필수 라이브러리 import

데이터 불러오기

import pandas as pd
pd.options.display.max_columns = 20
pd.options.display.max_rows = 10

absolute_path = '../Data/FoodAgricultureOrganization/Food_Agriculture_Organization_UN_Full.csv'
raw_data = pd.read_csv(absolute_path, sep=',')

데이터 확인하기

raw_data.head()

데이터 타입 확인하기

raw_data.dtypes

Area Abbreviation     object
Area Code              int64
Area                  object
Item Code              int64
Item                  object
                      ...
Y2009                float64
Y2010                float64
Y2011                float64
Y2012                  int64
Y2013                  int64
Length: 63, dtype: object

string타입으로 바꾸는 astype()

raw_data['Item Code'] = raw_data['Item Code'].astype(str)

데이터 정보 확인하기

raw_data.describe()

columns이 많을때는 describe().T로 transpose해주기!

raw_data.describe().T

데이터 전처리 iloc, loc 함수 뽀개기!

iloc[0]은 행을 뽑아 냅니다. 그런데 별로 안예쁘죠?

raw_data.iloc[0]

Area Abbreviation                    AF
Area Code                             2
Area                        Afghanistan
Item Code                          2511
Item                 Wheat and products
                            ...
Y2009                              4538
Y2010                              4605
Y2011                              4711
Y2012                              4810
Y2013                              4895
Name: 0, Length: 63, dtype: object

iloc[[0]] 이렇게 괄호 2개를 사용해서 dataframe형식 그대로 뽑아낼 수 있어요

raw_data.iloc[[1]]

iloc[행,열] 이 표현은 열을 뽑아낼 수 있어요

raw_data.iloc[:,0]

0        AF
1        AF
2        AF
3        AF
4        AF
         ..
21472    ZW
21473    ZW
21474    ZW
21475    ZW
21476    ZW
Name: Area Abbreviation, Length: 21477, dtype: object

iloc[행,열] 이 표현은 열을 뽑아낼 수 있어요, 위와 마찬가지로 dataframe처럼 예쁘게 보일려면 [[]] 괄호2개를 넣어주세요!

raw_data.iloc[:,[0]]

복습! 행을 여러개 뽑아보기

raw_data.iloc[0:5]

복습! 열을 여러개 뽑아보기

raw_data.iloc[:,0:2]

복습! 행과 열을 여러개 뽑아보기

raw_data.iloc[0:5,0:2]

loc()함수! 위의 iloc()와 어떤 차이점이 있을지 저도 매우 궁금했어요.

loc(), iloc() 비교 실습으로 알아봅시다.

loc여도 행1개를 추출할 수 있어요

raw_data.loc[[1]]

iloc와 똑같이 뽑을 수 있어요

raw_data.iloc[[1]]

loc함수는 여러개 행을 범위로 이렇게 뽑을 수 없네요

raw_data.loc[[1:3]]

  File "<ipython-input-17-32675df71253>", line 1
    raw_data.loc[[1:3]]
                   ^
SyntaxError: invalid syntax

loc함수는 여러개 행을 index를 지정해서 이렇게 뽑을 수 있어요..

raw_data.loc[[1,3]]

iloc함수도 마찬가지로 index로 몇개 추출할 수 있습니다.

raw_data.iloc[[1,3]]

loc함수는 범위로 뽑을려면 [[]] 괄호가 2개가 없어도 됩니다.

그런데, [1:3]은 대부분 1,2 까지 뽑아내지만, loc는 1,2,3 까지 뽑아 냅니다.

raw_data.loc[1:3]

iloc함수로 뽑으면 [1:3]이 1,2로 뽑힐 수 있습니다.

raw_data.iloc[1:3]

그럼 loc함수의 장점은 무엇일까요? 이제 나옵니다.

