常见的数据类型包括整数类型(如int32、int64)、浮点数类型(如float32、float64)、布尔类型(bool)以及复数类型(complex64、complex128)等。 查看Numpy数组的数据类型 代码语言:javascript 复制 importnumpyasnp # 创建一个整数类型的数组 arr_int=np.array([1,2,3,4])print("数组的数据类型:",arr_int.dtype)#...
try:int_array=mixed_array.astype(int)exceptValueErrorase:print("Error:",e) 1. 2. 3. 4. 5. 关系图——数据类型之间的关系 在了解数据转换的基础上,我们可以使用关系图展示整数、浮点数与 NumPy 数组之间的关系。如下图所示: FLOAT_ARRAYfloatvalueINT_ARRAYintvalueConvert 6. 结论 在本文中,我们深入探...
...(np.int32) print("布尔数组转换后的整数数组:", arr_bool_to_int) 输出结果: 布尔数组转换后的整数数组: [1 0 1 0] 在这个示例中,布尔数组中的True被转换为...1,False被转换为0。...特别是在将浮点数转换为整数或将复数转换为实数时,可能会丢失数据的部分信息。
# Convert to'int'datatype arr2d_f.astype('int')#>array([[0,1,2],#>[3,4,5],#>[6,7,8]])# Convert to int then to str datatype arr2d_f.astype('int').astype('str')#>array([['0','1','2'],#>['3','4','5'],#>['6','7','8']],#>dtype='U21') 数组中的每...
print(np.bool(42.0)) # 创建数组时定义元素的数据类型 print(np.arange(7, dtype=np.uint16)) print(np.arange(9, dtype=np.float64)) 数据类型转换 # 类型转换时出现的TypeError异常 try: print(np.int(42.0 + 1.j)) except TypeError: print("TypeError: can't convert complex to int!") arr =...
df[['day', 'month']] = df[['day', 'month']].astype(int) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 实例3: 实例3:自定义函数进行数据类型转换 def convert_currency(value): v = value.replace(',', '').replace('¥', '').replace('￥', '') ...
convert_float=True(默认) 将浮点数转换成int类型 例:1.0 -> 1 """ #读取第一个表格的内容 df = pd.read_excel("123.xlsx") print(df) #读取一个表格子表格名称叫“2月”的表格 df = pd.read_excel("123.xlsx",sheet_name="2月")
NumPy provides several functions to create arrays from tabular data. We focus here on thegenfromtxtfunction. In a nutshell,genfromtxtruns two main loops. The first loop converts each line of the file in a sequence of strings. The second loop converts each string to the appropriate data typ...
Check your Python script to make sure that you are using the correct attribute name for numpy. For instance, if you are trying to use the “int” attribute of numpy to convert an array to integers, use “numpy.astype(int)” instead. 3. Restart the Python interpreter In some cases, rest...
There are 5 basic numerical types representingbooleans (bool),integers (int),unsigned integers (uint),floating point (float)andcomplex. Data-types can be used as functions to convert python numbers to array scalars (see the array scalar section for an explanation), python sequences of numbers ...