Python | Pandas DataFrame.to_string

21 Feb, 2019
Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas.
Pandas DataFrame.to_string() function render a DataFrame to a console-friendly tabular output.
Syntax: 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, max_rows=None, max_cols=None, show_dimensions=False, decimal=’.’, line_width=None)
Parameter :buf : Buffer to write to.columns : The subset of columns to write. Writes all columns by default.col_space : The minimum width of each column.header : Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.index : Whether to print index (row) labels.na_rep : String representation of NAN to use.formatters : Formatter functions to apply to columns’ elements by position or name.float_format : Formatter function to apply to columns’ elements if they are floats. The result of this function must be a unicode string.sparsify : Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.index_names : Prints the names of the indexes.max_rows : Maximum number of rows to display in the console.max_cols : Maximum number of columns to display in the console.show_dimensions : Display DataFrame dimensions (number of rows by number of columns).decimal : Character recognized as decimal separator, e.g. ‘, ’ in Europe.line_width : Width to wrap a line in characters.
Returns : str (or unicode, depending on data and options)
Example #1: Use DataFrame.to_string() function to render the given DataFrame to a console-friendly tabular output. Do not include the index labels in the output.
# importing pandas as pdimport pandas as pd  # Creating the DataFramedf = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],                   'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],                   'Age':[14, 25, 55, 8, 21]})  # Create the indexindex_ = pd.date_range('2010-10-09 08:45', periods = 5, freq ='H')  # Set the indexdf.index = index_  # Print the DataFrameprint(df)
Output :
Now we will use DataFrame.to_string() function to render the given DataFrame to a console-friendly tabular output.
# print in tabular formatresult = df.to_string(index = False)  # Print the resultprint(result)
Output :As we can see in the output, the DataFrame.to_string() function has successfully rendered the given dataframe to the console friendly tabular output. Example #2: Use DataFrame.to_string() function to render the given DataFrame to a console-friendly tabular output. Represent the missing value in the given Dataframe by the string ‘Missing’.
# importing pandas as pdimport pandas as pd  # Creating the DataFramedf = pd.DataFrame({"A":[12, 4, 5, None, 1],                    "B":[7, 2, 54, 3, None],                    "C":[20, 16, 11, 3, 8],                    "D":[14, 3, None, 2, 6]})   # Create the indexindex_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']  # Set the indexdf.index = index_  # Print the DataFrameprint(df)
Output :
Now we will use DataFrame.to_string() function to render the given DataFrame to a console-friendly tabular output.
# print in tabular formatresult = df.to_string(na_rep = 'Missing')  # Print the resultprint(result)
Output :As we can see in the output, the DataFrame.to_string() function has successfully rendered the given dataframe to the console-friendly tabular output.
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