从 Python 中嵌套的 Json 中提取信息

Question

我有一个 dataset 包含嵌套的 json 对象。我希望从这个嵌套的 json 中提取信息并将其放入 python 中的 DataFrame 中。我使用了 json_normalize 方法，但在一定级别后无法解析。请帮忙。谢谢。

Answer 1

要“扁平化”嵌套的 json 文件，您可以使用以下函数：

def flatten_json(nested_json):       
    out = {}

    def flatten(x, name=''):
        if type(x) is dict:
            for a in x:
                flatten(x[a], name + a + '_')
        elif type(x) is list:
            i = 0
            for a in x:
                flatten(a, name + str(i) + '_')
                i += 1
        else:
            out[name[:-1]] = x

    flatten(nested_json)
    return out

假设您的 json 被称为 myjson:

df = pd.Series(flatten_json(myjson)).to_frame()

Answer 2

一直在开发一个可以扩展所有嵌入式列表和词典的功能。

from pathlib import Path

with open(Path.home().joinpath("Downloads").joinpath("Sample Json.txt")) as f: js = f.read()

def normalize(js, expand_all=False):
    df = pd.json_normalize(json.loads(js) if type(js) == str else js)
    # get first column that contains lists
    col = df.applymap(type).astype(str).eq("<class 'list'>").all().idxmax()
    # explode list and expand embedded dictionaries
    df = df.explode(col).reset_index(drop=True)
    df = df.drop(columns=[col]).join(df[col].apply(pd.Series), rsuffix=f".{col}")
    # any dictionary to expand?
    if df.applymap(type).astype(str).eq("<class 'dict'>").any().any():
        col = df.applymap(type).astype(str).eq("<class 'dict'>").all().idxmax()
        df = df.drop(columns=[col]).join(df[col].apply(pd.Series), rsuffix=f".{col}")

    # any lists left?
    while expand_all and df.applymap(type).astype(str).eq("<class 'list'>").any().any():
        df = normalize(df.to_dict("records"))
    return df

    
    
df = normalize(js, expand_all=True)

