问题描述
我有一个熊猫数据框，尺寸为89行x 13列.如果前五列中出现 NaN ，我想删除整个行.这是一个例子.
I have a pandas dataframe with dimensions 89 rows by 13 columns. I want to remove an entire row if NaN appears within the first five columns. Here is an example.
LotName C15 C16 C17 C18 C19 Spots15 Spots16 ...
Cherry St 439 464 555 239 420 101 101 ...
Springhurst NaN NaN NaN NaN NaN 12 12
Barton Lot 34 24 43 45 39 10 9 ...
在上面的示例中，我想删除 Springhurst 观察值，因为它在前五列中包含 NaN .我将如何在Python中做到这一点?
In the above example, I would want to remove the Springhurst observation, as it contains NaN within the first five columns. How would I be able to do this in Python?
推荐答案
如果要对前5列的所有行都严格检查 Nan ，
If you want to do a strict check of Nan in all rows for first 5 columns:
df.iloc[:, :5].dropna(how='all')
说明:
df.iloc [:,:5] :选择所有行和前5列
df.iloc[:, :5] : select all rows and first 5 columns
.dropna(how ='all'):检查一行中的所有值是否都是 NaN
.dropna(how='all') : check if all values in a row are NaN
如果要在5列中的任何一列中检查 Nan :
If you want to check for Nan in any of the 5 columns:
df.iloc[:, :5].dropna(how='any')
要将其分配回原始df，您可以执行以下操作:
In [2107]: ix = df.iloc[:, :5].dropna(how='all').index.tolist()
In [2110]: df = df.loc[ix]
In [2111]: df
Out[2111]:
LotName C15 C16 C17 C18 C19 Spots15 Spots16
Cherry St 439.0 464.0 555.0 239.0 420 101 101.0
Barton Lot 34.0 24.0 43.0 45.0 39 10 9.0
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