def place(
df: pd.DataFrame,
path: str,
filesystem: FileSystem = FileSystem(),
tmpdir: str = None,
**write_args,
) -> None:
"""Place a dataframe block onto the filesystem at the specified path
Parameters
----------
df : pd.DataFrame
The data to place
path : str
Path to the directory (possibly on GCS) in which to place the columns
write_args : dict
Any additional args to pass to the write function
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
"""
fname = os.path.basename(path)
tmp = os.path.join(tmpdir, fname)
write_df(df, tmp, **write_args)
filesystem.copy(tmp, path)
def _access(cgroups, filesystem: FileSystem,
tmpdir: str) -> Dict[cgroup, List[str]]:
"""Access potentially cloud stored files, preserving cgroups"""
updated = {}
for cgroup, paths in cgroups.items():
if filesystem._get_protocol_path(paths)[0] is None:
updated[cgroup] = paths
else:
tmp_cgroup = os.path.join(tmpdir, cgroup, "")
filesystem.copy(paths, tmp_cgroup)
updated[cgroup] = filesystem.ls(tmp_cgroup)
return updated
def unpickle(path: str, filesystem: FileSystem = FileSystem()):
"""Load an object from the pickle file at path
Parameters
----------
obj : Object
Any pickle compatible object
path : str
The path to the location of the saved pickle file, support gcs paths
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
"""
with filesystem.open(path, "rb") as f:
return _pickle.load(f)
def pickle(obj: Any, path: str, filesystem: FileSystem = FileSystem()):
"""Save a pickle of obj at the specified path
Parameters
----------
obj : Object
Any pickle compatible object
path : str
The path to the location to save the pickle file, support gcs paths
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
"""
with filesystem.open(path, "wb") as f:
_pickle.dump(obj, f)
def _expand(paths: Sequence[str], filesystem: FileSystem) -> List[str]:
"""For any directories in paths, expand into all the contained files"""
expanded = []
for path in paths:
if _has_ext(path):
# Has an extension so treat it as a file
expanded.append(path)
else:
# Otherwise try to read it like a directory
expanded += filesystem.ls(os.path.join(path, "**"))
return [p for p in expanded if _has_ext(p)]
def _expand(paths: Sequence[str], filesystem: FileSystem) -> List[str]:
"""For any directories in paths, expand into all the contained files"""
expanded = []
for path in paths:
if _has_ext(path):
# Has an extension so treat it as a file
expanded.append(path)
else:
# Otherwise try to read it like a directory
expanded += filesystem.ls(path + "**")
# Some cases might result in duplicates, so we convert to set and back
return sorted(set(p for p in expanded if _has_ext(p)))
def _collect(
path: str,
cgroups: Optional[Sequence[cgroup]],
rgroups: Optional[Sequence[rgroup]],
filesystem: FileSystem,
tmpdir: str,
) -> Dict[cgroup, Sequence[str]]:
"""Collect paths into cgroups and download any gcs files for local access
Parameters
----------
path : str
The glob-able path to all files to assemble into a frame
e.g. gs://example/*/*, gs://example/*/part.0.pq, gs://example/c[1-2]/*
See the README for a more detailed explanation
cgroups : list of str, optional
The list of cgroups (folder names) to include from the glob path
rgroups : list of str, optional
The list of rgroups (file names) to include from the glob path
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
tmpdir : str
The path of a temporary directory to use for copies of files
Returns
-------
grouped : {cgroup: list of paths}
Paths to local copies of the data, grouped by cgroup
"""
# ----------------------------------------
# Collect paths into cgroups
# ----------------------------------------
paths = filesystem.ls(path)
if not paths:
raise ValueError(f"Did not find any files at the path: {path}")
expanded = _expand(paths, filesystem)
filtered = _filter(expanded, cgroups, rgroups)
grouped = _cgroups(filtered)
accessed = _access(grouped, filesystem, tmpdir)
# Go in specified cgroup order, or alphabetical if not specified
if cgroups is None:
cgroups = sorted(grouped.keys())
return OrderedDict((k, accessed[k]) for k in cgroups)
def place(
df: pd.DataFrame, path: str, filesystem: FileSystem = GCSFileSystem(), **write_args
) -> None:
"""Place a dataframe block onto the filesystem at the specified path
Parameters
----------
df : pd.DataFrame
The data to place
path : str
Path to the directory (possibly on GCS) in which to place the columns
write_args : dict
Any additional args to pass to the write function
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
"""
bucket, fname = os.path.dirname(path), os.path.basename(path)
with filesystem.store(bucket, [fname]) as datafiles:
write_df(df, datafiles[0], **write_args)
def iterate(
path: str,
axis: int = -1,
cgroups: Optional[Sequence[cgroup]] = None,
rgroups: Optional[Sequence[rgroup]] = None,
read_args: Any = {},
cgroup_args: Dict[cgroup, Any] = {},
merge: str = "inner",
filesystem: FileSystem = FileSystem(),
tmpdir: str = None,
) -> Union[Iterator[Tuple[cgroup, rgroup, pd.DataFrame]], Iterator[Tuple[
str, pd.DataFrame]]]:
"""Iterate over dataframe blocks
Each file include in the path (or subdirs of that path) is opened as a
dataframe and returned in a generator of (cname, rname, dataframe).
