英文:
pandas changed column value condition of three other columns
问题
我有以下的pandas数据帧:
df = pd.DataFrame({'pred': [1, 2, 3, 4],
'a': [0.4, 0.6, 0.35, 0.5],
'b': [0.2, 0.4, 0.32, 0.1],
'c': [0.1, 0, 0.2, 0.2],
'd': [0.3, 0, 0.1, 0.2]})
我想根据列a、b、c、d的值更改'pred'列中的值,规则如下:
如果 列a的值大于列b、c、d的值之一
并且
如果 列b、c或d中的某一列的值大于0.25
那么将'pred'中的值更改为0。结果应如下所示:
pred a b c d
0 1 0.4 0.20 0.1 0.1
1 0 0.6 0.40 0.0 0.0
2 0 0.35 0.32 0.2 0.3
3 4 0.5 0.10 0.2 0.2
您可以如何做到这一点?
英文:
I have the following pandas dataframe:
df = pd.DataFrame({'pred': [1, 2, 3, 4],
'a': [0.4, 0.6, 0.35, 0.5],
'b': [0.2, 0.4, 0.32, 0.1],
'c': [0.1, 0, 0.2, 0.2],
'd': [0.3, 0, 0.1, 0.2]})
I want to change values on 'pred' column, based on columns a,b,c,d , as following:
if a has the value at column a is larger than the values of column b,c,d
and
if one of columns - b , c or d has value larger than 0.25
then change value in 'pred' to 0. so the results should be:
pred a b c d
0 1 0.4 0.2 0.1 0.1
1 0 0.6 0.4 0.0 0.0
2 0 0.35 0.32 0.2 0.3
3 4 0.5 0.1 0.2 0.2
How can I do this?
答案1
得分: 1
import pandas as pd
def row_cond(row):
m_val = max(row[2:])
if row[1] > m_val and m_val > 0.25:
row[0] = 0
return row
df = pd.DataFrame({'pred': [1, 2, 3, 4],
'a': [0.4, 0.6, 0.35, 0.5],
'b': [0.2, 0.4, 0.32, 0.1],
'c': [0.1, 0, 0.2, 0.2],
'd': [0.1, 0, 0.3, 0.2]})
new_df = df.apply(row_cond, axis=1)
Output:
pred a b c d
0 1.0 0.40 0.20 0.1 0.1
1 0.0 0.60 0.40 0.0 0.0
2 0.0 0.35 0.32 0.2 0.3
3 4.0 0.50 0.10 0.2 0.2
英文:
import pandas as pd
def row_cond(row):
m_val = max(row[2:])
if row[1]>m_val and m_val>0.25:
row[0] = 0
return row
df = pd.DataFrame({'pred': [1, 2, 3, 4],
'a': [0.4, 0.6, 0.35, 0.5],
'b': [0.2, 0.4, 0.32, 0.1],
'c': [0.1, 0, 0.2, 0.2],
'd': [0.1, 0, 0.3, 0.2]})
new_df = df.apply(row_cond,axis=1)
Output:
pred a b c d
0 1.0 0.40 0.20 0.1 0.1
1 0.0 0.60 0.40 0.0 0.0
2 0.0 0.35 0.32 0.2 0.3
3 4.0 0.50 0.10 0.2 0.2
答案2
得分: 1
创建一个布尔条件/掩码,然后使用 `loc` 将条件为 `True` 的值设置为 `0`
cols = ['b', 'c', 'd']
mask = df[cols].lt(df['a'], axis=0).all(1) & df[cols].gt(.25).any(1)
df.loc[mask, 'pred'] = 0
----------
pred a b c d
0 1 0.40 0.20 0.1 0.1
1 0 0.60 0.40 0.0 0.0
2 0 0.35 0.32 0.2 0.3
3 4 0.50 0.10 0.2 0.2
英文:
Create a boolean condition/mask then use loc
to set value to 0
where condition is True
cols = ['b', 'c', 'd']
mask = df[cols].lt(df['a'], axis=0).all(1) & df[cols].gt(.25).any(1)
df.loc[mask, 'pred'] = 0
pred a b c d
0 1 0.40 0.20 0.1 0.1
1 0 0.60 0.40 0.0 0.0
2 0 0.35 0.32 0.2 0.3
3 4 0.50 0.10 0.2 0.2
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