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Pandas處理csv英國降雨資料

文章目錄

匯入資料

英國降雨資料:http://data.defra.gov.uk/statistics_2015/env/water/uk_rain_2014.csv

import pandas as pd
#匯入資料
#uk_rain_2014.csv 第一行是標籤,可以做列索引
df=pd.read_csv('C:\\Users\\cc\\Desktop\\Pandas\\uk_rain_2014.csv'
,header=0)#預設引數header也為0,表示以第一行為列索引

pandas.read_csv()讀取csv檔案資料到dataframe
注意:csv中的資料都是用逗號隔開的。
引數:
filepath_or_buffer :字串、或者任何物件的read()方法。這個字串可以是URL,有效的URL方案包括http、ftp、s3和檔案。可以直接寫入"檔名.csv"
sep:分隔符,預設是‘,’,CSV檔案的分隔符
header:列名(列索引),預設第一行為列名(預設header=0)
header=None,說明第一行不是列名。這樣,它會給新的列名:0,1,2,3,4…
可以給加上新列名,見另一個引數
names:當csv檔案沒有列名時候,可以用names加上要用的列名
index_col:要用的行名(index),int或sequence或False,預設為None,即預設新增從0開始的index
若要用第一列作為行索引,寫index_col=0

print(df)
   Water Year  Rain (mm) Oct-Sep  Outflow (m3/s) Oct-Sep  Rain (mm) Dec-Feb  \
0     1980/81               1182                    5408                292   
1     1981/82               1098                    5112                257   
2     1982/83               1156                    5701                330   
3     1983/84                993                    4265                391   
4     1984/85               1182                    5364                217   
5     1985/86               1027                    4991                304   
6     1986/87               1151                    5196                295   
7     1987/88               1210                    5572                343   
8     1988/89                976                    4330                309   
9     1989/90               1130                    4973                470   
10    1990/91               1022                    4418                305   
11    1991/92               1151                    4506                246   
12    1992/93               1130                    5246                308   
13    1993/94               1162                    5583                422   
14    1994/95               1110                    5370                484   
15    1995/96                856                    3479                245   
16    1996/97               1047                    4019                258   
17    1997/98               1169                    4953                341   
18    1998/99               1268                    5824                360   
19    1999/00               1204                    5665                417   
20    2000/01               1239                    6092                328   
21    2001/02               1185                    5402                380   
22    2002/03               1021                    4366                272   
23    2003/04               1165                    4275                348   
24    2004/05               1095                    4547                309   
25    2005/06               1046                    4059                206   
26    2006/07               1387                    6391                437   
27    2007/08               1225                    5497                386   
28    2008/09               1139                    4941                268   
29    2009/10               1103                    4738                255   
30    2010/11               1053                    4521                265   
31    2011/12               1285                    5500                339   
32    2012/13               1090                    5329                350   

