JSON to pandas DataFrame
I found a quick and easy solution to what I wanted using json_normalize()
included in pandas 1.01
.
from urllib2 import Request, urlopen
import json
import pandas as pd
path1 = '42.974049,-81.205203|42.974298,-81.195755'
request=Request('http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&sensor=false')
response = urlopen(request)
elevations = response.read()
data = json.loads(elevations)
df = pd.json_normalize(data['results'])
This gives a nice flattened dataframe with the json data that I got from the Google Maps API.
Convert Pandas DataFrame to JSON format
The output that you get after DF.to_json
is a string
. So, you can simply slice it according to your requirement and remove the commas from it too.
out = df.to_json(orient='records')[1:-1].replace('},{', '} {')
To write the output to a text file, you could do:
with open('file_name.txt', 'w') as f:
f.write(out)
How to return a .csv file/Pandas DataFrame in JSON format using FastAPI?
The below shows four different ways of returning the data stored in a .csv
file/Pandas DataFrame.
Option 1
The first option is to convert the file data into JSON
and then parse it into a dict
. You can optionally change the orientation of the data using the orient
parameter in the .to_json()
method.
Note: Better not to use this option. See Updates below.
from fastapi import FastAPI
import pandas as pd
import json
app = FastAPI()
df = pd.read_csv("file.csv")
def parse_csv(df):
res = df.to_json(orient="records")
parsed = json.loads(res)
return parsed
@app.get("/questions")
def load_questions():
return parse_csv(df)
Update 1: Using
.to_dict()
method would be a better option, as it would return adict
directly, instead of converting the DataFrame into JSON (usingdf.to_json()
) and then that JSON string intodict
(usingjson.loads()
), as described earlier. Example:@app.get("/questions")
def load_questions():
return df.to_dict(orient="records")Update 2: When using
.to_dict()
method and returning thedict
, FastAPI, behind the scenes, automatically converts that return value intoJSON
, using thejsonable_encoder
. Thus, to avoid that extra processing, you could still use.to_json()
method, but this time, put theJSON
string in aResponse
and return it directly, as shown below.from fastapi import Response
@app.get("/questions")
def load_questions():
return Response(df.to_json(orient="records"), media_type="application/json")
Option 2
Another option is to return the data in string
format, using .to_string()
method.
@app.get("/questions")
def load_questions():
return df.to_string()
Option 3
You could also return the data as an HTML
table, using .to_html()
method.
from fastapi.responses import HTMLResponse
@app.get("/questions")
def load_questions():
return HTMLResponse(content=df.to_html(), status_code=200)
Option 4
Finally, you can always return the file
as is using FastAPI's FileResponse
.
from fastapi.responses import FileResponse
@app.get("/questions")
def load_questions():
return FileResponse(path="file.csv", filename="file.csv")
Convert URL Request from JSON to Pandas DataFrame
Try this
import pandas as pd
df = pd.read_json('https://data.epa.gov/efservice/PUB_DIM_FACILITY/ROWS/0:10/JSON')
FACILITY_ID LATITUDE LONGITUDE CITY STATE ZIP COUNTY_FIPS COUNTY ADDRESS1 ADDRESS2 ... REPORTED_INDUSTRY_TYPES FACILITY_TYPES SUBMISSION_ID UU_RD_EXEMPT REPORTING_STATUS PROCESS_STATIONARY_CML COMMENTS RR_MRV_PLAN_URL RR_MONITORING_PLAN RR_MONITORING_PLAN_FILENAME
0 1000001 48.828707 -122.685533 FERNDALE WA 98248 53073 WHATCOM COUNTY 5105 LAKE TERRELL ROAD NaN ... D Direct Emitter 176997 NaN NaN NaN NaN NaN NaN NaN
1 1000001 48.828707 -122.685533 FERNDALE WA 98248 53073 WHATCOM COUNTY 5105 LAKE TERRELL ROAD NaN ... C Direct Emitter 5752 NaN NaN NaN NaN NaN NaN NaN
Normalizing nested JSON object into Pandas dataframe
Personally, I would not use pd.json_normalize
for this case. Your JSON is quite complex, and unless you're really experienced with json_normalize
, the following code may take less time to understand for the average dev. In fact, you don't even need to see the JSON to understand exactly what this code does (although it would certainly help ;).
