Construct a DataFrame in Pandas using String Data

Constructing a DataFrame in Pandas using string data means creating a pandas DataFrame where the values are strings instead of numbers. For example: making a table of student names and their cities using text values like "Alice", "Bob", "Delhi", "London".

Using a dictionary of strings

Pandas easily converts this dictionary into a DataFrame. Each key represents a column name and values are stored as lists of strings.

Example: This example shows how to build a DataFrame directly from a dictionary of string lists.

 Date Event Cost
0 10/2/2011 Music 10000
1 11/2/2011 Poetry 12000
2 12/2/2011 Theatre 5000
3 13/2/2011 Comedy 8000

Using a list of strings

This method involves creating a list of rows (sublists), where each sublist holds string values. Column names are added separately.

Example: This example creates a DataFrame using a list of lists with string values.

 Date Event Cost
0 10/2/2011 Music 10000
1 11/2/2011 Poetry 12000
2 12/2/2011 Theatre 5000
3 13/2/2011 Comedy 8000

Using StringIO()

The StringIO() function from Python’s io module treats a string as a file-like object. This allows pd.read_csv() to parse it just like a real CSV file.

Example: This program loads a multi-line string into a DataFrame using StringIO().

 Date Event Cost
0 10/2/2011 Music 10000
1 11/2/2011 Poetry 12000
2 12/2/2011 Theatre 5000
3 13/2/2011 Comedy 8000

Using read_clipboard()

The pd.read_clipboard() function lets users copy tabular data and load it directly into a DataFrame. It is convenient but less reliable since it depends on manual copying and clipboard availability.

Example: This example reads string data from the clipboard into a DataFrame.

Output

Date Event Cost
0 10/2/2011 Music 10000
1 11/2/2011 Poetry 12000
2 12/2/2011 Theatre 5000
3 13/2/2011 Comedy 8000