问题描述
我有两个数据框 df1 df2 具有相同的行数和列数以及变量，我正在尝试比较布尔变量 choice 在两个数据框中.然后使用 if/else 来操作数据.但是当我尝试比较布尔变量时似乎有些错误.
I have two dataframes df1 df2with the same numbers of rows and columns and variables, and I'm trying to compare the boolean variable choice in the two dataframes. Then use if/else to manipulate the data. But something seems wrong when I try to compare the boolean var.
这是我的数据框示例和代码:
Here are my dataframes sample and codes:
#df1
v_100 choice #boolean
7 True
0 True
7 False
2 True
#df2
v_100 choice #boolean
1 False
2 True
74 True
6 True
def lastTwoTrials_outcome():
df1 = df.iloc[5::6, :] #df1 and df2 are extracted from the same dataframe first
df2 = df.iloc[4::6, :]
if df1['choice'] != df2['choice']: # if "choice" is different in the two dataframes
df1['v_100'] = (df1['choice'] + df2['choice']) * 0.5
这是错误:
if df1['choice'] != df2['choice']:
File "path", line 818, in wrapper
raise ValueError(msg)
ValueError: Can only compare identically-labeled Series objects
我在这里发现了同样的错误，和一个答案建议 sort_index 首先，但我真的不明白为什么?谁能详细解释一下(如果这是正确的解决方案)?
I found the same error here, and an answer suggests to sort_index first, but I don't really understand why though? Can anyone explain more in detail please (if that's the correct solution)?
谢谢！
推荐答案
我觉得你需要 reset_index 用于相同的索引值，然后是 comapare - 创建新列最好使用 mask 或 numpy.where:
I think you need reset_index for same index values and then comapare - for create new column is better use mask or numpy.where:
另外 + 使用 | 因为使用布尔值.
Also instead + use | because working with booleans.
df1 = df1.reset_index(drop=True)
df2 = df2.reset_index(drop=True)
df1['v_100'] = df1['choice'].mask(df1['choice'] != df2['choice'],
(df1['choice'] + df2['choice']) * 0.5)
df1['v_100'] = np.where(df1['choice'] != df2['choice'],
(df1['choice'] | df2['choice']) * 0.5,
df1['choice'])
样品:
print (df1)
v_100 choice
5 7 True
6 0 True
7 7 False
8 2 True
print (df2)
v_100 choice
4 1 False
5 2 True
6 74 True
7 6 True
<小时>
df1 = df1.reset_index(drop=True)
df2 = df2.reset_index(drop=True)
print (df1)
v_100 choice
0 7 True
1 0 True
2 7 False
3 2 True
print (df2)
v_100 choice
0 1 False
1 2 True
2 74 True
3 6 True
df1['v_100'] = df1['choice'].mask(df1['choice'] != df2['choice'],
(df1['choice'] | df2['choice']) * 0.5)
print (df1)
v_100 choice
0 0.5 True
1 1.0 True
2 0.5 False
3 1.0 True
这篇关于错误“只能比较标记相同的系列对象"和 sort_index的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！