Update list of dictionaries: Python-CodePudding

let's say I have the following data frame table:

Users    Data_Type
User1    String
User2    Integer
User3    String

I have the following sample list which dictionary elements inside it.

my_dicts

[{'name': 'User4', 'dtype': 'StringType'},
 {'name': 'User3', 'dtype': 'String'},
 {'name': 'User1', 'dtype': 'Boolean'},
 {'name': 'User2', 'dtype': 'String'}]

Based on the above table, I would like to update the above existing list of dictionarymy_dicts. I would like to get the following result.

[{'name': 'User1', 'dtype': 'String'},
 {'name': 'User2', 'dtype': 'Integer'},
 {'name': 'User3', 'dtype': 'String'}]

I was trying with this:

list_= df['Users'].tolist()
my_new_list= dict(zip((*my_dicts ,), list_))

Can anyone help me with this?

CodePudding user response：

You did not provide how the table is formatted, so I'm assuming this data structure:

updates = [
    ("User1", "String"),
    ("User2", "Integer"),
    ("User3", "String")
]

You can update my_dicts by going over each element and checking if the name key corresponds. If so, overwrite dtype.

for (name, dtype) in updates:
    for d in my_dicts:
        if d["name"] == name:
            d["dtype"] = dtype

I'm not entirely sure what the behavior should be for deleting rows. If any row in my_dict that is not in the new table should automatically be deleted, then what is the point of updating my_dict? It sounds like you want to convert the table to your dict datastructure.

CodePudding user response：

newlist = sorted(my_dicts, key=lambda d: d['name'])