def test_concat(join):
pdf1 = pd.DataFrame({'x': [1, 2, 3, 4, 6, 7],
'y': list('abcdef')},
index=[1, 2, 3, 4, 6, 7])
ddf1 = dd.from_pandas(pdf1, 2)
pdf2 = pd.DataFrame({'x': [1, 2, 3, 4, 6, 7],
'y': list('abcdef')},
index=[8, 9, 10, 11, 12, 13])
ddf2 = dd.from_pandas(pdf2, 2)
# different columns
pdf3 = pd.DataFrame({'x': [1, 2, 3, 4, 6, 7],
'z': list('abcdef')},
index=[8, 9, 10, 11, 12, 13])
ddf3 = dd.from_pandas(pdf3, 2)
for (dd1, dd2, pd1, pd2) in [(ddf1, ddf2, pdf1, pdf2),
(ddf1, ddf3, pdf1, pdf3)]:
result = dd.concat([dd1, dd2], join=join)
expected = pd.concat([pd1, pd2], join=join)
assert eq(result, expected)
# test outer only, inner has a problem on pandas side
for (dd1, dd2, pd1, pd2) in [(ddf1, ddf2, pdf1, pdf2),
(ddf1, ddf3, pdf1, pdf3),
(ddf1.x, ddf2.x, pdf1.x, pdf2.x),
(ddf1.x, ddf3.z, pdf1.x, pdf3.z),
(ddf1.x, ddf2.x, pdf1.x, pdf2.x),
(ddf1.x, ddf3.z, pdf1.x, pdf3.z)]:
result = dd.concat([dd1, dd2])
expected = pd.concat([pd1, pd2])
assert eq(result, expected)
def test_concat3():
pdf1 = pd.DataFrame(np.random.randn(6, 5),
columns=list('ABCDE'), index=list('abcdef'))
pdf2 = pd.DataFrame(np.random.randn(6, 5),
columns=list('ABCFG'), index=list('ghijkl'))
pdf3 = pd.DataFrame(np.random.randn(6, 5),
columns=list('ABCHI'), index=list('mnopqr'))
ddf1 = dd.from_pandas(pdf1, 2)
ddf2 = dd.from_pandas(pdf2, 3)
ddf3 = dd.from_pandas(pdf3, 2)
result = dd.concat([ddf1, ddf2])
assert result.divisions == ddf1.divisions[:-1] + ddf2.divisions
assert result.npartitions == ddf1.npartitions + ddf2.npartitions
assert_eq(result, pd.concat([pdf1, pdf2]))
assert_eq(dd.concat([ddf1, ddf2], interleave_partitions=True),
pd.concat([pdf1, pdf2]))
result = dd.concat([ddf1, ddf2, ddf3])
assert result.divisions == (ddf1.divisions[:-1] + ddf2.divisions[:-1] +
ddf3.divisions)
assert result.npartitions == (ddf1.npartitions + ddf2.npartitions +
ddf3.npartitions)
assert_eq(result, pd.concat([pdf1, pdf2, pdf3]))
assert_eq(dd.concat([ddf1, ddf2, ddf3], interleave_partitions=True),
pd.concat([pdf1, pdf2, pdf3]))
def test_concat_unknown_divisions_errors():
a = pd.Series([1, 2, 3, 4, 5, 6])
b = pd.Series([4, 3, 2, 1])
aa = dd.from_pandas(a, npartitions=2, sort=False)
bb = dd.from_pandas(b, npartitions=2, sort=False)
with pytest.raises(ValueError):
dd.concat([aa, bb], axis=1).compute()
def test_concat_one_series():
a = pd.Series([1, 2, 3, 4])
aa = dd.from_pandas(a, npartitions=2, sort=False)
c = dd.concat([aa], axis=0)
assert isinstance(c, dd.Series)
c = dd.concat([aa], axis=1)
assert isinstance(c, dd.DataFrame)
def test_concat_unknown_divisions():
a = pd.Series([1, 2, 3, 4])
b = pd.Series([4, 3, 2, 1])
aa = dd.from_pandas(a, npartitions=2, sort=False)
bb = dd.from_pandas(b, npartitions=2, sort=False)
assert not aa.known_divisions
