This is my JSON string, I want to make it read into dataframe in the following tabular format.
I have no idea what should I do after pd.Dataframe(json.loads(data))
JSON data, edited
{
"data":[
{
"data":{
"actual":"(0.2)",
"upper_end_of_central_tendency":"-"
},
"title":"2009"
},
{
"data":{
"actual":"2.8",
"upper_end_of_central_tendency":"-"
},
"title":"2010"
},
{
"data":{
"actual":"-",
"upper_end_of_central_tendency":"2.3"
},
"title":"longer_run"
}
],
"schedule_id":"2014-03-19"
}
That's a somewhat overly nested JSON. But if that's what you have to work with, and assuming your parsed JSON is in jdata:
datapts = jdata['data']
rownames = ['actual', 'upper_end_of_central_tendency']
colnames = [ item['title'] for item in datapts ] + ['schedule_id' ]
sched_id = jdata['schedule_id']
rows = [ [item['data'][rn] for item in datapts ] + [sched_id] for rn in rownames]
df = pd.DataFrame(rows, index=rownames, columns=colnames)
df is now:
If you wanted to simplify that a bit, you could construct the core data without the asymmetric schedule_id field, then add that after the fact:
datapts = jdata['data']
rownames = ['actual', 'upper_end_of_central_tendency']
colnames = [ item['title'] for item in datapts ]
rows = [ [item['data'][rn] for item in datapts ] for rn in rownames]
d2 = pd.DataFrame(rows, index=rownames, columns=colnames)
d2['schedule_id'] = jdata['schedule_id']
That will make an identical DataFrame (i.e. df == d2). It helps when learning pandas to try a few different construction strategies, and get a feel for what is more straightforward. There are more powerful tools for unfolding nested structures into flatter tables, but they're not as easy to understand first time out of the gate.
(Update) If you wanted a better structuring on your JSON to make it easier to put into this format, ask pandas what it likes. E.g. df.to_json() output, slightly prettified:
{
"2009": {
"actual": "(0.2)",
"upper_end_of_central_tendency": "-"
},
"2010": {
"actual": "2.8",
"upper_end_of_central_tendency": "-"
},
"longer_run": {
"actual": "-",
"upper_end_of_central_tendency": "2.3"
},
"schedule_id": {
"actual": "2014-03-19",
"upper_end_of_central_tendency": "2014-03-19"
}
}
That is a format from which pandas' read_json function will immediately construct the DataFrame you desire.
Related
I can't figure out how to correctly organize the JSON data that is created from my pandas dataframe. This is my code:
with open (spreadsheetName, 'rb') as spreadsheet:
newSheet = spreadsheet.read()
newSheet = pd.read_excel(newSheet)
exportSheet = newSheet.to_json('file.json', orient = 'index')
And I'd like for the JSON data to look something like
{
"cars": [
{
"Model": "Camry",
"Year": "2015"
},
{
"Model": "Model S",
"Year": "2018"
}
]
}
But instead I'm getting a single line of JSON data from the code I have. Any ideas on how I can make it so that each row is a JSON 'object' with it's own keys and values from the column headers (like model and year)?
Set an indent argument to desired value in to_json function.
exportSheet = newSheet.to_json('file.json', orient='index', indent=4)
I have an excel sheet which is in the below format
I want to convert this excel sheet into JSON format using Python. each JSON object is a diagonal value and column headings in the below format.
{
"Records": [
{
"RecordId": "F1",
"Assets": [
{
"AssetId": "A1",
"Support": "S11"
},
{
"AssetId": "A2",
"Support": "S12"
},
{
"AssetId": "A3",
"Support": "S13"
}
]
},
{
"RecordId": "F2",
"Assets": [
{
"AssetId": "A1",
"Support": "S21"
},
{
"AssetId": "A2",
"Support": "S22"
},
{
"AssetId": "A3",
"Support": "S23"
}
]
}
]
}
I have written some code it seems not working as I expected.
