pandas 在熊猫系列对象中查找非整数值
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finding non-integer values in pandas series object
提问by sumi1234
How to find non-integer values like float,string in pandas series object?
如何在pandas系列对象中找到浮点、字符串等非整数值？
Have a series object like this,
有一个这样的系列对象，
a=(1.2,3,4,5,6,2,8,5,9)
I tried to_numeric, but this is not helping to identify floatvalues. Is there a way to check integervalues?
我试过to_numeric，但这无助于识别float值。有没有办法检查integer值？
回答by jezrael
You can use list comprehensionfor checking non-integer values , if typeof values is stringand integer:
您可以list comprehension用于检查非整数值，如果type值是string和integer：
import pandas as pd
a=['a',3,4,5,6,2,8,5,9]
s = pd.Series(a)
print s
0 a
1 3
2 4
3 5
4 6
5 2
6 8
7 5
8 9
dtype: object
print [type(x) for x in s]
[<type 'str'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>]
print [type(x) == int for x in s]
[False, True, True, True, True, True, True, True, True]
Or by to_numericwith notnull:
print pd.to_numeric(s, errors='coerce').notnull()
0 False
1 True
2 True
3 True
4 True
5 True
6 True
7 True
8 True
dtype: bool
If values are intand float, Seriesconvert all values to float:
如果值为intand float，则将Series所有值转换为float：
import pandas as pd
a=[1.2,3,4,5,6,2,8,5,9]
s = pd.Series(a)
print s
0 1.2
1 3.0
2 4.0
3 5.0
4 6.0
5 2.0
6 8.0
7 5.0
8 9.0
dtype: float64
print [type(x) for x in s]
[<type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'numpy.float64'>]
回答by Gregory Miller
A naive solution to separate ints and floats is to compare the floats with their rounded values:
分离整数和浮点数的一个简单的解决方案是将浮点数与其舍入值进行比较：
import pandas as pd
a = (1.2,3,4,5,6,2,8,5,9)
df_floats = pd.to_numeric(a)
df_rounds = df_floats.round()
df_ints = df_rounds[df_rounds == df_floats].astype(int)
df_floats = df_floats[df_rounds != df_floats]
