'Normalizing nested JSON object into Pandas dataframe
Background: I am trying to normalize a json file, and save into a pandas dataframe, however I am having issues navigating the json structure and my code isn't working as expected.
Expected dataframe output: Given the following example json file (uses randomized data, but exactly the same format as the real one), this is the output I am trying to produce -
| New Entity Group | Entity ID | Adjusted Value (1/31/2022, No Div, USD) |
Adjusted TWR (Current Quarter No Div, USD)) |
Adjusted TWR (YTD, No Div, USD) |
Annualized Adjusted TWR (Since Inception, No Div, USD) |
Inception Date | Risk Target |
|---|---|---|---|---|---|---|---|
| Portfolio_1 | $260,786 | (44.55%) | (44.55%) | (44.55%) * | Apr 7, 2021 | N/A | |
| The FW Irrev Family Tr | 9552252 | $260,786 | 0.00% | 0.00% | 0.00% * | Jan 11, 2022 | N/A |
| Portfolio_2 | $18,396,664 | (5.78%) | (5.78%) | (5.47%) * | Sep 3, 2021 | Growth | |
| FW DAF | 10946585 | $18,396,664 | (5.78%) | (5.78%) | (5.47%) * | Sep 3, 2021 | Growth |
| Portfolio_3 | $60,143,818 | (4.42%) | (4.42%) | 7.75% * | Dec 17, 2020 | - | |
| The FW Family Trust | 13014080 | $475,356 | (6.10%) | (6.10%) | (3.97%) * | Apr 9, 2021 | Aggressive |
| FW Liquid Fund LP | 13396796 | $52,899,527 | (4.15%) | (4.15%) | (4.15%) * | Dec 30, 2021 | Aggressive |
| FW Holdings No. 2 LLC | 8413655 | $6,768,937 | (0.77%) | (0.77%) | 11.84% * | Mar 5, 2021 | N/A |
| FW and FR Joint | 9957007 | ($1) | - | - | - * | Dec 21, 2021 | N/A |
Actual dataframe output: despite my best efforts, I have only been able to get bolded rows to map into the dataframe:
| New Entity Group | Entity ID | Adjusted Value (1/31/2022, No Div, USD) |
Adjusted TWR (Current Quarter No Div, USD)) |
Adjusted TWR (YTD, No Div, USD) |
Annualized Adjusted TWR (Since Inception, No Div, USD) |
Inception Date | Risk Target |
|---|---|---|---|---|---|---|---|
| Portfolio_1 | $260,786 | (44.55%) | (44.55%) | (44.55%) * | Apr 7, 2021 | N/A | |
| Portfolio_2 | $18,396,664 | (5.78%) | (5.78%) | (5.47%) * | Sep 3, 2021 | Growth | |
| Portfolio_3 | $60,143,818 | (4.42%) | (4.42%) | 7.75% * | Dec 17, 2020 | - |
JSON file: this is the file I am trying to normalize and map into a dataframe:
{
"meta": {
"columns": [
{
"key": "node_id",
"display_name": "Entity ID",
"output_type": "Word"
},
{
"key": "value",
"display_name": "Adjusted Value (1/31/2022, No Div, USD)",
"output_type": "Number",
"currency": "USD"
},
{
"key": "time_weighted_return",
"display_name": "Adjusted TWR (Current Quarter, No Div, USD)",
"output_type": "Percent",
"currency": "USD"
},
{
"key": "time_weighted_return_2",
"display_name": "Adjusted TWR (YTD, No Div, USD)",
"output_type": "Percent",
"currency": "USD"
},
{
"key": "time_weighted_return_3",
"display_name": "Annualized Adjusted TWR (Since Inception, No Div, USD)",
"output_type": "Percent",
"currency": "USD"
},
{
"key": "inception_event_date",
"display_name": "Inception Date",
"output_type": "Date"
},
{
"key": "_custom_portfolio_target_347209",
"display_name": "Risk Target",
"output_type": "Word"
}
],
"groupings": [
{
"key": "_custom_new_entity_group_453577",
"display_name": "NEW Entity Group"
},
{
"key": "top_level_legal_entity",
"display_name": "Top Level Legal Entity"
}
]
},
"data": {
"type": "portfolio_views",
"attributes": {
"total": {
"name": "Total",
"columns": {
"time_weighted_return": -0.05001974888806926,
"inception_event_date": "2020-12-17",
"_custom_portfolio_target_347209": null,
"time_weighted_return_3": 0.0678647066340392,
"time_weighted_return_2": -0.05001974888806926,
"value": 7.880126780581851E7,
"node_id": null
},
"children": [
{
"name": "Portfolio_3",
"grouping": "_custom_new_entity_group_453577",
"columns": {
"time_weighted_return": -0.04420061615233983,
