How can I left justify text in a pandas DataFrame column in an IPython notebook

Question

I am trying to format the output in an IPython notebook. I tried using the to_string function, and this neatly lets me eliminate the index column. But the textual data is ri

Answer 1

I converted @unutbu's approach to a function so I could left-justify my dataframes.

my_df = pd.DataFrame({'StringVals': ["Text string One", "Text string Two", "Text string Three"]})

def left_justified(df):
    formatters = {}
    for li in list(df.columns):
        max = df[li].str.len().max()
        form = "{{:<{}s}}".format(max)
        formatters[li] = functools.partial(str.format, form)
    return df.to_string(formatters=formatters, index=False)

So now this:

print(my_df.to_string())

          StringVals
0    Text string One
1    Text string Two
2  Text string Three

becomes this:

print(left_justified(my_df))

StringVals
Text string One  
Text string Two  
Text string Three

Note, however, any non-string values in your dataframe will give you errors:

AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas

You'll have to pass different format strings to .to_string() if you want it to work with non-string values:

my_df2 = pd.DataFrame({'Booleans'  : [False, True, True],
                       'Floats'    : [1.0, 0.4, 1.5],           
                       'StringVals': ["Text string One", "Text string Two", "Text string Three"]})

FLOAT_COLUMNS = ('Floats',)
BOOLEAN_COLUMNS = ('Booleans',)

def left_justified2(df):
    formatters = {}

    # Pass a custom pattern to format(), based on
    # type of data
    for li in list(df.columns):
        if li in FLOAT_COLUMNS:
           form = "{{!s:<5}}".format()
        elif li in BOOLEAN_COLUMNS:
            form = "{{!s:<8}}".format()
        else:
            max = df[li].str.len().max()
            form = "{{:<{}s}}".format(max)
        formatters[li] = functools.partial(str.format, form)
    return df.to_string(formatters=formatters, index=False)

With floats and booleans:

print(left_justified2(my_df2))

Booleans Floats         StringVals
False     1.0    Text string One  
True      0.4    Text string Two  
True      1.5    Text string Three

Note this approach is a bit of a hack. Not only do you have to maintain column names in a separate lists, but you also have to best-guess at the data widths. Perhaps someone with better Pandas-Fu can demonstrate how to automate parsing the dataframe info to generate the formats automatically.