Python：如何删除特定列为空/NaN的行？
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Python: How to drop a row whose particular column is empty/NaN?
提问by Haroon S.
I have a csv file. I read it:
我有一个 csv 文件。我读了它：
import pandas as pd
data = pd.read_csv('my_data.csv', sep=',')
data.head()
It has output like:
它有如下输出：
id city department sms category
01 khi revenue NaN 0
02 lhr revenue good 1
03 lhr revenue NaN 0
I want to remove all the rows where smscolumn is empty/NaN. What is efficient way to do it?
我想删除sms列为空/NaN 的所有行。什么是有效的方法？
回答by jezrael
Use dropnawith parameter subsetfor specify column for check NaNs:
dropna与参数subset一起使用以指定用于检查NaN的列：
data = data.dropna(subset=['sms'])
print (data)
id city department sms category
1 2 lhr revenue good 1
Another solution with boolean indexingand notnull:
使用boolean indexing和的另一种解决方案notnull：
data = data[data['sms'].notnull()]
print (data)
id city department sms category
1 2 lhr revenue good 1
Alternative with query:
替代query：
print (data.query("sms == sms"))
id city department sms category
1 2 lhr revenue good 1
Timings
时间安排
#[300000 rows x 5 columns]
data = pd.concat([data]*100000).reset_index(drop=True)
In [123]: %timeit (data.dropna(subset=['sms']))
100 loops, best of 3: 19.5 ms per loop
In [124]: %timeit (data[data['sms'].notnull()])
100 loops, best of 3: 13.8 ms per loop
In [125]: %timeit (data.query("sms == sms"))
10 loops, best of 3: 23.6 ms per loop
回答by Thijs D
You can use the method dropnafor this:
您可以dropna为此使用该方法：
data.dropna(axis=0, subset=('sms', ))
See the documentationfor more details on the parameters.
有关参数的更多详细信息，请参阅文档。
Of course there are multiple ways to do this, and there are some slight performance differences. Unless performance is critical, I would prefer the use of dropna()as it is the most expressive.
当然有多种方法可以做到这一点，并且存在一些细微的性能差异。除非性能很关键，否则我更喜欢使用 ，dropna()因为它最具表现力。
import pandas as pd
import numpy as np
i = 10000000
# generate dataframe with a few columns
df = pd.DataFrame(dict(
a_number=np.random.randint(0,1e6,size=i),
with_nans=np.random.choice([np.nan, 'good', 'bad', 'ok'], size=i),
letter=np.random.choice(list('abcdefghijklmnop'), size=i))
)
# using notebook %%timeit
a = df.dropna(subset=['with_nans'])
#1.29 s ± 112 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# using notebook %%timeit
b = df[~df.with_nans.isnull()]
#890 ms ± 59.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# using notebook %%timeit
c = df.query('with_nans == with_nans')
#1.71 s ± 100 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