"inception_event_date": "2020-12-17",
"_custom_portfolio_target_347209": null,
"time_weighted_return_3": 0.07748325432684622,
"time_weighted_return_2": -0.04420061615233983,
"value": 6.014381761929752E7,
"node_id": null
},
"children": [
{
"entity_id": 9957007,
"name": "FW and FR Joint",
"grouping": "top_level_legal_entity",
"columns": {
"time_weighted_return": null,
"inception_event_date": "2021-12-21",
"_custom_portfolio_target_347209": "N/A",
"time_weighted_return_3": null,
"time_weighted_return_2": null,
"value": -1.44,
"node_id": "9957007"
},
"children": []
},
{
"entity_id": 8413655,
"name": "FW Holdings No. 2 LLC",
"grouping": "top_level_legal_entity",
"columns": {
"time_weighted_return": -0.0077309266066708515,
"inception_event_date": "2021-03-05",
"_custom_portfolio_target_347209": "N/A",
"time_weighted_return_3": 0.11844843557716445,
"time_weighted_return_2": -0.0077309266066708515,
"value": 6768936.74,
"node_id": "8413655"
},
"children": []
},
{
"entity_id": 13396796,
"name": "FW Liquid Fund LP",
"grouping": "top_level_legal_entity",
"columns": {
"time_weighted_return": -0.04149769229150746,
"inception_event_date": "2021-12-30",
"_custom_portfolio_target_347209": "Aggressive",
"time_weighted_return_3": -0.041497430478377395,
"time_weighted_return_2": -0.04149769229150746,
"value": 5.289952672686747E7,
"node_id": "13396796"
},
"children": []
},
{
"entity_id": 13014080,
"name": "The FW Family Trust",
"grouping": "top_level_legal_entity",
"columns": {
"time_weighted_return": -0.06102013456998856,
"inception_event_date": "2021-04-09",
"_custom_portfolio_target_347209": "Aggressive",
"time_weighted_return_3": -0.039685671858585514,
"time_weighted_return_2": -0.06102013456998856,
"value": 475355.59242999996,
"node_id": "13014080"
},
"children": []
}
]
},
{
"name": "Portfolio_1",
"grouping": "_custom_new_entity_group_453577",
"columns": {
"time_weighted_return": -0.44554958179309,
"inception_event_date": "2021-04-07",
"_custom_portfolio_target_347209": "N/A",
"time_weighted_return_3": -0.44554958179309,
"time_weighted_return_2": -0.44554958179309,
"value": 260786.03,
"node_id": null
},
"children": [
{
"entity_id": 9552252,
"name": "The FW Irrev Family Tr",
"grouping": "top_level_legal_entity",
"columns": {
"time_weighted_return": 0.0,
"inception_event_date": "2022-01-11",
"_custom_portfolio_target_347209": "N/A",
"time_weighted_return_3": 0.0,
"time_weighted_return_2": 0.0,
"value": 260786.03,
"node_id": "9552252"
},
"children": []
}
]
},
{
"name": "Portfolio_2",
"grouping": "_custom_new_entity_group_453577",
"columns": {
"time_weighted_return": -0.05780354507057972,
"inception_event_date": "2021-09-03",
"_custom_portfolio_target_347209": "Growth",
"time_weighted_return_3": -0.05470214863844658,
"time_weighted_return_2": -0.05780354507057972,
"value": 1.8396664156520825E7,
"node_id": null
},
"children": [
{
"entity_id": 10946585,
"name": "FW DAF",
"grouping": "top_level_legal_entity",
"columns": {
"time_weighted_return": -0.05780354507057972,
"inception_event_date": "2021-09-03",
"_custom_portfolio_target_347209": "Growth",
"time_weighted_return_3": -0.05470214863844658,
"time_weighted_return_2": -0.05780354507057972,
"value": 1.8396664156520832E7,
"node_id": "10946585"
},
"children": []
}
]
}
]
}
}
},
"included": []
}
My code: this is the function, which I built to try and normalize the JSON response and save in a pandas dataframe -
def unpack_response():
while True:
try:
api_response = response_writer()
df = pd.json_normalize(api_response['data']['attributes']['total']['children'])
df.columns = df.columns.str.replace(r'columns.', '', regex=False)
column_name_mapper = {column['key']: column['display_name'] for column in api_response['meta']['columns']}
df.rename(columns=column_name_mapper, inplace=True)
break
except KeyError:
