从 Python 中的文本文件索引矩阵并将其转换为嵌套字典

Question

I am trying to read this matrix from a.txt file:

-1 2 3 -1 -1 -1 -1 -1
2 -1 -1 4 -1 -1 -1 -1
3 -1 -1 -1 4 5 -1 -1
-1 4 -1 -1 -1 -1 3 -1
-1 -1 4 -1 -1 -1 -1 5
-1 -1 5 -1 -1 -1 2 3
-1 -1 -1 3 -1 2 -1 -1
-1 -1 -1 -1 5 3 -1 -1

and I want it to transform it into:

    A   B   C   D   E   F   G   H
A   -1  2   3   -1  -1  -1  -1  -1
B   2   -1  -1  4   -1  -1  -1  -1
C   3   -1  -1  -1  4   5   -1  -1
D   -1  4   -1  -1  -1  -1  3   -1
E   -1  -1  4   -1  -1  -1  -1  5
F   -1  -1  5   -1  -1  -1  2   3
G   -1  -1  -1  3   -1  2   -1  -1
H   -1  -1  -1  -1  5   3   -1  -1

I need to read the matrix as a dictionary so I can use it with my dijkstra algorithm to find a path. I also want to convert those "-1" to "9999" (a very high value) to make the algorithm work properly.
I'm using this to read the file and convert it into a dictionary. But it doesn't add an index such as 'A' 'B' 'C'... 'H' for each row and column as shown before. How can I do it? I want to know if there is a way to do it in vanilla python and if there is another way to do it maybe using a module.

with open('matrix.txt', 'r') as f:
    columns = next(f).split()
    matrix = collections.defaultdict(dict)
    for line in f:
        items = line.split()
        row, vals = items[0], items[1:]
        for col, val in zip(columns, vals):
            matrix[col][row] = int(val)
print(matrix)

It is important to mention that I cannot assign the letters by hand. Instead this program should be able to read any matrix (ie a 4x4 matrix or a 12x12 one) and assign an index to each row and column. (ie a 4x4 matrix should have only four letters on each column and row (AB C D), because it's a 4x4 matrix, and so on...).
I'm new to python so I was reading some information about "Pandas" but I don't know if I can do it "more generic" so it can work with even a 25x25 matrix and assign a letter for each column and row.

For example. When I use the code above with the SECOND MATRIX it prints:

defaultdict(<class 'dict'>, {'A': {'A': -1, 'B': 2, 'C': 3, 'D': -1, 'E': -1, 'F': -1, 'G': -1, 'H': -1}, 'B': {'A': 2, 'B': -1, 'C': -1, 'D': 4, 'E': -1, 'F': -1, 'G': -1, 'H': -1}, 'C': {'A': 3, 'B': -1, 'C': -1, 'D': -1, 'E': 4, 'F': 5, 'G': -1, 'H': -1}, 'D': {'A': -1, 'B': 4, 'C': -1, 'D': -1, 'E': -1, 'F': -1, 'G': 3, 'H': -1}, 'E': {'A': -1, 'B': -1, 'C': 4, 'D': -1, 'E': -1, 'F': -1, 'G': -1, 'H': 5}, 'F': {'A': -1, 'B': -1, 'C': 5, 'D': -1, 'E': -1, 'F': -1, 'G': 2, 'H': 3}, 'G': {'A': -1, 'B': -1, 'C': -1, 'D': 3, 'E': -1, 'F': 2, 'G': -1, 'H': -1}, 'H': {'A': -1, 'B': -1, 'C': -1, 'D': -1, 'E': 5, 'F': 3, 'G': -1, 'H': -1}})

But If I use it with the FIRST MATRIX it prints:

defaultdict(<class 'dict'>, {'-1': {'2': -1, '3': -1, '-1': -1}, '2': {'2': -1, '3': -1, '-1': -1}, '3': {'2': 4, '3': -1, '-1': -1}})
So how can I assign them automatically an index in each row and column?
Here is the full code and both.txt files if you are not sure what I'm trying to do:
dijkstra.py
matrixA
matrixB

Answer 1

You don't: those labels are not part of the matrix. Instead, simply use the upper-case alphabet string whenever needed. For instance:

letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# Read in your NxN matrix

print("  " + "  ".join(let[:N]))

for row in range(N):
    print(letters[row] + "  ", end = "")
    # Now print your row of values, properly formatted.