Files are opened by a method inferred from their extension
Parameters
----------
path : str
The glob-able path to all files to assemble into a frame
e.g. gs://example/*/*, gs://example/*/part.0.pq, gs://example/c[1-2]/*
See the README for a more detailed explanation
axis : int, default -1
The axis to iterate along
If -1 (the default), iterate over both columns and rows
If 0, iterate over the rgroups, combining any cgroups
If 1, iterate over the cgroups, combining any rgroups
cgroups : list of str, or {str: args} optional
The list of cgroups (folder names) to include from the glob path
rgroups : list of str, optional
The list of rgroups (file names) to include from the glob path
read_args : dict, optional
Any additional keyword args to pass to the read function
cgroup_args : {cgroup: kwargs}, optional
Any cgroup specific read arguments, where each key is the name
of the cgroup and each value is a dictionary of keyword args
merge : one of 'left', 'right', 'outer', 'inner', default 'inner'
The merge strategy to pass to pandas.merge, only used when axis=0
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
Returns
-------
data : generator
A generator of (cname, rname, dataframe) for each collected path
If axis=0, yields (rname, dataframe)
If axis=1, yields (cname, dataframe)
"""
grouped = _collect(path, cgroups, rgroups, filesystem, tmpdir)
if axis == -1:
for cgroup in grouped:
args = read_args.copy()
if cgroup in cgroup_args:
args.update(cgroup_args[cgroup])
for path in grouped[cgroup]:
yield _cname(path), _rname(path), read_df(path, **args)
elif axis == 0:
# find the shared files among all subfolders
rgroups = _shared_rgroups(grouped)
for rgroup in sorted(rgroups):
frames = []
for cgroup in grouped:
path = next(d for d in grouped[cgroup] if _rname(d) == rgroup)
args = read_args.copy()
if cgroup in cgroup_args:
args.update(cgroup_args[cgroup])
frames.append(read_df(path, **args))
yield rgroup, _merge_all(frames, merge=merge)
elif axis == 1:
for cgroup in grouped:
files = grouped[cgroup]
args = read_args.copy()
if cgroup in cgroup_args:
args.update(cgroup_args[cgroup])
yield cgroup, pd.concat(read_df(path, **args) for path in files)
else:
raise ValueError("Invalid choice for axis, options are -1, 0, 1")
def assemble(
path: str,
cgroups: Optional[Sequence[cgroup]] = None,
rgroups: Optional[Sequence[rgroup]] = None,
read_args: Any = {},
cgroup_args: Dict[cgroup, Any] = {},
merge: str = "inner",
filesystem: FileSystem = FileSystem(),
tmpdir: str = None,
) -> pd.DataFrame:
"""Assemble multiple dataframe blocks into a single frame
Each file included in the path (or subdirs of that path) is combined into
a single dataframe by first concatenating over row groups and then merging
over column groups. A row group is a subset of rows of the data stored in
different files. A column group is a subset of columns of the data stored in
different folders. The merges are performed in the order of listed cgroups if
provided, otherwise in alphabetic order. Files are opened by a method inferred
from their extension.
Parameters
----------
path : str
The glob-able path to all data files to assemble into a frame
e.g. gs://example/*/*, gs://example/*/part.0.pq, gs://example/c[1-2]/*
See the README for a more detailed explanation
cgroups : list of str, optional
The list of cgroups (folder names) to include from the glob path
rgroups : list of str, optional
The list of rgroups (file names) to include from the glob path
read_args : optional
Any additional keyword args to pass to the read function
cgroup_args : {cgroup: kwargs}, optional
Any cgroup specific read arguments, where each key is the name
of the cgroup and each value is a dictionary of keyword args
merge : one of 'left', 'right', 'outer', 'inner', default 'inner'
The merge strategy to pass to pandas.merge
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
Returns
-------
data : pd.DataFrame
The combined dataframe from all the blocks
"""
grouped = _collect(path, cgroups, rgroups, filesystem, tmpdir)
# ----------------------------------------
# Concatenate all rgroups
# ----------------------------------------
frames = []
for group in grouped:
files = grouped[group]
args = read_args.copy()
if group in cgroup_args:
args.update(cgroup_args[group])
frames.append(pd.concat(read_df(f, **args) for f in files))
# ----------------------------------------
# Merge all cgroups
# ----------------------------------------
df = _merge_all(frames, merge=merge)
return df
def divide(
df: pd.DataFrame,
path: str,
n_rgroup: int = 1,
rgroup_offset: int = 0,
cgroup_columns: Optional[Dict[Optional[cgroup], Sequence[str]]] = None,
extension: str = ".pq",
convert: bool = False,
filesystem: FileSystem = FileSystem(),
prefix=None,
tmpdir: str = None,
