How to Import and Manipulate Data from a Text File in Python?

In summary, you can take every p'th row in a text file and turn it into a \frac{m}{p}\times 2 matrix by using the methods described in the text.
  • #1
Avatrin
245
6
Hi

Lets say I have a txt file with m rows with n columns of numbers of the form:

1 .12222E+01 .27300E+01 -.41442E+01 -.49391E+00
1 .80375E+00 .15953E+00 .47715E+00 -.10432E+01
2 .11046E+01 .79376E-01 -.12639E+00 -.10389E+00
2 -.95291E-02 -.54210E+01 .36199E+00 -.13710E+01
2 .16524E+00 -.27779E+01 .74098E+00 -.34125E+00

Lets say I want to take every p'th row and take the second and third columns and turn it into a [itex]\frac{m}{p}\times 2[/itex] matrix. How would I go about doing that?
 
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  • #2
These things are very easy in Python. Below is the way I would do this, but there are many other ways. I'm assuming that you know m and p in advance. You must know p, but if you don't know m, you can read this file first and get m from len(lines). I'm also assuming that the text file is small enough that you can read the whole thing into memory, which is the easiest thing to do. If the file is too big for that, there are ways to read it line by line.
Code:
import numpy as np
m = <whatever>
p = <whatever>
data = np.zeros([m/p,2])
file = open(textfile, 'r')
lines = file.readlines()
file.close()
for i,line in enumerate(lines):
     if i%p == 0:
          items = line.split()
          data[i/p,0] = float(items[1])
          data[i/p,1] = float(items[2])
     else:
          continue
 
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Likes Avatrin
  • #3
My approach would be the following:
Code:
import numpy as np
# Import the data as a numpy array of arrays (a matrix). The elements of the array are the rows of the matrix.
raw_data = np.genfromtxt('data.txt', delimiter=' ', dtype=float)

# Select p'th rows
p_data = raw_data[::p]

# Select the 2nd and 3rd columns
data = p_data[:,[1,2]]

Of course don't forget to put a value for p. For p=2 you'd get that data is worth
Code:
array([[ 1.2222  ,  2.73    ], [ 1.1046  ,  0.079376], [ 0.16524 , -2.7779  ]])

I don't know whether this is what you were looking for, especially because m=5 and p=2 so m/p is not an integer while I obtain a 3x2 matrix. You could even easily condense the above code into a 3 liners, if you don't mind having no comment (bad practice of coding though) and lose some readability.
 
  • #4
  • #5
ChrisVer said:
Can't you use pandas (http://pandas.pydata.org/) to read tables from a file?

Well, Pandas is on my list of things I have to learn. However, I am not quite there yet. :)
 

Related to How to Import and Manipulate Data from a Text File in Python?

1. What is Numpy and why is it used for importing matrices?

Numpy is a popular Python library that is used for scientific computing. It provides efficient data structures and tools for working with multi-dimensional arrays, which are commonly used to represent matrices in scientific applications. Numpy is widely used for importing matrices because it offers high-performance operations on large datasets and has a wide range of functions for working with arrays and matrices.

2. How do I import a matrix into Numpy?

To import a matrix into Numpy, you can use the numpy.array() function. This function takes in a list, tuple, or other array-like object and converts it into a Numpy array. You can also specify the data type of the array using the dtype parameter. For example, numpy.array([[1, 2, 3], [4, 5, 6]], dtype=float) will create a 2x3 matrix with floating-point numbers.

3. Can I import a matrix from a file into Numpy?

Yes, Numpy provides functions for importing data from various file formats such as CSV, TXT, and HDF5. For example, you can use the numpy.loadtxt() function to read data from a text file and convert it into a Numpy array. You can also use numpy.genfromtxt() for more advanced options, such as handling missing values or skipping header rows.

4. How can I access and manipulate elements in a Numpy matrix?

You can access elements in a Numpy matrix using indexing and slicing. For example, my_array[0,0] will return the element in the first row and first column of the matrix. You can also use boolean indexing to select specific elements based on a condition. Numpy also provides various functions for performing operations on matrices, such as numpy.transpose() for transposing a matrix or numpy.reshape() for changing the shape of a matrix.

5. Are there any limitations when importing matrices into Numpy?

Numpy arrays are restricted to a single data type, so all elements in a matrix must have the same data type. Additionally, Numpy arrays are stored in contiguous memory, so the size of the matrix is limited by the available memory on your system. This means that very large matrices may not be able to be imported into Numpy. However, Numpy provides tools for working with out-of-memory arrays, such as numpy.memmap(), which allows you to work with arrays that are too large to fit in memory.

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