Learn NumPy data types in 8 minutes! 💱
Description: #python #coding #numpy
# dtype = Keyword argument that tells NumPy what kind of values are stored in an array
# Otherwise NumPy guesses the best data type based on your data
# Manually setting dtype improves performance
# & is more memory efficient (especially when working with large data sets)
# integer (int8, int16, int32, int64)
# float (float16, float32, float64)
# boolean (bool_)
# string (str_)
# object (object_)
# int8 = -128 to 127
# int16 = –32,768 to 32,767
# int32 = –2,147,483,648 to 2,147,483,647
# int64 = –9.22e18 to 9.22e18
# float16 = ~3-4 decimal digit precision
# float32 = ~7-8 decimal digit precision
# float64 = ~15-17 decimal digit precision Close description
# dtype = Keyword argument that tells NumPy what kind of values are stored in an array
# Otherwise NumPy guesses the best data type based on your data
# Manually setting dtype improves performance
# & is more memory efficient (especially when working with large data sets)
# integer (int8, int16, int32, int64)
# float (float16, float32, float64)
# boolean (bool_)
# string (str_)
# object (object_)
# int8 = -128 to 127
# int16 = –32,768 to 32,767
# int32 = –2,147,483,648 to 2,147,483,647
# int64 = –9.22e18 to 9.22e18
# float16 = ~3-4 decimal digit precision
# float32 = ~7-8 decimal digit precision
# float64 = ~15-17 decimal digit precision Close description