열을 뽑을 때 열의 명을 가지고 뽑을 수 있어요. 사실 열의 명으로 열을 뽑는게 더 쉽잖아요! 유용하게 쓸 수 있을 것 같아요

raw_data.loc[[1,3],['Area Code','Area']]

iloc함수로는 열의 이름으로 뽑을 수 없습니다.

raw_data.iloc[[1,3],['Area Code','Area']]

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-23-5375d4a0863b> in <module>
----> 1 raw_data.iloc[[1,3],['Area Code','Area']]

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
    871                     # AttributeError for IntervalTree get_value
    872                     pass
--> 873             return self._getitem_tuple(key)
    874         else:
    875             # we by definition only have the 0th axis

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
   1441     def _getitem_tuple(self, tup: Tuple):
   1442
-> 1443         self._has_valid_tuple(tup)
   1444         try:
   1445             return self._getitem_lowerdim(tup)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _has_valid_tuple(self, key)
    700                 raise IndexingError("Too many indexers")
    701             try:
--> 702                 self._validate_key(k, i)
    703             except ValueError as err:
    704                 raise ValueError(

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _validate_key(self, key, axis)
   1361             # check that the key has a numeric dtype
   1362             if not is_numeric_dtype(arr.dtype):
-> 1363                 raise IndexError(f".iloc requires numeric indexers, got {arr}")
   1364
   1365             # check that the key does not exceed the maximum size of the index

IndexError: .iloc requires numeric indexers, got ['Area Code' 'Area']

loc함수는 열의 명을 범위로 모두 뽑을 수 있더라구요!

유용하게 쓸 수 있을 것 같아요

raw_data.loc[[1,3],'Item':'Y2013']

내가 원하는 값 뽑아내기, 이것은 굳이 loc함수를 안써도 되어요

raw_data.loc[raw_data['Item']=='Sugar beet']

내가 원하는 값 뽑아내기, iloc는 할 수 없어요.

raw_data.iloc[raw_data['Item']=='Sugar beet']

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-26-24cc43d7f2af> in <module>
----> 1 raw_data.iloc[raw_data['Item']=='Sugar beet']

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
    877
    878             maybe_callable = com.apply_if_callable(key, self.obj)
--> 879             return self._getitem_axis(maybe_callable, axis=axis)
    880
    881     def _is_scalar_access(self, key: Tuple):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
   1480
   1481         if com.is_bool_indexer(key):
-> 1482             self._validate_key(key, axis)
   1483             return self._getbool_axis(key, axis=axis)
   1484

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexing.py in _validate_key(self, key, axis)
   1337                 if key.index.inferred_type == "integer":
   1338                     raise NotImplementedError(
-> 1339                         "iLocation based boolean "
   1340                         "indexing on an integer type "
   1341                         "is not available"

NotImplementedError: iLocation based boolean indexing on an integer type is not available

내가 원하는 값 뽑아내기, 이것은 굳이 loc함수를 안써도 되고 이렇게 그냥 list의 특성으로 뽑아 낼 수 있어요

raw_data[raw_data['Item']=='Sugar beet']

내가 원하는 값 뽑아내기, 같은 값을 2개 뽑아낼 수도 있어요 loc함수로!

raw_data.loc[raw_data['Item']=='Sugar beet','Area']

10                              Afghanistan
103                                 Albania
699                                 Armenia
832                               Australia
1099                             Azerbaijan
                        ...
20020                  United Arab Emirates
20676                            Uzbekistan
20900    Venezuela (Bolivarian Republic of)
21135                                 Yemen
21374                              Zimbabwe
Name: Area, Length: 66, dtype: object

저는 loc함수가 이 대목에서 대박이라 생각합니다. 원하는 값을 뽑아내면서, 열 명으로 원하는 열만 추출하기!

raw_data.loc[raw_data['Item']=='Sugar beet',['Area']]

list의 특성으로는 안되요

raw_data[raw_data['Item']=='Sugar beet',['Area']]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-30-d0ec0ffd7749> in <module>
----> 1 raw_data[raw_data['Item']=='Sugar beet',['Area']]

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2904             if self.columns.nlevels > 1:
   2905                 return self._getitem_multilevel(key)
-> 2906             indexer = self.columns.get_loc(key)
   2907             if is_integer(indexer):
   2908                 indexer = [indexer]

~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2896             casted_key = self._maybe_cast_indexer(key)
   2897             try:
-> 2898                 return self._engine.get_loc(casted_key)
   2899             except KeyError as err:
   2900                 raise KeyError(key) from err

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

TypeError: '(0        False
1        False
2        False
3        False
4        False
         ...
21472    False
21473    False
21474    False
21475    False
21476    False
Name: Item, Length: 21477, dtype: bool, ['Area'])' is an invalid key