    Outflow (m3/s) Dec-Feb  Rain (mm) Jun-Aug  Outflow (m3/s) Jun-Aug  
0                     7248                174                    2212  
1                     7316                242                    1936  
2                     8567                124                    1802  
3                     8905                141                    1078  
4                     5813                343                    4313  
5                     7951                229                    2595  
6                     7593                267                    2826  
7                     8456                294                    3154  
8                     6465                200                    1440  
9                    10520                209                    1740  
10                    7120                216                    1923  
11                    5493                280                    2118  
12                    8751                219                    2551  
13                   10109                193                    1638  
14                   11486                103                    1231  
15                    5515                172                    1439  
16                    5770                256                    2102  
17                    7747                285                    3206  
18                    8771                225                    2240  
19                   10021                197                    2166  
20                    9347                236                    2142  
21                    8891                259                    3187  
22                    7093                176                    1478  
23                    7493                315                    2959  
24                    7183                217                    1799  
25                    4578                188                    1474  
26                   10926                357                    5168  
27                    9485                320                    3505  
28                    6690                323                    3189  
29                    6435                244                    1958  
30                    6593                267                    2885  
31                    7630                379                    5261  
32                    9615                187                    1797  
df.index
RangeIndex(start=0, stop=33, step=1)
df.columns
Index([u'Water Year', u'Rain (mm) Oct-Sep', u'Outflow (m3/s) Oct-Sep',
       u'Rain (mm) Dec-Feb', u'Outflow (m3/s) Dec-Feb', u'Rain (mm) Jun-Aug',
       u'Outflow (m3/s) Jun-Aug'],
      dtype='object')
df.values
array([['1980/81', 1182L, 5408L, 292L, 7248L, 174L, 2212L],
       ['1981/82', 1098L, 5112L, 257L, 7316L, 242L, 1936L],
       ['1982/83', 1156L, 5701L, 330L, 8567L, 124L, 1802L],
       ['1983/84', 993L, 4265L, 391L, 8905L, 141L, 1078L],
       ['1984/85', 1182L, 5364L, 217L, 5813L, 343L, 4313L],
       ['1985/86', 1027L, 4991L, 304L, 7951L, 229L, 2595L],
       ['1986/87', 1151L, 5196L, 295L, 7593L, 267L, 2826L],
       ['1987/88', 1210L, 5572L, 343L, 8456L, 294L, 3154L],
       ['1988/89', 976L, 4330L, 309L, 6465L, 200L, 1440L],
       ['1989/90', 1130L, 4973L, 470L, 10520L, 209L, 1740L],
