![]() # Example Python program that converts a pandas DataFrame into a Python dictionaryĭataFrame = pds. Index orientation is specified with the string literal “index” for the parameter orient.Įxample – DataFrame to dictionary conversion in dict mode: All the dictionaries are returned in a dictionary, which is indexed by the row labels. In index orientation, each column is made a dictionary where the column elements are stored against the column name.Records orientation is specified with the string literal “records” for the parameter orient. All the dictionaries are returned as a list. In records orientation, each column is made a dictionary where the column elements are stored against the column name.Split orientation is specified with the string literal “split” for the parameter orient. The columns labels are stored in a list against the key "columns". The row labels are stored in a list against the key "index". In split orientation, each row is made a list and they are wrapped in another list and indexed with the key "data" in the returned dictionary object.Series orientation is specified with the string literal “series” for the parameter orient. In series orientation, each column is made a pandas Series, and the series instances are indexed against the row labels in the returned dictionary object.List orientation is specified with the string literal “list” for the parameter orient. In list orientation, each column is made a list and the lists are added to a dictionary against the column labels. ![]() Dictionary orientation is the default orientation for the conversion output. Dictionary orientation is specified with the string literal “dict” for the parameter orient. All these dictionaries are wrapped in another dictionary, which is indexed using column labels. In dictionary orientation, for each column of the DataFrame the column value is listed against the row label in a dictionary.The output can be specified of various orientations using the parameter orient. A pandas DataFrame can be converted into a Python dictionary using the DataFrame instance method to_dict().Note that while merging settings, exclude entries are merged by computing the "union" of keys, while include entries are merged by computing the "intersection" of keys. The resulting settings are merged per class with the explicit settings on dict, json, copy calls with the explicit settings taking priority. ![]() Field(., exclude=True)), with the field constructor taking priority.
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