import json
import pandas as pd
df = pd.read_excel (r'test.xlsx', sheet_name='Sheet2')
#initialize data
data=[0 for i in range(len(df))]
datac=[0 for c in range(len(df.columns))]
newset=dict()
for i in range(len(df)):
# data[i] = r'{"'+str(df.columns.values[0])+'": "' +str(df.loc[i][0])+'", '+str(df.columns.values[1])+'": "' +str(df.loc[i][1])+'", '+str(df.columns.values[2])+'": "' +str(df.loc[i][2])+'"}'
#data[i] = {str(df.columns.values[1]) : str(df.loc[i][0]), str(df.columns.values[1]): str(df.loc[i][1]), str(df.columns.values[2]): str(df.loc[i][2])}
for c in range(1,len(df.columns)):
#data[i] = {str('RecordId') : str(df.loc[i][0]),str('Assets'):[{"AssetId": str(df.columns.values[c]),"Support": str(df.loc[i][c])}]}
datac[c] = {"AssetId": str(df.columns.values[c]),"Support": str(df.loc[i][c])}
data[i]={str('RecordId') : str(df.loc[i][0]),str('Assets'):datac[c]}
print(data[i])
output_lines = [json.dumps(line)+",\n" for line in data]
output_lines[-1] = output_lines[-1][:-2] # remove ",\n" from last line
with open(r'Savedwork.json', 'w') as json_file:
json_file.writelines(output_lines)
What you need is the iterrows() method, it will iterate over the
dataframe's rows as (index, series) pairs. The columns() method will give you
the list of column names, so you'll be able to iterate over the columns in the
series, and access them by name.
import json
import pandas as pd
df = pd.read_excel('test.xlsx')
recs = []
for i, row in df.iterrows():
rec = {
'RecordId': row[0],
'Assets': [{'AssetId': c, 'Support': row[c]} for c in df.columns[1:]]
}
recs.append(rec)
out = {'Records': recs}
(yes, it could all be done in a single list comprehension, but abusing those hinders readability)
Also, you don't need to do json.dumps on lines, and then assemble them with
newlines (don't work at the text level): build a dictionary with the entire
data, and then json.dump that:
print(json.dumps(out, indent=4))
You can create the dicts directly in pandas.
First set the first column with F1, F2 as index:
df.set_index(0, inplace = True)
df.index.name = None
Then create the dicts in pandas with dict keys as column names, export it to a dict and save it to json:
import json
df = df.apply(lambda x: [{"AssetId": x.name, "Support": i} for i in x], axis =1).reset_index().rename(columns={'index': 'RecordId', 0: 'Assets'})
json_data = {"Records": df.to_dict('records')}
with open('r'Savedwork.json', 'w') as fp:
json.dump(json_data, fp)
another solution is to take a snapshot of the entire workbook in json format and reorganize it out of the box. Using the collect function of XLtoy is possible to do that via command line, this approach allows you more degrees of freedom.
[i'm the main developer of XLtoy]
I am using an API from collegefootballdata.com to get data on scores and betting lines. I want to use betting lines to infer expected win % and then compare that to actual results (I feel like my team loses too many games where we are big favorites and want to test that.) This code retrieves one game for example purposes.
parameters = {
"gameId": 401112435,
"year": 2019
}
response = requests.get("https://api.collegefootballdata.com/lines", params=parameters)
The JSON output is this:
[
{
"awayConference": "ACC",
"awayScore": 28,
"awayTeam": "Virginia Tech",
"homeConference": "ACC",
"homeScore": 35,
"homeTeam": "Boston College",
"id": 401112435,
"lines": [
{
"formattedSpread": "Virginia Tech -4.5",
"overUnder": "57.5",
"provider": "consensus",
"spread": "4.5"
},
{
"formattedSpread": "Virginia Tech -4.5",
"overUnder": "57",
"provider": "Caesars",
"spread": "4.5"
},
{
"formattedSpread": "Virginia Tech -4.5",
"overUnder": "58",
"provider": "numberfire",
"spread": "4.5"
},
{
"formattedSpread": "Virginia Tech -4.5",
"overUnder": "56.5",
"provider": "teamrankings",
"spread": "4.5"
}
],
"season": 2019,
"seasonType": "regular",
"week": 1
}
]
I'm then loading into a pandas dataframe with:
def jstring(obj):
# create a formatted string of the Python JSON object
text = json.dumps(obj, sort_keys=True, indent=4)
return text
json_str = jstring(response.json())
df = pd.read_json(json_str)
This creates a dataframe with a "lines" column that contains the entire lines section of the JSON as a string. Ultimately, I want to use the "spread" value in the block where "provider" = "consensus". Everything else is extraneous for my purposes. I've tried exploding the column with
df = df.explode('lines')
which gives me 4 rows with something like this for each game (as expected):
{'formattedSpread': 'Virginia Tech -4.5', 'overUnder': '57.5', 'provider': 'consensus', 'spread': '4.5'}
Here is where I'm stuck. I want to keep only the rows where 'provider' = 'consensus', and further I need to have 'spread' to use as a separate variable/column in my analysis. I've tried exploding a 2nd time, df.split, df.replace to change { to [ and explode as a list, all to no avail. Any help is appreciated!!