저는 loc함수가 이 대목에서 대박이라 생각합니다2. 원하는 값을 뽑아내면서, 열 명으로 원하는 여러 열도 추출하기!

raw_data.loc[raw_data['Item']=='Sugar beet',['Area','Item','latitude']]

endswith('many')함수는 값뒤에 'many'가 있으면 추출하는 함수입니다.

raw_data.loc[raw_data['Area'].str.endswith('many')]

저는 isin함수를 더 많이 쓰는데, isin(['Germany'])는 Germany 값과 같은 행을 뽑아 냅니다.

raw_data.loc[raw_data['Area'].isin(['Germany'])]

raw_data[raw_data['Area'].isin(['Germany'])]

저는 isin함수를 더 많이 쓰는데, isin([])는 2개이상으로 같은 값을 추출할 수 있어요

raw_data[raw_data['Area'].isin(['Germany','France'])]

raw_data.loc[(raw_data['Area'].str.endswith('many')) & (raw_data['Element']=='Feed')]

list특성으로 값을 추출할 수 있는데, & 기호(and 라는 뜻)를 이용해서 값을 추출 할 수 있습니다.

raw_data[(raw_data['Area'].str.endswith('many')) & (raw_data['Element']=='Feed')]

raw_data[(raw_data['Y2004'] < 1000) & (raw_data['Y2004'] > 990)]

loc함수의 가장 큰 장점을 이용하여 뽑을 수 있어요! 장점은 열의 명으로 열을 추출하기!

raw_data.loc[(raw_data['Y2004'] < 1000) & (raw_data['Y2004'] > 990), ['Area', 'Item', 'latitude']]

drop함수를 이용해서 열 삭제하기. 꼭 axis=1를 써야 열이 삭제가 되어요

raw_data.drop('Area',axis=1)

drop함수 내의 columns=를 지정해주면, axis=1을 안써도 됩니다.

raw_data.drop(columns="Area")

여러 열을 삭제하기

raw_data.drop(['Y2011','Y2012','Y2013'], axis=1)

unique()함수는 행의 값중에서 1개씩만 추출해주는 함수에요

raw_data['Item'].unique()

array(['Wheat and products', 'Rice (Milled Equivalent)',
       'Barley and products', 'Maize and products', 'Millet and products',
       'Cereals, Other', 'Potatoes and products', 'Sugar cane',
       'Sugar beet', 'Sugar (Raw Equivalent)', 'Sweeteners, Other',
       'Honey', 'Pulses, Other and products', 'Nuts and products',
       'Coconuts - Incl Copra', 'Sesame seed',
       'Olives (including preserved)', 'Soyabean Oil', 'Groundnut Oil',
       'Sunflowerseed Oil', 'Rape and Mustard Oil', 'Cottonseed Oil',
       'Palm Oil', 'Sesameseed Oil', 'Olive Oil', 'Oilcrops Oil, Other',
       'Tomatoes and products', 'Vegetables, Other',
       'Oranges, Mandarines', 'Citrus, Other', 'Bananas',
       'Apples and products', 'Pineapples and products', 'Dates',
       'Grapes and products (excl wine)', 'Fruits, Other',
       'Coffee and products', 'Cocoa Beans and products',
       'Tea (including mate)', 'Pepper', 'Spices, Other', 'Wine', 'Beer',
       'Beverages, Alcoholic', 'Bovine Meat', 'Mutton & Goat Meat',
       'Poultry Meat', 'Meat, Other', 'Offals, Edible', 'Butter, Ghee',
       'Cream', 'Fats, Animals, Raw', 'Eggs', 'Milk - Excluding Butter',
       'Freshwater Fish', 'Infant food', 'Cereals - Excluding Beer',
       'Starchy Roots', 'Sugar Crops', 'Sugar & Sweeteners', 'Pulses',
       'Treenuts', 'Oilcrops', 'Vegetable Oils', 'Vegetables',
       'Fruits - Excluding Wine', 'Stimulants', 'Spices',
       'Alcoholic Beverages', 'Meat', 'Offals', 'Animal fats',
       'Fish, Seafood', 'Miscellaneous', 'Rye and products', 'Oats',
       'Sorghum and products', 'Cassava and products', 'Sweet potatoes',
       'Roots, Other', 'Beans', 'Peas', 'Soyabeans',
       'Groundnuts (Shelled Eq)', 'Rape and Mustardseed',
       'Oilcrops, Other', 'Maize Germ Oil', 'Onions',
       'Lemons, Limes and products', 'Grapefruit and products',
       'Plantains', 'Pimento', 'Beverages, Fermented', 'Pigmeat',
       'Fish, Body Oil', 'Fish, Liver Oil', 'Demersal Fish',
       'Pelagic Fish', 'Marine Fish, Other', 'Crustaceans', 'Cephalopods',
       'Molluscs, Other', 'Aquatic Animals, Others', 'Aquatic Plants',
       'Aquatic Products, Other', 'Palm kernels', 'Coconut Oil', 'Cloves',
       'Palmkernel Oil', 'Yams', 'Sunflower seed', 'Cottonseed',
       'Sugar non-centrifugal', 'Ricebran Oil', 'Meat, Aquatic Mammals'],
      dtype=object)

