`np.arange('2005-02', '2005-03', dtype='datetime64[D]')` creates a NumPy array containing each day from `2005-02-01` through `2005-02-28`. The array has dtype `datetime64[D]`; the March endpoint is excluded, as shown by the supplied output.

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While Python natively provides built-in data types such as booleans, floats, and integers, NumPy offers additional specialized data types. These are often referred to by single-character codes:
- `i`: integer
- `b`: boolean
- `u`: unsigned integer
- `f`: float
- `c`: complex float
- `m`: timedelta
- `M`: datetime
- `O`: object
- `S`: string
- `U`: unicode string
- `V`: void (fixed chunk of memory for other types)

Data Types in NumPy

What will be the result of the following print statements:

Code inspection

uint8 --> Unsigned integer (0 to 255)

uint16 --> Unsigned integer (0 to 65535)

uint32 --> Unsigned integer (0 to 4294967295)

uint64 --> Unsigned integer (0 to 18446744073709551615)

NumPy Unassigned Integers

Complex number, represented by two 32-bit floats (real and imaginary components)

NumPy Complex Float

NumPy allows the subtraction of two Datetime values, an operation which produces a number with a time unit. Because NumPy doesn’t have a physical quantities system in its core, the timedelta64 data type was created to complement datetime64. The arguments for timedelta64 are a number, to represent the number of units, and a date/time unit, such as (D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. The timedelta64 data type also accepts the string “NAT” in place of the number for a “Not A Time” value.

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