更新 pandas 中满足特定条件的行值

问题描述

假设我有以下数据框:

更新 feat 和 another_feat 列的值的最有效方法是什么/strong>?

What is the most efficient way to update the values of the columns feat and another_feat where the stream is number 2?

是这个吗?

for index, row in df.iterrows():
    if df1.loc[index,'stream'] == 2:
       # do something

更新:如果我有超过 100 列怎么办?我不想明确命名要更新的列.我想将每列的值除以 2(流列除外).

UPDATE: What to do if I have more than a 100 columns? I don't want to explicitly name the columns that I want to update. I want to divide the value of each column by 2 (except for the stream column).

所以要明确我的目标是什么:

So to be clear what my goal is:

将所有具有流 2 的行的所有值除以 2，但不更改流列

推荐答案

我觉得你可以使用loc 如果您需要将两列更新为相同的值:

I think you can use loc if you need update two columns to same value:

df1.loc[df1['stream'] == 2, ['feat','another_feat']] = 'aaaa'
print df1
   stream        feat another_feat
a       1  some_value   some_value
b       2        aaaa         aaaa
c       2        aaaa         aaaa
d       3  some_value   some_value

如果您需要单独更新，一个选项是使用:

If you need update separate, one option is use:

df1.loc[df1['stream'] == 2, 'feat'] = 10
print df1
   stream        feat another_feat
a       1  some_value   some_value
b       2          10   some_value
c       2          10   some_value
d       3  some_value   some_value

另一个常见的选项是使用 numpy.where:

Another common option is use numpy.where:

df1['feat'] = np.where(df1['stream'] == 2, 10,20)
print df1
   stream  feat another_feat
a       1    20   some_value
b       2    10   some_value
c       2    10   some_value
d       3    20   some_value

如果您需要在条件为 True 的情况下划分所有不带 stream 的列，请使用:

If you need divide all columns without stream where condition is True, use:

print df1
   stream  feat  another_feat
a       1     4             5
b       2     4             5
c       2     2             9
d       3     1             7

#filter columns all without stream
cols = [col for col in df1.columns if col != 'stream']
print cols
['feat', 'another_feat']

df1.loc[df1['stream'] == 2, cols ] = df1 / 2
print df1
   stream  feat  another_feat
a       1   4.0           5.0
b       2   2.0           2.5
c       2   1.0           4.5
d       3   1.0           7.0

如果可以使用多个条件，请使用多个 numpy.在哪里或 numpy.select:

If working with multiple conditions is possible use multiple numpy.where or numpy.select:

df0 = pd.DataFrame({'Col':[5,0,-6]})

df0['New Col1'] = np.where((df0['Col'] > 0), 'Increasing', 
                          np.where((df0['Col'] < 0), 'Decreasing', 'No Change'))

df0['New Col2'] = np.select([df0['Col'] > 0, df0['Col'] < 0],
                            ['Increasing',  'Decreasing'], 
                            default='No Change')

print (df0)
   Col    New Col1    New Col2
0    5  Increasing  Increasing
1    0   No Change   No Change
2   -6  Decreasing  Decreasing

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