How to add a dataframe row in a field array Like i have my data frame.
import pandas as pd
inp = [{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}]
df = pd.DataFrame(inp)
print df
Output:
c1 c2
0 10 100
1 11 110
2 12 120
So i want to have something like this :
{
"fields": {
"c1": 10,
"c2": 100,
}
},
{
"fields": {
"c1": 11,
"c2": 110,
}
},
{
"fields": {
"c1": 12,
"c2": 120,
}
}
How can i do it ?
You can do:
a = df.transpose().to_dict()
a
>>> {0: {'c1': 10, 'c2': 100}, 1: {'c1': 11, 'c2': 110}, 2: {'c1': 12, 'c2': 120}}
res = [{'fields': a[i]} for i in a]
res
>>> [{'fields': {'c1': 10, 'c2': 100}}, {'fields': {'c1': 11, 'c2': 110}}, {'fields': {'c1': 12, 'c2': 120}}]
As @anky points out, defining a like so: a = df.to_dict('index') will also work, not sure which is more computationally efficient
You can try using df.to_dict with orient as records.
out = df.to_dict(orient='records')
# [{'c1': 10, 'c2': 100}, {'c1': 11, 'c2': 110}, {'c1': 12, 'c2': 120}]
out = [{'fields': val} for val in out]
[{'fields': {'c1': 10, 'c2': 100}},
{'fields': {'c1': 11, 'c2': 110}},
{'fields': {'c1': 12, 'c2': 120}}]
Try chain with df.to_dict
d = [{'field' : x} for x in df.to_dict('records')]
Out[167]:
[{'field': {'c1': 10, 'c2': 100}},
{'field': {'c1': 11, 'c2': 110}},
{'field': {'c1': 12, 'c2': 120}}]
Exception, the latest version throws FutureWarning. Works like charm in older versions.Pandas' built in method to_dict() allows converting a dataframe to a serialized list of records. Your desired output would require a transformation on these records:
# Get each row as a record
records = df.to_dict(orient='records')
# [{'c1': 10, 'c2': 100}, {'c1': 11, 'c2': 110}, {'c1': 12, 'c2': 120}]
# Transform each row
records = [{'fields':x} for x in records]
# [{'fields': {'c1': 10, 'c2': 100}},
# {'fields': {'c1': 11, 'c2': 110}},
# {'fields': {'c1': 12, 'c2': 120}}]