       ['1990/91', 1022L, 4418L, 305L, 7120L, 216L, 1923L],
       ['1991/92', 1151L, 4506L, 246L, 5493L, 280L, 2118L],
       ['1992/93', 1130L, 5246L, 308L, 8751L, 219L, 2551L],
       ['1993/94', 1162L, 5583L, 422L, 10109L, 193L, 1638L],
       ['1994/95', 1110L, 5370L, 484L, 11486L, 103L, 1231L],
       ['1995/96', 856L, 3479L, 245L, 5515L, 172L, 1439L],
       ['1996/97', 1047L, 4019L, 258L, 5770L, 256L, 2102L],
       ['1997/98', 1169L, 4953L, 341L, 7747L, 285L, 3206L],
       ['1998/99', 1268L, 5824L, 360L, 8771L, 225L, 2240L],
       ['1999/00', 1204L, 5665L, 417L, 10021L, 197L, 2166L],
       ['2000/01', 1239L, 6092L, 328L, 9347L, 236L, 2142L],
       ['2001/02', 1185L, 5402L, 380L, 8891L, 259L, 3187L],
       ['2002/03', 1021L, 4366L, 272L, 7093L, 176L, 1478L],
       ['2003/04', 1165L, 4275L, 348L, 7493L, 315L, 2959L],
       ['2004/05', 1095L, 4547L, 309L, 7183L, 217L, 1799L],
       ['2005/06', 1046L, 4059L, 206L, 4578L, 188L, 1474L],
       ['2006/07', 1387L, 6391L, 437L, 10926L, 357L, 5168L],
       ['2007/08', 1225L, 5497L, 386L, 9485L, 320L, 3505L],
       ['2008/09', 1139L, 4941L, 268L, 6690L, 323L, 3189L],
       ['2009/10', 1103L, 4738L, 255L, 6435L, 244L, 1958L],
       ['2010/11', 1053L, 4521L, 265L, 6593L, 267L, 2885L],
       ['2011/12', 1285L, 5500L, 339L, 7630L, 379L, 5261L],
       ['2012/13', 1090L, 5329L, 350L, 9615L, 187L, 1797L]], dtype=object)
#想知道一些基本統計資訊
df.describe()
Rain (mm) Oct-Sep Outflow (m3/s) Oct-Sep Rain (mm) Dec-Feb Outflow (m3/s) Dec-Feb Rain (mm) Jun-Aug Outflow (m3/s) Jun-Aug
count 33.000000 33.000000 33.000000 33.000000 33.000000 33.000000
mean 1129.000000 5019.181818 325.363636 7926.545455 237.484848 2439.757576
std 101.900074 658.587762 69.995008 1692.800049 66.167931 1025.914106
min 856.000000 3479.000000 206.000000 4578.000000 103.000000 1078.000000
25% 1053.000000 4506.000000 268.000000 6690.000000 193.000000 1797.000000
50% 1139.000000 5112.000000 309.000000 7630.000000 229.000000 2142.000000
75% 1182.000000 5497.000000 360.000000 8905.000000 280.000000 2959.000000
max 1387.000000 6391.000000 484.000000 11486.000000 379.000000 5261.000000
df.count()#查詢每個列的非空值的數量
df['Outflow (m3/s) Oct-Sep'].mean()#查詢某個列的均值
5019.181818181818
df.std()#查每個列的標準差
df.median()#查每列的中值
#按index(行標籤、列標籤)排序 預設升序
df.sort_index(axis=0, ascending=False)#axis=0 按行標籤  ascending=False降序
Water Year Rain (mm) Oct-Sep Outflow (m3/s) Oct-Sep Rain (mm) Dec-Feb Outflow (m3/s) Dec-Feb Rain (mm) Jun-Aug Outflow (m3/s) Jun-Aug
0 1980/81 1182 5408 292 7248 174 2212
1 1981/82 1098 5112 257 7316 242 1936
2 1982/83 1156 5701 330 8567 124 1802
3 1983/84 993 4265 391 8905 141 1078
4 1984/85 1182 5364 217 5813 343 4313
5 1985/86 1027 4991 304 7951 229 2595
6 1986/87 1151 5196 295 7593 267 2826
7 1987/88 1210 5572 343 8456 294 3154
8 1988/89 976 4330 309 6465 200 1440
9 1989/90 1130 4973 470 10520 209 1740
10 1990/91 1022 4418 305 7120 216 1923
11 1991/92 1151 4506 246 5493 280 2118
12 1992/93 1130 5246 308 8751 219 2551
13 1993/94 1162 5583 422 10109 193 1638
14 1994/95 1110 5370 484 11486 103 1231
15 1995/96 856 3479 245 5515 172 1439
16 1996/97 1047 4019 258 5770 256 2102
17 1997/98 1169 4953 341 7747 285 3206
18 1998/99 1268 5824 360 8771 225 2240
19 1999/00 1204 5665 417 10021 197 2166
20 2000/01 1239 6092 328 9347 236 2142
21 2001/02 1185 5402 380 8891 259 3187
22 2002/03 1021 4366 272 7093 176 1478
