np.where多个逻辑语句 pandas [英] np.where multiple logical statements pandas

问题描述

我知道有关使用np.where.

我有2个数据框:

df1
   A  B  C  D  E  F Postset
0  1  2  3  4  5  6     yes
1  1  2  3  4  5  6      no
2  1  2  3  4  5  6     yes

df2
   A  B  C  D  E  F Preset
0  1  2  3  4  5  6    yes
1  1  2  3  4  5  6    yes
2  1  2  3  4  5  6    yes

我想比较每个数据帧中行的唯一性.为此，我需要检查许多选定列的所有值是否相等.

I want to compare the uniqueness of the rows in each dataframe. To do this, I need to check that all values are equal for a number of selected columns.

来自此问题:如果我正在检查a b列c d e f我可以做到:

From this question: if I am checking columns a b c d e f I can do:

np.where((df1.A != df2.A) | (df1.B != df2.B) | (df1.C != df2.C) | (df1.D != df2.D) | (df1.E != df2.E) | (df1.F != df2.F))

正确给出的信息:

(array([], dtype=int64),)

即这两个数据帧的所有列中的值都独立相等.

i.e. the values in all columns are independently equal for both dataframes.

这对于较小的数据框来说很好，但是我的实际数据框具有大量必须检查的列. np.where条件太长，无法准确写出.

This is fine for a small dataframe, but my real dataframe has a high number of columns that I must check. The np.where condition is too long to write out with accuracy.

相反，我想将我的专栏放在列表中:

Instead, I would like to put my columns into a list:

columns_check_list = ['A','B','C','D','E','F'] 

并使用我的np.where语句自动检查所有列.

And use my np.where statement to perform my check over all columns automatically.

这显然行不通，但是它是我要查找的表格类型.像这样:

This obviously doesn't work, but its the type of form I am looking for. Something like:

check = np.where([df[column) != df[column] | for column in columns_check_list]) 

我该如何实现?

注意事项:

• 我有很多专栏
• 我的数据格式是固定的.
• 列中的值可以包含strings或floats.

• I have many columns
• The format of my data is fixed.
• The values within columns may contain either strings or floats.

推荐答案

似乎您需要或更可读，谢谢 IanS :

mask= (df1[columns_check_list] != df2[columns_check_list]).any(axis=1).values
print (mask)
[False False False]

也可以比较numpy array s:

mask= (df1[columns_check_list].values != df2[columns_check_list].values).any(axis=1)
print (mask)
[False False False]

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