pandas 根据浮点列是否为整数（`float.is_integer`）在由True、False填充的pandas df中创建新列
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Creating new column in pandas df populated by True,False depending on whether a float column is a whole number (`float.is_integer`)
提问by user
I have a pandas dfwhere df['value']is a series of floats.
我有一只Pandasdf，那里df['value']有一系列的花车。
- Some of the floats will be whole numbers (like 25.0). I want to create a new column,
df['is_it_whole'][i]with values1(orTrue) is the correspondingdf['value'][i]is a whole number,0orFalseotherwise. - I know I can do a for loop, but I am wondering if there is any trick I can use to do it fast (I have a large df).
- I tried using
df['is_it_whole'] = df['value'].is_integer()but pandas series do not support theis_integermethod, I am looking for something similar that would work.
- 一些浮点数将是整数（如 25.0）。我想创建一个新的列，
df['is_it_whole'][i]其值1（或True）被相应的df['value'][i]是一个整数，0或False以其他方式。 - 我知道我可以做一个 for 循环，但我想知道是否有任何技巧可以用来快速完成（我有一个很大的 df）。
- 我尝试使用
df['is_it_whole'] = df['value'].is_integer()但Pandas系列不支持该is_integer方法，我正在寻找类似的方法。
Suggestions?
建议？
回答by Dennis Golomazov
import pandas as pd
df = pd.DataFrame([['A', 1], ['B', 2.5], ['C', 3.0], ['D', 3.2]], columns=['label', 'value'])
df['is_it_whole'] = df['value'].map(lambda x: x.is_integer())
df
label value is_it_whole
0 A 1.0 True
1 B 2.5 False
2 C 3.0 True
3 D 3.2 False
回答by Psidom
You can try:
你可以试试：
df['is_it_whole'] = (df['value'].round() == df['value'])
Or to take into account float number inaccuracy:
或者考虑浮点数不准确：
tol = 0.001
df['is_it_whole'] = ((df['value'].round() - df['value']).abs() < tol)
回答by simon
This applies is_integer to each element in the series:
这将 is_integer 应用于系列中的每个元素：
df['is_it_whole'] = df['value'].apply(lambda x: x.is_integer())
回答by smci
You don't need to declare a lambda function. Just directly apply df['value'].map(float.is_integer)
您不需要声明 lambda 函数。直接申请就行df['value'].map(float.is_integer)
import pandas as pd
df = pd.DataFrame([['A', 1], ['B', 2.5], ['C', 3.0], ['D', 3.2]], columns=['label', 'value'])
df['is_it_whole'] = df['value'].map(float.is_integer)
>>> df
label value is_it_whole
0 A 1.0 True
1 B 2.5 False
2 C 3.0 True
3 D 3.2 False
