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pandas.DataFrame.to_string

DataFrame.to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, line_width=None, max_rows=None, max_cols=None, show_dimensions=False)[source]

Render a DataFrame to a console-friendly tabular output.

Parameters:

buf : StringIO-like, optional

buffer to write to

columns : sequence, optional

the subset of columns to write; default None writes all columns

col_space : int, optional

the minimum width of each column

header : bool, optional

Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names

index : bool, optional

whether to print index (row) labels, default True

na_rep : string, optional

string representation of NAN to use, default ‘NaN’

formatters : list or dict of one-parameter functions, optional

formatter functions to apply to columns’ elements by position or name, default None. The result of each function must be a unicode string. List must be of length equal to the number of columns.

float_format : one-parameter function, optional

formatter function to apply to columns’ elements if they are floats, default None. The result of this function must be a unicode string.

sparsify : bool, optional

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row, default True

index_names : bool, optional

Prints the names of the indexes, default True

line_width : int, optional

Width to wrap a line in characters, default no wrap

table_id : str, optional

id for the <table> element create by to_html

New in version 0.23.0.

justify : str, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

  • left
  • right
  • center
  • justify
  • justify-all
  • start
  • end
  • inherit
  • match-parent
  • initial
  • unset
Returns:
formatted : string (or unicode, depending on data and options)
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