23 2003/04 1165 4275 348 7493 315 2959
24 2004/05 1095 4547 309 7183 217 1799
25 2005/06 1046 4059 206 4578 188 1474
26 2006/07 1387 6391 437 10926 357 5168
27 2007/08 1225 5497 386 9485 320 3505
28 2008/09 1139 4941 268 6690 323 3189
29 2009/10 1103 4738 255 6435 244 1958
30 2010/11 1053 4521 265 6593 267 2885
31 2011/12 1285 5500 339 7630 379 5261
32 2012/13 1090 5329 350 9615 187 1797
df.sort_index(axis=1, ascending=False) #axis=1:按列標籤  降序
Water Year Rain (mm) Oct-Sep Rain (mm) Jun-Aug Rain (mm) Dec-Feb Outflow (m3/s) Oct-Sep Outflow (m3/s) Jun-Aug Outflow (m3/s) Dec-Feb
0 1980/81 1182 174 292 5408 2212 7248
1 1981/82 1098 242 257 5112 1936 7316
2 1982/83 1156 124 330 5701 1802 8567
3 1983/84 993 141 391 4265 1078 8905
4 1984/85 1182 343 217 5364 4313 5813
5 1985/86 1027 229 304 4991 2595 7951
6 1986/87 1151 267 295 5196 2826 7593
7 1987/88 1210 294 343 5572 3154 8456
8 1988/89 976 200 309 4330 1440 6465
9 1989/90 1130 209 470 4973 1740 10520
10 1990/91 1022 216 305 4418 1923 7120
11 1991/92 1151 280 246 4506 2118 5493
12 1992/93 1130 219 308 5246 2551 8751
13 1993/94 1162 193 422 5583 1638 10109
14 1994/95 1110 103 484 5370 1231 11486
15 1995/96 856 172 245 3479 1439 5515
16 1996/97 1047 256 258 4019 2102 5770
17 1997/98 1169 285 341 4953 3206 7747
18 1998/99 1268 225 360 5824 2240 8771
19 1999/00 1204 197 417 5665 2166 10021
20 2000/01 1239 236 328 6092 2142 9347
21 2001/02 1185 259 380 5402 3187 8891
22 2002/03 1021 176 272 4366 1478 7093
23 2003/04 1165 315 348 4275 2959 7493
24 2004/05 1095 217 309 4547 1799 7183
25 2005/06 1046 188 206 4059 1474 4578
26 2006/07 1387 357 437 6391 5168 10926
27 2007/08 1225 320 386 5497 3505 9485
28 2008/09 1139 323 268 4941 3189 6690
29 2009/10 1103 244 255 4738 1958 6435
30 2010/11 1053 267 265 4521 2885 6593
31 2011/12 1285 379 339 5500 5261 7630
32 2012/13 1090 187 350 5329 1797 9615
#按值排序
df.sort_values(by='Outflow (m3/s) Jun-Aug')#按某一列的資料排
Water Year Rain (mm) Oct-Sep Outflow (m3/s) Oct-Sep Rain (mm) Dec-Feb Outflow (m3/s) Dec-Feb Rain (mm) Jun-Aug Outflow (m3/s) Jun-Aug
3 1983/84 993 4265 391 8905 141 1078
14 1994/95 1110 5370 484 11486 103 1231
15 1995/96 856 3479 245 5515 172 1439
8 1988/89 976 4330 309 6465 200 1440
25 2005/06 1046 4059 206 4578 188 1474
22 2002/03 1021 4366 272 7093 176 1478
13 1993/94 1162 5583 422 10109 193 1638
9 1989/90 1130 4973 470 10520 209 1740
32 2012/13 1090 5329 350 9615 187 1797
24 2004/05 1095 4547 309 7183 217 1799
2 1982/83 1156 5701 330 8567 124 1802
10 1990/91 1022 4418 305 7120 216 1923
1 1981/82 1098 5112 257 7316 242 1936
29 2009/10 1103 4738 255 6435 244 1958
16 1996/97 1047 4019 258 5770 256 2102
11 1991/92 1151 4506 246 5493 280 2118
20 2000/01 1239 6092 328 9347 236 2142
19 1999/00 1204 5665 417 10021 197 2166
0 1980/81 1182 5408 292 7248 174 2212
18 1998/99 1268 5824 360 8771 225 2240
12 1992/93 1130 5246 308 8751 219 2551
5 1985/86 1027 4991 304 7951 229 2595
6 1986/87 1151 5196 295 7593 267 2826
30 2010/11 1053 4521 265 6593 267 2885
23 2003/04 1165 4275 348 7493 315 2959
7 1987/88 1210 5572 343 8456 294 3154
21 2001/02 1185 5402 380 8891 259 3187
28 2008/09 1139 4941 268 6690 323 3189
17 1997/98 1169 4953 341 7747 285 3206
27 2007/08 1225 5497 386 9485 320 3505
4 1984/85 1182 5364 217 5813 343 4313
26 2006/07 1387 6391 437 10926 357 5168
31 2011/12 1285 5500 339 7630 379 5261

