Loading data for plotting

• There are several common data structures we will keep coming across.

1. list eg: data = [1, 2, 3, 4, 5, 6]

2. numpy array eg: array = np.array(data)

3. pandas DataFrame eg: df = pd.DataFrame(data)

• Loading data from files

1. The basic Python way

evens = []
with open('file_path') as f:
    for line in f.readlines():
        evens.append(line.split()[1])

2. The Numpy way

import numpy as np
np.loadtxt('file_path', delimiter='\t', usecols=1, dtype=np.int32)

• The first parameter is the path of the data file. The delimiter parameter specifies the string used to separate values, which is a tab here. Because numpy.loadtxt() by default separate values separated by any whitespace into columns by default, this argument can be omitted here. We have set it for demonstration.
• For usecols and dtype that specify which columns to read and what data type each column corresponds to, you may pass a single value to each, or a sequence (such as list) for reading multiple columns.

3. The Pandas way(main way)

import pandas as pd
pd.read_csv('file_path', sep='\t', usecols=1)

Plotting

import matplotlib.pylot as plt
plt.plot(data)
plt.plot(data, data**2)
plt.plot(data, data**2, label='x^2')
plt.legend()       #To label the curve with a legend

• To label the curve with a legend by plt.legend()

Viewing and saving the figure

plt.savefig('output.png')        #save before show
plt.show()

• If you want to both view the image on screen and save it in file, remember to call plt.savefig() before plt.show() to make sure you don't save a blank canvas.