Add headers to colums and rows in Python - 2D array (list of lists)

Question

I have a 2D Python array (list of lists). I treat this as a 'table'. Now I want to replace the first row and the top row with a header row and column. The row and the header are exactly the same.

I try this using list comprehension. It works out for the 'row' part, but the column part is not very Pythonic yet.

# The header that i want to add headers = ['foo', 'bar', 'baz', 'other'] l = len(headers) + 1 # The final matrix is one element bigger # square matrix of random size, filled with data array = [['xxx' for i in range(l)] for j in range(l)] # The concise one - add headers to top row array[0] = ['Title'] + [category for category in headers] # The ugly one - add headers for the rows for i in range(l): array[i][0] = array[0][i] 

The final output should look like this (which it does):

[['Title', 'foo', 'bar', 'baz', 'other'], ['foo', 'xxx', 'xxx', 'xxx', 'xxx'], ['bar', 'xxx', 'xxx', 'xxx', 'xxx'], ['baz', 'xxx', 'xxx', 'xxx', 'xxx'], ['other', 'xxx', 'xxx', 'xxx', 'xxx']] 

I'm just not so happy with the 'for' loop. How can this be done more Pythonic?

Answer 1

Maybe this can help (if you don't want to use numpy):

headers = ['foo', 'bar', 'baz', 'other'] l = len(headers) arr = [["xxx" for i in range(l)] for j in range(l)] # adding top row arr = [headers] + arr # adding first column headers_mod = ['Title'] + headers new_arr = [[headers_mod[i]]+arr[i] for i in range(l+1)] for i in new_arr: print(*i) 

gives you the output as:

Title foo bar baz other foo xxx xxx xxx xxx bar xxx xxx xxx xxx baz xxx xxx xxx xxx other xxx xxx xxx xxx 

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Otherwise, when dealing with array manipulations in python try going with numpy, pandas, as they provide better operations like by giving option for axis, transpose, etc.

Answer 2

numpy is excellent for tables, but for a labeled table like this pandas might be better for your needs.

Solution using numpy:

import numpy as np # The header that i want to add headers = ['foo', 'bar', 'baz', 'other'] ll = len(headers)+1 data = [['xxx' for _ in range(ll)] for j in range(ll)] data = np.array(data, dtype=object) data[0,0] = 'Title' data[0,1:] = headers data[1:,0] = headers print(data) 

prints

[['Title' 'foo' 'bar' 'baz' 'other'] ['foo' 'xxx' 'xxx' 'xxx' 'xxx'] ['bar' 'xxx' 'xxx' 'xxx' 'xxx'] ['baz' 'xxx' 'xxx' 'xxx' 'xxx'] ['other' 'xxx' 'xxx' 'xxx' 'xxx']] 

Setting dtype to object allows your array to mix strings and other data types you might want to use. If your data is just strings then you can use 'UN' as the dtype, where N is the longest string you plan to use. (Numpy, when making an all string array automatically picks your longest string as the maximum length for the strings, which is fine unless your strings are all shorter than the headers you plan to add.)

Alternate version of the above code:

import numpy as np # The header that i want to add headers = ['foo', 'bar', 'baz', 'other'] # Add Title to headers to simply later assignment headers = ['Title'] + headers ll = len(headers) data = [['xxx' for _ in range(ll)] for j in range(ll)] data = np.array(data) data[0,:] = headers data[:,0] = headers print(data) 

pandas, on the other hand, is explicitly designed to handle headers

import numpy as np, pandas as pd # The header that i want to add headers = ['foo', 'bar', 'baz', 'other'] ll = len(headers) + 1 data = [['xxx' for _ in range(ll)] for j in range(ll)] data = np.array(data) data = pd.DataFrame(data[1:,1:], columns=headers, index=headers) data.columns.name = 'Title' data.loc['foo','bar'] = 'yes' print(data) print('') print(data['bar']) print('') print(data.loc['foo',:]) 

prints

Title foo bar baz other foo xxx yes xxx xxx bar xxx xxx xxx xxx baz xxx xxx xxx xxx other xxx xxx xxx xxx foo yes bar xxx baz xxx other xxx Name: bar, dtype: object Title foo xxx bar yes baz xxx other xxx Name: foo, dtype: object