# Random variables¶

In [1]:
import numpy as np
import seaborn as sns


## Normal¶

In [2]:
def source1(shape=None):
return np.random.normal(10, 2, shape)

In [3]:
source1()

Out[3]:
10.640236728514942
In [4]:
vals1 = source1(1000)

In [5]:
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


# Uniform¶

In [6]:
def source2(shape=None):
return np.random.uniform(0, 100, shape)

In [7]:
source2()

Out[7]:
16.94823906331788
In [8]:
vals2 = source2(1000)

In [9]:
sns.distplot(vals2, kde=True, rug=True);


# Binomial¶

In [10]:
def source3(shape=None):
return np.random.binomial(10, 0.5, shape)

In [11]:
source3()

Out[11]:
4
In [12]:
vals3 = source3(1000)

In [13]:
sns.distplot(vals3, kde=True, rug=True);


# Chi-square¶

In [14]:
def source4(shape=None):
return np.random.chisquare(1, shape)

In [15]:
source4()

Out[15]:
2.0901869671074977
In [16]:
vals4 = source4(1000)

In [17]:
sns.distplot(vals4, kde=True, rug=True);


# Pareto¶

In [18]:
def source5(shape=None):
return np.random.pareto(1.16, shape)

In [19]:
source5()

Out[19]:
0.28739400531779125
In [20]:
vals5 = source5(1000)

In [21]:
sns.distplot(vals5);


# Poisson¶

In [22]:
def source6(shape=None):
return np.random.poisson(3, shape)

In [23]:
source6()

Out[23]:
2
In [24]:
vals6 = source6(1000)

In [25]:
sns.distplot(vals6);


# Student's t¶

In [26]:
def source6(shape=None):
return np.random.standard_t(10, shape)

In [27]:
source6()

Out[27]:
-0.7910509164826577
In [28]:
vals6 = source6(1000)

In [29]:
sns.distplot(vals6);