Python API的安装
在已经安装了Python开发环境的电脑上，将DolphinDB提供的API安装包解压至任意目录下。进入该目录，确认setup.py文件的存在。然后运行以下命令就可以完成安装。
python setup.py install
我们可以使用以下命令更新已经安装的Python API：
python setup.py install --force
Python API的功能
我们可以在Python使用session方法创建DolphinDB连接对象，使用connect方法创建与DolphinDB数据库的连接。
import pandas as pd
import numpy as np
import dolphindb as ddb
conn=ddb.session()
conn.connect( 'localhost', 8848)
在下列例子中，我们连接正在运行的端口号为8848的本地DolphinDB服务器：
对于Python中的DolphinDB连接对象，我们提供以下方法：
>>> df = pd.DataFrame({'id': np.int32([1, 2, 3, 4, 3]), 'value': np.double([7.8, 4.6, 5.1, 9.6, 0.1]), 'x': np.int32([5, 4, 3, 2, 1])})
>>> conn.upload({'t1': df})
>>> conn.run('wavg', [100, 60, 300], [1, 1.5, 2])
164.0
>>> df = pd.DataFrame({'id': np.int32([1, 2, 3, 4, 3]), 'value': np.double([7.8, 4.6, 5.1, 9.6, 0.1]), 'x': np.int32([5, 4, 3, 2, 1])})
>>> conn.upload({'t1': df})
>>> conn.run('t1.value.avg()')
5.44
>>> conn.run('1 2 NULL')
array([ 1., 2., nan])
>>> z= conn.run("dict(1 2 3, `IBM`MSFT`GOOG)")
>>> z
{1: 'IBM', 2: 'MSFT', 3: 'GOOG'}
>>> z[1]
'IBM'
>>> conn.run('(1..6).reshape(3:2)')
(array([[1, 4],
[2, 5],
[3, 6]]), None, None)
>>> table_str = "n=20000\n"
>>> table_str += "t1=table(09:30:00+rand(18000, n) as timestamp, rand(100, n) as id, 100*(1+rand(100, n)) as qty, 5.0+rand(100.0,n) as price);\n"
>>> table_str += "select * from t1 where price>9"
>>> df = conn.run(table_str)
>>> df
timestamp id qty price
0 13:50:29 8 800 78.039556
1 09:45:13 39 6000 36.624369
2 09:34:39 45 5000 11.116828
3 11:54:26 26 8700 69.652368
4 12:07:52 20 9600 84.086700
... ... .. ... ...
19222 14:25:47 26 5500 78.223695
19223 13:08:36 49 7500 100.907622
19224 11:07:52 60 9500 57.600072
19225 11:47:36 31 9600 12.589440
19226 10:41:01 93 500 41.886152
[19227 rows x 4 columns]
>>> df.columns
Index([u'timestamp', u'id', u'qty', u'price'], dtype='object')
>>> dt = conn.table(data={'sym': ['A', 'B', 'B', 'A', 'A'], 'vol': [1, 3, 2, 5, 4], 'price': [16, 31, 28, 19, 22]})
>>> dt.tableName()
'Tb2e146a8'
请注意，每次使用tableName方法都会得到不同的表名，但是在下列例子中，表名保持不变。
>>> dt.showSQL()
'select price,sym,vol from Tb2e146a8'
>>> dt['price'].showSQL()
'select price from Tb2e146a8'
>>> dt.select('price').showSQL()
'select price from Tb2e146a8'
>>> dt.select("price").where(dt.price > 20).showSQL()
'select price from Tb2e146a8 where (price > 20)'
>>> dt.select("price").where(dt.price > 20).sort('price').showSQL()
'select price from Tb2e146a8 where (price > 20) order by price'
>>> dt.select(['sym','vol']).where(dt.price > 20).sort(['sym','vol desc']).showSQL()
'select sym,vol from Tb2e146a8 where (price > 20) order by sym,vol desc'
