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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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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
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
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
asked Mar 25 at 3:54
jalexbinjalexbin
173
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
json python-3.x pandas dictionary
asked Mar 25 at 3:54
jalexbinjalexbin
173
asked Mar 25 at 3:54
jalexbinjalexbin
173
asked Mar 25 at 3:54
jalexbinjalexbin
173
asked Mar 25 at 3:54
jalexbinjalexbin
173
asked Mar 25 at 3:54
jalexbinjalexbin
173
173
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
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')
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 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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')
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')
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')
answered Mar 25 at 4:26
Andy HaydenAndy Hayden
201k56451443
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')
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
answered Mar 25 at 4:26
Andy HaydenAndy Hayden
201k56451443
201k56451443
I knew there had to be an easier way - thanks!
– jalexbin
Mar 25 at 6:42
add a comment |
I knew there had to be an easier way - thanks!
– jalexbin
Mar 25 at 6:42
I knew there had to be an easier way - thanks!
– jalexbin
Mar 25 at 6:42
I knew there had to be an easier way - thanks!
– jalexbin
Mar 25 at 6:42
add a comment |
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