17.768\n",
      "2011-01-01         297       Addis Abeba       Ethiopia      17.708\n",
      "2011-01-01       11517           Nanjing          China       0.144\n",
      "2011-01-01       11847          New York  United States      -4.463\n",
      "\n",
      "[600 rows x 4 columns]\n",
      "2001-11-01\n",
      "   Unnamed: 0        date     city        country  avg_temp_c\n",
      "0           0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1           1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2           2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3           3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4           4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "          city        country\n",
      "0      Abidjan  Côte D'Ivoire\n",
      "1      Abidjan  Côte D'Ivoire\n",
      "2      Abidjan  Côte D'Ivoire\n",
      "3      Abidjan  Côte D'Ivoire\n",
      "4      Abidjan  Côte D'Ivoire\n",
      "5      Abidjan  Côte D'Ivoire\n",
      "6      Abidjan  Côte D'Ivoire\n",
      "7      Abidjan  Côte D'Ivoire\n",
      "8      Abidjan  Côte D'Ivoire\n",
      "9      Abidjan  Côte D'Ivoire\n",
      "10     Abidjan  Côte D'Ivoire\n",
      "11     Abidjan  Côte D'Ivoire\n",
      "12     Abidjan  Côte D'Ivoire\n",
      "13     Abidjan  Côte D'Ivoire\n",
      "14     Abidjan  Côte D'Ivoire\n",
      "15     Abidjan  Côte D'Ivoire\n",
      "16     Abidjan  Côte D'Ivoire\n",
      "17     Abidjan  Côte D'Ivoire\n",
      "18     Abidjan  Côte D'Ivoire\n",
      "19     Abidjan  Côte D'Ivoire\n",
      "20     Abidjan  Côte D'Ivoire\n",
      "21     Abidjan  Côte D'Ivoire\n",
      "22     Abidjan  Côte D'Ivoire\n",
      "23     Abidjan  Côte D'Ivoire\n",
      "24     Abidjan  Côte D'Ivoire\n",
      "25     Abidjan  Côte D'Ivoire\n",
      "26     Abidjan  Côte D'Ivoire\n",
      "27     Abidjan  Côte D'Ivoire\n",
      "28     Abidjan  Côte D'Ivoire\n",
      "29     Abidjan  Côte D'Ivoire\n",
      "...        ...            ...\n",
      "16470     Xian          China\n",
      "16471     Xian          China\n",
      "16472     Xian          China\n",
      "16473     Xian          China\n",
      "16474     Xian          China\n",
      "16475     Xian          China\n",
      "16476     Xian          China\n",
      "16477     Xian          China\n",
      "16478     Xian          China\n",
      "16479     Xian          China\n",
      "16480     Xian          China\n",
      "16481     Xian          China\n",
      "16482     Xian          China\n",
      "16483     Xian          China\n",
      "16484     Xian          China\n",
      "16485     Xian          China\n",
      "16486     Xian          China\n",
      "16487     Xian          China\n",
      "16488     Xian          China\n",
      "16489     Xian          China\n",
      "16490     Xian          China\n",
      "16491     Xian          China\n",
      "16492     Xian          China\n",
      "16493     Xian          China\n",
      "16494     Xian          China\n",
      "16495     Xian          China\n",
      "16496     Xian          China\n",
      "16497     Xian          China\n",
      "16498     Xian          China\n",
      "16499     Xian          China\n",
      "\n",
      "[16500 rows x 2 columns]\n",
      "      city        country\n",
      "0  Abidjan  Côte D'Ivoire\n",
      "1  Abidjan  Côte D'Ivoire\n",
      "2  Abidjan  Côte D'Ivoire\n",
      "3  Abidjan  Côte D'Ivoire\n",
      "4  Abidjan  Côte D'Ivoire\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "Can only use .dt accessor with datetimelike values",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-cb88a74f052e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m \u001b[1;31m# Add a year column to temperatures\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m \u001b[0mtemperatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemperatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myear\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m \u001b[1;31m# Pivot avg_temp_c by country and city vs year\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   4370\u001b[0m         if (name in self._internal_names_set or name in self._metadata or\n\u001b[0;32m   4371\u001b[0m                 name in self._accessors):\n\u001b[1;32m-> 4372\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4373\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4374\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[1;34m(self, obj, cls)\u001b[0m\n\u001b[0;32m    131\u001b[0m             \u001b[1;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 133\u001b[1;33m         \u001b[0maccessor_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    134\u001b[0m         \u001b[1;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[1;31m# http://www.pydanny.com/cached-property.html\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\accessors.py\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(cls, data)\u001b[0m\n\u001b[0;32m    323\u001b[0m             \u001b[1;32mpass\u001b[0m  \u001b[1;31m# we raise an attribute error anyway\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 325\u001b[1;33m         raise AttributeError(\"Can only use .dt accessor with datetimelike \"\n\u001b[0m\u001b[0;32m    326\u001b[0m                              \"values\")\n",
