Save and export dtypes information of a python pandas dataframe

Question

I have a pandas DataFrame named df. With df.dtypes I can print on screen:

arrival_time      object
departure_time    object
drop_off_type      int64
extra             object
pickup_type        int64
stop_headsign     object
stop_id           object
stop_sequence      int64
trip_id           object
dtype: object

I want to save this information so that I can compare it with other data, type-cast things elsewhere, etc. I want to save it into to a local file, recover it elsewhere in another program where the data can't go. But I'm not able to figure out how. Showing the results of various conversions.

df.dtypes.to_dict()
{'arrival_time': dtype('O'),
 'departure_time': dtype('O'),
 'drop_off_type': dtype('int64'),
 'extra': dtype('O'),
 'pickup_type': dtype('int64'),
 'stop_headsign': dtype('O'),
 'stop_id': dtype('O'),
 'stop_sequence': dtype('int64'),
 'trip_id': dtype('O')}
----
df.dtypes.to_json()
'{"arrival_time":{"alignment":4,"byteorder":"|","descr":[["","|O"]],"flags":63,"isalignedstruct":false,"isnative":true,"kind":"O","name":"object","ndim":0,"num":17,"str":"|O"},"departure_time":{"alignment":4,"byteorder":"|","descr":[["","|O"]],"flags":63,"isalignedstruct":false,"isnative":true,"kind":"O","name":"object","ndim":0,"num":17,"str":"|O"},"drop_off_type":{"alignment":4,"byteorder":"=","descr":[["","<i8"]],"flags":0,"isalignedstruct":false,"isnative":true,"kind":"i","name":"int64","ndim":0,"num":9,"str":"<i8"},"extra":{"alignment":4,"byteorder":"|","descr":[["","|O"]],"flags":63,"isalignedstruct":false,"isnative":true,"kind":"O","name":"object","ndim":0,"num":17,"str":"|O"},"pickup_type":{"alignment":4,"byteorder":"=","descr":[["","<i8"]],"flags":0,"isalignedstruct":false,"isnative":true,"kind":"i","name":"int64","ndim":0,"num":9,"str":"<i8"},"stop_headsign":{"alignment":4,"byteorder":"|","descr":[["","|O"]],"flags":63,"isalignedstruct":false,"isnative":true,"kind":"O","name":"object","ndim":0,"num":17,"str":"|O"},"stop_id":{"alignment":4,"byteorder":"|","descr":[["","|O"]],"flags":63,"isalignedstruct":false,"isnative":true,"kind":"O","name":"object","ndim":0,"num":17,"str":"|O"},"stop_sequence":{"alignment":4,"byteorder":"=","descr":[["","<i8"]],"flags":0,"isalignedstruct":false,"isnative":true,"kind":"i","name":"int64","ndim":0,"num":9,"str":"<i8"},"trip_id":{"alignment":4,"byteorder":"|","descr":[["","|O"]],"flags":63,"isalignedstruct":false,"isnative":true,"kind":"O","name":"object","ndim":0,"num":17,"str":"|O"}}'
----
json.dumps( df.dtypes.to_dict() )
...
TypeError: dtype('O') is not JSON serializable

----
list(xdf.dtypes)
[dtype('O'),
 dtype('O'),
 dtype('int64'),
 dtype('O'),
 dtype('int64'),
 dtype('O'),
 dtype('O'),
 dtype('int64'),
 dtype('O')]

How to save and export/archive dtype information of a pandas DataFrame?

Answer 1

pd.DataFrame.dtypes returns a pd.Series object. This means you can manipulate it as you would any regular series in Pandas:

df = pd.DataFrame({'A': [''], 'B': [1.0], 'C': [1], 'D': [True]})

res = df.dtypes.to_frame('dtypes').reset_index()

print(res)

  index   dtypes
0     A   object
1     B  float64
2     C    int64
3     D     bool

Output to csv / excel / pickle

You can then use any method you normally would to store a dataframe, such as to_csv, to_excel, to_pickle, etc. Note for distribution pickle is not recommended as it is version dependent.

Output to json

If you wish to easily store and load as a dictionary, a popular format is json. As you found, you need to convert to str type first:

import json

# first create dictionary
d = res.set_index('index')['dtypes'].astype(str).to_dict()

with open('types.json', 'w') as f:
    json.dump(d, f)

with open('types.json', 'r') as f:
    data_types = json.load(f)

print(data_types)

{'A': 'object', 'B': 'float64', 'C': 'int64', 'D': 'bool'}