【3】数据分析--10--科学计算--Pandas--3--Dataframe信息修改

一、例子

1.1 创建dataframe

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)

结果:

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
--------------------------------------------------

1.2 新增一列

例1

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)

结果:

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
--------------------------------------------------

例2 合并两列信息

dataframe["newColumn"] = dataframe["age"].map(str) + dataframe["phone"] + dataframe["address”]

1.3 修改某个数值

方法一: at

修改第一行,“A”列的数据

df.at[dates[0],"A"]=99
print("df.at[dates[0],'A']=99")
print(df)
print('-'*50)

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
--------------------------------------------------

方法二: iat

修改第一行,第一列的数据

print("df.iat[1,1]=-66")
df.iat[1,1]=-66
print(df)
print('-'*50)
print("df.loc[:,'D']=np.array([4]*len(df))")

结果

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
--------------------------------------------------

方法三: loc

新增”D“列

df.loc[:,"D"]=np.array([4]*len(df))
print(df)
print('-'*50)
df2=df.copy()#拷贝
print('-'*50)

结果:

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
--------------------------------------------------
--------------------------------------------------

方法四

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)

结果

                    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

1.4 新增一行

from pandas import *
from random import *
df = DataFrame(columns=('lib', 'qty1', 'qty2'))#生成空的pandas表
for i in range(5):#插入一行
    df.loc[i] = [randint(-1,1) for n in range(3)]
print df

1.5 对符合要求的列行进行修改

代码:

import pandas as pd

# df = pd.DataFrame({'a':[1,2,3]},'b':[2,3,4])

dt = {'one':[1,2,3,4],'two':[9,8,7,6],'three':[-1,-2,-3,-4]}
df = pd.DataFrame(dt,index =['a','b','c','d'])

# df[df['one']==2,'one'] = df[df['one']==2]['one'] * df[df['one']==2]['two']

print df

df.loc[df['one']==2,'one'] *=100
df.loc[((df['one']==3) & (df['three']==-3)  ),'two'] *=50
df.loc[df['three']==-2,'three'] = abs(df['three'] )

print df

结果示例:

   one  three  two
a    1     -1    9
b    2     -2    8
c    3     -3    7
d    4     -4    6
   one  three  two
a    1     -1    9
b  200      2    8
c    3     -3  350
d    4     -4    6

1.6 na的填充

数据

df = pd.DataFrame([[np.nan, 2, np.nan, 0],
     [3, 4, np.nan, 1],
    [np.nan, np.nan, np.nan, 5],
    [np.nan, 3, np.nan, 4]],
    columns=list('ABCD'))

print df

    A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

填充:

print df.fillna(0)

     A    B    C  D
0  0.0  2.0  0.0  0
1  3.0  4.0  0.0  1
2  0.0  0.0  0.0  5
3  0.0  3.0  0.0  4

某一列的填充

df['A'] = df['A'].fillna(0)
print df

     A    B   C  D
0  0.0  2.0 NaN  0
1  3.0  4.0 NaN  1
2  0.0  NaN NaN  5
3  0.0  3.0 NaN  4

二、讨论

用起来,是不是有点像R

参考资料

个人公众号,比较懒,很少更新,可以在上面提问题,如果回复不及时,可发邮件给我: tiehan@sina.cn

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