DataTypes in NumPy
A data type in NumPy is used to specify the type of data stored in a variable. Here is the list of characters to represent data types available in NumPy.
|
Character |
Meaning |
|---|---|
|
b |
Boolean |
|
f |
Float |
|
m |
Time Delta |
|
O |
Object |
|
U |
Unicode String |
|
i |
Integer |
|
u |
Unsigned Integer |
|
c |
Complex Float |
|
M |
DateTime |
|
S |
String |
|
V |
A fixed chunk of memory for other types (void) |
The list of various types of data types provided by NumPy are given below:
|
Data Type |
Description |
|---|---|
|
bool_ |
Boolean |
|
int_ |
Default integer type (int64 or int32) |
|
intc |
Identical to the integer in C (int32 or int64) |
|
intp |
Integer value used for indexing |
|
int8 |
8-bit integer value (-128 to 127) |
|
int16 |
16-bit integer value (-32768 to 32767) |
|
int32 |
32-bit integer value (-2147483648 to 2147483647) |
|
int64 |
64-bit integer value (-9223372036854775808 to 9223372036854775807) |
|
uint8 |
Unsigned 8-bit integer value (0 to 255) |
|
uint16 |
Unsigned 16-bit integer value (0 to 65535) |
|
uint32 |
Unsigned 32-bit integer value (0 to 4294967295) |
|
uint64 |
Unsigned 64-bit integer value (0 to 18446744073709551615) |
|
float_ |
Float values |
|
float16 |
Half precision float values |
|
float32 |
Single-precision float values |
|
float64 |
Double-precision float values |
|
complex_ |
Complex values |
|
complex64 |
Represent two 32-bit float complex values (real and imaginary) |
|
complex128 |
Represent two 64-bit float complex values (real and imaginary) |
Numpy data Types
NumPy is a powerful Python library that can manage different types of data. Here we will explore the Datatypes in NumPy and How we can check and create datatypes of the NumPy array.
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