pandas设置值、更改值

#!/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('', 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('', 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

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.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    10

2017-01-02  -0.438976 -66.000000  0.387825    11

2017-01-03   0.101879   0.469225  0.860522    12

2017-01-04  -0.670031   1.974935 -0.570337    13

2017-01-05   0.250046  -1.385470 -0.893637    14

2017-01-06   0.876446  -0.167285 -0.475356    15

2017-01-07   0.291258   0.676994 -1.953909    16

2017-01-08  -0.569716   0.749637  1.038614    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