Built-in, indexless, pretty-columns output with Pandas

Question

I frequently find myself writing code like this to get nicely formatted multi-column output (without the index) when debugging or studying my data in pandas:

   dfs = dfs[dfs['some_id'] == the_id]
    cols = [
      'some_col',
      'another_col',
      'yet_another',
    ]

    print("\t".join(cols))
    for row in dfs[cols].values:
      print("\t\t".join([str(val) for val in row]))

This works fine, but I was wondering if there's a built in way to get this sort of output with a pandas function or direct lookup syntax. Sample output:

some_col    another_col    yet_another
a           b              c
x           y              z

cs95 · Accepted Answer · 2017-09-03 00:08:36Z

2

Yes, you can call df.to_string with the parameter index=False.

dfs = dfs[dfs['some_id'] == the_id]
    cols = [
      'some_col',
      'another_col',
      'yet_another',
    ]

print(dfs[cols].to_string(index=False))

MCVE:

print(df)

          0         1
0  0.335232 -1.256177
1 -1.367855  0.746646
2  0.027753 -1.176076
3  0.230930 -0.679613
4  1.261967  0.570967

print(df.to_string(index=False, col_space=10))

0          1
 0.335232  -1.256177
-1.367855   0.746646
 0.027753  -1.176076
 0.230930  -0.679613
 1.261967   0.570967

edited Sep 3, 2017 at 0:08

answered Sep 3, 2017 at 0:05

cs95

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2 Comments

J B Over a year ago

I usually use that to see all or a single column. How can I use that to select a subset of the total columns?

cs95 Over a year ago

@JB Added the edit for your particular code at the top.

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