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NumPy – Data Types:

Nympy provides the below dataypes more than what exactly python holds.
 Data type Description bool_ Boolean (True or False) stored as a byte int_ Default integer type (same as C long; normally either int64 or int32) intc Identical to C int (normally int32 or int64) intp Integer used for indexing (same as C ssize_t; normally either int32 or int64) int8 Byte (-128 to 127) int16 Integer (-32768 to 32767) int32 Integer (-2147483648 to 2147483647) int64 Integer (-9223372036854775808 to 9223372036854775807) uint8 Unsigned integer (0 to 255) uint16 Unsigned integer (0 to 65535) uint32 Unsigned integer (0 to 4294967295) uint64 Unsigned integer (0 to 18446744073709551615) float_ Shorthand for float64. float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa float64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissa complex_ Shorthand for complex128. complex64 Complex number, represented by two 32-bit floats (real and imaginary components) complex128 Complex number, represented by two 64-bit floats (real and imaginary components)
Let us see some example below:
How identify the datatype ?
Below is the command. we will use the “dtype” method to identify the datatype
```>>> import numpy as np

>>> x=np.array([1,2,3,4,5])

>>> x
array([1, 2, 3, 4, 5])

>>> x.dtype
dtype('int32')```
What if you want to assign the integers as float datatype ?
Below is the command. Please observe that we have given the dtype as floating datatype.
```>>> x=np.array([1,2,3,4,5], dtype=float)

>>> x.dtype
dtype('float64')

>>> x
array([ 1.,  2.,  3.,  4.,  5.])```
Below example shows the floating dataype values.
```>>> y=np.array([.1,.2,.3,.4,.5])

>>> y
array([ 0.1,  0.2,  0.3,  0.4,  0.5])

>>> y.dtype
dtype('float64')```
Now let us see some more data types too.
This example will show the Boolean datatype.
```>>> eq=np.array([True, False])

>>> eq
array([ True, False], dtype=bool)

>>> eq.dtype
dtype('bool')```
This example will show the String datatype.
```>>> str=np.array(["This", "is", "NumPy"])

>>> str
array(['This', 'is', 'NumPy'],
dtype='<U5')

>>> str.dtype
dtype('<U5')```
This example will show the Complex datatype.
```>>> j=2

>>> solve=np.array([2+3j, 5+j, 9+2*3j])

>>> solve
array([ 2.+3.j,  7.+0.j,  9.+6.j])

>>> solve.dtype
dtype('complex128')

>>> solve.real
array([ 2.,  7.,  9.])```