print("-----------------------------------\n","API TIMEOUT ERROR: TRYING AGAIN...", "\n-----------------------------------\n")
df.rename(columns={'name': 'New Entity Group'}, inplace=True)
column_names = ["New Entity Group", "Entity ID", "Adjusted Value (1/31/2022, No Div, USD)", "Adjusted TWR (Current Quarter, No Div, USD)", "Adjusted TWR (YTD, No Div, USD)", "Annualized Adjusted TWR (Since Inception, No Div, USD)", "Inception Date"]
df = df.reindex(columns=column_names)
return df
unpack_response()
Comment about my code:
- Portfolio_1, Portfolio_2, Portfolio_3 - these bolded rows are first level of
childrenofdataand seem to be the only rows which are saving to thedf. I think this is because my code referencesdf = pd.json_normalize(api_response['data']['attributes']['total']['children'])so is only looking at these lists. I tried just appending['children']['children']to the end of that code snippet (given there are 3x level ofchildren, but received aTypeError: list indices must be integers or slices, not str.
I would be grateful for any suggestions on how I can improve or add to my function, so I can tap into the key:pair values, which are the 2x lower of the children levels.
Solution 1:[1]
This looks like you are trying to create and then stack three dataframes, which you may not really want to do or may be better achieved by mapping each Porfolio_ to every relevant line and then either
import itertools
...
portfolio_views_children = response['data']['attributes']['total']['children']
portfolios = []
for portfolio in portfolio_views_children:
entity_columns = []
# include portfolio itself within an iterable so the total is the header
for entity in itertools.chain([portfolio], portfolio["children"]):
entity_data = entity["columns"].copy() # don't mutate original response
entity_data["portfolio"] = portfolio["name"] # from outer
entity_data["name"] = entity["name"]
entity_columns.append(entity_data)
df = pd.DataFrame(entity_columns)
portfolios.append(df)
# combine dataframes
df = pd.concat(portfolios)
# reorder and rename
column_ordering = {"portfolio": "portfolio", "name": "name"}
column_ordering.update({c["key"]: c["display_name"] for c in response["meta"]["columns"]})
df = df[column_ordering.keys()] # beware: un-named cols will be dropped
df = df.rename(columns=column_ordering)
print(df.to_markdown(index=False)) # create output below (requires tabulate)
| portfolio | name | Entity ID | Adjusted Value (1/31/2022, No Div, USD) | Adjusted TWR (Current Quarter, No Div, USD) | Adjusted TWR (YTD, No Div, USD) | Annualized Adjusted TWR (Since Inception, No Div, USD) | Inception Date | Risk Target |
|---|---|---|---|---|---|---|---|---|
| Portfolio_3 | Portfolio_3 | 6.01438e+07 | -0.0442006 | -0.0442006 | 0.0774833 | 2020-12-17 | ||
| Portfolio_3 | FW and FR Joint | 9957007 | -1.44 | nan | nan | nan | 2021-12-21 | N/A |
| Portfolio_3 | FW Holdings No. 2 LLC | 8413655 | 6.76894e+06 | -0.00773093 | -0.00773093 | 0.118448 | 2021-03-05 | N/A |
| Portfolio_3 | FW Liquid Fund LP | 13396796 | 5.28995e+07 | -0.0414977 | -0.0414977 | -0.0414974 | 2021-12-30 | Aggressive |
| Portfolio_3 | The FW Family Trust | 13014080 | 475356 | -0.0610201 | -0.0610201 | -0.0396857 | 2021-04-09 | Aggressive |
| Portfolio_1 | Portfolio_1 | 260786 | -0.44555 | -0.44555 | -0.44555 | 2021-04-07 | N/A | |
| Portfolio_1 | The FW Irrev Family Tr | 9552252 | 260786 | 0 | 0 | 0 | 2022-01-11 | N/A |
| Portfolio_2 | Portfolio_2 | 1.83967e+07 | -0.0578035 | -0.0578035 | -0.0547021 | 2021-09-03 | Growth | |
| Portfolio_2 | FW DAF | 10946585 | 1.83967e+07 | -0.0578035 | -0.0578035 | -0.0547021 | 2021-09-03 | Growth |
Solution 2:[2]
Since your children's children has same structure as children, you can try using json_normalize twice separately and append it together.