assert eq(pd.concat([a, b], axis=1), dd.concat([aa, bb], axis=1))
cc = dd.from_pandas(b, npartitions=1, sort=False)
with pytest.raises(ValueError):
dd.concat([aa, cc], axis=1)
def test_concat5():
pdf1 = pd.DataFrame(np.random.randn(7, 5),
columns=list('ABCDE'), index=list('abcdefg'))
pdf2 = pd.DataFrame(np.random.randn(7, 6),
columns=list('FGHIJK'), index=list('abcdefg'))
pdf3 = pd.DataFrame(np.random.randn(7, 6),
columns=list('FGHIJK'), index=list('cdefghi'))
pdf4 = pd.DataFrame(np.random.randn(7, 5),
columns=list('FGHAB'), index=list('cdefghi'))
pdf5 = pd.DataFrame(np.random.randn(7, 5),
columns=list('FGHAB'), index=list('fklmnop'))
ddf1 = dd.from_pandas(pdf1, 2)
ddf2 = dd.from_pandas(pdf2, 3)
ddf3 = dd.from_pandas(pdf3, 2)
ddf4 = dd.from_pandas(pdf4, 2)
ddf5 = dd.from_pandas(pdf5, 3)
cases = [[ddf1, ddf2], [ddf1, ddf3], [ddf1, ddf4], [ddf1, ddf5],
[ddf3, ddf4], [ddf3, ddf5], [ddf5, ddf1, ddf4], [ddf5, ddf3],
[ddf1.A, ddf4.A], [ddf2.F, ddf3.F], [ddf4.A, ddf5.A],
[ddf1.A, ddf4.F], [ddf2.F, ddf3.H], [ddf4.A, ddf5.B],
[ddf1, ddf4.A], [ddf3.F, ddf2], [ddf5, ddf1.A, ddf2]]
for case in cases:
pdcase = [c.compute() for c in case]
with pytest.warns(None):
# some cases will raise warning directly from pandas
assert_eq(dd.concat(case, interleave_partitions=True),
pd.concat(pdcase))
assert_eq(dd.concat(case, join='inner', interleave_partitions=True),
pd.concat(pdcase, join='inner'))
assert_eq(dd.concat(case, axis=1), pd.concat(pdcase, axis=1))
assert_eq(dd.concat(case, axis=1, join='inner'),
pd.concat(pdcase, axis=1, join='inner'))
# Dask + pandas
cases = [[ddf1, pdf2], [ddf1, pdf3], [pdf1, ddf4],
[pdf1.A, ddf4.A], [ddf2.F, pdf3.F],
[ddf1, pdf4.A], [ddf3.F, pdf2], [ddf2, pdf1, ddf3.F]]
for case in cases:
pdcase = [c.compute() if isinstance(c, _Frame) else c for c in case]
assert_eq(dd.concat(case, interleave_partitions=True),
pd.concat(pdcase))
assert_eq(dd.concat(case, join='inner', interleave_partitions=True),
pd.concat(pdcase, join='inner'))
assert_eq(dd.concat(case, axis=1), pd.concat(pdcase, axis=1))
assert_eq(dd.concat(case, axis=1, join='inner'),
pd.concat(pdcase, axis=1, join='inner'))
def test_union_with_list_types(t, df, distinct):
expr = t.union(t, distinct=distinct)
result = expr.compile()
expected = (
df if distinct else dd.concat([df, df], axis=0, ignore_index=True)
)
tm.assert_frame_equal(result.compute(), expected.compute())
def predict_price(total_amount, trip_distance, passenger_count):
# Create a dataframe out of the three columns
# and pass it to dask-xgboost, to predict
# distributed
X = dd.concat([total_amount, trip_distance, passenger_count],
axis=1).astype("float64")
return dask_xgboost.predict(client, bst, X)
def _merge_xyz(xyz_data_frames: list):
"""Try to merge xyz data frames."""