>>> dt[dt.price > 20].showSQL()
'select price,sym,vol from Tb2e146a8 where (price > 20)'
>>> dt.toDF()
price sym vol
0 16 A 1
1 31 B 3
2 28 B 2
3 19 A 5
4 22 A 4
>>> dt['price'].toDF()
price
0 16
1 31
2 28
3 19
4 22
>>> dt.select('price').toDF()
price
0 16
1 31
2 28
3 19
4 22
>>> dt.select("price").where(dt.price > 20).toDF()
price
0 31
1 28
2 22
>>> dt.select("price").where(dt.price > 20).sort('price').toDF()
price
0 22
1 28
2 31
>>> dt.select(['sym','vol']).where(dt.price > 20).sort(['sym','vol desc']).toDF()
sym vol
0 A 4
1 B 3
2 B 2
>>> dt[dt.price > 20].toDF()
price sym vol
0 31 B 3
1 28 B 2
2 22 A 4
>>> dt[dt.price > 20].groupby('sym').agg('sum').showSQL()
'select sum(price),sum(vol) from Tb2e146a8 where (price > 20) group by sym'
>>> dt[dt.price > 20].groupby('sym').agg('sum').toDF()
sym sum_price sum_vol
0 B 59 5
1 A 22 4
>>> dt.groupby('sym').agg({'vol':[ddb.sum2]}).showSQL()
'select sum2(vol) from Tb2e146a8 group by sym'
>>> dt.groupby('sym').agg({'vol':[ddb.sum2]}).toDF()
sym sum2_vol
0 A 42
1 B 13
>>> dt.groupby('sym').agg2([ddb.wsum, ddb.wavg], [("price", "vol")]).showSQL()
'select wavg(price,vol),wsum(price,vol) from Tb2e146a8 group by sym'
>>> dt.groupby('sym').agg2([ddb.wsum, ddb.wavg], [("price", "vol")]).toDF()
sym wavg_price wsum_price
0 A 19.9 199
1 B 29.8 149
>>> dt.contextby('sym').agg({'price':[ddb.sum]}).showSQL()
'select sum(price),sym from Tb2e146a8 context by sym'
>>> dt.contextby('sym').agg({'price':[ddb.sum]}).toDF()
sum_price sym
0 57 A
1 57 A
2 57 A
3 59 B
4 59 B
>>> dt.contextby('sym').agg({'price':[ddb.cumsum]}).showSQL()
'select cumsum(price),sym from Tb2e146a8 context by sym'
>>> dt.contextby('sym').agg({'price':[ddb.cumsum]}).toDF()
cumsum_price sym
0 16 A
1 35 A
2 57 A
3 31 B
4 59 B
>>> dt.pivotby(index='vol', column='sym', value='price').showSQL()
'select price from Tb2e146a8 pivot by vol,sym'
>>> dt.pivotby(index='vol', column='sym', value='price').toDF()
vol A B
0 1 16.0 NaN
1 2 NaN 28.0
2 3 NaN 31.0
3 4 22.0 NaN
4 5 19.0 NaN
>>> dt1 = conn.table(data={'sym': ['AAPL', 'AAPL', 'AAPL', 'AMZN', 'AMZN'], 'date': ['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06'], 'price': [122, 125, 123, 756, 767]})
>>> dt2 = conn.table(data={'sym': ['AAPL', 'AAPL', 'AMZN', 'AMZN', 'AMZN'], 'date': ['2017-01-04', '2017-01-05', '2017-01-06', '2017-01-07', '2017-01-10'], 'vol': [1100, 1400, 1600, 2200, 1800]})
>>> dt1.toDF();
date price sym
0 2017-01-02 122 AAPL
1 2017-01-03 125 AAPL
2 2017-01-04 123 AAPL
3 2017-01-05 756 AMZN
4 2017-01-06 767 AMZN
>>> dt2.toDF();
date sym vol
0 2017-01-04 AAPL 1100
1 2017-01-05 AAPL 1400
2 2017-01-06 AMZN 1600
