import
numpy as np
import
matplotlib.pyplot as plt
import
seaborn as sns
import
scipy.stats as sc
import
statsmodels.graphics.gofplots as sm
sample_size
=
10000
standard_norm
=
np.random.normal(size
=
sample_size)
heavy_tailed_norm
=
np.random.normal(loc
=
0
, scale
=
2
, size
=
sample_size)
skewed_norm
=
sc.skewnorm.rvs(a
=
5
, size
=
sample_size)
skew_left_norm
=
sc.skewnorm.rvs(a
=
-
5
, size
=
sample_size)
fig, ax
=
plt.subplots(
1
,
2
, figsize
=
(
12
,
7
))
sns.histplot(standard_norm,kde
=
True
, color
=
'blue'
,ax
=
ax[
0
])
sm.ProbPlot(standard_norm).qqplot(line
=
's'
, ax
=
ax[
1
])
fig, ax
=
plt.subplots(
1
,
2
, figsize
=
(
12
,
7
))
sm.ProbPlot(skewed_norm).qqplot(line
=
's'
, ax
=
ax[
1
]);
sns.histplot(skewed_norm,kde
=
True
, color
=
'blue'
,ax
=
ax[
0
])
fig, ax
=
plt.subplots(
1
,
2
, figsize
=
(
12
,
7
))
sm.ProbPlot(skew_left_norm).qqplot(line
=
's'
,color
=
'red'
, ax
=
ax[
1
]);
sns.histplot(skew_left_norm,kde
=
True
, color
=
'red'
,ax
=
ax[
0
])
fig, ax
=
plt.subplots(
1
,
2
, figsize
=
(
12
,
7
))
sm.ProbPlot(heavy_tailed_norm).qqplot(line
=
's'
,color
=
'green'
, ax
=
ax[
1
]);
sns.histplot(heavy_tailed_norm,kde
=
True
, color
=
'green'
,ax
=
ax[
0
])
sns.histplot(standard_norm,kde
=
True
, color
=
'red'
,ax
=
ax[
0
])