Reputation: 4214
I have a dataframe of params and apply a function to each row. this function is essentially a couple of sql_queries and simple calculations on the result.
I am trying to leverage Dask's multiprocessing while keeping structure and ~ interface. The example below works and indeed has a significant boost:
def get_metrics(row):
record = {'areaName': row['name'],
'areaType': row.area_type,
'borough': row.Borough,
'fullDate': row['start'],
'yearMonth': row['start'],
}
Q = Qsi.format(unittypes=At,
start_date=row['start'],
end_date=row['end'],
freq='Q',
area_ids=row['descendent_ids'])
sales = _get_DF(Q)
record['salesInventory'] = len(sales)
record['medianAskingPrice'] = sales.price.median()
R.append(record)
R = []
x = ddf.map_partition(lambda x: x.apply(_metric, axis=1), meta={'result': None})
x.compute()
result2 = pd.DataFrame(R)
However, when I try to use .apply
method instead (see below), it throws me 'DataFrame' object has no attribute 'name'
...
R = list()
y = ddf.apply(_metrics, axis=1, meta={'result': None})
Yet, ddf.head() shows that there is a name
column in the dataframe
Upvotes: 10
Views: 10409
Reputation: 453
If the output of your _metric
function is a Series, maybe you should use meta=('your series's columns name','output's dtype')
This worked for me.
Upvotes: 10