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
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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