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MAINT: remove filling in missing value and ffill before coercing column

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This commit is contained in:
Maya Tydykov
2016-05-23 15:53:55 -04:00
parent c0eb798cc6
commit 3a3c7db844
1 changed files with 15 additions and 25 deletions
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zipline/pipeline/loaders/blaze/core.py
+15 -25
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@@ -1030,40 +1030,30 @@ class BlazeLoader(dict):
)
else:
last_in_group = last_in_group.reindex(dates)
# Unstack will fill all missing values with NaN; we need to fix
# this for all types that are not float.
if not df.empty:
for column in columns:
if df[column.name].dtype == categorical_dtype:
last_in_group[column.name] = last_in_group[
column.name
].where(pd.notnull(last_in_group[column.name]),
column.missing_value)
# Need to convert from float col to datetime col
elif df[column.name].dtype == datetime64ns_dtype:
last_in_group[column.name] = last_in_group[
column.name
].astype('datetime64[ns]')
else:
last_in_group[column.name] = last_in_group[
column.name
].fillna(column.missing_value)
return last_in_group
sparse_deltas = last_in_date_group(non_novel_deltas, reindex=False)
dense_output = last_in_date_group(sparse_output, reindex=True)
dense_output = dense_output.ffill()
# Unstack will fill all missing values with NaN; we need to fix
# this for all types that are not float.
for column in columns:
if have_sids:
if column.dtype == categorical_dtype:
dense_output[column.name] = dense_output[
column.name
].apply(lambda x: x.replace(
to_replace=column.missing_value, method='ffill'
))
].where(pd.notnull(dense_output[column.name]),
column.missing_value)
# Need to convert from float col to datetime col
elif column.dtype == datetime64ns_dtype:
dense_output[column.name] = dense_output[
column.name
].astype('datetime64[ns]')
else:
dense_output[column.name] = dense_output[column.name].replace(
to_replace=column.missing_value, method='ffill'
)
dense_output[column.name] = dense_output[
column.name
].fillna(column.missing_value)
if have_sids:
adjustments_from_deltas = adjustments_from_deltas_with_sids