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

You could use a['Text'].str.len().max() to compute the length of the longest string in a['Text'], and use that number, N, in a left-justified formatter '{:<Ns}'.format:

In [211]: print(a.to_string(formatters={'Text':'{{:<{}s}}'.format(a['Text'].str.len().max()).format}, index=False))
   Text  Value
 abcdef  12.34
 x        4.20

I like @unutbu's answer (not requiring any additional dependencies). @JS.'s additions are a step in the direction (towards something re-usable).

Since the construction of the formatter dict is the difficult part, let's create a function which creates the formatter dict from a DataFrame and an optional list of columns to format.

def make_lalign_formatter(df, cols=None):
    """
    Construct formatter dict to left-align columns.

    Parameters
    ----------
    df : pandas.core.frame.DataFrame
        The DataFrame to format
    cols : None or iterable of strings, optional
        The columns of df to left-align. The default, cols=None, will
        left-align all the columns of dtype object

    Returns
    -------
    dict
        Formatter dictionary

    """
    if cols is None:
       cols = df.columns[df.dtypes == 'object'] 

    return {col: f'{{:<{df[col].str.len().max()}s}}'.format for col in cols}

Let's create some example data to demonstrate using this function:

import pandas as pd

# Make some data
data = {'First': ['Tom', 'Dick', 'Harry'],
        'Last': ['Thumb', 'Whittington', 'Potter'],
        'Age': [183, 667, 23]}

# Make into a DataFrame
df = pd.DataFrame(data)

To align all the columns of type object in our DataFrame:

# Left align all columns
print(df.to_string(formatters=make_lalign_formatter(df), 
                   index=False,
                   justify='left'))

To align only the 'First' column:

# Left align 'First' column
print(df.to_string(formatters=make_lalign_formatter(df, cols=['First']), 
                   index=False,
                   justify='left'))

This works on Python 3.7 (functools is a part of that release now)

# pylint: disable=C0103,C0200,R0205
from __future__ import print_function
import pandas as pd
import functools

@staticmethod
def displayDataFrame(dataframe, displayNumRows=True, displayIndex=True, leftJustify=True):
    # type: (pd.DataFrame, bool, bool, bool) -> None
    """
    :param dataframe: pandas DataFrame
    :param displayNumRows: If True, show the number or rows in the output.
    :param displayIndex: If True, then show the indexes
    :param leftJustify: If True, then use technique to format columns left justified.
    :return: None
    """

    if leftJustify:
        formatters = {}

        for columnName in list(dataframe.columns):
            columnType = type(columnName)  # The magic!!
            # print("{} =>  {}".format(columnName, columnType))
            if columnType == type(bool):
                form = "{{!s:<8}}".format()
            elif columnType == type(float):
                form = "{{!s:<5}}".format()
            else:
                max = dataframe[columnName].str.len().max()
                form = "{{:<{}s}}".format(max)

            formatters[columnName] = functools.partial(str.format, form)

        print(dataframe.to_string(index=displayIndex, formatters=formatters), end="\n\n")
    else:
        print(dataframe.to_string(index=displayIndex), end="\n\n")

    if displayNumRows:
        print("Num Rows: {}".format(len(dataframe)), end="\n\n")

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.