Question: I have the following dictionary:

```
my_dict = {'fields': ['id': 1.0,
  'name': 'aaa',
  'type': 'string'},
 {'id': 3.0,
  'name': 'eee',
  'type': 'string'},

 {'id': nan,
  'name': 'bbb',
  'type': 'string'},

 {'id': 4.0,
  'name': 'ccc',
  'type': 'string'},

 {'id': nan,
  'name': 'ddd',
  'type': 'string'}],

'type': 'struct'
}

```

From this dictionary, I would like to drop the dictionary with the `id` value `nan` value and would like to get the following.

```
my_updated_dict = {'fields': ['id': 1.0,
  'name': 'aaa',
  'type': 'string'},

 {'id': 3.0,
  'name': 'eee',
  'type': 'string'},

 {'id': 4.0,
  'name': 'ccc',
  'type': 'string'}],

'type': 'struct'
}

```

I was trying changing to data frame and dropping the `id` value with the `nan` value and changing to dictionary back but couldn't get the intended result.

```
 my_updated_dict = pd.DataFrame(my_dict ).dropna().to_dict('list')

```

Answer: 
I do not know why would you need pandas for that if u can simply do:

```
my_dict["fields"] = [i for i in my_dict["fields"] if not np.isnan(i["id"])]

```

\*\* UPDATE \*\*

if you really do need for some reason to use pandas, you may try this constructiion:

```
my_dict["fields"] = pd.Series(my_dict["fields"]).apply(pd.Series).dropna().to_dict(orient="records")

```

though I do not see any advantages over simple list comprehension, except may be on big volume of information.
You can use `update()` to overwrite the value of the key, then you can try:

```
my_dict.update({'fields':[x for x in my_dict['fields'] if np.nan not in x.values()]})

```

Returning:

```
{'fields': [{'id': 1.0, 'name': 'aaa', 'type': 'string'},
  {'id': 3.0, 'name': 'eee', 'type': 'string'},
  {'id': 4.0, 'name': 'ccc', 'type': 'string'}],
 'type': 'struct'}

```