如何检查float pandas列是否只包含整数？

Question

我有一个数据框

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如何确保df = pd.DataFrame(data=np.arange(10),columns=['v']).astype(float) 中的数字是整数？ 我非常关心舍入/截断/浮点表示错误

Answer 1

与astype(int)

的比较

暂时将您的专栏转换为int并使用np.array_equal进行测试：

np.array_equal(df.v, df.v.astype(int))
True

float.is_integer

您可以将此python函数与apply：

结合使用

df.v.apply(float.is_integer).all()
True

或者，在生成器理解中使用python＆＃39; s all来提高空间效率：

all(x.is_integer() for x in df.v)
True

Answer 2

这是一种更简单，而且可能更快的方法：

(df[col] % 1  == 0).all()

要忽略空值：

(df[col].fillna(-9999) % 1  == 0).all()

Answer 3

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)

请记住，如果浮动列包含np.NaN值，则不会选择包含所有整数的浮动列。要将缺少值的浮点型列转换为整数，您需要填充/删除缺少的值，例如，使用中位插补：

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)

Answer 4

出于完整性考虑， Pandas v1.0 + 提供了convert_dtypes() utility，即（在其他3次转换中）对仅包含整数的所有数据框列（或序列）执行请求的操作

如果您只想将转换限制为单个列，则可以执行以下操作：

>>> df.dtypes          # inspect previous dtypes
v                      float64

>>> df["v"] = df["v"].convert_dtype()
>>> df.dtypes          # inspect converted dtypes
v                      Int64