	cfs	ctin	fldtr1	cfs3b	flprdr1	dtcancel	val	inv_typ	pos	idt	rchrg	inum	chksum	num	csamt	samt	rt	txval	camt	iamt
0	Y	03AZX	10-Aug-20	Y	Jul-20	nan	2390	R	03	27-07-2020	N	TI/20-21/111	24ea1a46933dd7c6f130cc7ddce3ad89f42194d84e358746f66716d0f1b8aef0	101	0	182.25	18	2025	182.25	0
1	Y	03AZY	02-Sep-20	Y	Jul-20	nan	10756	R	03	20-07-2020	N	70	164777293c8ce80595cd4803c3d0287bc544772fb9e5331602ed3d7d0534e82f	1801	0	820.35	18	9115	820.35	nan
2	Y	03A00P1Z7	10-Aug-20	Y	Jul-20	nan	411.82	R	03	01-07-2020	N	18IPB06013580804	0560d2b220de53f458ac65594f50bfa5ba736f95061c88201d91371fbeccabf8	1	0	31.41	18	349	31.41	nan
3	Y	03A00P1Z7	10-Aug-20	Y	Jul-20	nan	411.82	R	03	01-07-2020	N	18IPB06013580805	08ae71bcb591723318796e797da586ef9b8e5b6b920e9877be6afc9223486760	1	0	31.41	18	349	31.41	nan
4	Y	03A00P1Z7	10-Aug-20	Y	Jul-20	nan	383.5	R	03	01-07-2020	N	18IPB06013580806	4d22ddd1d05d22cc4707a89dd80e76a271b99a7ba2610e3b111489fd4f7950fc	1	0	29.25	18	325	29.25	nan
5	Y	03A00P1Z7	10-Aug-20	Y	Jul-20	nan	496.78	R	03	01-07-2020	N	18IPB06013580807	73e6e787493276151783d5ab1107bd0bac53780a5840964f7953bf3ba8a4efb0	1	0	37.89	18	421	37.89	nan
6	Y	03A00P1Z7	10-Aug-20	Y	Jul-20	nan	411.82	R	03	21-07-2020	N	18IPB07013893564	52ef0e7269de052c0353580cad5092ff1cc7a3c454318b2df1041a62a32f033f	1	0	31.41	18	349	31.41	nan
7	Y	03A00P1Z7	10-Aug-20	Y	Jul-20	nan	411.82	R	03	21-07-2020	N	18IPB07013893565	ab44c119f3db614dccfd3bc63c036eaca22a41c99e3e5090904e38aee056f4ac	1	0	31.41	18	349	31.41	nan
8	Y	03CAZD	10-Aug-20	Y	Jul-20	nan	162840	R	03	13-07-2020	N	T/20-21/56	92e52e48e812bb0bb2e34d9e400248730fdc40363459d05c4e9d6ebb7fe6165d	101	0	12420	18	138000	12420	0
9	Y	03AAE	22-Aug-20	Y	Jul-20	nan	46556	R	03	30-07-2020	N	S20/21-359	8138e35895114ae412e8256f3ce8382cdd8ae771f2780781085134618bb033c9	1801	0	3550.87	18	39454.2	3550.87	0
10	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	8417.98	R	03	02-07-2020	N	0000030301011976	70d17e281b22541b3d41eb3269d057b73140c203771365a892dd496ffc756adb	1	0	0	0	1024.84	0	nan
11	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	8417.98	R	03	02-07-2020	N	0000030301011976	70d17e281b22541b3d41eb3269d057b73140c203771365a892dd496ffc756adb	2	0	233.58	18	2595.37	233.58	nan
12	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	8417.98	R	03	02-07-2020	N	0000030301011976	70d17e281b22541b3d41eb3269d057b73140c203771365a892dd496ffc756adb	3	0	89.34	5	3573.99	89.34	nan
13	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	8417.98	R	03	02-07-2020	N	0000030301011976	70d17e281b22541b3d41eb3269d057b73140c203771365a892dd496ffc756adb	4	0	30.96	12	516.02	30.96	nan
14	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	2824.88	R	03	06-07-2020	N	0000030301012348	2e7978264e42a74a70aa35d39ca6856f4dfb333e76935667a8de2733f888a1f1	1	0	116.46	18	1293.94	116.46	nan
15	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	2824.88	R	03	06-07-2020	N	0000030301012348	2e7978264e42a74a70aa35d39ca6856f4dfb333e76935667a8de2733f888a1f1	2	0	37.27	12	621.18	37.27	nan
16	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	2824.88	R	03	06-07-2020	N	0000030301012348	2e7978264e42a74a70aa35d39ca6856f4dfb333e76935667a8de2733f888a1f1	3	0	0	0	85.26	0	nan
