[英]How to create a new column based on one of three other columns?
I have a Dataframe that has a movie name column and 3 other columns (let's call them A, B, and C) that are ratings from 3 different sources. 我有一个数据帧,其中包含电影名称列和其他3个列(分别称为A,B和C),它们分别来自3个不同来源。 There are many movies with only one rating, some movies with a combination from the 3 forums, and some with no ratings.
许多电影只有一个等级,有些电影是来自3个论坛的组合,有些则没有评级。 I want to create a new column that will:
我想创建一个新列,该列将:
This is what I have in my code so far: 到目前为止,这就是我的代码:
def check_rating(rating):
if newyear['Yahoo Rating'] != "\\N":
return rating
else:
if newyear['Movie Mom Rating'] != "\\N":
return rating
else:
if newyear['Critc Rating'] != "\\N":
return rating
else:
return "Unrated"
df['Rating'] = df.apply(check_rating, axis=1)
The error I get is: 我得到的错误是:
ValueError: ('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().', 'occurred at index 0')
For visual of my dataframe, here is newyear.head()
: 为了显示我的数据
newyear.head()
,这里是newyear.head()
:
I am not sure what this value error means to fix this problem and also if this is the right way to do it. 我不确定此值错误对解决此问题意味着什么,也不确定这样做是否正确。
I would do something like this: 我会做这样的事情:
df = df.replace('\\N', np.nan) # this requires import numpy as np
(df['Yahoo Rating'].fillna(df['Movie Mom Rating']
.fillna(df['Critic Rating']
.fillna("Unrated"))))
The reason that your code doesn't work is that newyear['Yahoo Rating'] != "\\\\N"
is a boolean array. 您的代码不起作用的原因是
newyear['Yahoo Rating'] != "\\\\N"
是一个布尔数组。 What you say here is something like if [True, False, True, False]:
. 您在这里说的话类似
if [True, False, True, False]:
That's the source of ambiguity. 这就是模棱两可的根源。 How do you evaluate such a condition?
您如何评估这种情况? Would you execute if all of them True or would just one of them be enough?
如果它们全部为True,您将执行该命令还是仅其中之一就足够了?
As M. Klugerford explained , you can change it so it is evaluated row by row (therefore returns a single value). 正如M. Klugerford解释的那样 ,您可以对其进行更改,以便逐行对其进行求值(因此将返回单个值)。 However, row by row apply operations are generally slow and pandas has great tools for handling missing data.
但是,逐行应用操作通常速度较慢,并且熊猫具有出色的工具来处理丢失的数据。 That's why I am suggesting this.
这就是为什么我建议这样做。
You are returning rating
in your original function .. but rating
is the row , not the value of any column 您将在原始函数中返回
rating
..但rating
是行 ,而不是任何列的值
>>> df
A B C Genre Title Year
0 7 6 \N g1 m1 y1
1 \N 5 7 g2 m2 y2
2 \N \N \N g3 m3 y3
3 \N 4 1 g4 m4 y4
4 \N \N 3 g5 m5 y5
>>> def rating(row):
if row['A'] != r'\N':
return row['A']
if row['B'] != r'\N':
return row['B']
if row['C'] != r'\N':
return row['C']
return 'Unrated'
>>> df['Rating'] = df.apply(rating, axis = 1)
>>> df
A B C Genre Title Year Rating
0 7 6 \N g1 m1 y1 7
1 \N 5 7 g2 m2 y2 5
2 \N \N \N g3 m3 y3 Unrated
3 \N 4 1 g4 m4 y4 4
4 \N \N 3 g5 m5 y5 3
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