#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/5/24 15:03
# @Author : zhang chao
# @File : s.py
from scipy import linalg as lg
#按标签选择
#通过标签选择多轴
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=8)
df = pd.DataFrame(np.random.randn(8,4), index=dates, columns=list('ABCD'))
print("df:")
print(df)
print('-'*50)
s=pd.Series(list(range(10,18)),index=pd.date_range('20170101', periods=8))
df["F"]=s#新加一列元素F
print("df['F']=s")
print(df)
print('-'*50)
df.at[dates[0],"A"]=99
print("df.at[dates[0],'A']=99")
print(df)
print('-'*50)
print("df.iat[1,1]=-66")
df.iat[1,1]=-66
print(df)
print('-'*50)
print("df.loc[:,'D']=np.array([4]*len(df))")
df.loc[:,"D"]=np.array([4]*len(df))
print(df)
print('-'*50)
df2=df.copy()#拷贝
print('-'*50)
print("")
df2[df2>0]=-df2#将df2中的所有大于0的元素值 都改为小于0的
print (df2)
D:\Download\python3\python3.exe D:/Download/pycharmworkspace/s.py
df:
A B C D
2017-01-01 -0.598774 1.076390 -0.642006 -0.089715
2017-01-02 -0.438976 1.063627 0.387825 1.312049
2017-01-03 0.101879 0.469225 0.860522 0.086417
2017-01-04 -0.670031 1.974935 -0.570337 0.478371
2017-01-05 0.250046 -1.385470 -0.893637 -1.786031
2017-01-06 0.876446 -0.167285 -0.475356 -0.145381
2017-01-07 0.291258 0.676994 -1.953909 -0.609507
2017-01-08 -0.569716 0.749637 1.038614 -0.502682
--------------------------------------------------
df['F']=s
A B C D F
2017-01-01 -0.598774 1.076390 -0.642006 -0.089715 10
2017-01-02 -0.438976 1.063627 0.387825 1.312049 11
2017-01-03 0.101879 0.469225 0.860522 0.086417 12
2017-01-04 -0.670031 1.974935 -0.570337 0.478371 13
2017-01-05 0.250046 -1.385470 -0.893637 -1.786031 14
2017-01-06 0.876446 -0.167285 -0.475356 -0.145381 15
2017-01-07 0.291258 0.676994 -1.953909 -0.609507 16
2017-01-08 -0.569716 0.749637 1.038614 -0.502682 17
--------------------------------------------------
df.at[dates[0],'A']=99
A B C D F
2017-01-01 99.000000 1.076390 -0.642006 -0.089715 10
2017-01-02 -0.438976 1.063627 0.387825 1.312049 11
2017-01-03 0.101879 0.469225 0.860522 0.086417 12
2017-01-04 -0.670031 1.974935 -0.570337 0.478371 13
2017-01-05 0.250046 -1.385470 -0.893637 -1.786031 14
2017-01-06 0.876446 -0.167285 -0.475356 -0.145381 15
2017-01-07 0.291258 0.676994 -1.953909 -0.609507 16
2017-01-08 -0.569716 0.749637 1.038614 -0.502682 17
--------------------------------------------------
df.iat[1,1]=-66
A B C D F
2017-01-01 99.000000 1.076390 -0.642006 -0.089715 10
2017-01-02 -0.438976 -66.000000 0.387825 1.312049 11
2017-01-03 0.101879 0.469225 0.860522 0.086417 12
2017-01-04 -0.670031 1.974935 -0.570337 0.478371 13
2017-01-05 0.250046 -1.385470 -0.893637 -1.786031 14
2017-01-06 0.876446 -0.167285 -0.475356 -0.145381 15
2017-01-07 0.291258 0.676994 -1.953909 -0.609507 16
2017-01-08 -0.569716 0.749637 1.038614 -0.502682 17
--------------------------------------------------
df.loc[:,'D']=np.array([4]*len(df))
A B C D F
2017-01-01 99.000000 1.076390 -0.642006 4 10
2017-01-02 -0.438976 -66.000000 0.387825 4 11
2017-01-03 0.101879 0.469225 0.860522 4 12
2017-01-04 -0.670031 1.974935 -0.570337 4 13
2017-01-05 0.250046 -1.385470 -0.893637 4 14
2017-01-06 0.876446 -0.167285 -0.475356 4 15
2017-01-07 0.291258 0.676994 -1.953909 4 16
2017-01-08 -0.569716 0.749637 1.038614 4 17
--------------------------------------------------
--------------------------------------------------
A B C D F
2017-01-01 -99.000000 -1.076390 -0.642006 -4 -10
2017-01-02 -0.438976 -66.000000 -0.387825 -4 -11
2017-01-03 -0.101879 -0.469225 -0.860522 -4 -12
2017-01-04 -0.670031 -1.974935 -0.570337 -4 -13
2017-01-05 -0.250046 -1.385470 -0.893637 -4 -14
2017-01-06 -0.876446 -0.167285 -0.475356 -4 -15
2017-01-07 -0.291258 -0.676994 -1.953909 -4 -16
2017-01-08 -0.569716 -0.749637 -1.038614 -4 -17
Process finished with exit code 0