      "\u001b[1;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
     ]
    }
   ],
   "source": [
    "temperatures = pd.read_csv('temperatures.csv')\n",
    "print(temperatures.info())\n",
    "\n",
    "# Look at temperatures\n",
    "print(temperatures)\n",
    "\n",
    "# Index temperatures by city\n",
    "temperatures_ind = temperatures.set_index('city')\n",
    "\n",
    "# Look at temperatures_ind\n",
    "print(temperatures_ind)\n",
    "\n",
    "# Reset the index, keeping its contents\n",
    "print(temperatures_ind.reset_index())\n",
    "\n",
    "# Reset the index, dropping its contents\n",
    "print(temperatures_ind.reset_index(drop=True))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Make a list of cities to subset on\n",
    "cities = [\"Moscow\", \"Saint Petersburg\"]\n",
    "\n",
    "# Subset temperatures using square brackets\n",
    "print(temperatures[temperatures[\"city\"].isin(cities)])\n",
    "\n",
    "# Subset temperatures_ind using .loc[]\n",
    "print(temperatures_ind.loc[cities])\n",
    "print(temperatures_ind.loc[['Moscow','Saint Petersburg']])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Index temperatures by country & city\n",
    "temperatures_ind = temperatures.set_index(['country','city'])\n",
    "\n",
    "# List of tuples: Brazil, Rio De Janeiro & Pakistan, Lahore\n",
    "rows_to_keep = [(\"Brazil\", \"Rio De Janeiro\"), ('Pakistan','Lahore')]\n",
    "\n",
    "# Subset for rows to keep\n",
    "print(temperatures_ind.loc[rows_to_keep])\n",
    "\n",
    "\n",
    "\n",
    "# Sort temperatures_ind by index values\n",
    "print(temperatures_ind.sort_index())\n",
    "\n",
    "# Sort temperatures_ind by index values at the city level\n",
    "print(temperatures_ind.sort_index(level=\"city\"))\n",
    "\n",
    "# Sort temperatures_ind by country then descending city\n",
    "print(temperatures_ind.sort_index(level=[\"country\",\"city\"], ascending=[True,False]))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Sort the index of temperatures_ind\n",
    "temperatures_srt = temperatures_ind.sort_index()\n",
    "\n",
    "# Subset rows from Pakistan to Russia\n",
    "print(temperatures_srt.loc[\"Pakistan\":\"Russia\"])\n",
    "\n",
    "# Try to subset rows from Lahore to Moscow\n",
    "print(temperatures_srt.loc[\"Lahore\":\"Moscow\"])\n",
    "\n",
    "# Subset rows from Pakistan, Lahore to Russia, Moscow\n",
    "print(temperatures_srt.loc[(\"Pakistan\",\"Lahore\"):(\"Russia\",\"Moscow\")])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Subset rows from India, Hyderabad to Iraq, Baghdad\n",
    "print(temperatures_srt.loc[('India','Hyderabad'):('Iraq','Baghdad')])\n",
    "\n",
    "# Subset columns from date to avg_temp_c\n",
    "print(temperatures_srt.loc[:, 'date':'avg_temp_c'])\n",
    "\n",
    "# Subset in both directions at once\n",
    "print(temperatures_srt.loc[('India','Hyderabad'):('Iraq','Baghdad'), 'date':'avg_temp_c'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Use Boolean conditions to subset temperatures for rows in 2010 and 2011\n",
    "temperatures_bool = temperatures[(temperatures[\"date\"] >= \"2010-01-01\") & (temperatures[\"date\"] <= \"2011-12-31\")]\n",
    "print(temperatures_bool)\n",
    "\n",
    "# Set date as an index and sort the index\n",
    "temperatures_ind = temperatures.set_index(\"date\").sort_index()\n",
    "\n",
    "# Use .loc[] to subset temperatures_ind for rows in 2010 and 2011\n",
    "print(temperatures_ind.loc[\"2010\":\"2011\"])\n",
    "\n",
    "# Use .loc[] to subset temperatures_ind for rows from Aug 2010 to Feb 2011\n",
    "print(temperatures_ind.loc[\"2010-08\":\"2011-02\"])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Get 23rd row, 2nd column (index 22, 1)\n",
    "print(temperatures.iloc[22, 1])\n",
    "\n",
    "# Use slicing to get the first 5 rows\n",
    "print(temperatures.iloc[0:5,])\n",
    "\n",
    "# Use slicing to get columns 3 to 4\n",
    "print(temperatures.iloc[:,2:4])\n",
    "\n",
    "# Use slicing in both directions at once\n",
    "print(temperatures.iloc[0:5,2:4])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Add a year column to temperatures\n",
    "temperatures['year'] = temperatures['date'].dt.year\n",
    "\n",
    "# Pivot avg_temp_c by country and city vs year\n",
    "temp_by_country_city_vs_year = temperatures.pivot_table(values=\"avg_temp_c\", columns='year', index=['country','city'])\n",
