pandas 如何检查浮动熊猫列是否只包含整数？
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How to check if float pandas column contains only integer numbers?
提问by 00__00__00
I have a dataframe
我有一个数据框
df = pd.DataFrame(data=np.arange(10),columns=['v']).astype(float)
How to make sure that the numbers in vare whole numbers?
I am very concerned about rounding/truncation/floating point representation errors
如何确保中的数字v是整数？我非常关心舍入/截断/浮点表示错误
回答by cs95
Comparison with astype(int)
与 astype(int)
Tentatively convert your column to intand test with np.array_equal:
暂时将您的列转换为int并测试np.array_equal：
np.array_equal(df.v, df.v.astype(int))
True
float.is_integer
float.is_integer
You can use this python function in conjunction with an apply:
您可以结合使用此 python 函数apply：
df.v.apply(float.is_integer).all()
True
Or, using python's allin a generator comprehension, for space efficiency:
或者，all在生成器理解中使用 python以提高空间效率：
all(x.is_integer() for x in df.v)
True
回答by mgoldwasser
If you want to check multiple float columns in your dataframe, you can do the following:
如果要检查数据框中的多个浮点列，可以执行以下操作：
col_should_be_int = df.select_dtypes(include=['float']).applymap(float.is_integer).all()
float_to_int_cols = col_should_be_int[col_should_be_int].index
df.loc[:, float_to_int_cols] = df.loc[:, float_to_int_cols].astype(int)
Keep in mind that a float column, containing all integers will not get selected if it has np.NaNvalues. To cast float columns with missing values to integer, you need to fill/remove missing values, for example, with median imputation:
请记住，包含所有整数的浮点列如果具有np.NaN值则不会被选中。要将具有缺失值的浮点列转换为整数，您需要填充/删除缺失值，例如，使用中值插补：
float_cols = df.select_dtypes(include=['float'])
float_cols = float_cols.fillna(float_cols.median().round()) # median imputation
col_should_be_int = float_cols.applymap(float.is_integer).all()
float_to_int_cols = col_should_be_int[col_should_be_int].index
df.loc[:, float_to_int_cols] = float_cols[float_to_int_cols].astype(int)
回答by scott
Here's a simpler, and probably faster, approach:
这是一种更简单且可能更快的方法：
(df[col] % 1 == 0).all()
To ignore nulls:
忽略空值：
(df[col].fillna(-9999) % 1 == 0).all()