17	Y	03AAD1ZA	11-Aug-20	Y	Jul-20	nan	2824.88	R	03	06-07-2020	N	0000030301012348	2e7978264e42a74a70aa35d39ca6856f4dfb333e76935667a8de2733f888a1f1	4	0	12.31	5	492.42	12.31	nan
18	Y	03AA1ZQ	17-Aug-20	Y	Jul-20	nan	39294	R	03	02-07-2020	N	TI/20-21/43	69f7931986ad9274d9595ca5221e3ce82aa389d659e83376ff1ec34571057670	101	0	2997	18	33300	2997	0
19	Y	03AGG3Z5	18-Aug-20	Y	Jul-20	22-Jan-20	593583	R	03	31-07-2020	N	25	623dcb5b65e34be4d0453c1783915bb8e66684a2e33a3c8a547e38754c4f1af9	1	0	45273.3	18	503036	45273.3	nan
20	Y	03AGG3Z5	18-Aug-20	Y	Jul-20	22-Jan-20	601409	R	03	31-07-2020	N	26	ef8b99f99fe090f0a2374d8d6c0b15c265740e6c6487ff68d510382ec21d8ce4	1	0	45870.2	18	509668	45870.2	nan
21	Y	03AGG3Z5	18-Aug-20	Y	Jul-20	22-Jan-20	767358	R	03	31-07-2020	N	27	9c1257eddeb8cdc7e6a832a3646969b71e49eeeb7d6742b26cfc6e0e3630438a	1	0	58527.3	18	650303	58527.3	nan
22	Y	03AGG3Z5	18-Aug-20	Y	Jul-20	22-Jan-20	597886	R	03	31-07-2020	N	28	29fc1b28aedd1545e7ea0fd8b67b8332a83f1ac3f62af9398af2dfa26c9f1d90	1	0	45601.4	18	506683	45601.4	nan
23	Y	03AA9	18-Aug-20	Y	Jul-20	nan	41914	R	03	29-07-2020	N	2020-21/K-916	d112ad384eb291d49509bdf4a005d509424fefee4caf3443bc9726cf41665295	1801	0	3196.8	18	35520	3196.8	nan
24	Y	03A1Z8	12-Aug-20	Y	Jul-20	nan	274893	R	03	20-07-2020	N	T/20-21/10	e5851fcc6b370714d7523080582a678a212f5dde90f5c2618880376018221f38	101	0	20966.4	18	232960	20966.4	0
25	Y	03AD1ZL	11-Aug-20	Y	Jul-20	nan	125375	R	03	03-07-2020	N	T/20-21/155	2bb398c7a0fedf11f1f1c1d196c43ad79910be52e6892f88915671025528eb2b	101	0	9562.5	18	106250	9562.5	0
26	Y	03AA3Z9	14-Aug-20	Y	Jul-20	nan	529.99	R	03	31-07-2020	N	0301072000000650	ad1e1d1572c9058fabd6d23fb5dc4b68f1a2a10d3dd3d7e73d73d3c502d92151	1	nan	40.42	18	449.15	40.42	nan
27	Y	03AA3Z9	14-Aug-20	Y	Jul-20	nan	1201	R	03	31-07-2020	N	0303072000000025	5a69229d907957c1d95eb464684891c202102b8589f5603b8ae14b07607f1655	1	nan	91.5	18	1018	91.5	nan
28	Y	03AB1ZV	11-Aug-20	Y	Jul-20	nan	30976	R	03	10-07-2020	N	70	69bbeb088634a88b30c6e6046b63b1977f5534b2f676b984ef78f2c3bad8ca35	1800	nan	2362.5	18	26250	2362.5	nan
29	Y	03AD1Z1	13-Aug-20	Y	Jul-20	nan	8968	R	03	01-07-2020	N	B25	5b98b819ca14a377c9304e7eab21957152c4819e82e37f2619fb2c547fb84ba6	1801	0	684	18	7600	684	nan
30	Y	03AAO	10-Aug-20	Y	Jul-20	nan	38940	R	03	13-07-2020	N	TI/20-21/30	bae339e580c2ab9ffee90533650e4e2acdc47310230ed54aabbb96f89d3fc7c4	101	0	2970	18	33000	2970	0
31	Y	07AH1ZU	11-Aug-20	Y	Jul-20	nan	13836.5	R	03	31-07-2020	N	DELR/EXP/12176	cb34f329adcd88c9e8794db9892fe47bd0a7afc0373a20860de046934f7923fa	1	0	nan	18	11725.9	nan	2110.65
32	Y	03A1ZT	18-Aug-20	Y	Jul-20	nan	41820	R	03	07-07-2020	N	TI/20-21/68	ad61c4dd8227b214dbe4bba24b57a2c976ce8438e53cf15b3530480116ca64da	101	0	3189.69	18	35441	3189.69	0
33	Y	03A1ZT	18-Aug-20	Y	Jul-20	nan	69773	R	03	10-07-2020	N	TI/20-21/71	1deca4741b91716bfabc8b2ab826be76342b0fd3e698b128c927f4b426c064d0	101	0	5321.7	18	59130	5321.7	0

从 Python 中嵌套的 Json 中提取信息

Extract information from nested Json in Python

python

json

dataframe

pandas

json-normalize