測試一下header

#匯入資料
#uk_rain_2014.csv 第一行不是列標籤
df1=pd.read_csv('C:\\Users\\cc\\Desktop\\Pandas\\uk_rain_2014NoColumns.csv')#預設header=0 看看結果
df1#j結果:把第一行的資料當成列標籤
1980/81 1182 5408 292 7248 174 2212
0 1981/82 1098 5112 257 7316 242 1936
1 1982/83 1156 5701 330 8567 124 1802
2 1983/84 993 4265 391 8905 141 1078
3 1984/85 1182 5364 217 5813 343 4313
4 1985/86 1027 4991 304 7951 229 2595
5 1986/87 1151 5196 295 7593 267 2826
6 1987/88 1210 5572 343 8456 294 3154
df2=pd.read_csv('C:\\Users\\cc\\Desktop\\Pandas\\uk_rain_2014NoColumns.csv', header=None)#看看結果
print(df2)#j結果:自動給了一個range(7)的columns
print(df2.columns)
         0     1     2    3     4    5     6
0  1980/81  1182  5408  292  7248  174  2212
1  1981/82  1098  5112  257  7316  242  1936
2  1982/83  1156  5701  330  8567  124  1802
3  1983/84   993  4265  391  8905  141  1078
4  1984/85  1182  5364  217  5813  343  4313
5  1985/86  1027  4991  304  7951  229  2595
6  1986/87  1151  5196  295  7593  267  2826
7  1987/88  1210  5572  343  8456  294  3154
Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64')
#手動給每一列的name;names引數(相當於columns)
df3=pd.read_csv('C:\\Users\\cc\\Desktop\\Pandas\\uk_rain_2014NoColumns.csv', names=[u'Water Year', u'Rain (mm) Oct-Sep', u'Outflow (m3/s) Oct-Sep',
       u'Rain (mm) Dec-Feb', u'Outflow (m3/s) Dec-Feb', u'Rain (mm) Jun-Aug',
       u'Outflow (m3/s) Jun-Aug'])
df3
Water Year Rain (mm) Oct-Sep Outflow (m3/s) Oct-Sep Rain (mm) Dec-Feb Outflow (m3/s) Dec-Feb Rain (mm) Jun-Aug Outflow (m3/s) Jun-Aug
0 1980/81 1182 5408 292 7248 174 2212
1 1981/82 1098 5112 257 7316 242 1936
2 1982/83 1156 5701 330 8567 124 1802
3 1983/84 993 4265 391 8905 141 1078
4 1984/85 1182 5364 217 5813 343 4313
5 1985/86 1027 4991 304 7951 229 2595
6 1986/87 1151 5196 295 7593 267 2826
7 1987/88 1210 5572 343 8456 294 3154
#檢視前x行的資料
df.head(5)
Water Year Rain (mm) Oct-Sep Outflow (m3/s) Oct-Sep Rain (mm) Dec-Feb Outflow (m3/s) Dec-Feb Rain (mm) Jun-Aug Outflow (m3/s) Jun-Aug
0 1980/81 1182 5408 292 7248 174 2212
1 1981/82 1098 5112 257 7316 242 1936
2 1982/83 1156 5701 330 8567 124 1802
3 1983/84 993 4265 391 8905 141 1078
4 1984/85 1182 5364 217 5813 343 4313
#c檢視後5行
df.tail(5)
Water Year Rain (mm) Oct-Sep Outflow (m3/s) Oct-Sep Rain (mm) Dec-Feb Outflow (m3/s) Dec-Feb Rain (mm) Jun-Aug Outflow (m3/s) Jun-Aug
28 2008/09 1139 4941 268 6690 323 3189
29 2009/10 1103 4738 255 6435 244 1958
30 2010/11 1053 4521 265 6593 267 2885
31 2011/12 1285 5500 339 7630 379 5261
32 2012/13 1090 5329 350 9615 187 1797
#改變列名
#列名太長,看著太煩
df.columns=['water_year','rain_octsep', 'outflow_octsep',
              'rain_decfeb', 'outflow_decfeb', 'rain_junaug', 'outflow_junaug']
df.head(5)
water_year rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
0 1980/81 1182 5408 292 7248 174 2212
1 1981/82 1098 5112 257 7316 242 1936
2 1982/83 1156 5701 330 8567 124 1802
3 1983/84 993 4265 391 8905 141 1078
4 1984/85 1182 5364 217 5813 343 4313
#檢視有多少條記錄
len(df)
33

過濾

df['rain_octsep']
df['rain_octsep'].head(8)
0    1182
1    1098
2    1156
3     993
4    1182
5    1027
6    1151
7    1210
Name: rain_octsep, dtype: int64
df.rain_octsep<1000
0     False
1     False
2     False
3      True
4     False
5     False
6     False
7     False
8      True
9     False
10    False
11    False
12    False
13    False
14    False
15     True
16    False
17    False
18    False
19    False
20    False
21    False
22    False
23    False
24    False
25    False
26    False
27    False
28    False
29    False
30    False
31    False
32    False
Name: rain_octsep, dtype: bool
#要9月-10月降雨量小於1000mm的記錄
df[df.rain_octsep<1000]
water_year rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
3 1983/84 993 4265 391 8905 141 1078
8 1988/89 976 4330 309 6465 200 1440
15 1995/96 856 3479 245 5515 172 1439
#rain_octsep<1000且outflow_octsep<4000的記錄
df[(df.rain_octsep<1000)&(df.outflow_octsep<4000)]
water_year rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
15 1995/96 856 3479 245 5515 172 1439
#要年份為199開頭的
df[df.water_year.str.startswith('199')]
water_year rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
10 1990/91 1022 4418 305 7120 216 1923
11 1991/92 1151 4506 246 5493 280 2118
12 1992/93 1130 5246 308 8751 219 2551
13 1993/94 1162 5583 422 10109 193 1638
14 1994/95 1110 5370 484 11486 103 1231
15 1995/96 856 3479 245 5515 172 1439
16 1996/97 1047 4019 258 5770 256 2102
17 1997/98 1169 4953 341 7747 285 3206
18 1998/99 1268 5824 360 8771 225 2240
19 1999/00 1204 5665 417 10021 197 2166

