def test_df_equivalent_after_sql(self):
# Parse the CSV
df_source = pandas_db.load_df_from_csvfile(
StringIO.StringIO(self.csv1), 0, 0)
# Store the DF in the DB
pandas_db.store_table(df_source, self.table_name)
# Load it from the DB
df_dst = pandas_db.load_table(self.table_name)
# Data frames mut be identical
assert df_source.equals(df_dst)
def df_equivalent_after_sql(self):
# Parse the CSV
df_source = pandas_db.load_df_from_csvfile(
StringIO.StringIO(self.csv1), 0, 0)
# Store the DF in the DB
pandas_db.store_table(df_source, self.table_name)
# Load it from the DB
df_dst = pandas_db.load_table(self.table_name)
# Columns have to have the same values (None and NaN are
# different)
for x in df_source.columns:
np.testing.assert_array_equal(
np.array(df_source[x], dtype=unicode),
np.array(df_dst[x], dtype=unicode))
def store_table_in_db(data_frame, pk, table_name, temporary=False):
"""
Update or create a table in the DB with the data in the data frame. It
also updates the corresponding column information
:param data_frame: Data frame to dump to DB
:param pk: Corresponding primary key of the workflow
:param table_name: Table to use in the DB
:param temporary: Boolean stating if the table is temporary,
or it belongs to an existing workflow.
:return: If temporary = True, then return a list with three lists:
- column names
- column types
- column is unique
If temporary = False, return None. All this info is stored in
the workflow
"""
if settings.DEBUG:
print('Storing table ', table_name)
# get column names
df_column_names = list(data_frame.columns)
# if the data frame is temporary, the procedure is much simpler
if temporary:
# Get the if the columns have unique values per row
column_unique = are_unique_columns(data_frame)
# Store the table in the DB
store_table(data_frame, table_name)
# Get the column types
df_column_types = df_column_types_rename(table_name)
# Return a list with three list with information about the
# data frame that will be needed in the next steps
return [df_column_names, df_column_types, column_unique]
# We are modifying an existing DF
# Get the workflow and its columns
workflow = Workflow.objects.get(id=pk)
wf_col_names = Column.objects.filter(workflow__id=pk).values_list(
"name", flat=True)
# Loop over the columns in the data frame and reconcile the column info
# with the column objects attached to the WF
for cname in df_column_names:
# See if this is a new column
if cname in wf_col_names:
# If column already exists in wf_col_names, no need to do anything
continue
# Create the new column
column = Column(
name=cname,
workflow=workflow,
data_type=pandas_datatype_names[data_frame[cname].dtype.name],
is_key=is_unique_column(data_frame[cname]),
position=Column.objects.filter(workflow=workflow).count() + 1,
)
column.save()
# Get now the new set of columns with names
wf_columns = Column.objects.filter(workflow__id=pk)
# Reorder the columns in the data frame
data_frame = data_frame[[x.name for x in wf_columns]]
# Store the table in the DB
store_table(data_frame, table_name)
# Review the column types because some "objects" are stored as booleans
column_types = df_column_types_rename(table_name)
for ctype, col in zip(column_types, wf_columns):
if col.data_type != ctype:
# If the column type in the DB is different from the one in the
# object, update
col.data_type = ctype
col.save()
# Update workflow fields and save
workflow.nrows = data_frame.shape[0]
workflow.ncols = data_frame.shape[1]
workflow.set_query_builder_ops()
workflow.data_frame_table_name = table_name
workflow.save()
return None
def test_eval_sql(self):
# Create the dataframe with the variables
df = pd.DataFrame([
(1, 2.0, True, 'xxx', datetime.datetime(2018, 01, 01, 00, 00, 00)),
(None, None, None, None, None)
],
columns=[
'v_integer', 'v_double', 'v_boolean', 'v_string',
'v_datetime'
])
# Store the data frame
pandas_db.store_table(df, 'TEST_TABLE')
#
# EQUAL
#
self.do_sql_operand('number', '{0}equal', 'integer', '1')
self.do_sql_operand('number', '{0}equal', 'double', '2.0')
self.do_sql_operand('number', '{0}equal', 'boolean', '1')
self.do_sql_operand('text', '{0}equal', 'string', 'xxx')
self.do_sql_operand('text', '{0}equal', 'datetime',
'2018-01-01T00:00:00')
#
# BEGINS WITH
#
self.do_sql_operand('text', '{0}begins_with', 'string', 'x')
#
# CONTAINS
#
self.do_sql_operand('text', '{0}contains', 'string', 'xx')
#
# ENDS WITH
#
self.do_sql_operand('text', '{0}ends_with', 'string', 'xx')
#
# IS EMPTY
#
self.do_sql_operand('number', 'is_{0}empty', 'integer', None)
self.do_sql_operand('number', 'is_{0}empty', 'double', None)
