>>> df = pd.DataFrame({'a': [7, 1, 5],'b': ['3','2','1']}, dtype='object')>>>df.dtypes a object b object dtype: object 然后使用infer_objects(),可以将列'a'的类型更改为int64: >>> df =df.infer_objects()>>>df.dtypes a int64 b object dtype: object 由于'b'的值是字...
pd.to_numeric是pandas库中的一个函数，用于将参数转换为数字类型。这个函数的默认返回类型是float64或i...
<executions> <execution> <id>repackage</id> <goals> <goal>repackage</goal> </g...
在最基本的层面上，Pandas 对象可以认为是 NumPy 结构化数组的增强版本，其中行和列用标签而不是简单的...
dtype: object 通过上述代码可知,现有所有的数据类型默认都是object。让我们看看推断的数据类型是什么: df2.infer_objects().dtypes A int64 B float64 C bool D object dtype: object 'infer_obejects'可能看起来微不足道,但在有很多列时作用巨大。
FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[320, 439)' has dtype incompatible with int64, please explicitly cast to a compatible dtype first. Cast a pandas object to a specified dtype ``dtype``. ...
importnumpyasnp# Convert "Fee" from float to int# Using DataFrame.apply(np.int64)df["Fee"]=df["Fee"].apply(np.int64)print(df.dtypes) Yields below output. # Output:Courses object Fee int64 Duration object Discount float64 dtype: object ...
# 需要导入模块: import pandas [as 别名]# 或者: from pandas importInt64Dtype[as 别名]deftest_to_pandas_nullable_int(self):importpandasaspdforcamel_casein[False,True]:assert( pd.Int64Dtype() == AssetList([Asset(parent_id=123), Asset(parent_id=None)]).to_pandas(camel_case=camel_case).dt...
('Int8') False >>> is_int64_dtype(pd.Int64Dtype) True >>> is_int64_dtype(float) False >>> is_int64_dtype(np.uint64) # unsigned False >>> is_int64_dtype(np.array(['a', 'b'])) False >>> is_int64_dtype(np.array([1, 2], dtype=np.int64)) True >>> is_int64_dtype(pd...
dtypes # col1 object # col2 object # col3 float64 # dtype: object Thanks for the suggestion, adding the bigInt cols with explicit datatype as Int64 solves the use case.Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment ...