Pandas to_dict modifying numbers

You could use pd.io.json.dumps to handle nested dicts with pandas objects.

Let's create a summary dict with dataframe records and custom metric.

In [137]: summary = {'df': df.to_dict(orient = 'records'), 'df_metric': df.sum() / df.min()}

In [138]: summary['df_metric']
Out[138]:
Column_1    9.454545
Column_2    9.000000
Column_3    8.615385
Column_4    8.285714
Column_5    8.000000
Column_6    7.750000
Column_7    7.529412
Column_8    7.333333
dtype: float64

In [139]: pd.io.json.dumps(summary)
Out[139]: '{"df":[{"Column_7":1.7,"Column_6":1.6,"Column_5":1.5,"Column_4":1.4,"Column_3":1.3,"Column_2":1.2,"Column_1":1.1,"Column_8":1.8},{"Column_7":2.7,"Column_6":2.6,"Column_5":2.5,"Column_4":2.4,"Column_3":2.3,"Column_2":2.2,"Column_1":2.1,"Column_8":2.8},{"Column_7":3.7,"Column_6":3.6,"Column_5":3.5,"Column_4":3.4,"Column_3":3.3,"Column_2":3.2,"Column_1":3.1,"Column_8":3.8},{"Column_7":4.7,"Column_6":4.6,"Column_5":4.5,"Column_4":4.4,"Column_3":4.3,"Column_2":4.2,"Column_1":4.1,"Column_8":4.8}],"df_metric":{"Column_1":9.4545454545,"Column_2":9.0,"Column_3":8.6153846154,"Column_4":8.2857142857,"Column_5":8.0,"Column_6":7.75,"Column_7":7.5294117647,"Column_8":7.3333333333}}'

Use, double_precision to alter the maximum digit precision of doubles. Notice. df_metric values.

In [140]: pd.io.json.dumps(summary, double_precision=2)
Out[140]: '{"df":[{"Column_7":1.7,"Column_6":1.6,"Column_5":1.5,"Column_4":1.4,"Column_3":1.3,"Column_2":1.2,"Column_1":1.1,"Column_8":1.8},{"Column_7":2.7,"Column_6":2.6,"Column_5":2.5,"Column_4":2.4,"Column_3":2.3,"Column_2":2.2,"Column_1":2.1,"Column_8":2.8},{"Column_7":3.7,"Column_6":3.6,"Column_5":3.5,"Column_4":3.4,"Column_3":3.3,"Column_2":3.2,"Column_1":3.1,"Column_8":3.8},{"Column_7":4.7,"Column_6":4.6,"Column_5":4.5,"Column_4":4.4,"Column_3":4.3,"Column_2":4.2,"Column_1":4.1,"Column_8":4.8}],"df_metric":{"Column_1":9.45,"Column_2":9.0,"Column_3":8.62,"Column_4":8.29,"Column_5":8.0,"Column_6":7.75,"Column_7":7.53,"Column_8":7.33}}'

You could use orient='records/index/..' to handle dataframe -> to_json construction.

In [144]: pd.io.json.dumps(summary, orient='records')
Out[144]: '{"df":[{"Column_7":1.7,"Column_6":1.6,"Column_5":1.5,"Column_4":1.4,"Column_3":1.3,"Column_2":1.2,"Column_1":1.1,"Column_8":1.8},{"Column_7":2.7,"Column_6":2.6,"Column_5":2.5,"Column_4":2.4,"Column_3":2.3,"Column_2":2.2,"Column_1":2.1,"Column_8":2.8},{"Column_7":3.7,"Column_6":3.6,"Column_5":3.5,"Column_4":3.4,"Column_3":3.3,"Column_2":3.2,"Column_1":3.1,"Column_8":3.8},{"Column_7":4.7,"Column_6":4.6,"Column_5":4.5,"Column_4":4.4,"Column_3":4.3,"Column_2":4.2,"Column_1":4.1,"Column_8":4.8}],"df_metric":[9.4545454545,9.0,8.6153846154,8.2857142857,8.0,7.75,7.5294117647,7.3333333333]}'

In essence, pd.io.json.dumps - converts arbitrary object recursively into JSON, which internally uses ultrajson