value_counts()는 유일한 값 중에서 몇개 있는지 값을 세어주는 함수에요. 유용합니다.

raw_data['Item'].value_counts()

Milk - Excluding Butter     558
Eggs                        360
Cereals - Excluding Beer    347
Fish, Seafood               337
Maize and products          333
                           ...
Sugar non-centrifugal        32
Palm kernels                 24
Cottonseed                   21
Ricebran Oil                 18
Meat, Aquatic Mammals         3
Name: Item, Length: 115, dtype: int64

sort_values()함수로 오름차순으로 추출해줄 수 있어요

raw_data['Item'].value_counts().sort_values()

Meat, Aquatic Mammals         3
Ricebran Oil                 18
Cottonseed                   21
Palm kernels                 24
Sugar non-centrifugal        32
                           ...
Maize and products          333
Fish, Seafood               337
Cereals - Excluding Beer    347
Eggs                        360
Milk - Excluding Butter     558
Name: Item, Length: 115, dtype: int64

raw_data['Item'].sort_values()

16297    Alcoholic Beverages
7390     Alcoholic Beverages
320      Alcoholic Beverages
14592    Alcoholic Beverages
16818    Alcoholic Beverages
                ...
5796                    Yams
9825                    Yams
20284                   Yams
13703                   Yams
15545                   Yams
Name: Item, Length: 21477, dtype: object

정말 유용한 groupby함수를 간단히 맛보기로 보자면,

groupby(['Area'])는 Area값별로 각 열의 카운트(count())를 세어주는 거에요

groupby는 단독으로 사용할수 없고, count(), sum(), max() 등과 같은 연산함수와 사용해야합니다.

raw_data.groupby(['Area']).count()

raw_data.groupby(['Area Code']).sum()

groupby함수를 사용해서, group화 하면서 내가 원하는 행만 추출할 수 있어요

raw_data.groupby(['Area Code'])['Y1961'].count()

Area Code
1        0
2       83
3      123
4      124
7      109
      ...
255      0
256      0
272      0
273      0
276      0
Name: Y1961, Length: 174, dtype: int64

groupby함수를 사용해서, group화 하면서 내가 원하는 행만 추출할 수 있어요. 예쁘게 할려면 [[]] 2개의 괄호를 써야되는거 위에서도 보셧죠?

raw_data.groupby(['Area Code'])[['Y1961']].count()

raw_data.groupby(['Area Code','Y1961']).count()

raw_data.groupby(['Item'])['Y2010'].sum()

Item
Alcoholic Beverages        247414.0
Animal fats                 24267.0
Apples and products         62436.0
Aquatic Animals, Others      1340.0
Aquatic Plants              12767.0
                             ...
Vegetables                 950541.0
Vegetables, Other          745438.0
Wheat and products         549926.0
Wine                        22297.0
Yams                        35898.0
Name: Y2010, Length: 115, dtype: float64

raw_data['Item'].isin(raw_data['Item']).value_counts()

