Pandas DataFrame.to_string-Python

Pandas is a powerful Python library for data manipulation, with DataFrame as its key two-dimensional, labeled data structure. It allows easy formatting and readable display of data. DataFrame.to_string() function in Pandas is specifically designed to render a DataFrame into a console-friendly tabular format as a string output. Example:

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'Weight': [45, 88, 56, 15, 71],'Name': ['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],'Age': [14, 25, 55, 8, 21]})
                   
print(df.to_string())

   Weight    Name  Age
0      45     Sam   14
1      88  Andrea   25
2      56    Alex   55
3      15   Robin    8
4      71     Kia   21

Explanation: This code creates a DataFrame from a dictionary with three columns (Weight, Name, Age), structures it into a tabular format using pd.DataFrame() and converts it into a fully visible string representation with df.to_string().

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)

Parameters:

• buf: Buffer to write the output string to (e.g., a file). Defaults to None, which means the output is returned as a string.
• columns: Specifies a subset of columns to include in the output. If None, all columns are printed.
• col_space: Defines the minimum width of each column.
• header: Whether to print column names. Can also accept a list of column name aliases.
• index: Whether to include index labels. Default is True.
• na_rep: String representation for missing values (NaN). Default is ‘NaN’.
• formatters: Dictionary or list of functions to apply to columns for formatting their output.
• float_format: Formatter function to apply specifically to floating-point numbers.
• sparsify: Controls hierarchical index formatting. If False, prints every multi-index key at each row.
• index_names: Whether to print index names. Default is True.
• justify: Alignment of column headers (‘left’, ‘right’, ‘center’, ‘justify’ or ‘justify-all’).
• max_rows: Maximum number of rows to display. If exceeded, truncates output.
• max_cols: Maximum number of columns to display. If exceeded, truncates output.
• show_dimensions: If True, displays the shape (rows x columns) of the DataFrame.
• decimal: Specifies the character for decimal separation (e.g., ‘,’ for European formatting).
• line_width: Defines the maximum character width of a row before wrapping text.

Returns: The function returns a str containing the formatted DataFrame.

Examples

1. Excluding index labels

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'Weight': [45, 88, 56, 15, 71],'Name': ['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],'Age': [14, 25, 55, 8, 21]})

print(df.to_string(index=False))

 Weight   Name  Age
     45    Sam   14
     88 Andrea   25
     56   Alex   55
     15  Robin    8
     71    Kia   21

Explanation: to_string(index=False) method removes the default index labels from the output. Instead of displaying row indices (0, 1, 2, etc.), only the column values are printed in a structured format.

2. Customizing Missing Values Representation

import pandas as pd

# Creating a DataFrame with missing values
df_missing = 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]})

print(df_missing.to_string(na_rep='Missing'))

        A       B   C       D
0    12.0     7.0  20    14.0
1     4.0     2.0  16     3.0
2     5.0    54.0  11 Missing
3 Missing     3.0   3     2.0
4     1.0 Missing   8     6.0

Explanation: When dealing with missing values (NaN), the na_rep=’Missing’ argument replaces them with the string “Missing”. This ensures better readability and prevents confusion with empty spaces or default NaN values.

Example 3. Custom Formatting for Floating-Point Numbers

import pandas as pd

# Creating a DataFrame with floating-point numbers
df = pd.DataFrame({'Value': [3.14159265, 2.718281828,1.618033988]})

res = df.to_string(float_format="{:.2f}".format)
print(res)

   Value
0   3.14
1   2.72
2   1.62

Explanation: float_format=”{:.2f}”.format argument rounds floating-point numbers to two decimal places. It ensures numerical data is formatted cleanly, making it easier to interpret without excessive decimal precision.

Example 4. Limiting the Number of Rows and Columns

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'Weight': [45, 88, 56, 15, 71],'Name': ['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],'Age': [14, 25, 55, 8, 21]})

print(df.to_string(max_rows=3, max_cols=2))

    Weight  ... Age
0       45  ...  14
..     ...  ...  ..
4       71  ...  21

Explanation: max_rows=3 and max_cols=2 arguments limit the number of rows and columns displayed. If the DataFrame exceeds these limits, Pandas inserts ellipses (…) to indicate truncated data.