    "\n",
    "# See the result\n",
    "print(temp_by_country_city_vs_year)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Subset for Egypt to India\n",
    "temp_by_country_city_vs_year.loc['Egypt':'India']\n",
    "\n",
    "# Subset for Egypt, Cairo to India, Delhi\n",
    "temp_by_country_city_vs_year.loc[('Egypt','Cairo'):('India','Delhi')]\n",
    "\n",
    "# Subset in both directions at once\n",
    "temp_by_country_city_vs_year.loc[('Egypt','Cairo'):('India','Delhi'), '2005':'2010']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Get the worldwide mean temp by year\n",
    "mean_temp_by_year = temp_by_country_city_vs_year.mean()\n",
    "\n",
    "# Filter for the year that had the highest mean temp\n",
    "print(mean_temp_by_year[mean_temp_by_year == mean_temp_by_year.max()])\n",
    "\n",
    "# Get the mean temp by city\n",
    "mean_temp_by_city = temp_by_country_city_vs_year.mean(axis=\"columns\")\n",
    "\n",
    "# Filter for the city that had the lowest mean temp\n",
    "print(mean_temp_by_city[mean_temp_by_city == mean_temp_by_city.min()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18249 entries, 0 to 18248\n",
      "Data columns (total 14 columns):\n",
      "Unnamed: 0      18249 non-null int64\n",
      "Date            18249 non-null object\n",
      "AveragePrice    18249 non-null float64\n",
      "Total Volume    18249 non-null float64\n",
      "4046            18249 non-null float64\n",
      "4225            18249 non-null float64\n",
      "4770            18249 non-null float64\n",
      "Total Bags      18249 non-null float64\n",
      "Small Bags      18249 non-null float64\n",
      "Large Bags      18249 non-null float64\n",
      "XLarge Bags     18249 non-null float64\n",
      "type            18249 non-null object\n",
      "year            18249 non-null int64\n",
      "region          18249 non-null object\n",
      "dtypes: float64(9), int64(2), object(3)\n",
      "memory usage: 1.9+ MB\n",
      "None\n",
      "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
      "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
      "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
      "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
      "3           3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
      "4           4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
      "\n",
      "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
      "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
      "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
      "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
      "3   72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
      "4   75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
      "\n",
      "   year  region  \n",
      "0  2015  Albany  \n",
      "1  2015  Albany  \n",
      "2  2015  Albany  \n",
      "3  2015  Albany  \n",
      "4  2015  Albany  \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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Import matplotlib.pyplot with alias plt\n",
    "avocados = pd.read_csv('avocado.csv')\n",
    "print(avocados.info())\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Look at the first few rows of data\n",
    "print(avocados.head())\n",
    "\n",
    "# Get the total number of avocados sold of each size\n",
    "nb_sold_by_size = avocados.groupby(\"type\")[\"Total Volume\"].sum()\n",
    "\n",
    "# Create a bar plot of the number of avocados sold by size\n",
    "nb_sold_by_size.plot(kind=\"bar\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Import matplotlib.pyplot with alias plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the total number of avocados sold on each date\n",
    "nb_sold_by_date = avocados.groupby('Date')[\"Total Volume\"].sum()\n",
    "\n",
    "# Create a line plot of the number of avocados sold by date\n",
    "nb_sold_by_date.plot(kind=\"line\", x=\"Date\", y=\"Total Volume\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Scatter plot of nb_sold vs avg_price with title\n",
    "avocados.plot(x=\"Total Volume\", y=\"AveragePrice\", kind=\"scatter\", title=\"Number of avocados sold vs. average price\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Histogram of conventional avg_price \n",
    "avocados[avocados['type'] == 'conventional']['AveragePrice'].hist(alpha=.6, bins=20)\n",
    "\n",
    "# Histogram of organic avg_price\n",
    "avocados[avocados['type'] == 'organic']['AveragePrice'].hist(alpha=.6, bins=20)\n",
    "\n",
    "# Add a legend\n",
    "plt.legend([\"conventional\",\"organic\"])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Unnamed: 0   Date  AveragePrice  Total Volume   4046   4225   4770  \\\n",
      "0           False  False         False         False  False  False  False   \n",