True    21477
Name: Item, dtype: int64

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
0	AF	2	Afghanistan	2511	Wheat and products	5142	Food	1000 tonnes	33.94	67.71	...	3249.0	3486.0	3704.0	4164.0	4252.0	4538.0	4605.0	4711.0	4810	4895
1	AF	2	Afghanistan	2805	Rice (Milled Equivalent)	5142	Food	1000 tonnes	33.94	67.71	...	419.0	445.0	546.0	455.0	490.0	415.0	442.0	476.0	425	422
2	AF	2	Afghanistan	2513	Barley and products	5521	Feed	1000 tonnes	33.94	67.71	...	58.0	236.0	262.0	263.0	230.0	379.0	315.0	203.0	367	360
3	AF	2	Afghanistan	2513	Barley and products	5142	Food	1000 tonnes	33.94	67.71	...	185.0	43.0	44.0	48.0	62.0	55.0	60.0	72.0	78	89
4	AF	2	Afghanistan	2514	Maize and products	5521	Feed	1000 tonnes	33.94	67.71	...	120.0	208.0	233.0	249.0	247.0	195.0	178.0	191.0	200	200

	Area Code	Element Code	latitude	longitude	Y1961	Y1962	Y1963	Y1964	Y1965	Y1966	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
count	21477.000000	21477.000000	21477.000000	21477.000000	17938.000000	17938.000000	17938.000000	17938.000000	17938.000000	17938.000000	...	21128.000000	21128.000000	21373.000000	21373.000000	21373.000000	21373.000000	21373.000000	21373.000000	21477.000000	21477.000000
mean	125.449411	5211.687154	20.450613	15.794445	195.262069	200.782250	205.464600	209.925577	217.556751	225.988962	...	486.690742	493.153256	496.319328	508.482104	522.844898	524.581996	535.492069	553.399242	560.569214	575.557480
std	72.868149	146.820079	24.628336	66.012104	1864.124336	1884.265591	1861.174739	1862.000116	2014.934333	2100.228354	...	5001.782008	5100.057036	5134.819373	5298.939807	5496.697513	5545.939303	5721.089425	5883.071604	6047.950804	6218.379479
min	1.000000	5142.000000	-40.900000	-172.100000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-169.000000	-246.000000
25%	63.000000	5142.000000	6.430000	-11.780000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	120.000000	5142.000000	20.590000	19.150000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	6.000000	6.000000	7.000000	7.000000	7.000000	7.000000	7.000000	8.000000	8.000000	8.000000
75%	188.000000	5142.000000	41.150000	46.870000	21.000000	22.000000	23.000000	24.000000	25.000000	26.000000	...	75.000000	77.000000	78.000000	80.000000	82.000000	83.000000	83.000000	86.000000	88.000000	90.000000
max	276.000000	5521.000000	64.960000	179.410000	112227.000000	109130.000000	106356.000000	104234.000000	119378.000000	118495.000000	...	360767.000000	373694.000000	388100.000000	402975.000000	425537.000000	434724.000000	451838.000000	462696.000000	479028.000000	489299.000000

	count	mean	std	min	25%	50%	75%	max
Area Code	21477.0	125.449411	72.868149	1.0	63.00	120.00	188.00	276.00
Element Code	21477.0	5211.687154	146.820079	5142.0	5142.00	5142.00	5142.00	5521.00
latitude	21477.0	20.450613	24.628336	-40.9	6.43	20.59	41.15	64.96
longitude	21477.0	15.794445	66.012104	-172.1	-11.78	19.15	46.87	179.41
Y1961	17938.0	195.262069	1864.124336	0.0	0.00	1.00	21.00	112227.00
...	...	...	...	...	...	...	...	...
Y2009	21373.0	524.581996	5545.939303	0.0	0.00	7.00	83.00	434724.00
Y2010	21373.0	535.492069	5721.089425	0.0	0.00	7.00	83.00	451838.00
Y2011	21373.0	553.399242	5883.071604	0.0	0.00	8.00	86.00	462696.00
Y2012	21477.0	560.569214	6047.950804	-169.0	0.00	8.00	88.00	479028.00
Y2013	21477.0	575.557480	6218.379479	-246.0	0.00	8.00	90.00	489299.00

