>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show()

February 14, 2026 - The numpy.logspace() function is used to generate numbers that are evenly spaced on a logarithmic scale.

>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show()

August 30, 2023 - The logspace() method creates an array with evenly spaced numbers on a log scale.

>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show()

April 22, 2026 - The numpy.logspace() function returns an array with numbers that are evenly spaced on a logarithmic scale.

>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show()

>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show()

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)[source]¶

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)[source]#

February 9, 2025 - You might be wondering: when should you use logspace versus linspace? Here’s the key difference: numpy.logspace generates values logarithmically spaced, meaning the intervals between numbers grow (or shrink) exponentially.

July 13, 2026 - logspace spaces samples evenly in exponent space, so the returned values grow multiplicatively rather than by a constant arithmetic step. numpy.logspace() creates numbers that are evenly spaced on a logarithmic scale.

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)[source]¶ · Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x2, y + 0.5, 'o') [<matplotlib.lines.Line2D object at 0x...>] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show()

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)[source]¶ · Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

November 14, 2024 - The numpy.logspace() function is a powerful tool from the NumPy library that allows for the creation of arrays with logarithmically spaced values between a specified start point and an end point.

2 weeks ago - numpy.logspace returns numbers spaced evenly on a log scale. The detail that trips most people up is that start and stop are exponents, not endpoints: np.logspace(0, 3, 4) gives 1, 10, 100, 1000 because the function computes 10**0 to 10**3.

August 11, 2026 - numpy.logspace(start, stop, num = 50, endpoint = True, base = 10.0, dtype=None, axis = 0)

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0)¶ · Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below). See also · arange ·

The numpy.logspace() function allows us to generate a NumPy array of values evenly spaced on a logarithmic scale.