NumPy Data Types¶

Data Types in Python¶

strings - used to represent text data, the text is given under quote marks. eg. “ABCD”
integer - used to represent integer numbers. eg. -1, -2, -3
float - used to represent real numbers. eg. 1.2, 42.42
boolean - used to represent True or False.
complex - used to represent a number in complex plain. eg. 1.0 + 2.0j, 1.5 + 2.5j

Data Types in NumPy¶

i - integer
b - boolean
u - unsigned integer
f - float
c - complex float
m - timedelta
M - datetime
O - object
S - string
U - unicode string
V - fixed chunk of memory for other type ( void )

Brief Overview of NumPy Data Types¶

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
complex128	Complex number, represented by two 64-bit floats

Creating NumPy Array¶

import numpy as np 

# Create a numpy array 
A = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
print(A)

[1 2 3 4 5 6 7 8 9]

# Create a numpy array with strings 
S = np.array(["Jim", "Tim", "Mim"])
print(S)

['Jim' 'Tim' 'Mim']

Checking the Data Type of an Array¶

The NumPy array object has a property called dtype that returns the data type of the array:

# Check the data type of `A`
A.dtype

dtype('int64')

# Check rthe data type of `S`
S.dtype

dtype('<U3')

Creating Arrays With a Defined Data Type¶

We use the array() function to create arrays, this function can take an optional argument: dtype that allows us to define the expected data type of the array elements

# Create an array of integers 
B = np.array([1, 2, 3, 4, 5], dtype='int')
print(B)

[1 2 3 4 5]

# Check data type of `B`
B.dtype

dtype('int64')

# Create an array of floats 
C = np.array([1, 2, 3, 4, 5], dtype='float32')
print(C)

[1. 2. 3. 4. 5.]

# Check the data type of `C`
C.dtype

dtype('float32')

Find Byte Size of an Array¶

# Check byte size of `A`
A.nbytes

# Check byte size of `S`
S.nbytes

# Check byte size of `B`
B.nbytes

# Check byte size of `C`
C.nbytes

Converting Data Type on Existing Arrays using `astype()`¶

# Create an array of floats 
arr = np.array([1, 2, 3, 4, 5], dtype='float32')
print(arr)

[1. 2. 3. 4. 5.]

# Convert the array into integer using astype()
new_arr = arr.astype('int')
print(new_arr)

[1 2 3 4 5]

# Now check the data type again 
new_arr.dtype

dtype('int64')

Introduction to NumPy