try:
df = dd.concat(xyz_data_frames, axis=0)
return df
except Exception as e:
raise (e)
def run(self):
dsk = None
if ParquetTarget(
env_workaround().return_env("local_location")
+ "rates/"
+ self.instrument
+ "/"
).exists():
input_target = next(iter(self.input()))
dsk = input_target.read()
df = self.fetch()
if dsk != None:
dsk2 = dd.from_pandas(df, chunksize=10000)
dsk = dd.concat([dsk, dsk2])
dsk = dsk.drop_duplicates()
else:
dsk = dd.from_pandas(df, chunksize=10000)
self.output().write(dsk)
if self.storage == "s3":
self.s3output().write(dsk)
def to_dc(
cls,
input_item: InputType,
table_name: str,
format: str = None,
persist: bool = True,
**kwargs,
) -> DataContainer:
"""
Turn possible input descriptions or formats (e.g. dask dataframes, pandas dataframes,
locations as string, hive tables) into the loaded data containers,
maybe persist them to cluster memory before.
"""
filled_get_dask_dataframe = lambda *args: cls._get_dask_dataframe(
*args, table_name=table_name, format=format, **kwargs,
)
if isinstance(input_item, list):
table = dd.concat([filled_get_dask_dataframe(item) for item in input_item])
else:
table = filled_get_dask_dataframe(input_item)
if persist:
table = table.persist()
return DataContainer(table.copy(), ColumnContainer(table.columns))
def _load_table(self, table):
df = None
for name, filepath in self._named_files.items():
filepath, ext = filepath
if '://' not in filepath:
filepath = os.path.join(self.root, filepath)
if name != table:
continue
load_fn, kwargs = self._load_fn(ext)
paths = self._resolve_template_vars(filepath)
if self.use_dask and ext in ('csv', 'json', 'parquet', 'parq'):
df = load_fn(paths, **kwargs)
else:
dfs = [load_fn(path, **kwargs) for path in paths]
if len(dfs) <= 1:
df = dfs[0] if dfs else None
elif self.use_dask and hasattr(dfs[0], 'compute'):
import dask.dataframe as dd
df = dd.concat(dfs)
else:
df = pd.concat(dfs)
if hasattr(df, 'persist'):
df = df.persist()
if df is None:
tables = list(self._named_files)
raise ValueError(
f"Table '{table}' not found. Available tables include: {tables}."
)
return df
def safe_concat(dfs: List[Union[dd.Series, dd.DataFrame]]) -> dd.DataFrame:
"""
Concat a list of `dd.Series` or `dd.DataFrame` objects into one DataFrame
This will use `DataFrame.concat` if all pieces are the same length.
Otherwise we will iterratively join.
When axis=1 and divisions are unknown, Dask `DataFrame.concat` can only
operate on objects with equal lengths, otherwise it will raise a
ValueError in `concat_and_check`.
See https://github.com/dask/dask/blob/2c2e837674895cafdb0612be81250ef2657d947e/dask/dataframe/multi.py#L907 # noqa
Note - this is likely to be quite slow, but this should be hit rarely in
real usage. A situtation that triggeres this slow path is aggregations
where aggregations return different numbers of rows (see
`test_aggregation_group_by` for a specific example).
TODO - performance.
"""
lengths = list(map(len, dfs))
if len(set(lengths)) != 1:
result = dfs[0].to_frame()
for other in dfs[1:]:
result = result.join(other.to_frame(), how="outer")
else:
result = dd.concat(dfs, axis=1)
return result
def from_excel(path: Union[str, List[str]], **params) -> dd.DataFrame:
"""Creates a `dask.dataframe.DataFrame` from one or several excel files.
Includes a "path column".
Parameters
----------
path
Path to files
params
Extra arguments passed on to `pandas.read_excel`
Returns
-------
df
A `dask.dataframe.DataFrame`
"""
path_list = _get_file_paths(path)
dds = []
for path_name in path_list:
parts = delayed(pd.read_excel)(path_name, **params)
data_frame = dd.from_delayed(parts).fillna("")
data_frame[PATH_COLUMN_NAME] = path_name
dds.append(data_frame)
return dd.concat(dds)
def cv_cat_mapping(X_mat, cat_feat_cols=cat_feat_cols, orig_colnames=orig_colnames):
"""Map the OHE categorical variables to their counts within CV.