# For first layer that includes Portfolio_1, Portfolio_2, Portfolio_3
df = pd.json_normalize(s, record_path=['data', 'attributes', 'total', 'children'])
# For second layer that includes The FW Irrev Family Tr, etc
# Use explode to convert list into rows
df_child = pd.json_normalize(df.explode('children').children)
# Combine both
df = pd.concat([df, df_child])
# You can use your column renaming and filtering
Solution 3:[3]
I prefer to use json_normalize. The following code doesn't deal with error handling, detailed formatting, etc, whereas I think the essence of what you want to do the most is included.
Code:
import json
import pandas as pd
# You have to change this path according to the actual json file location.
with open('./api_response.json', 'r') as f:
api_response = json.load(f)
def unpack_response(r):
df = pd.DataFrame()
df_src = pd.json_normalize(r, record_path=['data', 'attributes', 'total', 'children'])
for _, row in df_src.sort_values('name').iterrows():
df_p = pd.DataFrame(row).T
df_c = pd.json_normalize(row.children)
# I'm not sure what your expected sorting order is. Perhaps you might want to delete the next line.
df_c = df_c.sort_values(['columns._custom_portfolio_target_347209', 'columns.inception_event_date'])
df = pd.concat([df, df_p, df_c], axis=0, ignore_index=True)
column_name_mapper = {'columns.' + column['key']: column['display_name'] for column in api_response['meta']['columns']}
column_name_mapper.update({'name': 'New Entity Group'})
column_names = ["New Entity Group", "Entity ID", "Adjusted Value (1/31/2022, No Div, USD)", "Adjusted TWR (Current Quarter, No Div, USD)", "Adjusted TWR (YTD, No Div, USD)", "Annualized Adjusted TWR (Since Inception, No Div, USD)", "Inception Date", "Risk Target"]
df = df.rename(columns=column_name_mapper).reindex(columns=column_names)
return df
df = unpack_response(api_response)
Output:
| New Entity Group | Entity ID | Adjusted Value (1/31/2022, No Div, USD) | Adjusted TWR (Current Quarter, No Div, USD) | Adjusted TWR (YTD, No Div, USD) | Annualized Adjusted TWR (Since Inception, No Div, USD) | Inception Date | Risk Target |
|---|---|---|---|---|---|---|---|
| Portfolio_1 | 260786 | -0.44555 | -0.44555 | -0.44555 | 2021-04-07 | N/A | |
| The FW Irrev Family Tr | 9552252 | 260786 | 0 | 0 | 0 | 2022-01-11 | N/A |
| Portfolio_2 | 1.83967e+07 | -0.0578035 | -0.0578035 | -0.0547021 | 2021-09-03 | Growth | |
| FW DAF | 10946585 | 1.83967e+07 | -0.0578035 | -0.0578035 | -0.0547021 | 2021-09-03 | Growth |
| Portfolio_3 | 6.01438e+07 | -0.0442006 | -0.0442006 | 0.0774833 | 2020-12-17 | ||
| The FW Family Trust | 13014080 | 475356 | -0.0610201 | -0.0610201 | -0.0396857 | 2021-04-09 | Aggressive |
| FW Liquid Fund LP | 13396796 | 5.28995e+07 | -0.0414977 | -0.0414977 | -0.0414974 | 2021-12-30 | Aggressive |
| FW Holdings No. 2 LLC | 8413655 | 6.76894e+06 | -0.00773093 | -0.00773093 | 0.118448 | 2021-03-05 | N/A |
| FW and FR Joint | 9957007 | -1.44 | nan | nan | nan | 2021-12-21 | N/A |
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | ti7 |
| Solution 2 | |
| Solution 3 |