def _read_table(*, connection: tab_api.Connection,
table: TableType) -> pd.DataFrame:
if isinstance(table, str):
table = tab_api.TableName(table)
table_def = connection.catalog.get_table_definition(table)
columns = table_def.columns
dtypes: Dict[str, str] = {}
for column in columns:
column_type = pantab_types._ColumnType(column.type, column.nullability)
dtypes[column.name.unescaped] = _tableau_to_pandas_type(column_type)
with connection.execute_query(f"SELECT * from {table}") as result:
df = pd.DataFrame(result)
df.columns = dtypes.keys()
# The tableauhyperapi.Timestamp class is not implicitly convertible to a datetime
# so we need to run an apply against applicable types
for key, val in dtypes.items():
if val == "datetime64[ns]":
df[key] = df[key].apply(lambda x: x._to_datetime())
elif val == "datetime64[ns, UTC]":
df[key] = df[key].apply(lambda x: x._to_datetime()).dt.tz_localize(
"UTC")
elif val == "timedelta64[ns]":
df[key] = df[key].apply(_interval_to_timedelta)
df = df.astype(dtypes)
df = df.fillna(value=np.nan) # Replace any appearances of None
return df
def _read_table(*, connection: tab_api.Connection,
table: TableType) -> pd.DataFrame:
if isinstance(table, str):
table = tab_api.TableName(table)
table_def = connection.catalog.get_table_definition(table)
columns = table_def.columns
dtypes: Dict[str, str] = {}
for column in columns:
column_type = pantab_types._ColumnType(column.type, column.nullability)
try:
dtypes[column.name.
unescaped] = pantab_types._pandas_types[column_type]
except KeyError as e:
raise TypeError(
f"Column {column.name} has unsupported datatype {column.type} "
f"with nullability {column.nullability}") from e
query = f"SELECT * from {table}"
dtype_strs = tuple(dtypes.values())
df = pd.DataFrame(
libreader.read_hyper_query(connection._cdata, query, dtype_strs))
df.columns = dtypes.keys()
# TODO: remove this hackery...
for k, v in dtypes.items():
dtypes[k] = "object"
df = df.astype(dtypes)
df = df.fillna(value=np.nan) # Replace any appearances of None
return df
def _read_table(*, connection: tab_api.Connection,
table: TableType) -> pd.DataFrame:
if isinstance(table, str):
table = tab_api.TableName(table)
table_def = connection.catalog.get_table_definition(table)
columns = table_def.columns
dtypes: Dict[str, str] = {}
for column in columns:
column_type = pantab_types._ColumnType(column.type, column.nullability)
try:
dtypes[column.name.
unescaped] = pantab_types._pandas_types[column_type]
except KeyError as e:
raise TypeError(
f"Column {column.name} has unsupported datatype {column.type} "
f"with nullability {column.nullability}") from e
query = f"SELECT * from {table}"
with connection.execute_query(query) as result:
return _read_query_result(result, dtypes)
def _read_table(*, connection: tab_api.Connection, table: TableType) -> pd.DataFrame:
if isinstance(table, str):
table = tab_api.TableName(table)
table_def = connection.catalog.get_table_definition(table)
columns = table_def.columns
dtypes: Dict[str, str] = {}
for column in columns:
column_type = pantab_types._ColumnType(column.type, column.nullability)
dtypes[column.name.unescaped] = _tableau_to_pandas_type(column_type)
address = int(str(connection._cdata)[:-1].split()[-1], base=16) # HACK :-X
query = f"SELECT * from {table}"
dtype_strs = tuple(dtypes.values())
df = pd.DataFrame(libreader.read_hyper_query(address, query, dtype_strs))
df.columns = dtypes.keys()
df = df.astype(dtypes)
df = df.fillna(value=np.nan) # Replace any appearances of None
return df
def _read_query_result(
result: tab_api.Result,
dtypes: Optional[Dict[str, str]],
) -> pd.DataFrame:
if dtypes is None:
dtypes = {}
# Construct data types from result
for column in result.schema.columns:
# `result.schema` does not provide nullability information.
# Lwt's err on the safe side and always assume they are nullable
nullability = tab_api.Nullability.NULLABLE
column_type = pantab_types._ColumnType(column.type, nullability)
try:
dtypes[column.name.
unescaped] = pantab_types._pandas_types[column_type]
except KeyError as e:
raise TypeError(
f"Column {column.name} has unsupported datatype {column.type} "
f"with nullability {column.nullability}") from e
# Call native library to read tuples from result set
dtype_strs = tuple(dtypes.values())
df = pd.DataFrame(
libreader.read_hyper_query(result._Result__cdata, dtype_strs))
df.columns = dtypes.keys()
# TODO: remove this hackery...
for k, v in dtypes.items():
if v == "date":
dtypes[k] = "datetime64[ns]"
df = df.astype(dtypes)
df = df.fillna(value=np.nan) # Replace any appearances of None
return df