from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

arr = np.random.normal(size=100)

# plot histogram
plt.subplot(221)
plt.hist(arr)

# obtain histogram data
plt.subplot(222)
hist, bin_edges = np.histogram(arr)
plt.plot(hist)

# fit histogram curve
plt.subplot(223)
sns.distplot(arr, kde=False, fit=stats.gamma, rug=True)
plt.show()

from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

arr = np.random.normal(size=100)

plt.subplot(121)
hist, bin_edges = np.histogram(arr)
cdf = np.cumsum(hist)
plt.plot(cdf)

plt.subplot(122)
cdf = stats.cumfreq(arr)
plt.plot(cdf[0])

plt.show()

from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

arr = np.random.normal(size=100)

hist, bin_edges = np.histogram(arr)
width = (bin_edges[1] - bin_edges[0]) * 0.8
plt.bar(bin_edges[1:], hist/max(hist), width=width, color='#5B9BD5')

cdf = np.cumsum(hist/sum(hist))
plt.plot(bin_edges[1:], cdf, '-*', color='#ED7D31')

plt.xlim([-2, 2])
plt.ylim([0, 1])
plt.grid()

plt.show()