def csv_reader_infer_nb_pandas_type(
filepath_or_buffer, delimiter=',', names=None, usecols=None, dtype=None, skiprows=None, parse_dates=False
):
# infer column types from the first block (similarly as Arrow does this)
# TO-DO: tune the block size or allow user configure it via env var
rows_to_read = 1000
df = pd.read_csv(filepath_or_buffer, delimiter=delimiter, names=names,
usecols=usecols, dtype=dtype, skiprows=skiprows, nrows=rows_to_read,
parse_dates=parse_dates)
try:
df_type = numba.typeof(df)
except ValueError:
nb_col_types = []
for col_name in df.columns:
try:
series_type = numba.typeof(df[col_name])
col_type = series_type.data
except ValueError:
col_type = string_array_type
nb_col_types.append(col_type)
nb_col_types = tuple(nb_col_types)
nb_col_names = tuple(df.columns)
column_loc, _, _ = get_structure_maps(nb_col_types, nb_col_names)
df_type = DataFrameType(nb_col_types, PositionalIndexType(), nb_col_names, column_loc=column_loc)
return df_type
def typeof_pd_dataframe(val, c):
col_names = tuple(val.columns.tolist())
# TODO: support other types like string and timestamp
col_types = get_hiframes_dtypes(val)
index_type = _infer_index_type(val.index)
column_loc, _, _ = get_structure_maps(col_types, col_names)
return DataFrameType(col_types, index_type, col_names, True, column_loc=column_loc)
def unbox_dataframe(typ, val, c):
"""unbox dataframe to an empty DataFrame struct
columns will be extracted later if necessary.
"""
n_cols = len(typ.columns)
# create dataframe struct and store values
dataframe = cgutils.create_struct_proxy(typ)(c.context, c.builder)
errorptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
_, data_typs_map, types_order = get_structure_maps(typ.data, typ.columns)
for col_typ in types_order:
type_id, col_indices = data_typs_map[col_typ]
n_type_cols = len(col_indices)
list_type = types.List(col_typ)
ok, inst = listobj.ListInstance.allocate_ex(c.context, c.builder, list_type, n_type_cols)
with c.builder.if_else(ok, likely=True) as (if_ok, if_not_ok):
with if_ok:
inst.size = c.context.get_constant(types.intp, n_type_cols)
for i, col_idx in enumerate(col_indices):
series_obj = c.pyapi.object_getattr_string(val, typ.columns[col_idx])
arr_obj = c.pyapi.object_getattr_string(series_obj, "values")
ty_series = typ.data[col_idx]
# FIXME: CategoricalType has wrong dtype attribute value (i.e. dtype of codes)
# current implementation offers pd_dtype for this purpose, so use it
column_dtype = ty_series.pd_dtype if isinstance(ty_series, Categorical) else ty_series.dtype
native_val = _unbox_series_data(column_dtype, ty_series, arr_obj, c)
inst.setitem(c.context.get_constant(types.intp, i), native_val.value, incref=False)
c.pyapi.decref(arr_obj)
c.pyapi.decref(series_obj)
dataframe.data = c.builder.insert_value(dataframe.data, inst.value, type_id)
with if_not_ok:
c.builder.store(cgutils.true_bit, errorptr)
# If an error occurred, drop the whole native list
with c.builder.if_then(c.builder.load(errorptr)):
c.context.nrt.decref(c.builder, list_type, inst.value)
index_obj = c.pyapi.object_getattr_string(val, "index")
dataframe.index = _unbox_index_data(typ.index, index_obj, c).value
c.pyapi.decref(index_obj)
dataframe.parent = val
return NativeValue(dataframe._getvalue(), is_error=c.builder.load(errorptr))
def _gen_pandas_read_csv_func_text(col_names,
col_typs,
py_col_dtypes,
usecols,
signature=None):
func_name = 'csv_reader_py'
return_columns = usecols if usecols and isinstance(usecols[0],
str) else col_names
column_loc, _, _ = get_structure_maps(col_typs, return_columns)
df_type = DataFrameType(tuple(col_typs),
types.none,
tuple(col_names),
column_loc=column_loc)
df_type_repr = repr(df_type)
# for some reason pandas and pyarrow read_csv() return CategoricalDtype with
# ordered=False in case when dtype is with ordered=None
df_type_repr = df_type_repr.replace('ordered=None', 'ordered=False')
# TODO: support non-numpy types like strings
date_inds = ", ".join(
str(i) for i, t in enumerate(col_typs)
if t.dtype == types.NPDatetime('ns'))
return_columns = usecols if usecols and isinstance(usecols[0],
str) else col_names
if signature is None:
signature = "filepath_or_buffer"
