[FIXED] prettytable is updating all rows in the table

Issue

I am facing a difficulty with a package for building beautiful tables. I would like gradJ to be updated in parts, just like the term i.

I would like to see this:

  +---+---------------------+
  | i |        GradJ        |
  +---+---------------------+
  | 0 | [1. 0. 0. 0. 0. 0.] |
  | 1 | [1. 2. 0. 0. 0. 0.] |
  | 2 | [1. 2. 3. 0. 0. 0.] |
  | 3 | [1. 2. 3. 4. 0. 0.] |
  | 4 | [1. 2. 3. 4. 5. 0.] |
  | 5 | [1. 2. 3. 4. 5. 6.] |
  +---+---------------------+

However, my code returns this:

  +---+---------------------+
  | i |        GradJ        |
  +---+---------------------+
  | 0 | [1. 2. 3. 4. 5. 6.] |
  | 1 | [1. 2. 3. 4. 5. 6.] |
  | 2 | [1. 2. 3. 4. 5. 6.] |
  | 3 | [1. 2. 3. 4. 5. 6.] |
  | 4 | [1. 2. 3. 4. 5. 6.] |
  | 5 | [1. 2. 3. 4. 5. 6.] |
  +---+---------------------+

I simplified the code that is causing the problem.

from prettytable import PrettyTable
import numpy

myTable = PrettyTable(["i", "GradJ"])
gradJ = numpy.zeros(6)
for i in range(6):
    gradJ[i] = i+1
    myTable.add_row([i, gradJ[:]])
print(myTable)

I have read the PrettyTable documentation, but I still can't figure out what I'm doing wrong.

Solution

Since a NumPy array is not a standard list, calling [:] will not properly create a copy.

Here is the correct usage using numpy.ndarray.copy:

from prettytable import PrettyTable
import numpy

myTable = PrettyTable(["i", "GradJ"])
gradJ = numpy.zeros(6)
for i in range(6):
    gradJ[i] = i+1
    myTable.add_row([i, gradJ.copy()]) # Use numpy.ndarray.copy
print(myTable)

---

Here is a modified version that removes the dependency on PrettyTable and demonstrates NumPy's copy() method.

import numpy

size, rows = 6, []

# Populate the table
row = numpy.zeros(size)
for i in range(size):
    row[i] = i + 1 # Fill in the corresponding column
    rows.append([i, row.copy()]) # Call .copy(), not [:]

# Display the table
for row in rows:
    print(row)

Output:

[0, array([1., 0., 0., 0., 0., 0.])]
[1, array([1., 2., 0., 0., 0., 0.])]
[2, array([1., 2., 3., 0., 0., 0.])]
[3, array([1., 2., 3., 4., 0., 0.])]
[4, array([1., 2., 3., 4., 5., 0.])]
[5, array([1., 2., 3., 4., 5., 6.])]

Answered By - Mr. Polywhirl