Goal¶

This post aims to introduce how to calculate the average, variance and standard deviation of matrix using pandas.

Libraries¶

In [2]:

import pandas as pd
import numpy as np

Create a matrix¶

In [13]:

n = 1000
df = pd.DataFrame({'rand': np.random.rand(n),
                   'randint': np.random.randint(low=0, high=100, size=n),
                   'randn': np.random.randn(n),
                   'random_sample': np.random.random_sample(size=n),
                   'binomial': np.random.binomial(n=1, p=.5, size=n),
                   'beta': np.random.beta(a=1, b=1, size=n),
                   })
df.head()

Out[13]:

	rand	randint	randn	random_sample	binomial	beta
0	0.689690	59	0.416245	0.607567	1	0.532052
1	0.288356	2	0.092351	0.311634	0	0.192651
2	0.173002	50	-0.626691	0.920702	0	0.342812
3	0.953088	17	-0.149677	0.316060	1	0.792191
4	0.693120	94	0.264678	0.060313	1	0.059370

Calculate average, variance, and standard deviation¶

Calculate by each function¶

In [16]:

df.mean()

Out[16]:

rand              0.497015
randint          49.224000
randn            -0.054651
random_sample     0.504412
binomial          0.490000
beta              0.508469
dtype: float64

In [15]:

df.var()

Out[15]:

rand               0.083301
randint          791.485309
randn              1.033378
random_sample      0.081552
binomial           0.250150
beta               0.083489
dtype: float64

In [17]:

df.std()

Out[17]:

rand              0.288619
randint          28.133349
randn             1.016552
random_sample     0.285573
binomial          0.500150
beta              0.288944
dtype: float64

Calculate using `describe`¶

In [18]:

df.describe()

Out[18]:

	rand	randint	randn	random_sample	binomial	beta
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.00000	1000.000000
mean	0.497015	49.224000	-0.054651	0.504412	0.49000	0.508469
std	0.288619	28.133349	1.016552	0.285573	0.50015	0.288944
min	0.000525	0.000000	-3.405606	0.001359	0.00000	0.000373
25%	0.241000	25.000000	-0.741640	0.264121	0.00000	0.256070
50%	0.497571	48.000000	-0.074852	0.505738	0.00000	0.523674
75%	0.742702	73.000000	0.602928	0.743445	1.00000	0.758901
max	0.999275	99.000000	3.861652	0.995010	1.00000	0.999007