      "1           False  False         False         False  False  False  False   \n",
      "2           False  False         False         False  False  False  False   \n",
      "3           False  False         False         False  False  False  False   \n",
      "4           False  False         False         False  False  False  False   \n",
      "5           False  False         False         False  False  False  False   \n",
      "6           False  False         False         False  False  False  False   \n",
      "7           False  False         False         False  False  False  False   \n",
      "8           False  False         False         False  False  False  False   \n",
      "9           False  False         False         False  False  False  False   \n",
      "10          False  False         False         False  False  False  False   \n",
      "11          False  False         False         False  False  False  False   \n",
      "12          False  False         False         False  False  False  False   \n",
      "13          False  False         False         False  False  False  False   \n",
      "14          False  False         False         False  False  False  False   \n",
      "15          False  False         False         False  False  False  False   \n",
      "16          False  False         False         False  False  False  False   \n",
      "17          False  False         False         False  False  False  False   \n",
      "18          False  False         False         False  False  False  False   \n",
      "19          False  False         False         False  False  False  False   \n",
      "20          False  False         False         False  False  False  False   \n",
      "21          False  False         False         False  False  False  False   \n",
      "22          False  False         False         False  False  False  False   \n",
      "23          False  False         False         False  False  False  False   \n",
      "24          False  False         False         False  False  False  False   \n",
      "25          False  False         False         False  False  False  False   \n",
      "26          False  False         False         False  False  False  False   \n",
      "27          False  False         False         False  False  False  False   \n",
      "28          False  False         False         False  False  False  False   \n",
      "29          False  False         False         False  False  False  False   \n",
      "...           ...    ...           ...           ...    ...    ...    ...   \n",
      "18219       False  False         False         False  False  False  False   \n",
      "18220       False  False         False         False  False  False  False   \n",
      "18221       False  False         False         False  False  False  False   \n",
      "18222       False  False         False         False  False  False  False   \n",
      "18223       False  False         False         False  False  False  False   \n",
      "18224       False  False         False         False  False  False  False   \n",
      "18225       False  False         False         False  False  False  False   \n",
      "18226       False  False         False         False  False  False  False   \n",
      "18227       False  False         False         False  False  False  False   \n",
      "18228       False  False         False         False  False  False  False   \n",
      "18229       False  False         False         False  False  False  False   \n",
      "18230       False  False         False         False  False  False  False   \n",
      "18231       False  False         False         False  False  False  False   \n",
      "18232       False  False         False         False  False  False  False   \n",
      "18233       False  False         False         False  False  False  False   \n",
      "18234       False  False         False         False  False  False  False   \n",
      "18235       False  False         False         False  False  False  False   \n",
      "18236       False  False         False         False  False  False  False   \n",
      "18237       False  False         False         False  False  False  False   \n",
      "18238       False  False         False         False  False  False  False   \n",
      "18239       False  False         False         False  False  False  False   \n",
      "18240       False  False         False         False  False  False  False   \n",
      "18241       False  False         False         False  False  False  False   \n",
      "18242       False  False         False         False  False  False  False   \n",
      "18243       False  False         False         False  False  False  False   \n",
      "18244       False  False         False         False  False  False  False   \n",
