These functions calculate moments of the probability density distribution (that's why it takes only one parameter) and doesn't care about the "functional form" of the values.

These are meant for "random datasets" (think of them as measures like mean, standard deviation, variance):

import numpy as np
from scipy.stats import kurtosis, skew

x = np.random.normal(0, 2, 10000)   # create random values based on a normal distribution

print( 'excess kurtosis of normal distribution (should be 0): {}'.format( kurtosis(x) ))
print( 'skewness of normal distribution (should be 0): {}'.format( skew(x) ))

which gives:

excess kurtosis of normal distribution (should be 0): -0.024291887786943356
skewness of normal distribution (should be 0): 0.009666157036010928

changing the number of random values increases the accuracy:

x = np.random.normal(0, 2, 10000000)

Leading to:

excess kurtosis of normal distribution (should be 0): -0.00010309478605163847
skewness of normal distribution (should be 0): -0.0006751744848755031

In your case the function "assumes" that each value has the same "probability" (because the values are equally distributed and each value occurs only once) so from the point of view of skew and kurtosis it's dealing with a non-gaussian probability density (not sure what exactly this is) which explains why the resulting values aren't even close to 0:

import numpy as np
from scipy.stats import kurtosis, skew

x_random = np.random.normal(0, 2, 10000)

x = np.linspace( -5, 5, 10000 )
y = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x)**2  )  # normal distribution

import matplotlib.pyplot as plt

f, (ax1, ax2) = plt.subplots(1, 2)
ax1.hist(x_random, bins='auto')
ax1.set_title('probability density (random)')
ax2.hist(y, bins='auto')
ax2.set_title('(your dataset)')
plt.tight_layout()

Numpy is restricted to fairly basic array operations, you need to reach out to it's more educated brother, Scipy, to get more advanced stats functions.

scipy.stats.kurtosis(a, axis=0, fisher=True, bias=True)

Computes the kurtosis (Fisher or Pearson) of a dataset.

So, from scipy.stats import kurtosis, then kurtosis(x).

In general, the methods on Numpy arrays are restricted to only the most basic operations (max, min, etc.). Slightly more functionality is eked out of Numpy methods (e.g. numpy.diff), and more still from Scipy (scipy.optimize.[whatever] scipy.signal.[whatever], scipy.stats.[whatever])

If you read the documentation of scipy's kurtosis function, scipy is calculting Excess Kurtosis by default (with the minus 3):

scipy.stats.kurtosis(a, axis=0, fisher=True, bias=True)

Computes the kurtosis (Fisher or Pearson) of a dataset.

Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution.

If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators

Setting fisher to False will give you what you expected:

In [1]: a = np.array([   1. ,    2. ,    2.5,  400. ,    6. ,    0. ])

In [2]: scipy.stats.kurtosis(a, fisher=False)
Out[2]: 4.198860780044809