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- np.array()
- np.asarray()
- np.zeros()
- np.ones()
- np.empty()
- np.arange()
- np.linspace()
- np.eye()
- np.full()
- np.fromfunction()
- np.empty_like()
- np.identity()
- np.ones_like()
- np.zeros_like()
- np.full_like()
- np.frombuffer()
- np.from_dlpack()
- np.fromfile()
- np.fromiter()
- np.fromstring()
- np.loadtxt()
- np.rec.array()
- np.logspace()
- np.geomspace()
- np.meshgrid()
- np.mgrid()
- np.ogrid()
- np.diag()
- np.diagflat()
- np.tri()
- np.tril()
- np.triu()
- np.vander()
Creation | np.logspace()
Method:
np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=np.float64, axis=0)
Generates numbers spaced evenly on a log scale.
Returns:
np.ndarray
Parameters:
start: int-
Starting value of the NumPy array.
import numpy as np
arr = np.logspace(start=0, stop=2, num=5, base=10.0)
# Both 'start' & 'stop' are included as endpoint=True
print(arr)
'''
Output:
[ 1. 3.16227766 10. 31.6227766 100. ]
'''Firstly the values are converted into np.linspace() and the base value is raised to the power of the returned linspace values.
The values in the above array are - [10.00.0, 10.00.5, 10.01.0, 10.01.5, 10.02.0]
stop: int-
End of the interval value of the NumPy array.
import numpy as np
arr = np.logspace(start=0, stop=1, num=5)
# Both 'start' & 'stop' are included as endpoint=True
print(arr)
'''
Output:
[ 1. 1.77827941 3.16227766 5.62341325 10. ]
'''If stop > start, an array is returned with data in decreasing order.
If stop < start, an array is returned with data in ascending order.
If stop = start, an array of basestart is returned.
num: int-
endpoint: (True or False) Optional-
If endpoint is set to True, stop value is included in the returned NumPy array.
endpoint = True (default) +
base: (int or float)-
dtype: np.float64, Optional-
The desired data-type for the NumPy array is inferred from the dtype specified.
import numpy as np
arr = np.logspace(start=0, stop=4, num=5, dtype=np.int32)
# Both 'start' & 'stop' are included
print(arr)
'''
Output:
[ 1 10 100 1000 10000]
'''Values: +
Integer Types
np.int8: 8-bit signed integer (range: -128 to 127).
np.int16: 16-bit signed integer (range: -32,768 to 32,767).
np.int32: 32-bit signed integer (range: -2,147,483,648 to 2,147,483,647).
np.int64: 64-bit signed integer (large integer range).
np.uint8: 8-bit unsigned integer (range: 0 to 255).
np.uint16: 16-bit unsigned integer (range: 0 to 65,535).
np.uint32: 32-bit unsigned integer (range: 0 to 4,294,967,295).
np.uint64: 64-bit unsigned integer (large positive integer range).
Floating Types
np.float16: Half precision floating-point (16-bit, for low-precision computations).
np.float32: Single precision floating-point (32-bit).
np.float64: Double precision floating-point (64-bit, the default float in NumPy).
np.float128: Extended precision floating-point (128-bit, availability depends on system).
Comples Number Types
np.complex64: Complex number represented by two 32-bit floats (for real and imaginary parts).
np.complex128: Complex number represented by two 64-bit floats (default complex dtype).
np.complex256: Complex number represented by two 128-bit floats (system-dependent).
Boolean Type
np.bool_: Boolean type, can be either True or False (stored as 1-bit but takes up a full byte).
String Types
np.str_: Fixed-length Unicode string, specified by S + length (e.g., S10 for a 10-character string).
np.unicode_: Fixed-length Unicode string with support for multiple characters (uses U).
Object Type
np.object_: Allows storing any Python object, including mixed types, strings, or other arrays. Useful for heterogeneous data but slower than native NumPy types.
Datetime & Timedelta Types
np.datetime64: Stores dates and times with varying precisions (e.g., Y, M, D, h, m, s, ms, us, ns, ps, fs, as). Example: np.datetime64('2003-10-02')
np.timedelta64: Represents time durations with units (same units as datetime64).
By passing the dtype, you are implicitly converting the values to the passed dtype.
axis: int Optional-
The axis in the result to store the samples. Relevant only if start or stop are array-like.
axis = 0 (default) +
A new NumPy array is returned with values generated along rows (number of rows = num).
axis = -1 +
A new NumPy array is returned with values along the columns instead of rows, which can be especially useful in multidimensional arrays.
axis is only useful when dealing with array-like input.
Plays a vital role in transformation of the input along your axis (columns or rows).