根据列值的长度过滤数据框行 [英] filter dataframe rows based on length of column values

问题描述

我有一个熊猫数据框，如下所示:

df = pd.DataFrame([ [1,2], [np.NaN,1], ['test string1', 5]], columns=['A','B'] )

df
              A  B
0             1  2
1           NaN  1
2  test string1  5

我使用的是熊猫0.20.删除任何"列值的长度大于10的行的最有效方法是什么?

len('test string1') 12

因此对于上述示例，我期望输出如下:

df
              A  B
0             1  2
1           NaN  1

解决方案

如果基于列A

In [865]: df[~(df.A.str.len() > 10)]
Out[865]:
     A  B
0    1  2
1  NaN  1

如果基于所有列

In [866]: df[~df.applymap(lambda x: len(str(x)) > 10).any(axis=1)]
Out[866]:
     A  B
0    1  2
1  NaN  1

I have a pandas dataframe as follows:

df = pd.DataFrame([ [1,2], [np.NaN,1], ['test string1', 5]], columns=['A','B'] )

df
              A  B
0             1  2
1           NaN  1
2  test string1  5

I am using pandas 0.20. What is the most efficient way to remove any rows where 'any' of its column values has length > 10?

len('test string1') 12

So for the above e.g., I am expecting an output as follows:

df
              A  B
0             1  2
1           NaN  1

解决方案

If based on column A

In [865]: df[~(df.A.str.len() > 10)]
Out[865]:
     A  B
0    1  2
1  NaN  1

If based on all columns

In [866]: df[~df.applymap(lambda x: len(str(x)) > 10).any(axis=1)]
Out[866]:
     A  B
0    1  2
1  NaN  1

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