DOC: Fix image rendering on README file and update contents.

1. The distribution plots were not rendered correctly in table format on
   github. Fixed that by adding extra vertical bars enclosing the image.
2. Used `_bit_generator` instead of `bit_generator` for RandomState in
   example code.
3. Remove the redundant python version entry in requirements list since
   NumPy v1.19 supports Python>=3.6, so there requirement is met
   implicitely by successfully installing 1.19.

Author: zoj613

README.md

@@ -58,8 +58,8 @@ from polyagamma import random_polyagamma
 # generate a PG(1, 0) sample
 o = random_polyagamma()
 
-# Get a 5 by 10 array of PG(1, 2) variates.
-o = random_polyagamma(z=2, size=(5, 10))
+# Get a 5 by 1 array of PG(1, 2) variates.
+o = random_polyagamma(z=2, size=5)
 
 # We can pass sequences as input. Numpy's broadcasting rules apply here.
 # Get a 10 by 2 array where column 1 is PG(2, -10) and column 2 is PG(1, 10)
@@ -90,7 +90,7 @@ o = random_polyagamma(random_state=rng)
 
 # If one is using a `numpy.random.RandomState` instance instead of the `Generator`
 # class, the object's underlying bitgenerator can be passed as the value of random_state
-bit_gen = np.random.RandomState(12345).bit_generator
+bit_gen = np.random.RandomState(12345)._bit_generator
 o = random_polyagamma(random_state=bit_gen)
 
 # When passing a large input array for the shape parameter `h`, parameter value
@@ -148,7 +148,6 @@ For an example of how to use `polyagamma` in a C program, see [here][1].
 
 
 ## Dependencies
-- Python >= 3.6
 - Numpy >= 1.19.0
 
 
@@ -182,10 +181,10 @@ and `z`, using each method. We restrict `h` to integer values to accomodate the
 `devroye` method, which cannot be used for non-integer `h`. The version of the
 package used to generate them is `v1.3.1`.
 
-![](./scripts/img/perf_methods_0.0.svg) | ![](./scripts/img/perf_methods_2.5.svg)
+|![](./scripts/img/perf_methods_0.0.svg) | ![](./scripts/img/perf_methods_2.5.svg)|
 | --- | --- |
 
-![](./scripts/img/perf_methods_5.0.svg) | ![](./scripts/img/perf_methods_10.0.svg)
+|![](./scripts/img/perf_methods_5.0.svg) | ![](./scripts/img/perf_methods_10.0.svg)|
 | --- | --- |
 
 Generally:
@@ -205,10 +204,10 @@ sampler to the `pgdrawv` functions provided by the package. Below are runtime pl
 samples for each value of `h` and `z`. Values of `h` range from 0.1 to 50, while `z` is set
 to 0, 2.5, 5, and 10.
 
-![](./scripts/img/perf_samplers_0.0.svg) | ![](./scripts/img/perf_samplers_2.5.svg)
+|![](./scripts/img/perf_samplers_0.0.svg) | ![](./scripts/img/perf_samplers_2.5.svg)|
 | --- | --- |
 
-![](./scripts/img/perf_samplers_5.0.svg) | ![](./scripts/img/perf_samplers_10.0.svg)
+|![](./scripts/img/perf_samplers_5.0.svg) | ![](./scripts/img/perf_samplers_10.0.svg)|
 | --- | --- |
 
 It can be seen that when generating many samples at once for any given combination of 
@@ -239,12 +238,12 @@ is ran on.
 ## Distribution Plots
 Below is a visualization of the Cumulative distribution and density functions for
 various values of the parameters.
-![](./scripts/img/pdf.svg) | ![](./scripts/img/cdf.svg)
+|![](./scripts/img/pdf.svg) | ![](./scripts/img/cdf.svg)|
 | --- | --- |
 
 We can compare these plots to the Kernel density estimate and empirical CDF plots
 generated from 20000 random samples using each of the available methods.
-![](./scripts/img/kde.svg) | ![](./scripts/img/ecdf.svg)
+|![](./scripts/img/kde.svg) | ![](./scripts/img/ecdf.svg)|
 | --- | --- |