class Asset(object):
share=0
from datetime import datetime
buyTime=datetime.strptime("1990-01-01","%Y-%m-%d")
sellTime=datetime.strptime("1990-01-01","%Y-%m-%d")
def __init__(self,id,price):
self.id=id
self.price=price
def buy(self,share,time):
self.share+=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
def sell(self,share,time):
self.share-=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
class NonShortableAsset(Asset):
def sell(self,share,time):
if self.share>=share:
self.share-=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
else:
print("not permitted!There are not enough share to sell!")
print("Current share is %s"%self.share)
return
arrange、list切片 首包含，尾不包含，linspace 首尾都包含,list、array 索引从0开始
a=float(input("please input a num"))
if a is not int:
print("ERROR,Please input an integer")
elif (a%2)!=0:
print(a)
else:
print(0)
import random
for i in range(5):
a=random.randint(-1000,1000)
print(a)
if a>=0:
print("Big")
else:
print("Small")
class Asset(object):
share=0
from datetime import datetime
buyTime=datetime.strptime("1990-01-01","%Y-%m-%d")
sellTime=datetime.strptime("1990-01-01","%Y-%m-%d")
def __init__(self,id,price):
self.id=id
self.price=price
def buy(self,share,time):
self.share+=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
def sell(self,share,time):
self.share-=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
class NonShortableAsset(Asset):
def sell(self,share,time):
if self.share>=share:
self.share-=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
else:
print("not permitted!There are not enough share to sell!")
print("Current share is %s"%self.share)
return
def OpenPrice(priceSequence):
Open=priceSequence[0]
return Open
from datetime import datetime
import pandas as pd
date=datetime(2016,1,1)
date=pd.Timestamp(date)
print(date)
ts=pd.Series(1,index=[date])
print(ts)
dates=[datetime(2016,1,1),datetime(2016,1,2),datetime(2016,1,3)]
ts=pd.Series([1,2,3],index=dates)
print(ts.index[0])
print(ts.truncate(after="2016-01-02"))
print(ts)
print(ts.shift(1))
Price=pd.Series([20.34,20.56,21.01,20.55,21.34],index=pd.to_datetime(["2016-01-01","2016-01-02","2016-01-03","2016-01-04","2016-01-05"]))
Roe=(Price-Price.shift(1))/Price.shift(1)
print(Roe)
rts=ts.resample("M",how="mean")
print(rts)
dates=["2016-01-01","2016-01-02","2016-01-03","2016-01-04","2016-01-05","2016-01-06"]
dates=pd.to_datetime(dates)
df=pd.DataFrame(np.random.rand(6,4),index=dates,columns=list("ABCD"))
print(df.info())
print(df.head(3))
print(df.tail(4))
print(df.columns)
print(df.index)
print(df.values)
print(df.describe())
print(df[1:3])
print(df["A"])
print(df["A"]>0)
print(df.loc[:,"A"])
print(df.loc[:,"A":"C"])
print(df.loc[dates[0:2],"A":"C"])
print(df)
print(df.iloc[2])#第3行的数据，起始为0
print(df.ix[2:5])
print(df.T)
a=float(input("please input a num"))
if a is not int:
print("ERROR,Please input an integer")
elif (a%2)!=0:
print(a)
else:
print(0)
import random
for i in range(5):
a=random.randint(-1000,1000)
print(a)
if a>=0:
print("Big")
else:
print("Small")
class Asset(object):
share=0
from datetime import datetime
buyTime=datetime.strptime("1990-01-01","%Y-%m-%d")
sellTime=datetime.strptime("1990-01-01","%Y-%m-%d")
def __init__(self,id,price):
self.id=id
self.price=price
def buy(self,share,time):
self.share+=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
def sell(self,share,time):
self.share-=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
class NonShortableAsset(Asset):
def sell(self,share,time):
if self.share>=share:
self.share-=share
self.sellTime=datetime.strptime(time,"%Y-%m-%d")
else:
print("not permitted!There are not enough share to sell!")
