How to use print() along with other operations in list comprehension? [duplicate]

Question

I have a directory of CSV data files and I load all of them in one line using pandas.read_csv() within a list comprehension statement.

import glob
import pandas as pd
file_list = glob.glob('../data/')
df_list = [pd.read_csv(f) for f in file_list]
df = pd.concat(df_list, ignore_index=True)

Now I want to print the file path every time when it loads a data file, but I cannot find a way to use multiple statements in list comprehension. For example, something like [pd.read_csv(f); print(f) for f in file_list] will cause a SyntaxError.

The closest thing I can get is to let print() to return None in an if-statement, which works like a pass after printing.

df_list = [pd.read_csv(f) for f in file_list if print(f) is None]

Is there a proper way of doing this? I like list comprehension for its conciseness, but it does not seem to allow multiple statements.

Answer 1

If you want a list comprehension (understandable given the speed improvement over a for loop), you could slightly modify your solution because None is falsy:

df_list = [pd.read_csv(f) for f in file_list if not print(f)]

Alternatively make a function that does the work:

def read_and_print(f):
    print(f)
    return pd.read_csv(f)

df_list = [read_and_print(f) for f in file_list]

However, the approaches violate the Command–query separation principle that Python generally follows because the function has both a side-effect and a return value of interest. Nonetheless, I think these are quite pragmatic, particularly if you want to print() now to view the data, but later you plan to remove the print() calls.

Answer 2

List comprehension was not designed for that. Rather, just for populating a list looping over some iterable, and (optionally) if a condition is met. Python likes to emphasise readability over lines of code.

The proper way to do what you want is to not use list comprehension at all, rather a for loop:

for f in file_list:
    print(f)
    df_list.append(pd.read_csv(f))