As a supplement to the other answers, even a SELECT statement that returns no rows will expose the column names to you and to application code.

select *
from table_name
where false;

I intend this to be executable by almost any client software for almost any DBMS. (Almost? Yes, some don't support a clause like where false. Instead, they require an expression like where 1 = 0.)

Permissions might come into play with any of these approaches.

Open psql command line and type :

\d+ table_name

As per the Documentation

SELECT
    table_schema || '.' || table_name as show_tables
FROM
    information_schema.tables
WHERE
    table_type = 'BASE TABLE'
AND
    table_schema NOT IN ('pg_catalog', 'information_schema');

for more convenience make it as a function

create or replace function show_tables() returns SETOF text as $$
SELECT
    table_schema || '.' || table_name as show_tables
FROM
    information_schema.tables
WHERE
    table_type = 'BASE TABLE'
AND
    table_schema NOT IN ('pg_catalog', 'information_schema');
$$
language sql; 

So we can get the tables using

select show_tables()

For the table description

 select column_name, data_type, character_maximum_length
 from INFORMATION_SCHEMA.COLUMNS where table_name ='table_name';

as a Function

create or replace function describe_table(tbl_name text) returns table(column_name   
varchar, data_type varchar,character_maximum_length int) as $$
select column_name, data_type, character_maximum_length
from INFORMATION_SCHEMA.COLUMNS where table_name = $1;
$$
language 'sql';

select  *  from describe_table('a_table_name');

Since you're working in Python, clearest if you handle this in two steps I think. First, use this query to retrieve table/column name pairs:

select table_name, column_name 
from information_schema.columns 
where table_name in (
    select tablename from pg_tables where schemaname = 'public');

Then, stick the results into a defaultdict:

from collections import defaultdict

my_cols = <your code to execute the query above and fetch all rows>
column_mapping = defaultdict(list)
for tablename, colname in my_cols:
    column_mapping[tablename].append(colname)

This will give you:

>>> column_mapping
defaultdict(<type 'list'>, {'table_1': ['time', 'col'], 'table_2': ['time', 'col'], 'table_3': ['time', 'col]})

Which you can convert trivially with:

>>> column_mapping.items()
[('table_1', ['time', 'col']), ('table_2', ['time', 'col']), ('table_3', ['time', 'col])]