"""
X_df = pd.DataFrame(X_mat) # Make matrix into pd.df
X_df.columns = orig_colnames
# Create indices for each OHE cat variable
cat_feat_1 = cat_feat_cols[0:2]
cat_feat_2 = cat_feat_cols[2:5]
cat_feat_3 = cat_feat_cols[5:7]
cat_feat_3 = cat_feat_cols[5:7]
cat_feat_4 = cat_feat_cols[7:19]
cat_feat_col_list = [cat_feat_1, cat_feat_2, cat_feat_3, cat_feat_4]
cat_mapping_list = [] # Map each OHE variable to their counts
for cat_idx in range(len(cat_feat_col_list)):
# Apply to X_df
ohe_var = X_df.loc[:,cat_feat_col_list[cat_idx]]
mapping_fun = mapping_fun_list[cat_idx]
cat_mapping_list.append(ohe_var.apply(lambda x: mapping_fun(x), axis=1))
cat_counts_df = pd.DataFrame(cat_mapping_list).T # Put results into a df
cat_counts_df.columns=['type_counts', 'time_counts', 'dose_counts', 'new_feature_counts']
X_df = dd.concat([X_df, cat_counts_df], axis=1) # Combine with X_df
X_df = X_df.compute() # Return Dask to Pandas
return X_df
def create_12_mon_features(joined_df, **kwargs):
#ddf.to_parquet(joined_df, 'dask_create_12_mon_features.pq')
testdfs = []
n_months = 12
for y in range(1, n_months + 1):
tmpdf = joined_df[[
'loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12',
'upb_12'
]]
tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf[
'timestamp_month']
tmpdf['josh_mody_n'] = np.floor(
(tmpdf['josh_months'].astype('float64') - 24000 - y) / 12)
grp = tmpdf.groupby(['loan_id', 'josh_mody_n']).agg({
'delinquency_12': 'max',
'upb_12': 'min'
}).reset_index()
tmpdf['delinquency_12'] = (tmpdf['delinquency_12'] > 3).astype('int32')
tmpdf['delinquency_12'] += (tmpdf['upb_12'] == 0).astype('int32')
tmpdf['timestamp_year'] = np.floor(
((tmpdf['josh_mody_n'] * n_months) + 24000 +
(y - 1)) / 12).astype('int16')
tmpdf['timestamp_month'] = np.int8(y)
tmpdf = tmpdf.drop(['josh_mody_n'], axis=1)
testdfs.append(tmpdf)
del (tmpdf)
del (joined_df)
return ddf.concat(testdfs)
def create_date_dataframe(dss, fields_shp, doa, class_col_name,
class_use_dict):
#Put intermediate data frames in a list
field_dataframes = []
#Iterate geo-dataframe
for idx, shape in fields_shp.iterrows():
# Indicate column of field ID
IDLote = shape['IDLote']
# Indicate column of phenology class value
class_value = shape[class_col_name]
# Indicate the geometry column
polygon = shape['geometry']
if not np.isnan(class_value):
df = get_field_dataset(dss, polygon, class_value, doa, IDLote)
df['IDLote'] = IDLote
df['time'] = doa
try:
df['tt'] = class_use_dict[IDLote]
except KeyError:
df['tt'] = 'nd'
field_dataframes.append(df)
#Concatenate dask dataframes for all fields
data = dd.concat(field_dataframes, axis=0, interleave_partitions=True)
return data
def from_parquet(path: Union[str, List[str]], **params) -> dd.DataFrame:
"""Creates a `dd.DataFrame` from one or several parquet files.
Includes a "path column".
Parameters
----------
path
Path to files
**params
Extra arguments passed on to `pandas.read_parquet`
Returns
-------
df
A `dd.DataFrame`
"""
path_list = _get_file_paths(path)
dds = []
for path_name in path_list:
ddf = dd.read_parquet(path_name, engine="pyarrow", **params)
ddf[PATH_COLUMN_NAME] = path_name
dds.append(ddf)
return dd.concat(dds)
def concat(dfs: List[DataframeLike], engine: Engine):
if engine == Engine.PANDAS:
return pd.concat(dfs, ignore_index=True, sort=False)
if engine == Engine.DASK:
import dask.dataframe
return dask.dataframe.concat(dfs).reset_index(drop=True)
if engine == Engine.CUDF:
import cudf
try:
return cudf.concat(dfs, ignore_index=True)
except TypeError as e:
logger.warning(
'Failed to concat, likely due to column type issue, try converting to a string; columns'
)
for df in dfs:
logger.warning('df types :: %s', df.dtypes)
raise e
if engine == Engine.DASK:
import dask.dataframe as dd
return dd.concat(dfs)
if engine == Engine.DASK_CUDF:
import dask_cudf
return dask_cudf.concat(dfs)
raise NotImplementedError('Unknown engine')
def from_json(path: Union[str, List[str]],
flatten: bool = False,
**params) -> dd.DataFrame:
"""Creates a `dd.DataFrame` from one or several json files.