索引

#可以用iloc  iloc[行] iloc[行,列]獲取某一區域的資料
df.iloc[19]
water_year        1999/00
rain_octsep          1204
outflow_octsep       5665
rain_decfeb           417
outflow_decfeb      10021
rain_junaug           197
outflow_junaug       2166
Name: 19, dtype: object
df.iloc[0:5]  #相當於df.head(5)
water_year rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
0 1980/81 1182 5408 292 7248 174 2212
1 1981/82 1098 5112 257 7316 242 1936
2 1982/83 1156 5701 330 8567 124 1802
3 1983/84 993 4265 391 8905 141 1078
4 1984/85 1182 5364 217 5813 343 4313

改變行索引

df= pd.read_csv('C:\\Users\\cc\\Desktop\\Pandas\\uk_rain_2014.csv',header=0)
df.columns=['water_year','rain_octsep', 'outflow_octsep',
              'rain_decfeb', 'outflow_decfeb', 'rain_junaug', 'outflow_junaug']
df.head(5)
water_year rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
0 1980/81 1182 5408 292 7248 174 2212
1 1981/82 1098 5112 257 7316 242 1936
2 1982/83 1156 5701 330 8567 124 1802
3 1983/84 993 4265 391 8905 141 1078
4 1984/85 1182 5364 217 5813 343 4313
#將某一列設定成行索引
df= df.set_index("water_year")
df.head(5)
#恢復: df.reset_index("water_year")
rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
water_year
1980/81 1182 5408 292 7248 174 2212
1981/82 1098 5112 257 7316 242 1936
1982/83 1156 5701 330 8567 124 1802
1983/84 993 4265 391 8905 141 1078
1984/85 1182 5364 217 5813 343 4313
df.iloc[2]
rain_octsep       1156
outflow_octsep    5701
rain_decfeb        330
outflow_decfeb    8567
rain_junaug        124
outflow_junaug    1802
Name: 1982/83, dtype: int64
df.loc['1982/83']
rain_octsep       1156
outflow_octsep    5701
rain_decfeb        330
outflow_decfeb    8567
rain_junaug        124
outflow_junaug    1802
Name: 1982/83, dtype: int64
df.loc['1982/83','rain_octsep']
1156
df.loc['1982/83':'1984/85','rain_octsep':'outflow_decfeb']
rain_octsep outflow_octsep rain_decfeb outflow_decfeb
water_year
1982/83 1156 5701 330 8567
1983/84 993 4265 391 8905
1984/85 1182 5364 217 5813
#按索引降序
df.sort_index(axis=0, ascending=False)
rain_octsep outflow_octsep rain_decfeb outflow_decfeb rain_junaug outflow_junaug
water_year
2012/13 1090 5329 350 9615 187 1797
2011/12 1285 5500 339 7630 379 5261
2010/11 1053 4521 265 6593 267 2885
2009/10 1103 4738 255 6435 244 1958
2008/09 1139 4941 268 6690 323 3189
2007/08 1225 5497 386 9485 320 3505
2006/07 1387 6391 437 10926 357 5168
2005/06 1046 4059 206 4578 188 1474
2004/05 1095 4547 309 7183 217 1799
2003/04 1165 4275 348 7493 315 2959
2002/03 1021 4366 272 7093 176 1478
2001/02 1185 5402 380 8891 259 3187
2000/01 1239 6092 328 9347 236 2142
1999/00 1204 5665 417 10021 197 2166
1998/99 1268 5824 360 8771 225 2240
1997/98 1169 4953 341 7747 285 3206
1996/97 1047 4019 258 5770 256 2102
1995/96 856 3479 245 5515 172 1439
1994/95 1110 5370 484 11486 103 1231
1993/94 1162 5583 422 10109 193 1638
1992/93 1130 5246 308 8751 219 2551
1991/92 1151 4506 246 5493 280 2118
1990/91 1022 4418 305 7120 216 1923
1989/90 1130 4973 470 10520 209 1740
1988/89 976 4330 309 6465 200 1440
1987/88 1210 5572 343 8456 294 3154
1986/87 1151 5196 295 7593 267 2826
1985/86 1027 4991 304 7951 229 2595
1984/85 1182 5364 217 5813