df.append() with dicts converts booleans to 1s and 0s

Question

Say I create an empty dataframe:

df = pd.DataFrame()

and I add a dict via df.append():

df.append({'A': 'foo', 'B': 'bar'}, ignore_index=True)

This gives me the intended result of

     A    B
0  foo  bar

However, if there are any booleans in the dict values, i.e.,

df.append({'A': True, 'B': False}, ignore_index=True)

The booleans are converted into floats.

     A    B
0  1.0  0.0

Why this is happening / how can I prevent this conversion? I'd prefer not do anything to the finished dataframe if possible (i.e., prefer not to coerce from float back to boolean).

EDIT: found my own solution, but still would like to know why the above behavior is happening. My solution is:

df.append(pd.DataFrame.from_dict({'A': True, 'B': False}, orient='index').T, ignore_index=True)

Which gives the desired

      A      B
0  True  False

Answer 1

You can convert your dict to a DataFrame before appending to keep the data types consistent:

df = pd.DataFrame()
df.append(pd.DataFrame({'A': True, 'B': False}, index = [0]))


     A      B
0   True    False