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
0	AF	2	Afghanistan	2511	Wheat and products	5142	Food	1000 tonnes	33.94	67.71	...	3249.0	3486.0	3704.0	4164.0	4252.0	4538.0	4605.0	4711.0	4810	4895
1	AF	2	Afghanistan	2805	Rice (Milled Equivalent)	5142	Food	1000 tonnes	33.94	67.71	...	419.0	445.0	546.0	455.0	490.0	415.0	442.0	476.0	425	422
2	AF	2	Afghanistan	2513	Barley and products	5521	Feed	1000 tonnes	33.94	67.71	...	58.0	236.0	262.0	263.0	230.0	379.0	315.0	203.0	367	360
3	AF	2	Afghanistan	2513	Barley and products	5142	Food	1000 tonnes	33.94	67.71	...	185.0	43.0	44.0	48.0	62.0	55.0	60.0	72.0	78	89
4	AF	2	Afghanistan	2514	Maize and products	5521	Feed	1000 tonnes	33.94	67.71	...	120.0	208.0	233.0	249.0	247.0	195.0	178.0	191.0	200	200

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
1	AF	2	Afghanistan	2805	Rice (Milled Equivalent)	5142	Food	1000 tonnes	33.94	67.71	...	419.0	445.0	546.0	455.0	490.0	415.0	442.0	476.0	425	422
3	AF	2	Afghanistan	2513	Barley and products	5142	Food	1000 tonnes	33.94	67.71	...	185.0	43.0	44.0	48.0	62.0	55.0	60.0	72.0	78	89

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	Area Abbreviation	Area Code
0	AF	2
1	AF	2
2	AF	2
3	AF	2
4	AF	2
...	...	...
21472	ZW	181
21473	ZW	181
21474	ZW	181
21475	ZW	181
21476	ZW	181

