def apply(self,
func,
col=None,
columns=None,
max_rows=cfg.MAX_ROWS,
fetch=True,
dest=None,
chunksize=10000):
'''idea is to (in a majorly hacky way) allow arbitrary python "udfs" but pulling each row locally and applying the python function, then writing back to bq'''
# TODO make work and allow user to provide arguments to function
if col is None:
col = self.active_col
startrow = 0
while startrow < len(self):
fields, data = self.con.client.ReadSchemaAndRows(
bqutil.dictify(self.remote),
start_row=startrow,
max_rows=chunksize)
ndf = bqresult_2_df(fields, data)
ndf[col + '_mod'] = ndf[col].apply(func)
if dest is None:
dest = self.remote + '_mod_%s' % col
ndf = ndf[[col + '_mod']]
_, _ = write_df_to_remote(self.con,
ndf,
overwrite_method='append',
**bqutil.dictify(dest))
startrow += chunksize
if not self._check_write(dest):
warnings.warn('remote writing of UDF apply function failed')
combined_df = BQDF(self.con, dest)
return combined_df
def slice(self, start=0, end=10):
# NOTE need to fit slice locally
# see if there is a bigquery way to do this
fields, data = self.con.client.ReadSchemaAndRows(
bqutil.dictify(self.remote), start_row=start, max_rows=end - start)
ndf = bqresult_2_df(fields, data)
dest = self.remote + '_slice_%sto%s' % (start, end)
_ = write_df_to_remote(self.con, ndf, **bqutil.dictify(dest))
if not self._check_write(dest):
warnings.warn('failed to write new slice to bigquery')
ndf = BQDF(self.con, dest)
ndf.refresh()
return ndf
def slice(self, start=0, end=10):
# NOTE need to fit slice locally
# see if there is a bigquery way to do this
fields, data = self.con.client.ReadSchemaAndRows(bqutil.dictify(
self.remote),
start_row=start,
max_rows=end - start)
ndf = bqresult_2_df(fields, data)
dest = self.remote + '_slice_%sto%s' % (start, end)
_ = write_df_to_remote(self.con, ndf, **bqutil.dictify(dest))
if not self._check_write(dest):
warnings.warn('failed to write new slice to bigquery')
ndf = BQDF(self.con, dest)
ndf.refresh()
return ndf
def _get_nth_row(self, n):
fields, data = con.client.ReadSchemaAndRows(bqutil.dictify(
self.remote),
start_row=n,
max_rows=1)
result = {f['name']: d for f, d in zip(fields, data[0])}
return result
def groupby_apply(self, groupingcol, func, columns=None, max_rows=cfg.MAX_ROWS, fetch=True, dest=None):
''' same as apply (python udf hack) but for groups analogous to df.groupby('col').apply(myfunc)
# TODO make work and allow user to provide arguments
groups data by grouping column and performs requested operations on other columns
INPUTS:
groupingcol (str): column to group on
func (python function): takes arbitrary python function that acts on all data in a group
columns (list): list of column names to touch with function
OUTPUTS:
ndf: BQDF instance for result
'''
dest = None
if columns is None:
columns is self.columns
for group in self.unique(groupingcol):
group_query = "SELECT %s FROM %s WHERE %s == %s" (
', '.join(columns), self.tablename, groupingcol, group)
ndf = self.query(group_query, fetch=True, dest=dest)
applied_ndf = func(ndf.local)
if dest is None:
gdf = self.query(group_query, fetch=True, dest=None)
dest = gdf.remote
_, _ = write_df_to_remote(
self.con, applied_ndf, overwrite_method='append', **bqutil.dictify(dest))
if not self._check_write(dest):
warnings.warn(
'remote writing of UDF groupby-apply function failed')
gdf = BQDF(self.con, '%s' % dest)
return gdf
def apply(self, func, col=None, columns=None, max_rows=cfg.MAX_ROWS, fetch=True, dest=None, chunksize=10000):
'''idea is to (in a majorly hacky way) allow arbitrary python "udfs" but pulling each row locally and applying the python function, then writing back to bq'''
# TODO make work and allow user to provide arguments to function
if col is None:
col = self.active_col
startrow = 0
while startrow < len(self):
fields, data = self.con.client.ReadSchemaAndRows(
bqutil.dictify(self.remote), start_row=startrow, max_rows=chunksize)
ndf = bqresult_2_df(fields, data)
ndf[col + '_mod'] = ndf[col].apply(func)
if dest is None:
dest = self.remote + '_mod_%s' % col
ndf = ndf[[col + '_mod']]
_, _ = write_df_to_remote(
self.con, ndf, overwrite_method='append', **bqutil.dictify(dest))
startrow += chunksize
if not self._check_write(dest):
warnings.warn('remote writing of UDF apply function failed')
combined_df = BQDF(self.con, dest)
return combined_df
def create_column_from_values(con, col, content, remotetable, length=None):
'''create new dataframe with column content (which can then be joined with existing table)'''
d = bqutil.dictify(remotetable)
d['tableId'] = d['tableId'] + '_newcol_' + \
str(np.random.randint(1000, 10000))
if not hasattr(content, '__iter__'):
try:
np.isnan(content)
except TypeError:
content = unicode(content)
content = [content for i in range(length)]
df = pd.DataFrame({col: content})
con, dest = write_df_to_remote(
con, df, overwrite_method='fail', projectId=d['projectId'], datasetId=d['datasetId'], tableId=d['tableId'])
return dest
def _check_write(self, newremote, timeout=10):
'''query from a newly created table (waits until table has been fully inserted)'''
loaded = False
start_time = time()
elapsed_time = 0