Includes a "path column".
Parameters
----------
path
Path to files
flatten
If true, flatten nested data (default false).
**params
Extra arguments passed on to `pandas.read_json`
Returns
-------
dataframe
A `dd.DataFrame`
"""
def json_engine(*args, **kwargs) -> pd.DataFrame:
data_frame = pd.read_json(*args, **kwargs)
return flatten_dataframe(data_frame) if flatten else data_frame
path_list = _get_file_paths(path)
dds = []
for path_name in path_list:
ddf = dd.read_json(path_name, engine=json_engine, **params)
ddf[PATH_COLUMN_NAME] = path_name
dds.append(ddf)
return dd.concat(dds)
def test_set_index_sorts():
# https://github.com/dask/dask/issues/2288
vals = np.array([1348550149000000000, 1348550149000000000, 1348558142000000000,
1348558142000000000, 1348585928000000000, 1348585928000000000,
1348600739000000000, 1348601706000000000, 1348600739000000000,
1348601706000000000, 1348614789000000000, 1348614789000000000,
1348621037000000000, 1348621038000000000, 1348621040000000000,
1348621037000000000, 1348621038000000000, 1348621040000000000,
1348637628000000000, 1348638159000000000, 1348638160000000000,
1348638159000000000, 1348638160000000000, 1348637628000000000,
1348646354000000000, 1348646354000000000, 1348659107000000000,
1348657111000000000, 1348659107000000000, 1348657111000000000,
1348672876000000000, 1348672876000000000, 1348682787000000000,
1348681985000000000, 1348682787000000000, 1348681985000000000,
1348728167000000000, 1348728167000000000, 1348730745000000000,
1348730745000000000, 1348750198000000000, 1348750198000000000,
1348750198000000000, 1348753539000000000, 1348753539000000000,
1348753539000000000, 1348754449000000000, 1348754449000000000,
1348761333000000000, 1348761554000000000, 1348761610000000000,
1348761333000000000, 1348761554000000000, 1348761610000000000,
1348782624000000000, 1348782624000000000, 1348782624000000000,
1348782624000000000])
vals = pd.to_datetime(vals, unit='ns')
breaks = [10, 36, 58]
dfs = []
for i in range(len(breaks)):
lo = sum(breaks[:i])
hi = sum(breaks[i:i + 1])
dfs.append(pd.DataFrame({"timestamp": vals[lo:hi]}, index=range(lo, hi)))
ddf = dd.concat(dfs).clear_divisions()
assert ddf.set_index("timestamp").index.compute().is_monotonic is True
def test_set_index_empty_partition():
test_vals = [1, 2, 3]
converters = [
int,
float,
str,
lambda x: pd.to_datetime(x, unit='ns'),
]
for conv in converters:
df = pd.DataFrame([{
'x': conv(i),
'y': i
} for i in test_vals],
columns=['x', 'y'])
ddf = dd.concat([
dd.from_pandas(df, npartitions=1),
dd.from_pandas(df[df.y > df.y.max()], npartitions=1),
])
assert any(
ddf.get_partition(p).compute().empty
for p in range(ddf.npartitions))
assert assert_eq(ddf.set_index('x'), df.set_index('x'))
def test_set_index_sorts():
# https://github.com/dask/dask/issues/2288
vals = np.array([1348550149000000000, 1348550149000000000, 1348558142000000000,
1348558142000000000, 1348585928000000000, 1348585928000000000,
1348600739000000000, 1348601706000000000, 1348600739000000000,
1348601706000000000, 1348614789000000000, 1348614789000000000,
1348621037000000000, 1348621038000000000, 1348621040000000000,
1348621037000000000, 1348621038000000000, 1348621040000000000,
1348637628000000000, 1348638159000000000, 1348638160000000000,
1348638159000000000, 1348638160000000000, 1348637628000000000,
1348646354000000000, 1348646354000000000, 1348659107000000000,
1348657111000000000, 1348659107000000000, 1348657111000000000,
