Python NumPy: Data Types List

Numpy provides the wider range of numeric data types than that provided by the Python. It can be used as function.It can also be referred by character code.

A list of Numpy data types are given below:

  • bool_: It is stored as bytes. It is used to indicate boolean value weather it is True or False. 
  • int_: It is used to store default type of integer. It is also identical to long type of C that contains 64 bit or 32 bit      integer.
  • intc: It is similar to the C integer, it is used to represent 32 or 64-bit int.
  • intp: This type of integer are used for for indexing.
  • int8: It is 8-bit integer identical to bytes. Its value range from -128 to 127.
  • int16: It is 16-bit integer ranging from -32768 to 32767.
  • int32: It is 32-bit integer ranging from -2147483648 to 2147483647.
  • int64: It is 64-bit integer ranging from -9223372036854775808 to 9223372036854775807.
  • uint8: It is 8-bit unsigned integer.
  • uint16: It is 16-bit unsigned integer.
  • uint32: It is 32-bit unsigned integer.
  • uint64: It is 64-bit unsigned integer.
  • float_: It is identical to float of 64 bit.
  • float16: It is the half-precision float of 5 bits which is reserved for the exponent ,10 bits which is reserved for mantissa, and 1 bit  which is reserved for the sign.
  • float32: It is the single precision float o f8 bits which is reserved for the exponent, 23 bits which is reserved for mantissa, 1 bit is used for the sign.
  • float64:  It is the double precision float of 11 bits which is reserved for the exponent, 52 bits which is reserved for mantissa, 1 bit is used for the sign.
  • complex_: It is identical to complex of 128-bits.
  • complex64: This type of data type is used to represent the complex number where real and imaginary part shares 32 bits each.
  • complex128: This type of data type is used to represent the complex number where real and imaginary part shares 64 bits each.
Sat, 07/31/2021 - 19:59

Authored by

Devanshi, is working as a Data Scientist with iVagus. She has expertise in Python, NumPy, Pandas and other data science technologies.