Answer 2

So, I think this could work:

import string

with open('matrix-a.txt', 'r') as f:
    lines = f.readlines()

letters = string.ascii_uppercase

lines = [line.split() for line in lines]
matrix = {}
for row in range(len(lines)):
    inner = {}
    for letter, line in zip(letters, lines[row]):
        line = int(line)
        if line == -1:
            line = 9999
        inner[letter] = line
    matrix[letters[row]] = inner

If I print out matrix , I get:

{'A': {'A': 9999, 'B': 2, 'C': 3, 'D': 9999, 'E': 9999, 'F': 9999, 'G': 9999, 'H': 9999}, 'B': {'A': 2, 'B': 9999, 'C': 9999, 'D': 4, 'E': 9999, 'F': 9999, 'G': 9999, 'H': 9999}, 'C': {'A': 3, 'B': 9999, 'C': 9999, 'D': 9999, 'E': 4, 'F': 5, 'G': 9999, 'H': 9999}, 'D': {'A': 9999, 'B': 4, 'C': 9999, 'D': 9999, 'E': 9999, 'F': 9999, 'G': 3, 'H': 9999}, 'E': {'A': 9999, 'B': 9999, 'C': 4, 'D': 9999, 'E': 9999, 'F': 9999, 'G': 9999, 'H': 5}, 'F': {'A': 9999, 'B': 9999, 'C': 5, 'D': 9999, 'E': 9999, 'F': 9999, 'G': 2, 'H': 3}, 'G': {'A': 9999, 'B': 9999, 'C': 9999, 'D': 3, 'E': 9999, 'F': 2, 'G': 9999, 'H': 9999}, 'H': {'A': 9999, 'B': 9999, 'C': 9999, 'D': 9999, 'E': 5, 'F': 3, 'G': 9999, 'H': 9999}}

Which seems to be the dictionary behavior you are looking for, correct?

If you want to play code golf:

matrix = {letters[row]: {letter: int(line) for letter, line in zip(letters, lines[row])} for row in range(len(lines))}

Answer 3

Use pandas and string

• List Comprehensions for renaming the columns

import pandas as pd
import string

# read file
df = pd.read_csv('matrix.txt', sep='\\s+', header=None)

# convert -1 to 9999 if you want. I didn't do it for the output shown
df.replace(-1, 9999, inplace=True)

# letters
letters = string.ascii_uppercase

# create column names and index
df.columns = [letter for letter in letters[:len(df.columns)]]
df.index = [letter for letter in letters[:len(df.columns)]]

# convert to dict
my_dict = df.to_dict()

print(my_dict)

{'A': {'A': -1, 'B': 2, 'C': 3, 'D': -1, 'E': -1, 'F': -1, 'G': -1, 'H': -1},
 'B': {'A': 2, 'B': -1, 'C': -1, 'D': 4, 'E': -1, 'F': -1, 'G': -1, 'H': -1},
 'C': {'A': 3, 'B': -1, 'C': -1, 'D': -1, 'E': 4, 'F': 5, 'G': -1, 'H': -1},
 'D': {'A': -1, 'B': 4, 'C': -1, 'D': -1, 'E': -1, 'F': -1, 'G': 3, 'H': -1},
 'E': {'A': -1, 'B': -1, 'C': 4, 'D': -1, 'E': -1, 'F': -1, 'G': -1, 'H': 5},
 'F': {'A': -1, 'B': -1, 'C': 5, 'D': -1, 'E': -1, 'F': -1, 'G': 2, 'H': 3},
 'G': {'A': -1, 'B': -1, 'C': -1, 'D': 3, 'E': -1, 'F': 2, 'G': -1, 'H': -1},
 'H': {'A': -1, 'B': -1, 'C': -1, 'D': -1, 'E': 5, 'F': 3, 'G': -1, 'H': -1}}

Answer 4

Solution

Try this. You can get a nested dictionary (json-like) from the dataframe ( df ) using df.to_dict() .

import pandas as pd
import string
filename = 'matrix.txt'
df = (pd
      .read_csv(filename, sep='\s+', header=None)
      .replace(-1, 9999, inplace=False)
)
headers = string.ascii_uppercase
df.columns = list(headers[:len(df.columns)])
df.index = list(headers[:len(df.index)])
print(df)
nested_dict = df.to_dict()

Example

import pandas as pd
import string
from io import StringIO

s = """
-1 2 3 -1 -1 -1 -1 -1
2 -1 -1 4 -1 -1 -1 -1
3 -1 -1 -1 4 5 -1 -1
-1 4 -1 -1 -1 -1 3 -1
-1 -1 4 -1 -1 -1 -1 5
-1 -1 5 -1 -1 -1 2 3
-1 -1 -1 3 -1 2 -1 -1
-1 -1 -1 -1 5 3 -1 -1
"""
df = (pd
      .read_csv(StringIO(s), sep='\s+', header=None)
      .replace(-1, 9999, inplace=False)
)
headers = string.ascii_uppercase
df.columns = list(headers[:len(df.columns)])
df.index = list(headers[:len(df.index)])
print(df)

Output :

      A     B     C     D     E     F     G     H
A  9999     2     3  9999  9999  9999  9999  9999
B     2  9999  9999     4  9999  9999  9999  9999
C     3  9999  9999  9999     4     5  9999  9999
D  9999     4  9999  9999  9999  9999     3  9999
E  9999  9999     4  9999  9999  9999  9999     5
F  9999  9999     5  9999  9999  9999     2     3
G  9999  9999  9999     3  9999     2  9999  9999
H  9999  9999  9999  9999     5     3  9999  9999