self.do_sql_operand('text', 'is_{0}empty', 'string', None)
self.do_sql_operand('text', 'is_{0}empty', 'boolean', None)
self.do_sql_operand('text', 'is_{0}empty', 'datetime', None)
#
# LESS
#
self.do_sql_operand('number', 'less', 'integer', '2')
self.do_sql_operand('number', 'less', 'double', '3.2')
self.do_sql_operand('text', 'less', 'datetime', '2018-01-02T00:00:00')
#
# LESS OR EQUAL
#
self.do_sql_operand('number', 'less_or_equal', 'integer', '1')
self.do_sql_operand('number', 'less_or_equal', 'double', '2.0')
self.do_sql_operand('text', 'less_or_equal', 'datetime',
'2018-01-01T00:00:00')
#
# GREATER
#
self.do_sql_operand('number', 'greater', 'integer', '0')
self.do_sql_operand('number', 'greater', 'double', '1.2')
self.do_sql_operand('text', 'greater', 'datetime',
'2017-01-01T00:00:00')
#
# GREATER OR EQUAL
#
self.do_sql_operand('number', 'greater_or_equal', 'integer', '1')
self.do_sql_operand('number', 'greater_or_equal', 'double', '2.0')
self.do_sql_operand('text', 'greater_or_equal', 'datetime',
'2018-01-01T00:00:00')
#
# BETWEEN
#
self.do_sql_operand('number', '{0}between', 'integer', ['0', '2'])
self.do_sql_operand('number', '{0}between', 'double', ['1.2', '2.2'])
self.do_sql_operand('text', '{0}between', 'datetime',
['2017-01-01T00:00:00', '2018-09-13T00:00:00'])
def store_table_in_db(data_frame,
pk,
table_name,
temporary=False,
reset_keys=True):
"""
Update or create a table in the DB with the data in the data frame. It
also updates the corresponding column information
:param data_frame: Data frame to dump to DB
:param pk: Corresponding primary key of the workflow
:param table_name: Table to use in the DB
:param temporary: Boolean stating if the table is temporary,
or it belongs to an existing workflow.
:param reset_keys: Reset the value of the field is_key computing it from
scratch
:return: If temporary = True, then return a list with three lists:
- column names
- column types
- column is unique
If temporary = False, return None. All this info is stored in
the workflow
"""
if settings.DEBUG:
print('Storing table ', table_name)
# get column names
df_column_names = list(data_frame.columns)
# if the data frame is temporary, the procedure is much simpler
if temporary:
# Get the if the columns have unique values per row
column_unique = are_unique_columns(data_frame)
# Store the table in the DB
store_table(data_frame, table_name)
# Get the column types
df_column_types = df_column_types_rename(table_name)
# Return a list with three list with information about the
# data frame that will be needed in the next steps
return [df_column_names, df_column_types, column_unique]
# We are modifying an existing DF
# Get the workflow and its columns
workflow = Workflow.objects.get(id=pk)
wf_cols = workflow.columns.all()
# Loop over the columns in the Workflow to refresh the is_key value. There
# may be values that have been added to the column, so this field needs to
# be reassessed
for col in wf_cols:
if reset_keys:
new_val = is_unique_column(data_frame[col.name])
if col.is_key and not new_val:
# Only set the is_key value if the column states that it is a
# key column, but the values say no. Othe other way around
# is_key is false in the column will be ignored as it may have
# been set by the user
col.is_key = new_val
col.save()
# Remove this column name from wf_col_names, no further processing is
# needed.
df_column_names.remove(col.name)
# Loop over the remaining columns in the data frame and create the new
# column objects in the workflow
for cname in df_column_names:
# Create the new column
column = Column(
name=cname,
workflow=workflow,
data_type=pandas_datatype_names[data_frame[cname].dtype.name],
is_key=is_unique_column(data_frame[cname]),
position=workflow.columns.count() + 1
)
column.save()
# Get the new set of columns with names
wf_columns = workflow.columns.all()
# Reorder the columns in the data frame
data_frame = data_frame[[x.name for x in wf_columns]]
# Store the table in the DB
store_table(data_frame, table_name)
# Review the column types because some "objects" are stored as booleans
column_types = df_column_types_rename(table_name)
for ctype, col in zip(column_types, wf_columns):
if col.data_type != ctype:
# If the column type in the DB is different from the one in the
# object, update
col.data_type = ctype
col.save()
# Update workflow fields and save
workflow.nrows = data_frame.shape[0]
workflow.ncols = data_frame.shape[1]
workflow.set_query_builder_ops()
workflow.data_frame_table_name = table_name
workflow.save()
return None