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Nesting/grouping a range of columns when converting a Pandas DataFrame to a dictionary

Convert two lists into a dictionary in PythonConvert a String representation of a Dictionary to a dictionary?Selecting multiple columns in a pandas dataframeRenaming columns in pandasAdding new column to existing DataFrame in Python pandasDelete column from pandas DataFrameHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headersConvert list of dictionaries to a pandas DataFrame

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1

I've been trying to work out how to convert a Pandas DataFrame into a list of nested dictionaries and I haven't been having any luck.

My first thought was to convert the DataFrame into a list of dictionaries (with users = users.to_dict(orient='records')) and then merge the 'address' and 'color_preference' items into sublists but there must be a better way to do it!

I have a dataframe like this:

import pandas as pd
users = pd.DataFrame('email_address': ["email@email.com"], 'status': ["active"], 'address': ["1 Eagle St"], 'suburb': ["BROOKLYN"], 'state': ["NY"], 'postcode': ["11201"], 'country': ["USA"], 'red': [False], 'orange': [True], 'yellow': [True], 'green': [True], 'blue': [False], 'indigo': [False], 'violet': [False])

and I'm trying to convert it into this format:

 
 "email_address":"email@email.com",
 "status":"active",
 "address": 
 "address":"1 Eagle St",
 "suburb":"Brooklyn",
 "state":"NY",
 "postcode":"11201",
 "country":"USA"
 ,
 "color_preference": 
 "red":false,
 "orange":true,
 "yellow":true,
 "green":true,
 "blue":false,
 "indigo":false,
 "violet":false
 

json python-3.x pandas dictionary

share|improve this question

asked Mar 25 at 3:54

jalexbinjalexbin

173

add a comment | 

1

I've been trying to work out how to convert a Pandas DataFrame into a list of nested dictionaries and I haven't been having any luck.

My first thought was to convert the DataFrame into a list of dictionaries (with users = users.to_dict(orient='records')) and then merge the 'address' and 'color_preference' items into sublists but there must be a better way to do it!

I have a dataframe like this:

import pandas as pd
users = pd.DataFrame('email_address': ["email@email.com"], 'status': ["active"], 'address': ["1 Eagle St"], 'suburb': ["BROOKLYN"], 'state': ["NY"], 'postcode': ["11201"], 'country': ["USA"], 'red': [False], 'orange': [True], 'yellow': [True], 'green': [True], 'blue': [False], 'indigo': [False], 'violet': [False])

and I'm trying to convert it into this format:

 
 "email_address":"email@email.com",
 "status":"active",
 "address": 
 "address":"1 Eagle St",
 "suburb":"Brooklyn",
 "state":"NY",
 "postcode":"11201",
 "country":"USA"
 ,
 "color_preference": 
 "red":false,
 "orange":true,
 "yellow":true,
 "green":true,
 "blue":false,
 "indigo":false,
 "violet":false
 

json python-3.x pandas dictionary

share|improve this question

asked Mar 25 at 3:54

jalexbinjalexbin

173

add a comment | 

I've been trying to work out how to convert a Pandas DataFrame into a list of nested dictionaries and I haven't been having any luck.

My first thought was to convert the DataFrame into a list of dictionaries (with users = users.to_dict(orient='records')) and then merge the 'address' and 'color_preference' items into sublists but there must be a better way to do it!

I have a dataframe like this:

import pandas as pd
users = pd.DataFrame('email_address': ["email@email.com"], 'status': ["active"], 'address': ["1 Eagle St"], 'suburb': ["BROOKLYN"], 'state': ["NY"], 'postcode': ["11201"], 'country': ["USA"], 'red': [False], 'orange': [True], 'yellow': [True], 'green': [True], 'blue': [False], 'indigo': [False], 'violet': [False])

and I'm trying to convert it into this format:

 
 "email_address":"email@email.com",
 "status":"active",
 "address": 
 "address":"1 Eagle St",
 "suburb":"Brooklyn",
 "state":"NY",
 "postcode":"11201",
 "country":"USA"
 ,
 "color_preference": 
 "red":false,
 "orange":true,
 "yellow":true,
 "green":true,
 "blue":false,
 "indigo":false,
 "violet":false
 

json python-3.x pandas dictionary

share|improve this question

asked Mar 25 at 3:54

jalexbinjalexbin

173

import pandas as pd
users = pd.DataFrame('email_address': ["email@email.com"], 'status': ["active"], 'address': ["1 Eagle St"], 'suburb': ["BROOKLYN"], 'state': ["NY"], 'postcode': ["11201"], 'country': ["USA"], 'red': [False], 'orange': [True], 'yellow': [True], 'green': [True], 'blue': [False], 'indigo': [False], 'violet': [False])

 
 "email_address":"email@email.com",
 "status":"active",
 "address": 
 "address":"1 Eagle St",
 "suburb":"Brooklyn",
 "state":"NY",
 "postcode":"11201",
 "country":"USA"
 ,
 "color_preference": 
 "red":false,
 "orange":true,
 "yellow":true,
 "green":true,
 "blue":false,
 "indigo":false,
 "violet":false
 

share|improve this question

asked Mar 25 at 3:54

jalexbinjalexbin

173

share|improve this question

asked Mar 25 at 3:54

jalexbinjalexbin

173

asked Mar 25 at 3:54

jalexbinjalexbin

173

asked Mar 25 at 3:54

jalexbinjalexbin

173

1 Answer
1

active

oldest

votes

1

You can do this explicitly with apply (I've done the first couple but you could do all the address/colors):

def extract_json(row):
 return 
 "email_address": row.loc["email_address"],
 "status": row.loc["status"],
 "address": row.loc[["address", "suburb"]].to_dict(),
 "color_preference": row.loc[["red", "orange"]].to_dict()
 

