Write Json Format Using Pandas Series And Dataframe
I'm working with csvfiles. My goal is to write a json format with csvfile information. Especifically, I want to get a similar format as miserables.json Example: {'source': 'Napoleo
Solution 1:
Consider removing the Series()
around the scalar value, country. By doing so and then upsizing the dictionaries of series into a dataframe, you force NaN
(later converted to null
in json) into the series to match the lengths of other series. You can see this by printing out the dfForce dataframe:
from pandas import Series
from pandas import DataFrame
country = 'Germany'
sourceTemp = ['Mexico', 'USA', 'Argentina']
value = [1, 2, 3]
forceData = {'source': Series(country),
'target': Series(sourceTemp),
'value': Series(value)}
dfForce = DataFrame(forceData)
# source target value# 0 Germany Mexico 1# 1 NaN USA 2# 2 NaN Argentina 3
To resolve, simply keep country as scalar in dictionary of series:
forceData = {'source': country,
'target': Series(sourceTemp),
'value': Series(value)}
dfForce = DataFrame(forceData)
# source target value# 0 Germany Mexico 1# 1 Germany USA 2# 2 Germany Argentina 3
By the way, you do not need a dataframe object to output to json. Simply use a list of dictionaries. Consider the following using an Ordered Dictionary collection (to maintain the order of keys). In this way the growing list dumps into a text file without appending which would render an invalid json as opposite facing adjacent square brackets ...][...
are not allowed.
from collections import OrderedDict
...
data = []
for element in newcountries:
bills = csvdata['target'][csvdata['country'] == element]
frquency = Counter(bills)
for k,v in frquency.items():
inner = OrderedDict()
inner['source'] = element
inner['target'] = k
inner['value'] = int(v)
data.append(inner)
newData = json.dumps(data, indent=4)
with open('data.json', 'w') as savetxt:
savetxt.write(newData)
Post a Comment for "Write Json Format Using Pandas Series And Dataframe"