Remove the automatic two spaces between columns that Pandas DataFrame.to_string inserts

Question:

I’m looking for a solution to remove/turn off the 2 spaces between columns that df.to_string creates automatically.

Example:

from pandas import DataFrame

df = DataFrame()
df = df.append({'a':'12345', 'b': '12345'})
df.to_string(index=False, header=False)
'12345  1235'

For clarity, the result is: ‘12345..12345’ where the dots represent actual spaces.

I already tried the pandas.set_option and pandas.to_string documentation.

EDIT: The above example is overly simplified. I am working with an existing df that has spaces all over the place and the output text files are consumed by another blackbox program that is based off char-widths for each line. I’ve already figured out how to reformat the columns with formatters and make sure my columns are not cutoff by pandas default so I am 90% there (minus these auto spaces).
FYI here are some good links on to_string() formatting and data-truncation:

Appreciate the help!

Asked By: PydPiper

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Source

Answers:

You can use the pd.Series.str.cat method, which accepts a sep keyword argument. By default sep is set to '' so there is no separation between values. Here are the docs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.cat.html

You can also use pd.Series.str.strip to remove any leading or trailing whitespace from each value. Here are the docs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.strip.html

Here’s an example based on what you have:

df = pd.DataFrame({'a': ['12345'], 'b': ['12345']})
df.iloc[0].fillna('').str.strip().str.cat(sep=' ')

Note that fillna('') is required if there are any empty values.

Answered By: Henry Woody

I also had the same problem. There is a justify option in to_string() which is supposed to help in this case. But I ended up doing it the old way:

[row['a']+ row['b'] for index, row in df.iterrows()]
Answered By: user11766756

Even if this post is old, just in case that someone else comes nowadays like me:

df.to_string(header=False, index=False).strip().replace(' ', ''))

Answered By: Lucia