1348672876000000000, 1348672876000000000, 1348682787000000000,
1348681985000000000, 1348682787000000000, 1348681985000000000,
1348728167000000000, 1348728167000000000, 1348730745000000000,
1348730745000000000, 1348750198000000000, 1348750198000000000,
1348750198000000000, 1348753539000000000, 1348753539000000000,
1348753539000000000, 1348754449000000000, 1348754449000000000,
1348761333000000000, 1348761554000000000, 1348761610000000000,
1348761333000000000, 1348761554000000000, 1348761610000000000,
1348782624000000000, 1348782624000000000, 1348782624000000000,
1348782624000000000])
vals = pd.to_datetime(vals, unit='ns')
breaks = [10, 36, 58]
dfs = []
for i in range(len(breaks)):
lo = sum(breaks[:i])
hi = sum(breaks[i:i + 1])
dfs.append(pd.DataFrame({"timestamp": vals[lo:hi]}, index=range(lo, hi)))
ddf = dd.concat(dfs).clear_divisions()
assert ddf.set_index("timestamp").index.compute().is_monotonic is True
def run(self):
# The main function. Gets the script, creates graph and saves the result
dsk = dd.read_parquet(env_workaround().return_env("local_location") +
"trading_history/*.parquet")
self.instruments = dsk["instrument"].drop_duplicates().compute()
self.instruments = list(self.instruments.values)[1:]
self.requires()
a = self.extract(self.instruments.pop(), self.granularity)
for i in self.instruments:
b = self.extract(i, self.granularity)
a = dd.concat([a, b], axis=1)
fig, ax = plt.subplots(figsize=(12, 7))
sns_plot = sns.heatmap(
a.corr(),
xticklabels=a.columns,
yticklabels=a.columns,
annot=True,
linewidths=0.3,
)
fig = sns_plot.get_figure()
plt.title(
"Correlation of instruments in the portfolio with granularity {}".
format(self.granularity))
name = "correlation" + self.granularity + ".png"
if not os.path.exists(env_workaround().return_env("local_location") +
"images/"):
os.makedirs(env_workaround().return_env("local_location") +
"images/")
fig.savefig(env_workaround().return_env("local_location") + "images/" +
name)
self.fig = fig
def simulate_state_lines_losses(eventlookup, freq_mean, states, lines, sims):
'''assembles state line level events based on the year event
'''
logger = logging.getLogger(__name__)
logger.info('start state lines losses')
numberofevents = dd(np.random.poisson(freq_mean, sims),
index=np.arange(1, sims + 1),
columns=['events'])
catevents = simulate_events(numberofevents, eventlookup, sims)
simsevents = list(range(len(catevents)))
#combinedResults = xr.DataArray(np.empty((len(states), len(lines), len(catevents), 4)),name="catevents", coords=[states['state'], lines['line'], simsevents, ["sim", "eventseq", "eventid", "rand"]], dims=['state', 'line', 'eventsim', 'data'] )
logger.info(
'start to build full array of losses, combining state lines with events'
)
sim_events = dd()
firstloop = True
for state in states['state']:
print(f'start {state}')
for line in lines['line']:
#combinedResults.loc[state, line] = catevents.copy()
print(f'start {line}')
b = catevents.copy()
b['state'] = state
b['line'] = line
if firstloop:
sim_events = b
firstloop = False
else:
sim_events = dd.concat([sim_events, b])
#sim_events = pd.concat(a, ignore_index=True, axis=0, )
logger.info('Completed combined state lines with events')
return combinedResults
def load_dataframe(self, file_resources, npartitions=None):
"""
Args:
file_resources:
npartitions:
"""
dfs = []
for filename, content in file_resources.items():
