我有一个float列,它包含NaN值和float值。如何筛选出那些不以.0结尾的值?
例如:

Col1
0.7
1.0
1.1
9.0
9.5
NaN

期望结果将是:
Col1
0.7
1.1
9.2

最佳答案

您可以使用boolean indexing

#convert to string and compare last value
print ((df.Col1.astype(str).str[-1] != '0') & (df.Col1.notnull()))
0     True
1    False
2     True
3    False
4     True
5    False
Name: Col1, dtype: bool

print (df[(df.Col1.astype(str).str[-1] != '0') & (df.Col1.notnull())])
   Col1
0   0.7
2   1.1
4   9.5

另一种比较转换值与ìnt的方法,但首先需要fillna
s = df.Col1.fillna(1)
print (df[s.astype(int) != s])
   Col1
0   0.7
2   1.1
4   9.5

时间安排:
#[30000 rows x 1 columns]
df = pd.concat([df]*10000).reset_index(drop=True)

def jez2(df):
    s = df.Col1.fillna(1)
    return (df[s.astype(int) != s])

In [179]: %timeit (df[(df.Col1.astype(str).str[-1] != '0') & (df.Col1.notnull())])
10 loops, best of 3: 80.2 ms per loop

In [180]: %timeit (jez2(df))
1000 loops, best of 3: 1.16 ms per loop

In [181]: %timeit (df[df.Col1 // 1 != df.Col1].dropna())
100 loops, best of 3: 3.04 ms per loop

In [182]: %timeit (df[df['Col1'].mod(1) > 0].dropna())
100 loops, best of 3: 2.58 ms per loop

10-05 23:00