pandas 根据其他列的值创建新列/逐行应用多列的功能 [英] pandas create new column based on values from other columns / apply a function of multiple columns, row-wise
问题描述
我想将我的自定义函数(它使用if-else梯形图)应用于每个这六个列(ERI_Hispanic，ERI_AmerInd_AKNatv，ERI_Asian，ERI_Black_Afr.Amer，ERI_HI_PacIsl，ERI_White)我的数据框的一行.
I want to apply my custom function (it uses an if-else ladder) to these six columns (ERI_Hispanic, ERI_AmerInd_AKNatv, ERI_Asian, ERI_Black_Afr.Amer, ERI_HI_PacIsl, ERI_White) in each row of my dataframe.
我尝试了与其他问题不同的方法，但似乎仍然找不到适合我问题的正确答案.关键在于，如果该人被视为西班牙裔，就不能被视为其他任何人.即使他们在另一个种族栏中的得分为"1"，他们仍然被视为西班牙裔，而不是两个或两个以上的种族.同样，如果所有ERI列的总和大于1，则将它们计为两个或两个以上的种族，并且不能计为唯一的种族(西班牙裔除外).希望这是有道理的.任何帮助将不胜感激.
I've tried different methods from other questions but still can't seem to find the right answer for my problem. The critical piece of this is that if the person is counted as Hispanic they can't be counted as anything else. Even if they have a "1" in another ethnicity column they still are counted as Hispanic not two or more races. Similarly, if the sum of all the ERI columns is greater than 1 they are counted as two or more races and can't be counted as a unique ethnicity(except for Hispanic). Hopefully this makes sense. Any help will be greatly appreciated.
这几乎就像在每行中进行一个for循环一样，如果每条记录都符合条件，则将它们添加到一个列表中并从原始列表中删除.
Its almost like doing a for loop through each row and if each record meets a criterion they are added to one list and eliminated from the original.
从下面的数据框中，我需要根据以下SQL规范来计算新列:
From the dataframe below I need to calculate a new column based on the following spec in SQL:
================================================= =========
========================= CRITERIA ===============================
IF [ERI_Hispanic] = 1 THEN RETURN "Hispanic"
ELSE IF SUM([ERI_AmerInd_AKNatv] + [ERI_Asian] + [ERI_Black_Afr.Amer] + [ERI_HI_PacIsl] + [ERI_White]) > 1 THEN RETURN "Two or More"
ELSE IF [ERI_AmerInd_AKNatv] = 1 THEN RETURN "A/I AK Native"
ELSE IF [ERI_Asian] = 1 THEN RETURN "Asian"
ELSE IF [ERI_Black_Afr.Amer] = 1 THEN RETURN "Black/AA"
ELSE IF [ERI_HI_PacIsl] = 1 THEN RETURN "Haw/Pac Isl."
ELSE IF [ERI_White] = 1 THEN RETURN "White"
注释:如果针对西班牙裔的ERI标志为True(1)，则该雇员被归类为西班牙裔"
Comment: If the ERI Flag for Hispanic is True (1), the employee is classified as "Hispanic"
注释:如果多个非西班牙裔ERI标志为真，则返回两个或更多"
Comment: If more than 1 non-Hispanic ERI Flag is true, return "Two or More"
======================数据帧======================== ===
====================== DATAFRAME ===========================
lname fname rno_cd eri_afr_amer eri_asian eri_hawaiian eri_hispanic eri_nat_amer eri_white rno_defined
0 MOST JEFF E 0 0 0 0 0 1 White
1 CRUISE TOM E 0 0 0 1 0 0 White
2 DEPP JOHNNY 0 0 0 0 0 1 Unknown
3 DICAP LEO 0 0 0 0 0 1 Unknown
4 BRANDO MARLON E 0 0 0 0 0 0 White
5 HANKS TOM 0 0 0 0 0 1 Unknown
6 DENIRO ROBERT E 0 1 0 0 0 1 White
7 PACINO AL E 0 0 0 0 0 1 White
8 WILLIAMS ROBIN E 0 0 1 0 0 0 White
9 EASTWOOD CLINT E 0 0 0 0 0 1 White
推荐答案
好吧，这有两个步骤-首先是编写一个可以执行您想要的翻译的函数-我已经根据您的伪代码整理了一个示例:
OK, two steps to this - first is to write a function that does the translation you want - I've put an example together based on your pseudo-code:
def label_race (row):
if row['eri_hispanic'] == 1 :
return 'Hispanic'
if row['eri_afr_amer'] + row['eri_asian'] + row['eri_hawaiian'] + row['eri_nat_amer'] + row['eri_white'] > 1 :
return 'Two Or More'
if row['eri_nat_amer'] == 1 :
return 'A/I AK Native'
if row['eri_asian'] == 1:
return 'Asian'
if row['eri_afr_amer'] == 1:
return 'Black/AA'
if row['eri_hawaiian'] == 1:
return 'Haw/Pac Isl.'
if row['eri_white'] == 1:
return 'White'
return 'Other'
您可能想了解一下，但这似乎可以解决问题-请注意，进入该函数的参数被认为是一个标有行"的Series对象.
You may want to go over this, but it seems to do the trick - notice that the parameter going into the function is considered to be a Series object labelled "row".
接下来，在熊猫中使用apply函数来应用该功能-例如
Next, use the apply function in pandas to apply the function - e.g.
df.apply (lambda row: label_race(row), axis=1)
请注意axis = 1说明符，这意味着应用程序是在行而不是列级别完成的.结果在这里:
Note the axis=1 specifier, that means that the application is done at a row, rather than a column level. The results are here:
0 White
1 Hispanic
2 White
3 White
4 Other
5 White
6 Two Or More
7 White
8 Haw/Pac Isl.
9 White
如果您对这些结果感到满意，请再次运行它，将结果保存到原始数据框中的新列中.
If you're happy with those results, then run it again, saving the results into a new column in your original dataframe.
df['race_label'] = df.apply (lambda row: label_race(row), axis=1)
结果数据框如下所示(向右滚动以查看新列):
The resultant dataframe looks like this (scroll to the right to see the new column):
lname fname rno_cd eri_afr_amer eri_asian eri_hawaiian eri_hispanic eri_nat_amer eri_white rno_defined race_label
0 MOST JEFF E 0 0 0 0 0 1 White White
1 CRUISE TOM E 0 0 0 1 0 0 White Hispanic
2 DEPP JOHNNY NaN 0 0 0 0 0 1 Unknown White
3 DICAP LEO NaN 0 0 0 0 0 1 Unknown White
4 BRANDO MARLON E 0 0 0 0 0 0 White Other
5 HANKS TOM NaN 0 0 0 0 0 1 Unknown White
6 DENIRO ROBERT E 0 1 0 0 0 1 White Two Or More
7 PACINO AL E 0 0 0 0 0 1 White White
8 WILLIAMS ROBIN E 0 0 1 0 0 0 White Haw/Pac Isl.
9 EASTWOOD CLINT E 0 0 0 0 0 1 White White
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