if ".gtf" in filename:
df = read_gtf(content,
npartitions=npartitions,
compression="gzip")
dfs.append(df)
if npartitions:
annotation_df = dd.concat(dfs)
else:
annotation_df = pd.concat(dfs)
if self.remove_version_num:
annotation_df["gene_id"] = annotation_df["gene_id"].str.replace(
"[.].*", "", regex=True)
annotation_df["transcript_id"] = annotation_df[
"transcript_id"].str.replace("[.].*", "", regex=True)
return annotation_df
def fastasToDF(fastas , verbose=False, ecodDB = False):
regex = re.compile('[^a-zA-Z0-9]')
regexAA = re.compile('[^ARDNCEQGHILKMFPSTWYV]')
DFdict={}
count = 0
total = []
DDF =None
for fasta in fastas:
if verbose == True:
print(fasta)
fastaIter = SeqIO.parse(fasta, "fasta")
for seq in fastaIter:
seqstr = regexAA.sub('', str(seq.seq))
desc =str(seq.description)
fastastr = '>'+desc+'\n'+seqstr+'\n'
if desc not in total:
#check for duclipcates within a folder
total.append(desc)
DFdict[desc] = { 'desc': desc.encode(), 'seq':seqstr, 'fasta': fastastr}
if ecodDB == True:
labels = ['ECOD uid','ECOD domain' , 'EOCD hierearchy string', 'ECOD pdb_range']
for i,ecodb in enumerate(seq.description.split('|')[1:]):
DFdict[desc][labels[i]] = ecodb
count +=1
if count % 400 == 0:
df = pd.DataFrame.from_dict(DFdict, orient = 'index' )
if df is not None and len(df)>0:
if DDF is None:
DDF = dd.from_pandas(df , chunksize = 200)
else:
DDF = dd.concat([ DDF, dd.from_pandas(df , chunksize = 200) ] , interleave_partitions=True )
DFdict={}
else:
df = pd.DataFrame.from_dict(DFdict, orient = 'index')
if df is not None and len(df)>0:
if DDF is None:
DDF = dd.from_pandas(df , chunksize = 200)
else:
DDF = dd.concat([ DDF, dd.from_pandas(df , chunksize = 200) ] , interleave_partitions=True)
DFdict={}
if verbose == True:
print(df)
return DDF
async def f():
async with Scheduler(protocol=protocol, interface='ib0',
dashboard_address=':8789') as s:
async with Nanny(s.address, protocol=protocol, nthreads=1,
memory_limit='32GB',
env={'CUDA_VISIBLE_DEVICES': '2'},
) as w:
async with Nanny(s.address, protocol=protocol,memory_limit='32gb',
env={'CUDA_VISIBLE_DEVICES': '3'},
nthreads=1) as w2:
async with Client(s.address, asynchronous=True) as c:
with log_errors(pdb=True):
# Create a simple random array
#n_rows = 50000000
#n_keys = 5000000
# working!!!
n_rows = 5000000
n_keys = 500000
#n_rows = 5000000
#n_keys = 2500000
chunks = n_rows // 100
left = dd.concat([
da.random.random(n_rows, chunks=chunks).to_dask_dataframe(columns='x'),
da.random.randint(0, n_keys, size=n_rows,
chunks=chunks).to_dask_dataframe(columns='id'),], axis=1).persist()
right = dd.concat([
da.random.random(n_rows, chunks=chunks).to_dask_dataframe(columns='y'),
da.random.randint(0, n_keys, size=n_rows,
chunks=chunks).to_dask_dataframe(columns='id'),], axis=1).persist()
gright = right.map_partitions(cudf.from_pandas)
gleft = left.map_partitions(cudf.from_pandas)
#print(format_bytes(await c.compute(left.size) * 8 * 2))
#print(format_bytes(await c.compute(right.size) * 8 * 2))
res = gleft.merge(gright, on=['id'])
res = await res.persist()
print("COMPUTING HEAD()")
out = await c.compute(res.head(compute=False))
#breakpoint()
print(out)
def h5_append_dummy_row(
df: Union[pd.DataFrame, dd.DataFrame],
freq=None,
tim: Optional[Sequence[Any]] = None
) -> Union[pd.DataFrame, dd.DataFrame]:
"""
Add row of NaN with index value that will between one of last data and one of next data start
:param df: dataframe
