dleal dleal - 1 year ago 272
Python Question

dask dataframe how to convert column to to_datetime

I am trying to convert one column of my dataframe to datetime. Following the discussion here https://github.com/dask/dask/issues/863 I tried the following code:

import dask.dataframe as dd
df['time'].map_partitions(pd.to_datetime, columns='time').compute()

But I am getting the following error message

ValueError: Metadata inference failed, please provide `meta` keyword

What exactly should I put under meta? should I put a dictionary of ALL the columns in df or only of the 'time' column? and what type should I put? I have tried dtype and datetime64 but none of them work so far.

Thank you and I appreciate your guidance,


I will include here the new error messages:

1) Using Timestamp


TypeError: Cannot convert input to Timestamp

2) Using datetime and meta

meta = ('time', pd.Timestamp)
TypeError: to_datetime() got an unexpected keyword argument 'meta'

3) Just using date time: gets stuck at 2%

In [14]: df['trd_exctn_dt'].map_partitions(pd.to_datetime).compute()
[ ] | 2% Completed | 2min 20.3s

Also, I would like to be able to specify the format in the date, as i would do in pandas:

pd.to_datetime(df['time'], format = '%m%d%Y'

Update 2

After updating to Dask 0.11, I no longer have problems with the meta keyword. Still, I can't get it past 2% on a 2GB dataframe.

df['trd_exctn_dt'].map_partitions(pd.to_datetime, meta=meta).compute()
[ ] | 2% Completed | 30min 45.7s

Update 3

worked better this way:

def parse_dates(df):
return pd.to_datetime(df['time'], format = '%m/%d/%Y')

df.map_partitions(parse_dates, meta=meta)

I'm not sure whether it's the right approach or not

Answer Source

Use astype

You can use the astype method to convert the dtype of a series to a NumPy dtype


There is probably a way to specify a Pandas style dtype as well (edits welcome)

Use map_partitions and meta

When using black-box methods like map_partitions, dask.dataframe needs to know the type and names of the output. There are a few ways to do this listed in the docstring for map_partitions.

You can supply an empty Pandas object with the right dtype and name

meta = pd.Series([], name='time', dtype=pd.Timestamp)

Or you can provide a tuple of (name, dtype) for a Series or a dict for a DataFrame

meta = ('time', pd.Timestamp)

Then everything should be fine

df.time.map_partitions(pd.Timestamp, meta=meta)

If you were calling map_partitions on df instead then you would need to provide the dtypes for everything. That isn't the case in your example though.

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