pandas 逆json_normalize [英] Inverse of Pandas json_normalize

问题描述

我刚刚发现了json_normalize函数，该函数在获取JSON对象并给我一个Pandas Dataframe时效果很好.现在，我想要反向操作，该操作采用相同的Dataframe并给我一个与原始json具有相同结构的json(或类似json的字典，我可以轻松地将其转换为json).

I just discovered the json_normalize function which works great in taking a JSON object and giving me a pandas Dataframe. Now I want the reverse operation which takes that same Dataframe and gives me a json (or json-like dictionary which I can easily turn to json) with the same structure as the original json.

这里是一个示例: https://hackersandslackers.com/json-into-pandas-数据帧/.

他们采用JSON对象(或类似JSON的python字典)并将其转换为数据框，但是我现在想采用该数据框并将其转换回类似JSON的字典(以便以后转储至json文件).

They take a JSON object (or JSON-like python dictionary) and turn it into a dataframe, but I now want to take that dataframe and turn it back into a JSON-like dictionary (to later dump to json file).

推荐答案

我通过几个函数实现了它

I implemented it with a couple functions

def set_for_keys(my_dict, key_arr, val):
    """
    Set val at path in my_dict defined by the string (or serializable object) array key_arr
    """
    current = my_dict
    for i in range(len(key_arr)):
        key = key_arr[i]
        if key not in current:
            if i==len(key_arr)-1:
                current[key] = val
            else:
                current[key] = {}
        else:
            if type(current[key]) is not dict:
                print("Given dictionary is not compatible with key structure requested")
                raise ValueError("Dictionary key already occupied")

        current = current[key]

    return my_dict

def to_formatted_json(df, sep="."):
    result = []
    for _, row in df.iterrows():
        parsed_row = {}
        for idx, val in row.iteritems():
            keys = idx.split(sep)
            parsed_row = set_for_keys(parsed_row, keys, val)

        result.append(parsed_row)
    return result


#Where df was parsed from json-dict using json_normalize
to_formatted_json(df, sep=".")

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