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
10	AF	2	Afghanistan	2537	Sugar beet	5521	Feed	1000 tonnes	33.94	67.71	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0
103	AL	3	Albania	2537	Sugar beet	5521	Feed	1000 tonnes	41.15	20.17	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1	1
699	AM	1	Armenia	2537	Sugar beet	5521	Feed	1000 tonnes	40.07	45.04	...	1.0	2.0	1.0	2.0	1.0	3.0	1.0	1.0	0	1
832	AU	10	Australia	2537	Sugar beet	5521	Feed	1000 tonnes	-25.27	133.78	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0
1099	AZ	52	Azerbaijan	2537	Sugar beet	5521	Feed	1000 tonnes	40.14	47.58	...	1.0	1.0	4.0	3.0	4.0	4.0	6.0	6.0	4	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
20020	AE	225	United Arab Emirates	2537	Sugar beet	5521	Feed	1000 tonnes	23.42	53.85	...	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0	0
20676	UZ	235	Uzbekistan	2537	Sugar beet	5521	Feed	1000 tonnes	41.38	64.59	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0
20900	VE	236	Venezuela (Bolivarian Republic of)	2537	Sugar beet	5142	Food	1000 tonnes	6.42	-66.59	...	17.0	20.0	23.0	21.0	21.0	21.0	30.0	35.0	20	22
21135	YE	249	Yemen	2537	Sugar beet	5521	Feed	1000 tonnes	15.55	48.52	...	0.0	0.0	1.0	13.0	0.0	0.0	0.0	0.0	0	0
21374	ZW	181	Zimbabwe	2537	Sugar beet	5142	Food	1000 tonnes	-19.02	29.15	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
7532	DE	79	Germany	2511	Wheat and products	5521	Feed	1000 tonnes	51.17	10.45	...	8006.0	11084.0	10644.0	8993.0	10669.0	10608.0	8189.0	9242.0	7868	7494
7533	DE	79	Germany	2511	Wheat and products	5142	Food	1000 tonnes	51.17	10.45	...	6524.0	6931.0	6796.0	6886.0	6868.0	7137.0	7235.0	7204.0	6712	6900
7534	DE	79	Germany	2805	Rice (Milled Equivalent)	5521	Feed	1000 tonnes	51.17	10.45	...	7.0	9.0	12.0	17.0	8.0	18.0	13.0	12.0	7	7
7535	DE	79	Germany	2805	Rice (Milled Equivalent)	5142	Food	1000 tonnes	51.17	10.45	...	206.0	212.0	226.0	244.0	251.0	257.0	243.0	271.0	275	277
7536	DE	79	Germany	2513	Barley and products	5521	Feed	1000 tonnes	51.17	10.45	...	7571.0	6878.0	7845.0	6940.0	7255.0	7592.0	7543.0	6062.0	6316	6598
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7674	DE	79	Germany	2948	Milk - Excluding Butter	5142	Food	1000 tonnes	51.17	10.45	...	20252.0	20878.0	21323.0	21661.0	21091.0	22077.0	21431.0	21171.0	21169	21401
7675	DE	79	Germany	2960	Fish, Seafood	5521	Feed	1000 tonnes	51.17	10.45	...	11.0	11.0	32.0	51.0	28.0	25.0	29.0	25.0	6	5
7676	DE	79	Germany	2960	Fish, Seafood	5142	Food	1000 tonnes	51.17	10.45	...	1122.0	1177.0	1238.0	1287.0	1226.0	1187.0	1175.0	1188.0	1128	1039
7677	DE	79	Germany	2961	Aquatic Products, Other	5142	Food	1000 tonnes	51.17	10.45	...	3.0	3.0	4.0	4.0	4.0	4.0	4.0	5.0	1	0
7678	DE	79	Germany	2928	Miscellaneous	5142	Food	1000 tonnes	51.17	10.45	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
6910	FR	68	France	2511	Wheat and products	5521	Feed	1000 tonnes	46.23	2.21	...	9867.0	11311.0	10091.0	8483.0	10283.0	8351.0	6262.0	7727.0	7179	7822
6911	FR	68	France	2511	Wheat and products	5142	Food	1000 tonnes	46.23	2.21	...	6057.0	6140.0	6402.0	6102.0	6677.0	6331.0	6986.0	6765.0	6984	6971
6912	FR	68	France	2805	Rice (Milled Equivalent)	5521	Feed	1000 tonnes	46.23	2.21	...	80.0	75.0	84.0	88.0	92.0	82.0	81.0	91.0	99	101
6913	FR	68	France	2805	Rice (Milled Equivalent)	5142	Food	1000 tonnes	46.23	2.21	...	296.0	325.0	317.0	367.0	350.0	347.0	339.0	361.0	339	314
6914	FR	68	France	2513	Barley and products	5521	Feed	1000 tonnes	46.23	2.21	...	3537.0	3560.0	4014.0	3446.0	4275.0	4428.0	4332.0	3137.0	3163	2865
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7674	DE	79	Germany	2948	Milk - Excluding Butter	5142	Food	1000 tonnes	51.17	10.45	...	20252.0	20878.0	21323.0	21661.0	21091.0	22077.0	21431.0	21171.0	21169	21401
7675	DE	79	Germany	2960	Fish, Seafood	5521	Feed	1000 tonnes	51.17	10.45	...	11.0	11.0	32.0	51.0	28.0	25.0	29.0	25.0	6	5
7676	DE	79	Germany	2960	Fish, Seafood	5142	Food	1000 tonnes	51.17	10.45	...	1122.0	1177.0	1238.0	1287.0	1226.0	1187.0	1175.0	1188.0	1128	1039
7677	DE	79	Germany	2961	Aquatic Products, Other	5142	Food	1000 tonnes	51.17	10.45	...	3.0	3.0	4.0	4.0	4.0	4.0	4.0	5.0	1	0
7678	DE	79	Germany	2928	Miscellaneous	5142	Food	1000 tonnes	51.17	10.45	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0