This is probably what you're looking for -
EDIT: Handling special case.
import pandas as pd
import requests
params = {
"gameId": 401112435,
"year": 2019,
}
r = requests.get("https://api.collegefootballdata.com/lines", params=params)
df = pd.DataFrame(r.json()) # Create a DataFrame with a lines column that contains JSON
df = df.explode('lines') # Explode the DataFrame so that each line gets its own row
df = df.reset_index(drop=True) # After explosion, the indices are all the same - this resets them so that you can align the DataFrame below cleanly
def fill_na_lines(lines):
if pd.isna(lines):
return {k: None for k in ['provider', 'spread', 'formattedSpread', 'overUnder']}
return lines
df.lines = df.lines.apply(fill_na_lines)
lines_df = pd.DataFrame(df.lines.tolist()) # A separate lines DataFrame created from the lines JSON column
df = pd.concat([df, lines_df], axis=1) # Concatenating the two DataFrames along the vertical axis.
# Now you can filter down to whichever rows you need.
df = df[df.provider == 'consensus']
The documentation on joining DataFrames in different ways is probably useful.
I have a JSON which is in nested form. I would like to extract specific data from json and put into csv using pandas python.
data = {
"class":"hudson.model.Hudson",
"jobs":[
{
"_class":"hudson.model.FreeStyleProject",
"name":"git_checkout",
"url":"http://localhost:8080/job/git_checkout/",
"builds":[
{
"_class":"hudson.model.FreeStyleBuild",
"duration":1201,
"number":6,
"result":"FAILURE",
"url":"http://localhost:8080/job/git_checkout/6/"
}
]
},
{
"_class":"hudson.model.FreeStyleProject",
"name":"output",
"url":"http://localhost:8080/job/output/",
"builds":[
]
},
{
"_class":"org.jenkinsci.plugins.workflow.job.WorkflowJob",
"name":"pipeline_test",
"url":"http://localhost:8080/job/pipeline_test/",
"builds":[
{
"_class":"org.jenkinsci.plugins.workflow.job.WorkflowRun",
"duration":9274,
"number":85,
"result":"SUCCESS",
"url":"http://localhost:8080/job/pipeline_test/85/"
},
{
"_class":"org.jenkinsci.plugins.workflow.job.WorkflowRun",
"duration":4251,
"number":84,
"result":"SUCCESS",
"url":"http://localhost:8080/job/pipeline_test/84/"
}
]
}
]
}
From the above JSON i want to fetch jobs name value and builds result value . I am new to python any help will be appreciated .
Till now i have tried
main_data = data['jobs]
json_normalize(main_data,['builds'],
record_prefix='jobs_', errors='ignore')
which gives information only build key values and not the name of job .
Can anyone help ?
Expected Output:
Considering only first build result value you can need to be in csv column you can achieve this using pandas.