while not loaded:
if elapsed_time < timeout:
resource = bqutil.get_table_resource(self.con,
bqutil.dictify(newremote))
# won't contain this attribute while actively streaming
# insertions
if 'numRows' in resource:
if int(resource['numRows']) > 0:
return True
elapsed_time = time() - start_time
sleep(.5)
else:
return False
def _check_write(self, newremote, timeout=10):
'''query from a newly created table (waits until table has been fully inserted)'''
loaded = False
start_time = time()
elapsed_time = 0
while not loaded:
if elapsed_time < timeout:
resource = bqutil.get_table_resource(
self.con, bqutil.dictify(newremote))
# won't contain this attribute while actively streaming
# insertions
if 'numRows' in resource:
if int(resource['numRows']) > 0:
return True
elapsed_time = time() - start_time
sleep(.5)
else:
return False
def create_column_from_values(con, col, content, remotetable, length=None):
'''create new dataframe with column content (which can then be joined with existing table)'''
d = bqutil.dictify(remotetable)
d['tableId'] = d['tableId'] + '_newcol_' + \
str(np.random.randint(1000, 10000))
if not hasattr(content, '__iter__'):
try:
np.isnan(content)
except TypeError:
content = unicode(content)
content = [content for i in range(length)]
df = pd.DataFrame({col: content})
con, dest = write_df_to_remote(con,
df,
overwrite_method='fail',
projectId=d['projectId'],
datasetId=d['datasetId'],
tableId=d['tableId'])
return dest
def batch_df_to_remote(con,
df,
overwrite_method='fail',
delete='True',
name=None,
projectId=None,
datasetId=None,
tableId=None):
'''write pandas dataframe as bigquery table'''
schema = {"fields": bqutil.bqjson_from_df(df, dumpjson=False)}
table_ref = {
'tableId': tableId,
'datasetId': datasetId,
'projectId': projectId
}
if overwrite_method == 'append':
write_disposition = 'WRITE_APPEND'
elif overwrite_method == 'overwrite':
write_disposition = 'WRITE_TRUNCATE'
else:
write_disposition = 'WRITE_EMPTY'
df.to_csv(tableId + '.csv', index=False)
filename = os.path.join(os.getcwd(), tableId + '.csv')
project = bqutil.dictify(self.remote)['projectId']
if name is None:
name = datasetId + tableId
bqutil.file_to_bucket(con, project, self.bucket, filename, name=name)
jobref = bucket_to_bq(con,
table_ref,
projectId,
bucket,
name,
schema=schema,
write_disposition=write_disposition,
wait=True)
if delete:
delete_from_bucket(con, project, bucket, name)
return con, bqutil.stringify(table_ref)
def batch_df_to_remote(con, df, overwrite_method='fail', delete='True', name=None, projectId=None, datasetId=None, tableId=None):
'''write pandas dataframe as bigquery table'''
schema = {"fields": bqutil.bqjson_from_df(df, dumpjson=False)}
table_ref = {'tableId': tableId,
'datasetId': datasetId,
'projectId': projectId}
if overwrite_method == 'append':
write_disposition = 'WRITE_APPEND'
elif overwrite_method == 'overwrite':
write_disposition = 'WRITE_TRUNCATE'
else:
write_disposition = 'WRITE_EMPTY'
df.to_csv(tableId + '.csv', index=False)
filename = os.path.join(os.getcwd(), tableId + '.csv')
project = bqutil.dictify(self.remote)['projectId']
if name is None:
name = datasetId + tableId
bqutil.file_to_bucket(con, project, self.bucket, filename, name=name)
jobref = bucket_to_bq(con, table_ref, projectId, bucket, name,
schema=schema, write_disposition=write_disposition, wait=True)
if delete:
delete_from_bucket(con, project, bucket, name)
return con, bqutil.stringify(table_ref)
def groupby_apply(self,
groupingcol,
func,
columns=None,
max_rows=cfg.MAX_ROWS,
fetch=True,
dest=None):
''' same as apply (python udf hack) but for groups analogous to df.groupby('col').apply(myfunc)
# TODO make work and allow user to provide arguments
groups data by grouping column and performs requested operations on other columns
INPUTS:
groupingcol (str): column to group on
func (python function): takes arbitrary python function that acts on all data in a group
columns (list): list of column names to touch with function
OUTPUTS:
ndf: BQDF instance for result
'''
dest = None
if columns is None:
columns is self.columns
for group in self.unique(groupingcol):
group_query = "SELECT %s FROM %s WHERE %s == %s" (
', '.join(columns), self.tablename, groupingcol, group)
ndf = self.query(group_query, fetch=True, dest=dest)
applied_ndf = func(ndf.local)
if dest is None:
gdf = self.query(group_query, fetch=True, dest=None)
dest = gdf.remote
_, _ = write_df_to_remote(self.con,
applied_ndf,
overwrite_method='append',
**bqutil.dictify(dest))
if not self._check_write(dest):
warnings.warn(
'remote writing of UDF groupby-apply function failed')
gdf = BQDF(self.con, '%s' % dest)
return gdf
def get_resource(self, remote):
'''fetch info about remote table'''
return self.con.client._apiclient.tables().get(
**bqutil.dictify(remote)).execute()
def _get_nth_row(self, n):
fields, data = con.client.ReadSchemaAndRows(
bqutil.dictify(self.remote), start_row=n, max_rows=1)
result = {f['name']: d for f, d in zip(fields, data[0])}
return result
def get_resource(self, remote):
'''fetch info about remote table'''
return self.con.client._apiclient.tables().get(**bqutil.dictify(remote)).execute()