      "18245       False  False         False         False  False  False  False   \n",
      "18246       False  False         False         False  False  False  False   \n",
      "18247       False  False         False         False  False  False  False   \n",
      "18248       False  False         False         False  False  False  False   \n",
      "\n",
      "       Total Bags  Small Bags  Large Bags  XLarge Bags   type   year  region  \n",
      "0           False       False       False        False  False  False   False  \n",
      "1           False       False       False        False  False  False   False  \n",
      "2           False       False       False        False  False  False   False  \n",
      "3           False       False       False        False  False  False   False  \n",
      "4           False       False       False        False  False  False   False  \n",
      "5           False       False       False        False  False  False   False  \n",
      "6           False       False       False        False  False  False   False  \n",
      "7           False       False       False        False  False  False   False  \n",
      "8           False       False       False        False  False  False   False  \n",
      "9           False       False       False        False  False  False   False  \n",
      "10          False       False       False        False  False  False   False  \n",
      "11          False       False       False        False  False  False   False  \n",
      "12          False       False       False        False  False  False   False  \n",
      "13          False       False       False        False  False  False   False  \n",
      "14          False       False       False        False  False  False   False  \n",
      "15          False       False       False        False  False  False   False  \n",
      "16          False       False       False        False  False  False   False  \n",
      "17          False       False       False        False  False  False   False  \n",
      "18          False       False       False        False  False  False   False  \n",
      "19          False       False       False        False  False  False   False  \n",
      "20          False       False       False        False  False  False   False  \n",
      "21          False       False       False        False  False  False   False  \n",
      "22          False       False       False        False  False  False   False  \n",
      "23          False       False       False        False  False  False   False  \n",
      "24          False       False       False        False  False  False   False  \n",
      "25          False       False       False        False  False  False   False  \n",
      "26          False       False       False        False  False  False   False  \n",
      "27          False       False       False        False  False  False   False  \n",
      "28          False       False       False        False  False  False   False  \n",
      "29          False       False       False        False  False  False   False  \n",
      "...           ...         ...         ...          ...    ...    ...     ...  \n",
      "18219       False       False       False        False  False  False   False  \n",
      "18220       False       False       False        False  False  False   False  \n",
      "18221       False       False       False        False  False  False   False  \n",
      "18222       False       False       False        False  False  False   False  \n",
      "18223       False       False       False        False  False  False   False  \n",
      "18224       False       False       False        False  False  False   False  \n",
      "18225       False       False       False        False  False  False   False  \n",
      "18226       False       False       False        False  False  False   False  \n",
      "18227       False       False       False        False  False  False   False  \n",
      "18228       False       False       False        False  False  False   False  \n",
      "18229       False       False       False        False  False  False   False  \n",
      "18230       False       False       False        False  False  False   False  \n",
      "18231       False       False       False        False  False  False   False  \n",
      "18232       False       False       False        False  False  False   False  \n",
      "18233       False       False       False        False  False  False   False  \n",
      "18234       False       False       False        False  False  False   False  \n",
      "18235       False       False       False        False  False  False   False  \n",
      "18236       False       False       False        False  False  False   False  \n",
