**Introduction** A random walk is a mathematical object, known as a stochastic or random process, that describes a path that consists of a succession of random steps on some mathematical space such as the integers. An elementary example of a random walk is the random walk on the integer number line, which starts at 0 and at each step moves +1 or -1 with equal probability. Other examples include the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging animal, the price of a fluctuating stock and the financial status of a gambler can all be approximated by random walk models, even though they may not be truly random in reality. As illustrated by those examples, random walks have applications to many scientific fields including ecology, psychology, computer science, physics, chemistry, biology as well as economics. Random walks explain the observed behaviors of many processes in these fields, and thus serve as a fundamental model for the recorded stochastic activity. As a more mathematical application, the value of pi can be approximated by the usage of random walk in agent-based modelling environment.

Enough with the boring theory. Let’s take a break while getting some knowledge for the code. So, to code out the random walk we will basically require some libraries in python some to do maths and some others to plot the curve.

**Libraries Required**

**matplotlib**Its a external library which help you to plot the curve. To install this library type the following code in you cmd.pip install matplotlib

It would be enough to get you through the installation.

**numpy**It’s also an external library in python it helps you to work with array and matrices. To install the library type the following code in cmd.pip install numpy

**random**It’s a built-in library of python we will use it to generate random points.

**One-dimensional random walk** An elementary example of a random walk is the random walk on the integer number line, which starts at 0 and at each step moves +1 or ?1 with equal probability.

So lets try to implement the 1-D random walk in python.

`# Python code for 1-D random walk.` `import` `random` `import` `numpy as np` `import` `matplotlib.pyplot as plt` ` ` `# Probability to move up or down` `prob ` `=` `[` `0.05` `, ` `0.95` `] ` ` ` `# statically defining the starting position` `start ` `=` `2` `positions ` `=` `[start]` ` ` `# creating the random points` `rr ` `=` `np.random.random(` `1000` `)` `downp ` `=` `rr < prob[` `0` `]` `upp ` `=` `rr > prob[` `1` `]` ` ` ` ` `for` `idownp, iupp ` `in` `zip` `(downp, upp):` ` ` `down ` `=` `idownp ` `and` `positions[` `-` `1` `] > ` `1` ` ` `up ` `=` `iupp ` `and` `positions[` `-` `1` `] < ` `4` ` ` `positions.append(positions[` `-` `1` `] ` `-` `down ` `+` `up)` ` ` `# plotting down the graph of the random walk in 1D` `plt.plot(positions)` `plt.show()` |

Output:

**Higher dimensions** In higher dimensions, the set of randomly walked points has interesting geometric properties. In fact, one gets a discrete fractal, that is, a set which exhibits stochastic self-similarity on large scales. On small scales, one can observe “jaggedness” resulting from the grid on which the walk is performed. Two books of Lawler referenced below are a good source on this topic. The trajectory of a random walk is the collection of points visited, considered as a set with disregard to when the walk arrived at the point. In one dimension, the trajectory is simply all points between the minimum height and the maximum height the walk achieved (both are, on average, on the order of ?n).

Let’s try to create random walk in 2D.

`# Python code for 2D random walk.` `import` `numpy` `import` `pylab` `import` `random` ` ` `# defining the number of steps` `n ` `=` `100000` ` ` `#creating two array for containing x and y coordinate` `#of size equals to the number of size and filled up with 0's` `x ` `=` `numpy.zeros(n)` `y ` `=` `numpy.zeros(n)` ` ` `# filling the coordinates with random variables` `for` `i ` `in` `range` `(` `1` `, n):` ` ` `val ` `=` `random.randint(` `1` `, ` `4` `)` ` ` `if` `val ` `=` `=` `1` `:` ` ` `x[i] ` `=` `x[i ` `-` `1` `] ` `+` `1` ` ` `y[i] ` `=` `y[i ` `-` `1` `]` ` ` `elif` `val ` `=` `=` `2` `:` ` ` `x[i] ` `=` `x[i ` `-` `1` `] ` `-` `1` ` ` `y[i] ` `=` `y[i ` `-` `1` `]` ` ` `elif` `val ` `=` `=` `3` `:` ` ` `x[i] ` `=` `x[i ` `-` `1` `]` ` ` `y[i] ` `=` `y[i ` `-` `1` `] ` `+` `1` ` ` `else` `:` ` ` `x[i] ` `=` `x[i ` `-` `1` `]` ` ` `y[i] ` `=` `y[i ` `-` `1` `] ` `-` `1` ` ` ` ` `# plotting stuff:` `pylab.title(` `"Random Walk ($n = "` `+` `str` `(n) ` `+` `"$ steps)"` `)` `pylab.plot(x, y)` `pylab.savefig(` `"rand_walk"` `+` `str` `(n)` `+` `".png"` `,bbox_inches` `=` `"tight"` `,dpi` `=` `600` `)` `pylab.show()` |

Output:

**Applications**

- In computer networks, random walks can model the number of transmission packets buffered at a server.
- In population genetics, random walk describes the statistical properties of genetic drift.
- In image segmentation, random walks are used to determine the labels (i.e., “object” or “background”) to associate with each pixel.
- In brain research, random walks and reinforced random walks are used to model cascades of neuron firing in the brain.
- Random walks have also been used to sample massive online graphs such as online social networks.

**References**

1. Wikipedia – Random Walk

2. Stackoverflow – Random Walk 1D

3. matplotlib documentation

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