In [11]: users.apply(extract_json, axis=1)
Out[11]:
0 {'email_address': 'email@email.com', 'status':...
dtype: object

In [12]: users.apply(extract_json, axis=1).tolist()
Out[12]:
['email_address': 'email@email.com',
 'status': 'active',
 'address': 'address': '1 Eagle St', 'suburb': 'BROOKLYN',
 'color_preference': 'red': False, 'orange': True]

You could pull out all the address/colors by position:

In [21]: users.columns[2:7]
Out[21]: Index(['address', 'suburb', 'state', 'postcode', 'country'], dtype='object')

In [22]: users.columns[7:]
Out[22]: Index(['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'], dtype='object')

share|improve this answer

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I knew there had to be an easier way - thanks!
  
  – jalexbin
  Mar 25 at 6:42
  
  

  
  
  
  

add a comment | 

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1

You can do this explicitly with apply (I've done the first couple but you could do all the address/colors):

def extract_json(row):
 return 
 "email_address": row.loc["email_address"],
 "status": row.loc["status"],
 "address": row.loc[["address", "suburb"]].to_dict(),
 "color_preference": row.loc[["red", "orange"]].to_dict()
 

In [11]: users.apply(extract_json, axis=1)
Out[11]:
0 {'email_address': 'email@email.com', 'status':...
dtype: object

In [12]: users.apply(extract_json, axis=1).tolist()
Out[12]:
['email_address': 'email@email.com',
 'status': 'active',
 'address': 'address': '1 Eagle St', 'suburb': 'BROOKLYN',
 'color_preference': 'red': False, 'orange': True]

You could pull out all the address/colors by position:

In [21]: users.columns[2:7]
Out[21]: Index(['address', 'suburb', 'state', 'postcode', 'country'], dtype='object')

In [22]: users.columns[7:]
Out[22]: Index(['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'], dtype='object')

share|improve this answer

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I knew there had to be an easier way - thanks!
  
  – jalexbin
  Mar 25 at 6:42
  
  

  
  
  
  

add a comment | 

1

You can do this explicitly with apply (I've done the first couple but you could do all the address/colors):

def extract_json(row):
 return 
 "email_address": row.loc["email_address"],
 "status": row.loc["status"],
 "address": row.loc[["address", "suburb"]].to_dict(),
 "color_preference": row.loc[["red", "orange"]].to_dict()
 

In [11]: users.apply(extract_json, axis=1)
Out[11]:
0 {'email_address': 'email@email.com', 'status':...
dtype: object

In [12]: users.apply(extract_json, axis=1).tolist()
Out[12]:
['email_address': 'email@email.com',
 'status': 'active',
 'address': 'address': '1 Eagle St', 'suburb': 'BROOKLYN',
 'color_preference': 'red': False, 'orange': True]

You could pull out all the address/colors by position:

In [21]: users.columns[2:7]
Out[21]: Index(['address', 'suburb', 'state', 'postcode', 'country'], dtype='object')

In [22]: users.columns[7:]
Out[22]: Index(['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'], dtype='object')

share|improve this answer

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I knew there had to be an easier way - thanks!
  
  – jalexbin
  Mar 25 at 6:42
  
  

  
  
  
  

add a comment | 

You can do this explicitly with apply (I've done the first couple but you could do all the address/colors):

def extract_json(row):
 return 
 "email_address": row.loc["email_address"],
 "status": row.loc["status"],
 "address": row.loc[["address", "suburb"]].to_dict(),
 "color_preference": row.loc[["red", "orange"]].to_dict()
 

In [11]: users.apply(extract_json, axis=1)
Out[11]:
0 {'email_address': 'email@email.com', 'status':...
dtype: object

In [12]: users.apply(extract_json, axis=1).tolist()
Out[12]:
['email_address': 'email@email.com',
 'status': 'active',
 'address': 'address': '1 Eagle St', 'suburb': 'BROOKLYN',
 'color_preference': 'red': False, 'orange': True]

You could pull out all the address/colors by position:

In [21]: users.columns[2:7]
Out[21]: Index(['address', 'suburb', 'state', 'postcode', 'country'], dtype='object')

In [22]: users.columns[7:]
Out[22]: Index(['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'], dtype='object')

share|improve this answer

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443

def extract_json(row):
 return 
 "email_address": row.loc["email_address"],
 "status": row.loc["status"],
 "address": row.loc[["address", "suburb"]].to_dict(),
 "color_preference": row.loc[["red", "orange"]].to_dict()
 

In [11]: users.apply(extract_json, axis=1)
Out[11]:
0 {'email_address': 'email@email.com', 'status':...
dtype: object

In [12]: users.apply(extract_json, axis=1).tolist()
Out[12]:
['email_address': 'email@email.com',
 'status': 'active',
 'address': 'address': '1 Eagle St', 'suburb': 'BROOKLYN',
 'color_preference': 'red': False, 'orange': True]

In [21]: users.columns[2:7]
Out[21]: Index(['address', 'suburb', 'state', 'postcode', 'country'], dtype='object')

In [22]: users.columns[7:]
Out[22]: Index(['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'], dtype='object')

share|improve this answer

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443

answered Mar 25 at 4:26

Andy HaydenAndy Hayden

201k56451443