First, we can extract the objects (portfolios and their children) from the JSON into a list, and use a series of steps to get them in the right form and order:
def prep_obj(o):
"""Prepares an object (portfolio/child) from the JSON to be inserted into a dataframe."""
return {
'New Entity Group': o['name'],
} | o['columns']
# Get a list of lists, where each sub-list contains the portfolio object at index 0 and then the portfolio object's children:
groups = [[prep_obj(o), *[prep_obj(child) for child in o['children']]] for o in api_response['data']['attributes']['total']['children']]
# Sort the portfolio groups by their number:
groups.sort(key=lambda g: int(g[0]['New Entity Group'].split('_')[1]))
# Reverse the children of each portfolio group:
groups = [[g[0]] + g[1:][::-1] for g in groups]
# Flatten out the groups into one large list of objects:
objects = [obj for group in groups for obj in group]
# The above is exactly equivalent to the following:
# objects = []
# for group in groups:
# for obj in group:
# objects.append(obj)
Next, create the dataframe:
# Create a mapping for column names so that their display names can be used:
mapping = {col['key']: col['display_name'] for col in api_response['meta']['columns']}
# Create a dataframe from the list of objects:
df = pd.DataFrame(objects)
# Correct column names:
df = df.rename(mapping, axis=1)
# Reorder columns:
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[column_names]
And formatting:
def format_twr_col(col):
return (
col
.abs()
.mul(100)
.round(2)
.pipe(lambda s: s.where(s.eq(0) | s.isna(), '(' + s.astype(str) + '%)'))
.pipe(lambda s: s.where(s.ne(0) | s.isna(), s.astype(str) + '%'))
.fillna('-')
)
def format_value_col(col):
positive_mask = col.ge(0)
col[positive_mask] = (
col[positive_mask]
.round()
.astype(int)
.map('${:,}'.format)
)
col[~positive_mask] = (
col[~positive_mask]
.astype(float)
.round()
.astype(int)
.abs()
.map('(${:,})'.format)
)
return col
df['Adjusted TWR (Current Quarter, No Div, USD)'] = format_twr_col(df['Adjusted TWR (Current Quarter, No Div, USD)'])
df['Annualized Adjusted TWR (Since Inception, No Div, USD)'] = format_twr_col(df['Annualized Adjusted TWR (Since Inception, No Div, USD)'])
df['Adjusted TWR (YTD, No Div, USD)'] = format_twr_col(df['Adjusted TWR (YTD, No Div, USD)'])
df['Adjusted Value (1/31/2022, No Div, USD)'] = format_value_col(df['Adjusted Value (1/31/2022, No Div, USD)'].copy())
df['Inception Date'] = pd.to_datetime(df['Inception Date']).dt.strftime('%b %d, %Y')
df['Entity ID'] = df['Entity ID'].fillna('')
And... voilà:
>>> pd.options.display.max_columns = None
>>> df
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
0 Portfolio_1 $260,786 (44.55%) (44.55%) (44.55%) Apr 07, 2021 N/A
1 The FW Irrev Family Tr 9552252 $260,786 0.0% 0.0% 0.0% Jan 11, 2022 N/A
2 Portfolio_2 $18,396,664 (5.78%) (5.78%) (5.47%) Sep 03, 2021 Growth
3 FW DAF 10946585 $18,396,664 (5.78%) (5.78%) (5.47%) Sep 03, 2021 Growth
4 Portfolio_3 $60,143,818 (4.42%) (4.42%) (7.75%) Dec 17, 2020 NaN
5 The FW Family Trust 13014080 $475,356 (6.1%) (6.1%) (3.97%) Apr 09, 2021 Aggressive
6 FW Liquid Fund LP 13396796 $52,899,527 (4.15%) (4.15%) (4.15%) Dec 30, 2021 Aggressive
7 FW Holdings No. 2 LLC 8413655 $6,768,937 (0.77%) (0.77%) (11.84%) Mar 05, 2021 N/A
8 FW and FR Joint 9957007 ($1) - - - Dec 21, 2021 N/A
convert text into json using pandas dataframe with customer delimiter
It seems the problem is with the status
part not being separated by a delimiter. You can fight it by adding some processing in pandas to split the date column on the status keyword and stripping out the colon before writing to json:
# Splits the date part and the status part into two columns (your status is being dragged into the date column)
cam_details[['date', 'status']] = cam_details['date'].map(lambda x: x.split('status')).tolist()
# Clean up the status column which still has the colons and extra whitespaces
cam_details['status'] = cam_details['status'].map(lambda x: x.replace(':', '').strip())
Convert Nested DateTime json to Pandas DataFrame
Why don't you just do this?
pd.DataFrame(data["Time Series (Daily)"]).T.reset_index().rename(columns = {"index":"Time Series (Daily)"})
Output -
Time Series (Daily) | 1. open | 4. close | |
---|---|---|---|
0 | 2001-06-31 | 113.2000 | 113.8000 |
1 | 2001-07-01 | 114.2000 | 114.2000 |
Mapping complex JSON to Pandas Dataframe
jsonpath-ng
can parse even such a nested json object very easily. You can install this convenient library by the following command:
pip install --upgrade jsonpath-ng
Code:
import json
import jsonpath_ng as jp
import pandas as pd
def unpack_response(r):
# Create a dataframe from extracted data
expr = jp.parse('$..children.[*]')
data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)
# Append a portfolio column
df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
df['portfolio'].fillna(method='ffill', inplace=True)
# Deal with columns
trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
df = df.rename(columns=trans)[cols]
return df
# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
# api_response = json.load(f)
# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "le
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