import numpy as np
import seaborn as sns
def source1(shape=None):
return np.random.normal(10, 2, shape)
source1()
10.640236728514942
vals1 = source1(1000)
sns.distplot(vals1, kde=True, rug=True);
C:\Users\mclou\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
def source2(shape=None):
return np.random.uniform(0, 100, shape)
source2()
16.94823906331788
vals2 = source2(1000)
sns.distplot(vals2, kde=True, rug=True);
def source3(shape=None):
return np.random.binomial(10, 0.5, shape)
source3()
4
vals3 = source3(1000)
sns.distplot(vals3, kde=True, rug=True);
def source4(shape=None):
return np.random.chisquare(1, shape)
source4()
2.0901869671074977
vals4 = source4(1000)
sns.distplot(vals4, kde=True, rug=True);
def source5(shape=None):
return np.random.pareto(1.16, shape)
source5()
0.28739400531779125
vals5 = source5(1000)
sns.distplot(vals5);
def source6(shape=None):
return np.random.poisson(3, shape)
source6()
2
vals6 = source6(1000)
sns.distplot(vals6);
def source6(shape=None):
return np.random.standard_t(10, shape)
source6()
-0.7910509164826577
vals6 = source6(1000)
sns.distplot(vals6);