Let's assume that we have time series stored in a plain text file named my_data.txt as follows:

0  0
1  1
2  4
4 16
5 25
6 36

A minimalistic pure Python approach to read and plot that data would go as follows:

import matplotlib.pyplot as plt

X, Y = [], []
for line in open('my_data.txt', 'r'):
  values = [float(s) for s in line.split()]
  X.append(values[0])
  Y.append(values[1])

plt.plot(X, Y)
plt.show()

This script, together with the data stored in my_data.txt, will produce the following graph:

The following are some explanations on how the preceding script works:

The following equivalent one-liner to read a text file may bring a smile to those more familiar with Python:

import matplotlib.pyplot as plt

with open('my_data.txt', 'r') as f:
  X, Y = zip(*[[float(s) for s in line.split()] for line in f])

plt.plot(X, Y)
plt.show()

In our data loading code, note that there is no serious checking or error handling going on. In any case, one might remember that a good programmer is a lazy programmer. Indeed, since NumPy is so often used with matplotlib, why not use it here? Run the following script to enable NumPy:

import numpy as np
import matplotlib.pyplot as plt

data = np.loadtxt('my_data.txt')

plt.plot(data[:,0], data[:,1])
plt.show()

This is as short as the one-liner shown in the preceding section, yet easier to read, and it will handle many error cases that our pure Python code does not handle. The following point describes the preceding script:

Along with making the code shorter and simpler, using NumPy brings additional advantages. For large files, using NumPy will be noticeably faster (the NumPy module is mostly written in C), and storing the whole dataset as a NumPy array can save memory as well. Finally, using NumPy allows you to support other common file formats (CVS and Matlab) for numerical data without much effort.

As a way to demonstrate all that we have seen so far, let's consider the following task. A file contains N columns of values, describing N–1 curves. The first column contains the x coordinates, the second column contains the y coordinates of the first curve, the third column contains the y coordinates of the second curve, and so on. We want to display those N–1 curves. We will do so by using the following code:

import numpy as np
import matplotlib.pyplot as plt

data = np.loadtxt('my_data.txt')
for column in data.T:
  plt.plot(data[:,0], column)

plt.show()

The file my_data.txt should contain the following content:

0 0 6
1 1 5
2 4 4
4 16 3
5 25 2
6 36 1

Then we get the following graph:

We did the job with little effort by exploiting two tricks. In NumPy notation, data.T is a transposed view of the 2D array data—rows are seen as columns and columns are seen as rows. Also, we can iterate over the rows of a multidimensional array by doing for row in data. Thus, doing for column in data.T will iterate over the columns of an array. With a few lines of code, we have a fairly general plotting generic script.