print("Current share is %s"%self.share)
return
def OpenPrice(priceSequence):
Open=priceSequence[0]
return Open
from datetime import datetime
import pandas as pd
date=datetime(2016,1,1)
date=pd.Timestamp(date)
print(date)
ts=pd.Series(1,index=[date])
print(ts)
dates=[datetime(2016,1,1),datetime(2016,1,2),datetime(2016,1,3)]
ts=pd.Series([1,2,3],index=dates)
print(ts.index[0])
print(ts.truncate(after="2016-01-02"))
print(ts)
print(ts.shift(1))
Price=pd.Series([20.34,20.56,21.01,20.55,21.34],index=pd.to_datetime(["2016-01-01","2016-01-02","2016-01-03","2016-01-04","2016-01-05"]))
Roe=(Price-Price.shift(1))/Price.shift(1)
print(Roe)
rts=ts.resample("M",how="mean")
print(rts)
dates=["2016-01-01","2016-01-02","2016-01-03","2016-01-04","2016-01-05","2016-01-06"]
dates=pd.to_datetime(dates)
df=pd.DataFrame(np.random.rand(6,4),index=dates,columns=list("ABCD"))
print(df.info())
print(df.head(3))
print(df.tail(4))
print(df.columns)
print(df.index)
print(df.values)
print(df.describe())
print(df[1:3])
print(df["A"])
print(df["A"]>0)
print(df.loc[:,"A"])
print(df.loc[:,"A":"C"])
print(df.loc[dates[0:2],"A":"C"])
print(df)
print(df.iloc[2])#第3行的数据，起始为0
print(df.ix[2:5])
print(df.T)
dates=["2016-01-01","2016-01-02","2016-01-03","2016-01-04","2016-01-05","2016-01-06"]
dates=pd.to_datetime(dates)
df=pd.DataFrame(np.random.rand(6,4),index=dates,columns=list("ABCD"))
# =============================================================================
# print(df)
# print(df.T)
# =============================================================================
df.sort_values(axis=0,by="A",ascending=False)
df.sort_values(axis=1,by="2016-01-02",ascending=False)
df=df.rank(axis=0)
print(df)
dates=["2016-01-01","2016-01-02","2016-01-03","2016-01-04","2016-01-05","2016-01-06"]
dates=pd.to_datetime(dates)
df=pd.DataFrame(np.random.rand(6,4),index=dates,columns=list("ABCD"))
s1=pd.Series([1,2,3,4,5,6],index=pd.date_range("20160101",periods=6,freq="D"))
df["E"]=s1
df=pd.concat([df,s1],axis=1)
df1=pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9]},index=pd.date_range("20160110",periods=3))
df.append(df1)
pd.concat([df,df1],join="inner")
df.drop("A",axis=1)
df.loc[dates[2],"C"]=0
df.iloc[0:1]=0
df.loc[:,"B"]=np.arange(0,len(df))
new_index=pd.date_range("20160102",periods=7)
df.reindex(new_index,columns=list("ABCD"))
s1=pd.Series([1,2,3],index=list("ABC"))
s2=pd.Series([4,5,6],index=list("BCD"))
df1=pd.DataFrame(np.arange(1,13).reshape(3,4),index=list("abc"),columns=list("ABCD"))
df2=pd.DataFrame(np.arange(1,13).reshape(4,3),index=list("bcde"),columns=list("CDE"))
df1.apply(max,axis=1)
f=lambda x: x.max()-x.min()
df=df.apply(f,axis=1)
df3=df1.mul(df2,fill_value=0)
print(df3)
df4=df3.B[df3.B.notnull()]
df3.fillna(method="bfill")
df3.dropna(axis=1)
df3.duplicated()
df3=df3.drop_duplicates()
print(df3)
iloc[a:b,c:d] 从a到b-1行c到d-1列
axis=0 行 axis=1列