Reputation: 139
I wrote some python code to pull data from SQL server and I'm currently trying to merge the data. I tried to pull the data into a Dataframe and then work with it, but wasn't able to do that.
The current form that I set the data up as is like this :
[ { a : { 1 : ( x,y,z,...) }},
{ a : { 2 : ( x,y,z,...) }},
{ a : { 3 : ( x,y,z,...) }} ]
This is where I want to get to
[ { a : { 1 : ( x,y,z,...) , 2 : (x,y,...) , 3 : (x,y,z,...) } ]
Upvotes: 0
Views: 79
Reputation: 8078
You can make use of the reduce
function and dict.update
to transform the data. Assuming that 'a'
is your only key, you can do this:
a = [
{'a': {1: (1, 2, 3)}},
{'a': {2: (4, 5, 6)}},
{'a': {3: (7, 8, 9)}}
]
def update(d, c):
d['a'].update(c['a'])
return d
print reduce(update, a, {'a':{}}) #Prints {'a': {1: (1, 2, 3), 2: (4, 5, 6), 3: (7, 8, 9)}}
Upvotes: 0
Reputation: 36013
How's this?
data = [{'a': {1: 4}, 'b': {7: 8}},
{'a': {2: 5}, 'b': {9: 10}},
{'a': {3: 6}}]
all_keys = set().union(*data)
result = {}
for key in all_keys:
result[key] = {}
for d in data:
if key in d:
result[key].update(d[key])
print(result) # {'b': {7: 8, 9: 10}, 'a': {1: 4, 2: 5, 3: 6}}
Upvotes: 1
Reputation: 164623
Use a nested dictionary structure via collections.defaultdict
.
Note that in this implementation duplicate inner keys are not permitted; for example, you cannot have two dictionaries with outer key 'a' and inner key 1. In this case, the last will take precedence.
from collections import defaultdict
lst = [ { 'a' : { 1 : ( 3, 4, 5 ) }},
{ 'a' : { 2 : ( 6, 7, 8 ) }},
{ 'a' : { 3 : ( 1, 2, 3 ) }},
{ 'c' : { 4 : ( 5, 9, 8 ) }},
{ 'b' : { 1 : ( 6, 6, 8 ) }},
{ 'c' : { 3 : ( 2, 5, 7 ) }}]
d = defaultdict(dict)
for item in lst:
key = next(iter(item))
d[key].update(item[key])
# defaultdict(dict,
# {'a': {1: (3, 4, 5), 2: (6, 7, 8), 3: (1, 2, 3)},
# 'b': {1: (6, 6, 8)},
# 'c': {3: (2, 5, 7), 4: (5, 9, 8)}})
Upvotes: 2