3 2017-01-07 AMZN 2200
4 2017-01-10 AMZN 1800
>>> dt1.merge(dt2, left_on=['sym','date'], right_on=['sym','date']).showSQL()
'select Tc288e78d.date as rhs_date,Tc288e78d.sym as rhs_sym,Tc288e78d.vol as rhs_vol,Td8323cd0.date as lhs_date,Td8323cd0.price as lhs_price,Td8323cd0.sym as lhs_sym from ej(Td8323cd0,Tc288e78d,`sym`date,`sym`date)'
>>> dt1.merge(dt2, left_on=['sym','date'], right_on=['sym','date']).toDF()
lhs_date lhs_price lhs_sym rhs_date rhs_sym rhs_vol
0 2017-01-04 123 AAPL 2017-01-04 AAPL 1100
1 2017-01-06 767 AMZN 2017-01-06 AMZN 1600
>>> dt1.merge(dt2, left_on=['sym','date'], right_on=['sym','date'], how='left').showSQL()
'select Tc288e78d.date as rhs_date,Tc288e78d.sym as rhs_sym,Tc288e78d.vol as rhs_vol,Td8323cd0.date as lhs_date,Td8323cd0.price as lhs_price,Td8323cd0.sym as lhs_sym from lj(Td8323cd0,Tc288e78d,`sym`date,`sym`date)'
>>> dt1.merge(dt2, left_on=['sym','date'], right_on=['sym','date'], how='left').toDF()
lhs_date lhs_price lhs_sym rhs_date rhs_sym rhs_vol
0 2017-01-02 122 AAPL NaN
1 2017-01-03 125 AAPL NaN
2 2017-01-04 123 AAPL 2017-01-04 AAPL 1100.0
3 2017-01-05 756 AMZN NaN
4 2017-01-06 767 AMZN 2017-01-06 AMZN 1600.0
>>> dt1 = conn.table(data={'id': ['A','A','A','B','B'], 'date': pd.to_datetime(['2017-02-06', '2017-02-08', '2017-02-10', '2017-02-07', '2017-02-09']), 'price': [22, 23, 20, 100, 102]})
>>> dt2 = conn.table(data={'id': ['A','A','B','B','B'], 'date': pd.to_datetime(['2017-02-07', '2017-02-10', '2017-02-07', '2017-02-08', '2017-02-10'])})
>>> dt1.toDF()
date id price
0 2017.02.06T00:00:00.000000000 A 22
1 2017.02.08T00:00:00.000000000 A 23
2 2017.02.10T00:00:00.000000000 A 20
3 2017.02.07T00:00:00.000000000 B 100
4 2017.02.09T00:00:00.000000000 B 102
>>> dt2.toDF()
date id
0 2017.02.07T00:00:00.000000000 A
1 2017.02.10T00:00:00.000000000 A
2 2017.02.07T00:00:00.000000000 B
3 2017.02.08T00:00:00.000000000 B
4 2017.02.10T00:00:00.000000000 B
>>> dt2.merge_asof(dt1, on=['id','date']).showSQL()
'select T38907df4.date as lhs_date,T38907df4.id as lhs_id,T97b7d897.date as rhs_date,T97b7d897.price as rhs_price,T97b7d897.id as rhs_id from aj(T38907df4,T97b7d897,`id`date,`id`date)'
>>> dt2.merge_asof(dt1, on=['id','date']).toDF()
lhs_date lhs_id rhs_date rhs_price rhs_id
0 2017.02.07T00:00:00.000000000 A 2017.02.06T00:00:00.000000000 22 A
1 2017.02.10T00:00:00.000000000 A 2017.02.10T00:00:00.000000000 20 A
2 2017.02.07T00:00:00.000000000 B 2017.02.07T00:00:00.000000000 100 B
3 2017.02.08T00:00:00.000000000 B 2017.02.07T00:00:00.000000000 100 B
4 2017.02.10T00:00:00.000000000 B 2017.02.09T00:00:00.000000000 102 B