data = {
"class": "hudson.model.Hudson",
"jobs": [
{
"_class": "hudson.model.FreeStyleProject",
"name": "git_checkout",
"url": "http://localhost:8080/job/git_checkout/",
"builds": [
{
"_class": "hudson.model.FreeStyleBuild",
"duration": 1201,
"number": 6,
"result": "FAILURE",
"url": "http://localhost:8080/job/git_checkout/6/"
}
]
},
{
"_class": "hudson.model.FreeStyleProject",
"name": "output",
"url": "http://localhost:8080/job/output/",
"builds": []
},
{
"_class": "org.jenkinsci.plugins.workflow.job.WorkflowJob",
"name": "pipeline_test",
"url": "http://localhost:8080/job/pipeline_test/",
"builds": [
{
"_class": "org.jenkinsci.plugins.workflow.job.WorkflowRun",
"duration": 9274,
"number": 85,
"result": "SUCCESS",
"url": "http://localhost:8080/job/pipeline_test/85/"
},
{
"_class": "org.jenkinsci.plugins.workflow.job.WorkflowRun",
"duration": 4251,
"number": 84,
"result": "SUCCESS",
"url": "http://localhost:8080/job/pipeline_test/84/"
}
]
}
]
}
main_data = data.get('jobs')
res = {'name':[], 'result':[]}
for name_dict in main_data:
res['name'].append(name_dict.get('name','NA'))
resultval = name_dict['builds'][0].get('result') if len(name_dict['builds'])>0 else 'NA'
res['result'].append(resultval)
print(res)
import pandas as pd
df = pd.DataFrame(res)
df.to_csv("/home/file_timer/jobs.csv", index=False)
Check the csv file output
name,result
git_checkout,FAILURE
output,NA
pipeline_test,SUCCESS
If 'NA' result want to skip then
main_data = data.get('jobs')
res = {'name':[], 'result':[]}
for name_dict in main_data:
if len(name_dict['builds'])==0:
continue
res['name'].append(name_dict.get('name', 'NA'))
resultval = name_dict['builds'][0].get('result')
res['result'].append(resultval)
print(res)
import pandas as pd
df = pd.DataFrame(res)
df.to_csv("/home/akash.pagar/shell_learning/file_timer/jobs.csv", index=False)
Output will bw like
name,result
git_checkout,FAILURE
pipeline_test,SUCCESS
Simply with build number,
for job in data.get('jobs'):
for build in job.get('builds'):
print(job.get('name'), build.get('number'), build.get('result'))
gives the result
git_checkout 6 FAILURE
pipeline_test 85 SUCCESS
pipeline_test 84 SUCCESS
If you want to get the result of latest build, and pretty sure about the build number always in decending order,
for job in data.get('jobs'):
if job.get('builds'):
print(job.get('name'), job.get('builds')[0].get('result'))
and if you are not sure the order,
for job in data.get('jobs'):
if job.get('builds'):
print(job.get('name'), sorted(job.get('builds'), key=lambda k: k.get('number'))[-1].get('result'))
then the result will be:
git_checkout FAILURE
pipeline_test SUCCESS
Assuming last build is the last element of its list and you don't care about jobs with no builds, this does:
import pandas as pd
#data = ... #same format as in the question
z = [(job["name"], job["builds"][-1]["result"]) for job in data["jobs"] if len(job["builds"])]
df = pd.DataFrame(data=z, columns=["name", "result"])
#df.to_csv #TODO
Also we don't necessarily need pandas to create the csv file.
You could do:
import csv
#z = ... #see previous code block
with open("f.csv", 'w') as fp:
csv.writer(fp).writerows([("name", "result")] + z)
I have a nested json, and i want to transform it into a pandas dataframe. I was able to normalize with json_normalize.
However, there are still json layer within the dataframe, which i also want to unpack. How can i do it in the best way? I will likely have to deal with this a few more times within the project i am doing currently
The json i have is the following
{
"data": {
"allOpportunityApplication": {
"data": [
{
"id": "111111111",
"opportunity": {
"programme": {
"short_name": "XX"
}
},
"person": {
"home_lc": {
"name": "NAME"
}
},
"standards": [
{
"constant_name": "constant1",
"standard_option": {
"option": "true"
}
},
{
"constant_name": "constant2",
"standard_option": {
"option": "true"
}
}
]
}
]
}
}
}
Used json_normalize
standards_df = json_normalize(
standard_json['allOpportunityApplication']['data'],
record_path=['standards'],
meta=['id','person','opportunity']
)
with that i get a dataframe with the columns: constant_name, standard_option, id, person, opportunity. The problem is that the data standard_option, person and opportunity are json, with a single option inside.
The current ouput and expected output for each column is as follow
Standard_option
Currently an item in the column "standard_option" looks like:
{'option': 'true'}
I want it to be just true
Person
Currently an item in the column "person" looks like:
{'programme': {'short_name': 'XX'}}
I want it to look like: XX
Opportunity
Currently an item in the column "opportunity" looks like:
{'home_lc': {'name': 'NAME'}}
I want it to look like: NAME
Might not be the best way, but I think it works.
standards_df['person'] = (standards_df.loc[:, 'person']
.apply(lambda x: x['home_lc']['name']))
standards_df['opportunity'] = (standards_df.loc[:, 'opportunity']
.apply(lambda x: x['programme']['short_name']))
constant_name standard_option.option id person opportunity
0 constant1 true 111111111 NAME XX
1 constant2 true 111111111 NAME XX
standard_option was already fine when I run your code