Mixing
Pandas JSON_Normalize only specific columns
I have a nested JSON structure which I need to flatten. On using JSON normalize it flattens all the keys. But, I want to flatten specific keys while preserving the other keys nested. How to achieve this with JSON normalize. The detail description of what I am trying to do is as follows.
The JSON data that looks something like this
data = {"Attachment":[{"url":"URL001", "type":"pdf"},
{"url":"URL002", "type":"pdf"}],
"Image":{"url":"URL001", "type":"png"},
"Lookup":{"ProductName":"Item001", "ProductId":"001"}}
On running the following snippet it flattens bothImage and Lookup field.
from pandas.io.json import json_normalize
df = json_normalize(data)
df.to_json(orient="records")
The output looks something like,
Attachment Image.URL Image.Type Lookup.ProductName Lookup.ProductId
[{...}, {...}] URL001 png Item001 001
But I don't want to flatten the Image key and preserve it as it is.
The expected Output looks like
Attachment Image Lookup.ProductName Lookup.ProductId
[{...}, {...}] {"url":...,} Item001 001
Is there a way to achieve this using JSON normalize.
python pandas scikit-learn pandas-groupby sklearn-pandas
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
add a comment |
I have a nested JSON structure which I need to flatten. On using JSON normalize it flattens all the keys. But, I want to flatten specific keys while preserving the other keys nested. How to achieve this with JSON normalize. The detail description of what I am trying to do is as follows.
The JSON data that looks something like this
data = {"Attachment":[{"url":"URL001", "type":"pdf"},
{"url":"URL002", "type":"pdf"}],
"Image":{"url":"URL001", "type":"png"},
"Lookup":{"ProductName":"Item001", "ProductId":"001"}}
On running the following snippet it flattens bothImage and Lookup field.
from pandas.io.json import json_normalize
df = json_normalize(data)
df.to_json(orient="records")
The output looks something like,
Attachment Image.URL Image.Type Lookup.ProductName Lookup.ProductId
[{...}, {...}] URL001 png Item001 001
But I don't want to flatten the Image key and preserve it as it is.
The expected Output looks like
Attachment Image Lookup.ProductName Lookup.ProductId
[{...}, {...}] {"url":...,} Item001 001
Is there a way to achieve this using JSON normalize.
python pandas scikit-learn pandas-groupby sklearn-pandas
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
add a comment |
I have a nested JSON structure which I need to flatten. On using JSON normalize it flattens all the keys. But, I want to flatten specific keys while preserving the other keys nested. How to achieve this with JSON normalize. The detail description of what I am trying to do is as follows.
The JSON data that looks something like this
data = {"Attachment":[{"url":"URL001", "type":"pdf"},
{"url":"URL002", "type":"pdf"}],
"Image":{"url":"URL001", "type":"png"},
"Lookup":{"ProductName":"Item001", "ProductId":"001"}}
On running the following snippet it flattens bothImage and Lookup field.
from pandas.io.json import json_normalize
df = json_normalize(data)
df.to_json(orient="records")
The output looks something like,
Attachment Image.URL Image.Type Lookup.ProductName Lookup.ProductId
[{...}, {...}] URL001 png Item001 001
But I don't want to flatten the Image key and preserve it as it is.
The expected Output looks like
Attachment Image Lookup.ProductName Lookup.ProductId
[{...}, {...}] {"url":...,} Item001 001
Is there a way to achieve this using JSON normalize.
python pandas scikit-learn pandas-groupby sklearn-pandas
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
I have a nested JSON structure which I need to flatten. On using JSON normalize it flattens all the keys. But, I want to flatten specific keys while preserving the other keys nested. How to achieve this with JSON normalize. The detail description of what I am trying to do is as follows.
The JSON data that looks something like this
data = {"Attachment":[{"url":"URL001", "type":"pdf"},
{"url":"URL002", "type":"pdf"}],
"Image":{"url":"URL001", "type":"png"},
"Lookup":{"ProductName":"Item001", "ProductId":"001"}}
On running the following snippet it flattens bothImage and Lookup field.
from pandas.io.json import json_normalize
df = json_normalize(data)
df.to_json(orient="records")
The output looks something like,
Attachment Image.URL Image.Type Lookup.ProductName Lookup.ProductId
[{...}, {...}] URL001 png Item001 001
But I don't want to flatten the Image key and preserve it as it is.
The expected Output looks like
Attachment Image Lookup.ProductName Lookup.ProductId
[{...}, {...}] {"url":...,} Item001 001
Is there a way to achieve this using JSON normalize.
python pandas scikit-learn pandas-groupby sklearn-pandas
python pandas scikit-learn pandas-groupby sklearn-pandas
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
edited Nov 19 '18 at 14:37
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
asked Nov 19 '18 at 14:17
Bhavani Ravi
721423
721423
add a comment |
add a comment |
1 Answer
1
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oldest
votes
How about you just separate data in to two separate dictionaries. Perform 2 different transform operations and then join the respective dataframes:
data1 = {k:v for k,v in data.iteritems() if k!='Image'}
data2 = {k:v for k,v in data.iteritems() if k=='Image'}
df = pd.io.json.json_normalize(data1).join(pd.DataFrame([data2]))
answered Nov 19 '18 at 15:20
Robert
33429
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
How about you just separate data in to two separate dictionaries. Perform 2 different transform operations and then join the respective dataframes:
data1 = {k:v for k,v in data.iteritems() if k!='Image'}
data2 = {k:v for k,v in data.iteritems() if k=='Image'}
df = pd.io.json.json_normalize(data1).join(pd.DataFrame([data2]))
answered Nov 19 '18 at 15:20
Robert
33429
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
add a comment |
How about you just separate data in to two separate dictionaries. Perform 2 different transform operations and then join the respective dataframes:
data1 = {k:v for k,v in data.iteritems() if k!='Image'}
data2 = {k:v for k,v in data.iteritems() if k=='Image'}
df = pd.io.json.json_normalize(data1).join(pd.DataFrame([data2]))
answered Nov 19 '18 at 15:20
Robert
33429
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
add a comment |
How about you just separate data in to two separate dictionaries. Perform 2 different transform operations and then join the respective dataframes:
data1 = {k:v for k,v in data.iteritems() if k!='Image'}
data2 = {k:v for k,v in data.iteritems() if k=='Image'}
df = pd.io.json.json_normalize(data1).join(pd.DataFrame([data2]))
answered Nov 19 '18 at 15:20
Robert
33429
How about you just separate data in to two separate dictionaries. Perform 2 different transform operations and then join the respective dataframes:
data1 = {k:v for k,v in data.iteritems() if k!='Image'}
data2 = {k:v for k,v in data.iteritems() if k=='Image'}
df = pd.io.json.json_normalize(data1).join(pd.DataFrame([data2]))
answered Nov 19 '18 at 15:20
Robert
33429
answered Nov 19 '18 at 15:20
Robert
33429
answered Nov 19 '18 at 15:20
Robert
33429
answered Nov 19 '18 at 15:20
Robert
33429
33429
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
add a comment |
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
No that would be costly also its not just two fields. I have a huge dictionary to work with.
– Bhavani Ravi
Nov 19 '18 at 15:53
add a comment |
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