Dask: outer join read from multiple csv files - dask

Question

import dask.dataframe as dd
import numpy as np
from dask import delayed
df1 = pd.DataFrame({'a': np.arange(10), 'b': np.random.rand()})
df1 = df1.astype({'a':np.float64})
df2 = pd.DataFrame({'a': np.random.rand(5), 'c': 1})
df1.to_csv('df1.csv')
df2.to_csv('df2.csv')
dd.read_csv('*.csv').compute()
Gives inner join result:
Unnamed: 0 a b
0 0 0.000000 0.218319
1 1 1.000000 0.218319
2 2 2.000000 0.218319
...
And:
df1_delayed = delayed(lambda: df1)()
df2_delayed = delayed(lambda: df2)()
dd.from_delayed([df1_delayed, df2_delayed]).compute()
Gives outer join result:
a b c
0 0.000000 0.218319 NaN
1 1.000000 0.218319 NaN
2 2.000000 0.218319 NaN
...
How to make read_csv work in the same mode?
EDIT:
Even passing dtype schema down to pandas doesn't work:
dd.read_csv('*.csv', dtype={'a':np.float64, 'b': np.float64, 'c': np.float64}).compute()

Answer 1

Generally dask.dataframe assumes that all Pandas dataframes that form the dask.dataframe have the same columns and dtype. Behavior is ill-defined if this is not the case.
If your CSVs have different columns and dtypes then I recommend using dask.delayed as you've done in your second example and explicitly add the new empty columns before calling dask.dataframe.from_delayed.