Importing Data

Ascii files

Tables of numbers

Simplest way to import data aranged in table from an ascii file is to use numpy:

import numpy as np
data = np.genfromtxt('data/data.csv')
print(data)

[[37.70587485 25.87930691 11.1807849   6.29982498]
 [ 8.56702615  6.42440682 26.43331203  4.75911353]
 [46.62942399 30.2224285  49.35137806 16.32100564]
 [38.99391383 31.54382599 14.88671628  9.92786169]
 [15.50027605 48.45142251 30.16164798 18.29314173]
 [ 6.6336436   4.23085519 33.69157162 39.33629762]
 [ 3.64350001  0.61175817 18.83570963 13.17930294]
 [ 2.48434325 33.77391739  1.89173126 27.08342268]
 [15.48219208 49.78432857 48.17905851 35.66727198]
 [22.16530832 32.91388506 38.37315601 32.00800632]
 [32.5199885  45.86655672 12.45190816 33.92922999]
 [49.21332445 43.04899737 41.55144366 42.84145576]
 [37.2348323  33.27424985  1.9561426  46.64086924]]

Exporting arrays of numbers is as simple

new_data = data*4.3
np.savetxt('data/new_data.csv', new_data)

Mixed table of numbers and text

The pandas package allows to deal with complex sets of data including text and numbers.

import pandas as pd
data = pd.read_csv('data/data_mixed.csv')
print(data)

  yellow   125   [0.18848372 0.54320351 0.1604927 ]
0   blue    24   [0.33882547 0.87313498 0.46219675]
1    red    93   [0.30873892 0.21764292 0.38400272]
2   grey    23   [0.5043959  0.548625   0.60459919]

Complex data

Data than are not organized in a table (like text, result measurements from an exotic machine) can be imported using the builtins python functions.

with open('data/data_complex.txt', 'r') as f:
    data = f.readlines()
for line in data:
    print(line)

From wikipedia: 

Python is an interpreted high-level programming language for

general-purpose programming. Created by Guido van Rossum and

first released in 1991, Python has a design philosophy that

emphasizes code readability, and a syntax that allows programmers

to express concepts in fewer lines of code,[25][26]

notably using significant whitespace. It provides constructs that

enable clear programming on both small and large scales.[27]

Images

The scipy package makes it particularly easy to import on work with images.

from imageio import imread
image = imread('data/image.bmp')

Images can then be displayed it with matplotlib.

import matplotlib.pyplot as plt
plt.figure()
plt.imshow(image)
plt.show()

Images are stored as arrays of numbers, so pixel values are easily accessible.

print("Pixel values:\n{}".format(image))
print("Value of the pixel at (10,10): {}".format(image[10, 10]))

Pixel values:
[[180 186 191 ... 224 225 232]
 [192 180 179 ... 221 226 226]
 [184 186 185 ... 230 225 225]
 ...
 [ 30  30  30 ... 153 155 156]
 [ 29  28  28 ... 150 138 147]
 [ 26  26  28 ... 136 137 140]]
Value of the pixel at (10,10): 189

Videos

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Excel files

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Matlab files

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Compressed files

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