**write_args,
) -> None:
"""Split a dataframe into rgroups/cgroups and save to disk
Note that this splitting does not preserve the original index, so make sure
to have another column to track values
Parameters
----------
df : pd.DataFrame
The data to divide
path : str
Path to the directory (possibly on GCS) in which to place the columns
n_rgroup : int, default 1
The number of row groups to partition the data into
The rgroups will have approximately equal sizes
rgroup_offset : int, default 0
The index to start from in the name of file parts
e.g. If rgroup_offset=10 then the first file will be `part_00010.pq`
cgroup_columns : {cgroup: list of column names}
The column lists to form cgroups; if None, do not make cgroups
Each key is the name of the cgroup, and each value is the list of columns to include
To reassemble later make sure to include join keys for each cgroup
extension : str, default .pq
The file extension for the dataframe (file type inferred from this extension
convert : bool, default False
If true attempt to coerce types to numeric. This can avoid issues with ambiguous
object columns but requires additional time
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
prefix: str
Prefix to add to written filenames
write_args : dict
Any additional args to pass to the write function
"""
# Use a single dummy cgroup if None wanted
if cgroup_columns is None:
cgroup_columns = {None: df.columns}
# Add leading dot if not in extension
if extension[0] != ".":
extension = "." + extension
if convert:
for col in df.columns:
df[col] = pd.to_numeric(df[col], errors="ignore")
files = []
for cname, columns in cgroup_columns.items():
cgroup = df[columns]
bucket = os.path.join(path, cname) if cname else path
tmp_cgroup = os.path.join(tmpdir, cname) if cname else tmpdir
if not filesystem.isdir(tmp_cgroup):
filesystem.mkdir(tmp_cgroup)
rnames = [
"part_{:05d}{}".format(i + rgroup_offset, extension)
for i in range(n_rgroup)
]
if prefix is not None:
rnames = [prefix + "_" + rn for rn in rnames]
for rgroup, rname in zip(np.array_split(cgroup, n_rgroup), rnames):
tmp = os.path.join(tmp_cgroup, rname)
write_df(rgroup.reset_index(drop=True), tmp, **write_args)
files.append((cname, rname) if cname else (rname, ))
filesystem.copy(
[os.path.join(tmpdir, *f) for f in files],
[os.path.join(path, *f) for f in files],
)
def partitioned(
path: str,
cgroups: Sequence[cgroup] = None,
rgroups: Sequence[rgroup] = None,
read_args: Any = {},
cgroup_args: Dict[cgroup, Any] = {},
merge: str = "inner",
filesystem: FileSystem = FileSystem(),
tmpdir: str = None,
):
"""Return a partitioned dask dataframe, where each partition is a row group
The results are the same as iterate with axis=0, except that it returns a dask dataframe
instead of a generator. Note that this requires dask to be installed
Parameters
----------
path : str
The glob-able path to all files to assemble into a frame
e.g. gs://example/*/*, gs://example/*/part.0.pq, gs://example/c[1-2]/*
See the README for a more detailed explanation
cgroups : list of str, or {str: args} optional
The list of cgroups (folder names) to include from the glob path
rgroups : list of str, optional
The list of rgroups (file names) to include from the glob path
read_args : dict, optional
Any additional keyword args to pass to the read function
cgroup_args : {cgroup: kwargs}, optional
Any cgroup specific read arguments, where each key is the name
of the cgroup and each value is a dictionary of keyword args
merge : one of 'left', 'right', 'outer', 'inner', default 'inner'
The merge strategy to pass to pandas.merge, only used when axis=0
filesystem : blocks.filesystem.FileSystem or similar
A filesystem object that implements the blocks.FileSystem API
Returns
-------
data : dask.dataframe
A dask dataframe partitioned by row groups, with all cgroups merged
"""
try:
import dask
import dask.dataframe as dd
except ImportError:
raise ImportError(
"Partitioned requires dask[dataframe] to be installed")
grouped = _collect(path, cgroups, rgroups, filesystem, tmpdir)
blocks = []
@dask.delayed()
def merged(rgroup):
frames = []
for cgroup in grouped:
p = next(p for p in grouped[cgroup]
if os.path.basename(p) == rgroup)
args = read_args.copy()
if cgroup in cgroup_args:
args.update(cgroup_args[cgroup])
frames.append(read_df(p, **args))
return _merge_all(frames, merge=merge)
for rgroup in _shared_rgroups(grouped):
blocks.append(merged(rgroup))
return dd.from_delayed(blocks)
def _access(cgroups, filesystem: FileSystem) -> Dict[cgroup, List[DataFile]]:
"""Access potentially cloud stored files, preserving cgroups"""
updated = {}
for cgroup, paths in cgroups.items():
updated[cgroup] = filesystem.access(paths)
return updated
def fs(request):
return FileSystem()