      "18237       False       False       False        False  False  False   False  \n",
      "18238       False       False       False        False  False  False   False  \n",
      "18239       False       False       False        False  False  False   False  \n",
      "18240       False       False       False        False  False  False   False  \n",
      "18241       False       False       False        False  False  False   False  \n",
      "18242       False       False       False        False  False  False   False  \n",
      "18243       False       False       False        False  False  False   False  \n",
      "18244       False       False       False        False  False  False   False  \n",
      "18245       False       False       False        False  False  False   False  \n",
      "18246       False       False       False        False  False  False   False  \n",
      "18247       False       False       False        False  False  False   False  \n",
      "18248       False       False       False        False  False  False   False  \n",
      "\n",
      "[18249 rows x 14 columns]\n",
      "Unnamed: 0      False\n",
      "Date            False\n",
      "AveragePrice    False\n",
      "Total Volume    False\n",
      "4046            False\n",
      "4225            False\n",
      "4770            False\n",
      "Total Bags      False\n",
      "Small Bags      False\n",
      "Large Bags      False\n",
      "XLarge Bags     False\n",
      "type            False\n",
      "year            False\n",
      "region          False\n",
      "dtype: bool\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0      False\n",
      "Date            False\n",
      "AveragePrice    False\n",
      "Total Volume    False\n",
      "4046            False\n",
      "4225            False\n",
      "4770            False\n",
      "Total Bags      False\n",
      "Small Bags      False\n",
      "Large Bags      False\n",
      "XLarge Bags     False\n",
      "type            False\n",
      "year            False\n",
      "region          False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# Import matplotlib.pyplot with alias plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Check individual values for missing values\n",
    "print(avocados.isna())\n",
    "\n",
    "# Check each column for missing values\n",
    "print(avocados.isna().any())\n",
    "\n",
    "# Bar plot of missing values by variable\n",
    "avocados.isna().sum().plot(kind=\"bar\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Remove rows with missing values\n",
    "avocados_complete = avocados.dropna()\n",
    "\n",
    "# Check if any columns contain missing values\n",
    "print(avocados_complete.isna().any())\n",
    "\n",
    "\n",
    "\n",
    "# List the columns with missing values\n",
    "cols_with_missing = [\"small_sold\", \"large_sold\", \"xl_sold\"]\n",
    "\n",
    "# Create histograms showing the distributions cols_with_missing\n",
    "avocados[cols_with_missing].hist()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of dictionaries with new data\n",
    "avocados_list = [\n",
    "    {\"date\": \"2019-11-03\", \"small_sold\": 10376832, \"large_sold\": 7835071},\n",
    "    {\"date\": \"2019-11-10\", \"small_sold\": 10717154, \"large_sold\": 8561348},\n",
    "]\n",
    "\n",
    "# Convert list into DataFrame\n",
    "avocados_2019 = pd.DataFrame(avocados_list)\n",
    "\n",
    "# Print the new DataFrame\n",
    "print(avocados_2019)\n",
    "\n",
    "\n",
    "\n",
    "# Create a dictionary of lists with new data\n",
    "avocados_dict = {\n",
    "  \"date\": [\"2019-11-17\", \"2019-12-01\"],\n",
    "  \"small_sold\": [10859987, 9291631],\n",
    "  \"large_sold\": [7674135, 6238096]\n",
    "}\n",
    "\n",
    "# Convert dictionary into DataFrame\n",
    "avocados_2019 = pd.DataFrame(avocados_dict)\n",
    "\n",
    "# Print the new DataFrame\n",
    "print(avocados_2019)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# From previous step\n",
    "airline_bumping = pd.read_csv(\"airline_bumping.csv\")\n",
    "print(airline_bumping.head())\n",
    "\n",
    "# For each airline, select nb_bumped and total_passengers and sum\n",
    "airline_totals = airline_bumping.groupby('airline')[['nb_bumped','total_passengers']].sum()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# From previous steps\n",
    "airline_bumping = pd.read_csv(\"airline_bumping.csv\")\n",
    "print(airline_bumping.head())\n",
    "airline_totals = airline_bumping.groupby(\"airline\")[[\"nb_bumped\", \"total_passengers\"]].sum()\n",
    "\n",
    "# Create new col, bumps_per_10k: no. of bumps per 10k passengers for each airline\n",
    "airline_totals[\"bumps_per_10k\"] = airline_totals[\"nb_bumped\"] / airline_totals[\"total_passengers\"] * 10000"
   ]
  }
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