Python pandas从一列字符串的数据选择中过滤掉nan
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Python pandas Filtering out nan from a data selection of a column of strings
提问by ccsv
Without using groupbyhow would I filter out data without NaN?
如果不使用groupby，我将如何过滤掉没有的数据NaN？
Let say I have a matrix where customers will fill in 'N/A','n/a' or any of its variations and others leave it blank:
假设我有一个矩阵，客户将在其中填写“N/A”、“n/a”或其任何变体，而其他人将其留空：
import pandas as pd
import numpy as np
df = pd.DataFrame({'movie': ['thg', 'thg', 'mol', 'mol', 'lob', 'lob'],
'rating': [3., 4., 5., np.nan, np.nan, np.nan],
'name': ['John', np.nan, 'N/A', 'Graham', np.nan, np.nan]})
nbs = df['name'].str.extract('^(N/A|NA|na|n/a)')
nms=df[(df['name'] != nbs) ]
output:
输出：
>>> nms
movie name rating
0 thg John 3
1 thg NaN 4
3 mol Graham NaN
4 lob NaN NaN
5 lob NaN NaN
How would I filter out NaN values so I can get results to work with like this:
我将如何过滤掉 NaN 值，以便我可以得到这样的结果：
movie name rating
0 thg John 3
3 mol Graham NaN
I am guessing I need something like ~np.isnanbut the tilda does not work with strings.
我猜我需要类似的东西，~np.isnan但 tilda 不适用于字符串。
采纳答案by EdChum
Just drop them:
只需放下它们：
nms.dropna(thresh=2)
this will drop all rows where there are at least two non-NaN.
这将删除至少有两个非NaN.
Then you could then drop where name is NaN:
然后你可以删除 name 所在的位置NaN：
In [87]:
nms
Out[87]:
movie name rating
0 thg John 3
1 thg NaN 4
3 mol Graham NaN
4 lob NaN NaN
5 lob NaN NaN
[5 rows x 3 columns]
In [89]:
nms = nms.dropna(thresh=2)
In [90]:
nms[nms.name.notnull()]
Out[90]:
movie name rating
0 thg John 3
3 mol Graham NaN
[2 rows x 3 columns]
EDIT
编辑
Actually looking at what you originally want you can do just this without the dropnacall:
实际上，查看您最初想要的内容，您可以在不dropna调用的情况下执行此操作：
nms[nms.name.notnull()]
UPDATE
更新
Looking at this question 3 years later, there is a mistake, firstly thresharg looks for at least nnon-NaNvalues so in fact the output should be:
3 年后看这个问题，有一个错误，首先thresharg 寻找至少n非NaN值，所以实际上输出应该是：
In [4]:
nms.dropna(thresh=2)
Out[4]:
movie name rating
0 thg John 3.0
1 thg NaN 4.0
3 mol Graham NaN
It's possible that I was either mistaken 3 years ago or that the version of pandas I was running had a bug, both scenarios are entirely possible.
可能是我 3 年前弄错了，或者我运行的 Pandas 版本有错误，这两种情况都是完全可能的。
回答by Gil Baggio
Simplest of all solutions:
最简单的解决方案：
filtered_df = df[df['name'].notnull()]
Thus, it filters out only rows that doesn't have NaN values in 'name' column.
因此，它仅过滤掉“名称”列中没有 NaN 值的行。
For multiple columns:
对于多列：
filtered_df = df[df[['name', 'country', 'region']].notnull().all(1)]
回答by Bashar Mohammad
df = pd.DataFrame({'movie': ['thg', 'thg', 'mol', 'mol', 'lob', 'lob'],'rating': [3., 4., 5., np.nan, np.nan, np.nan],'name': ['John','James', np.nan, np.nan, np.nan,np.nan]})
for col in df.columns:
df = df[~pd.isnull(df[col])]
回答by JacoSolari
df.dropna(subset=['columnName1', 'columnName2'])