# map generated func params into values used in inner call of pandas_read_csv
# if no transformation is needed just use outer param name (since APIs match)
# otherwise use value in the dictionary
inner_call_params = {'parse_dates': f"[{date_inds}]"}
used_read_csv_params = ('filepath_or_buffer', 'names', 'skiprows',
'parse_dates', 'dtype', 'usecols', 'sep',
'delimiter')
# pyarrow reads unnamed header as " ", pandas reads it as "Unnamed: N"
# during inference from file names should be raplaced with "Unnamed: N"
# passing names to pyarrow means that one row is header and should be skipped
if col_names and any(map(lambda x: x.startswith('Unnamed: '), col_names)):
inner_call_params['names'] = str(col_names)
inner_call_params['skiprows'] = "(skiprows and skiprows + 1) or 1"
# dtype parameter of compiled function is not used at all, instead a python dict
# of columns dtypes is captured at compile time, because some dtypes (like datetime)
# are converted and also to avoid penalty of creating dict in objmode
inner_call_params['dtype'] = 'read_as_dtypes'
params_str = '\n'.join([
f" {param}={inner_call_params.get(param, param)},"
for param in used_read_csv_params
])
func_text = '\n'.join([
f"def {func_name}({signature}):",
f" with objmode(df=\"{df_type_repr}\"):",
f" df = pandas_read_csv(\n{params_str}", f" )", f" return df"
])
global_vars = {
'read_as_dtypes': py_col_dtypes,
'objmode': objmode,
'pandas_read_csv': pandas_read_csv,
}
return func_text, func_name, global_vars
def unbox_dataframe(typ, val, c):
"""unbox dataframe to an empty DataFrame struct
columns will be extracted later if necessary.
"""
n_cols = len(typ.columns)
column_strs = [
numba.cpython.unicode.make_string_from_constant(
c.context, c.builder, string_type, a) for a in typ.columns
]
# create dataframe struct and store values
dataframe = cgutils.create_struct_proxy(typ)(c.context, c.builder)
errorptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
col_list_type = types.List(string_type)
ok, inst = listobj.ListInstance.allocate_ex(c.context, c.builder,
col_list_type, n_cols)
with c.builder.if_else(ok, likely=True) as (if_ok, if_not_ok):
with if_ok:
inst.size = c.context.get_constant(types.intp, n_cols)
for i, column_str in enumerate(column_strs):
inst.setitem(c.context.get_constant(types.intp, i),
column_str,
incref=False)
dataframe.columns = inst.value
with if_not_ok:
c.builder.store(cgutils.true_bit, errorptr)
# If an error occurred, drop the whole native list
with c.builder.if_then(c.builder.load(errorptr)):
c.context.nrt.decref(c.builder, col_list_type, inst.value)
_, data_typs_map, types_order = get_structure_maps(typ.data, typ.columns)
for col_typ in types_order:
type_id, col_indices = data_typs_map[col_typ]
n_type_cols = len(col_indices)
list_type = types.List(col_typ)
ok, inst = listobj.ListInstance.allocate_ex(c.context, c.builder,
list_type, n_type_cols)
with c.builder.if_else(ok, likely=True) as (if_ok, if_not_ok):
with if_ok:
inst.size = c.context.get_constant(types.intp, n_type_cols)
for i, col_idx in enumerate(col_indices):
series_obj = c.pyapi.object_getattr_string(
val, typ.columns[col_idx])
arr_obj = c.pyapi.object_getattr_string(
series_obj, "values")
ty_series = typ.data[col_idx]
if isinstance(ty_series, types.Array):
native_val = unbox_array(typ.data[col_idx], arr_obj, c)
elif ty_series == string_array_type:
native_val = unbox_str_series(string_array_type,
series_obj, c)
inst.setitem(c.context.get_constant(types.intp, i),
native_val.value,
incref=False)
dataframe.data = c.builder.insert_value(
dataframe.data, inst.value, type_id)
with if_not_ok:
c.builder.store(cgutils.true_bit, errorptr)
# If an error occurred, drop the whole native list
with c.builder.if_then(c.builder.load(errorptr)):
c.context.nrt.decref(c.builder, list_type, inst.value)
index_obj = c.pyapi.object_getattr_string(val, "index")
dataframe.index = _unbox_index_data(typ.index, index_obj, c).value
c.pyapi.decref(index_obj)
dataframe.parent = val
# increase refcount of stored values
if c.context.enable_nrt:
# TODO: other objects?
for var in column_strs:
c.context.nrt.incref(c.builder, string_type, var)
return NativeValue(dataframe._getvalue(),
is_error=c.builder.load(errorptr))