	Area Abbreviation	Area Code	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
3754	CL	40	Chile	2531	Potatoes and products	5142	Food	1000 tonnes	-35.68	-71.54	...	998.0	980.0	1018.0	875.0	884.0	850.0	1003.0	1132.0	1096	1089
7630	DE	79	Germany	2744	Eggs	5142	Food	1000 tonnes	51.17	10.45	...	999.0	974.0	1011.0	997.0	1011.0	1016.0	1032.0	1052.0	990	1010
7672	DE	79	Germany	2949	Eggs	5142	Food	1000 tonnes	51.17	10.45	...	999.0	974.0	1011.0	997.0	1011.0	1016.0	1032.0	1052.0	990	1010
9038	IN	100	India	2513	Barley and products	5142	Food	1000 tonnes	20.59	78.96	...	995.0	954.0	944.0	691.0	740.0	1276.0	984.0	1217.0	1152	835
9104	IN	100	India	2641	Pimento	5142	Food	1000 tonnes	20.59	78.96	...	998.0	844.0	1012.0	1010.0	1019.0	941.0	893.0	954.0	871	1018
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13436	MM	28	Myanmar	2948	Milk - Excluding Butter	5142	Food	1000 tonnes	21.91	95.96	...	992.0	1069.0	1117.0	1253.0	1298.0	1472.0	1581.0	1660.0	1600	1677
13669	NP	149	Nepal	2919	Fruits - Excluding Wine	5142	Food	1000 tonnes	28.39	84.12	...	993.0	1050.0	1044.0	1098.0	1195.0	1178.0	1301.0	1428.0	1859	1700
15525	PT	174	Portugal	2511	Wheat and products	5142	Food	1000 tonnes	39.40	-8.22	...	991.0	982.0	995.0	1002.0	976.0	970.0	978.0	980.0	994	1000
19714	TM	213	Turkmenistan	2905	Cereals - Excluding Beer	5142	Food	1000 tonnes	38.97	59.56	...	995.0	1009.0	1031.0	1019.0	1017.0	1023.0	1019.0	1039.0	1036	1059
20768	UZ	235	Uzbekistan	2919	Fruits - Excluding Wine	5142	Food	1000 tonnes	41.38	64.59	...	993.0	1079.0	1448.0	1538.0	1693.0	1875.0	2108.0	2159.0	2422	2638

	Element Code	latitude	longitude	Y1961	Y1962	Y1963	Y1964	Y1965	Y1966	Y1967	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
Area Code
1	697151	5329.31	5990.32	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	5515.0	5992.0	6228.0	6753.0	6958.0	6824.0	5908.0	6423.0	6978	7175
2	430576	2817.02	5619.93	9481.0	9414.0	9194.0	10170.0	10473.0	10169.0	11289.0	...	16542.0	17658.0	18317.0	19248.0	19381.0	20661.0	21030.0	21100.0	22706	23007
3	641941	5061.45	2480.91	1706.0	1749.0	1767.0	1889.0	1884.0	1995.0	2046.0	...	6637.0	6719.0	6911.0	6744.0	7168.0	7316.0	7907.0	8114.0	8221	8271
4	645946	3475.72	205.84	7488.0	7235.0	6861.0	7255.0	7509.0	7536.0	7986.0	...	48619.0	49562.0	51067.0	49933.0	50916.0	57505.0	60071.0	65852.0	69365	72161
7	566163	-1220.80	1947.83	4834.0	4775.0	5240.0	5286.0	5527.0	5677.0	5833.0	...	25541.0	26696.0	28247.0	29877.0	32053.0	36985.0	38400.0	40573.0	38064	48639
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
255	712577	6868.00	607.92	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	35273.0	32353.0	32234.0	35016.0	34902.0	34964.0	32708.0	32196.0	31326	33336
256	665162	6327.14	778.51	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1308.0	1290.0	1295.0	1300.0	1377.0	1345.0	1375.0	1432.0	1501	1511
272	664025	5590.54	2668.27	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	25423.0	22473.0	26902.0	25401.0	25350.0	24799.0	16587	25399
273	614715	5039.78	2285.66	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	1605.0	1659.0	1860.0	1891.0	1810.0	1887.0	1786	1848
276	538558	1337.44	3142.88	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	42543	43661

		Area Abbreviation	Area	Item Code	Item	Element Code	Element	Unit	latitude	longitude	Y1962	...	Y2004	Y2005	Y2006	Y2007	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013
Area Code	Y1961
2	0.0	27	27	27	27	27	27	27	27	27	27	...	27	27	27	27	27	27	27	27	27	27
	1.0	2	2	2	2	2	2	2	2	2	2	...	2	2	2	2	2	2	2	2	2	2
	2.0	4	4	4	4	4	4	4	4	4	4	...	4	4	4	4	4	4	4	4	4	4
	3.0	3	3	3	3	3	3	3	3	3	3	...	3	3	3	3	3	3	3	3	3	3
	4.0	3	3	3	3	3	3	3	3	3	3	...	3	3	3	3	3	3	3	3	3	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
251	165.0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
	358.0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
	373.0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
	499.0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
	578.0	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1

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