[英]How to create dataframe by randomly selecting from another dataframe?
DP 1 DP 2 DP 3 DP 4 DP 5 DP 6 DP 7 DP 8 DP 9 DP 10
(0.519) (1.117) (1.152) 0.772 1.490 (0.850) (1.189) (0.759)
0.030 0.047 0.632 (0.608) (0.322) 0.939 0.346 0.651
1.290 (0.179) 0.006 0.850 (1.141) 0.758 0.682
1.500 (1.228) 1.840 (1.594) (0.282) (0.907)
(1.540) 0.689 (0.683) 0.005 0.543
(0.197) (0.664) (0.636) 0.878
(0.942) 0.764 (0.137)
0.693 1.647
0.197
I have above dataframe:我上面有dataframe:
i need below dataframe using random value from above dataframe:我需要使用 dataframe 上方的随机值在 dataframe 以下:
DP 1 DP 2 DP 3 DP 4 DP 5 DP 6 DP 7 DP 8 DP 9 DP 10
(0.664) 1.290 0.682 0.030 (0.683) (0.636) (0.683) 1.840 (1.540)
1.490 (0.907) (0.850) (0.197) (1.228) 0.682 1.290 0.939
0.047 0.682 0.346 0.689 (0.137) 1.490 0.197
0.047 0.878 0.651 0.047 0.047 (0.197)
(1.141) 0.758 0.878 1.490 0.651
1.647 1.490 0.772 1.490
(0.519) 0.693 0.346
(0.137) 0.850
0.197
I've tried this code:我试过这段代码:
df2= df1.sample(len(df1)) df2=df1.sample(len(df1))
print(df2)打印(df2)
But Output is但是 Output 是
DP1 DP2 DP3 DP4 DP5 DP6 DP7 DP8 DP9
OP8 0.735590 1.762630 NaN NaN NaN NaN NaN NaN NaN
OP7 -0.999665 0.817949 -0.147698 NaN NaN NaN NaN NaN NaN
OP2 0.031430 0.049994 0.682040 -0.667445 -0.360034 1.089516 0.426642 0.916619 NaN
OP3 1.368955 -0.191781 0.006623 0.932736 -1.277548 0.880056 0.841018 NaN NaN
OP1 -0.551065 -1.195305 -1.243199 0.847178 1.668630 -0.986300 -1.465904 -1.069986 NaN
OP4 1.592201 -1.314628 1.985683 -1.749389 -0.315828 -1.052629 NaN NaN NaN
OP6 -0.208647 -0.710424 -0.686654 0.963221 NaN NaN NaN NaN NaN
OP10 NaN NaN NaN NaN NaN NaN NaN NaN NaN
OP9 0.209244 NaN NaN NaN NaN NaN NaN NaN NaN
OP5 -1.635306 0.737937 -0.736907 0.005545 0.607974 NaN NaN NaN NaN
You can use np.random.choice()
for the sampling.您可以使用
np.random.choice()
进行采样。
Assuming df
is something like this:假设
df
是这样的:
df = pd.DataFrame({'DP 1': ['(0.519)','0.030','1.290','1.500','(1.540)','(0.197)','(0.942)','0.693','0.197'],'DP 2': ['(1.117)','0.047','(0.179)','(1.228)','0.689','(0.664)','0.764','1.647',np.nan],'DP 3': ['(1.152)','0.632','0.006','1.840','(0.683)','(0.636)','(0.137)',np.nan,np.nan],'DP 4': ['0.772','(0.608)','0.850','(1.594)','0.005','0.878',np.nan,np.nan,np.nan],'DP 5': ['1.490','(0.322)','(1.141)','(0.282)','0.543',np.nan,np.nan,np.nan,np.nan],'DP 6': ['(0.850)','0.939','0.758','(0.907)',np.nan,np.nan,np.nan,np.nan,np.nan],'DP 7': ['(1.189)','0.346','0.682',np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],'DP 8': ['(0.759)','0.651',np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],'DP 9': [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],'DP 10': [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]})
# DP 1 DP 2 DP 3 DP 4 DP 5 DP 6 DP 7 DP 8 DP 9 DP 10
# 0 (0.519) (1.117) (1.152) 0.772 1.490 (0.850) (1.189) (0.759) NaN NaN
# 1 0.030 0.047 0.632 (0.608) (0.322) 0.939 0.346 0.651 NaN NaN
# 2 1.290 (0.179) 0.006 0.850 (1.141) 0.758 0.682 NaN NaN NaN
# 3 1.500 (1.228) 1.840 (1.594) (0.282) (0.907) NaN NaN NaN NaN
# 4 (1.540) 0.689 (0.683) 0.005 0.543 NaN NaN NaN NaN NaN
# 5 (0.197) (0.664) (0.636) 0.878 NaN NaN NaN NaN NaN NaN
# 6 (0.942) 0.764 (0.137) NaN NaN NaN NaN NaN NaN NaN
# 7 0.693 1.647 NaN NaN NaN NaN NaN NaN NaN NaN
# 8 0.197 NaN NaN NaN NaN NaN NaN NaN NaN NaN
First extract the choices
from all non-null values of df
:首先从
df
的所有非空值中提取choices
:
choices = df.values[~pd.isnull(df.values)]
# array(['(0.519)', '(1.117)', '(1.152)', '0.772', '1.490', '(0.850)',
# '(1.189)', '(0.759)', '0.030', '0.047', '0.632', '(0.608)',
# '(0.322)', '0.939', '0.346', '0.651', '1.290', '(0.179)', '0.006',
# '0.850', '(1.141)', '0.758', '0.682', '1.500', '(1.228)', '1.840',
# '(1.594)', '(0.282)', '(0.907)', '(1.540)', '0.689', '(0.683)',
# '0.005', '0.543', '(0.197)', '(0.664)', '(0.636)', '0.878',
# '(0.942)', '0.764', '(0.137)', '0.693', '1.647', '0.197'],
# dtype=object)
Then take a np.random.choice()
from choices
for all non-null cells:然后从所有非空单元格的
choices
中获取一个np.random.choice()
:
df = df.applymap(lambda x: np.random.choice(choices) if not pd.isnull(x) else x)
# DP 1 DP 2 DP 3 DP 4 DP 5 DP 6 DP 7 DP 8 DP 9 DP 10
# 0 (0.179) 0.682 0.758 (1.152) (0.137) (1.152) 0.939 (0.759) NaN NaN
# 1 1.500 (1.152) (0.197) 0.772 1.840 1.840 0.772 (0.850) NaN NaN
# 2 0.878 0.005 (1.540) 0.764 (0.519) 0.682 (1.152) NaN NaN NaN
# 3 0.758 (0.137) 1.840 1.647 1.647 (0.942) NaN NaN NaN NaN
# 4 0.693 (0.683) (0.759) 1.500 (0.197) NaN NaN NaN NaN NaN
# 5 0.006 (0.137) 0.764 (1.117) NaN NaN NaN NaN NaN NaN
# 6 (0.664) 0.632 (1.141) NaN NaN NaN NaN NaN NaN NaN
# 7 0.543 (0.664) NaN NaN NaN NaN NaN NaN NaN NaN
# 8 (0.137) NaN NaN NaN NaN NaN NaN NaN NaN NaN
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