How Can I Convert a Pandas DataFrame to a Dictionary with Specific Orientations?

Convert a Pandas DataFrame to a Dictionary

In Python, converting a Pandas DataFrame into a dictionary involves restructuring the data frame to align with the desired dictionary format. Specifically, the column names become dictionary keys, while the values in each row become the values associated with those keys.

To achieve this transformation, the to_dict() method proves valuable. This method accepts an 'orient' parameter that dictates the arrangement of the data. By default, it adopts the 'dict' orientation, where column names act as keys, and the values are dictionaries containing index-value pairs.

To produce a list of values for each column instead, as demonstrated in the sample output, the 'orient' parameter should be set to 'list':

df.set_index('ID').T.to_dict('list')

This line accomplishes several tasks:

• It sets the 'ID' column as the DataFrame's index.
• It transposes the DataFrame to move the columns into rows.
• It applies the to_dict() method with the 'orient' argument set to 'list', which outputs a dictionary with column names as keys and lists of column values as values.

The resulting dictionary takes the desired format, where the first column elements are keys, and the corresponding row elements are values.

While the 'list' orientation is suitable for the given scenario, other orient options exist, each producing a distinct dictionary format:

• 'dict': Column names are keys, values are dictionaries of index:data pairs.
• 'series': Like 'list', but values are Series.
• 'split': Splits columns/data/index into keys, with values being column names, data values by row, and index labels respectively.
• 'records': Each row becomes a dictionary with column names as keys and data in the cell as values.
• 'index': Similar to 'records', but a dictionary of dictionaries with keys as index labels and column names and data values as values.

By understanding the different orientations, you can tailor the conversion to the desired dictionary structure.

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