:param freq: frequency to calc index. If logically equal to False, then will be calculated using tim
:param tim: sequence having in last elements time of 2 last rows
:return: appended dataframe
"""
if tim is not None:
try:
dindex = pd.Timedelta(
seconds=0.5 / freq) if freq else np.abs(tim[-1] - tim[-2]) / 2
except IndexError: # only one element => we think they are seldom so use 1s
dindex = pd.Timedelta(seconds=1)
ind_new = [tim[-1] + dindex]
else:
df_index, itm = multiindex_timeindex(df.index)
try:
dindex = pd.Timedelta(
seconds=0.5 /
freq) if freq else np.abs(df_index[-1] - df_index[-2]) / 2
except (IndexError, NotImplementedError):
# only one element => we think they are seldom so use 1s or NotImplemented in Dask
dindex = pd.Timedelta(seconds=1)
ind_new = multiindex_replace(df.index[-1:], df_index[-1:] + dindex,
itm)
dict_dummy = {}
tip0 = None
same_types = True # tries prevent fall down to object type (which is bad handled by pandas.pytables) if possible
for name, field in df.dtypes.iteritems():
typ = field.type
dict_dummy[name] = typ(0) if np.issubdtype(
typ,
np.integer) else np.NaN if np.issubdtype(typ, np.floating) else ''
if same_types:
if typ != tip0:
if tip0 is None:
tip0 = typ
else:
same_types = False
df_dummy = pd.DataFrame(
dict_dummy,
columns=df.columns.values,
index=ind_new,
dtype=tip0 if same_types else None).rename_axis('Time')
if isinstance(df, dd.DataFrame):
return dd.concat(
[df, df_dummy], axis=0,
interleave_partitions=True) # buggish dask not always can append
else:
return df.append(df_dummy)
def load_dataframe(self, file_resources, npartitions=None):
"""
Args:
file_resources:
npartitions:
"""
go_terms = pd.read_table(
file_resources["rnacentral_rfam_annotations.tsv"],
low_memory=True,
header=None,
names=["RNAcentral id", "GO terms", "Rfams"])
go_terms["RNAcentral id"] = go_terms["RNAcentral id"].str.split(
"_", expand=True, n=2)[0]
gene_ids = []
for file in file_resources:
if "database_mappings" in file:
if npartitions:
id_mapping = dd.read_table(file_resources[file],
header=None,
names=[
"RNAcentral id", "database",
"external id", "species",
"RNA type", "gene symbol"
])
else:
id_mapping = pd.read_table(file_resources[file],
low_memory=True,
header=None,
names=[
"RNAcentral id", "database",
"external id", "species",
"RNA type", "gene symbol"
])
gene_ids.append(id_mapping)
if npartitions:
gene_ids = dd.concat(gene_ids, join="inner")
else:
gene_ids = pd.concat(gene_ids, join="inner")
gene_ids["species"] = gene_ids["species"].astype("O")
if self.species is not None:
gene_ids = gene_ids[gene_ids["species"] == self.species]
lnc_go_terms = go_terms[go_terms["RNAcentral id"].isin(
gene_ids["RNAcentral id"])].groupby("RNAcentral id")[
"GO terms"].apply(lambda x: "|".join(x.unique()))
lnc_rfams = go_terms[go_terms["RNAcentral id"].isin(
gene_ids["RNAcentral id"])].groupby(
"RNAcentral id")["Rfams"].apply(lambda x: "|".join(x.unique()))
gene_ids["GO terms"] = gene_ids["RNAcentral id"].map(lnc_go_terms)
gene_ids["Rfams"] = gene_ids["RNAcentral id"].map(lnc_rfams)
gene_ids = gene_ids[gene_ids["GO terms"].notnull()
| gene_ids["Rfams"].notnull()]
return gene_ids
def channels(self):
def get_biometa(array):
df = dd.from_dask_array(array.channels.data)
df["Array"] = array.name
return df
datasets = self.bag.map(get_biometa)
return datasets.fold(lambda x, y: dd.concat([x, y]))
def test_to_hdf_modes_multiple_nodes():
pytest.importorskip("tables")
df = pd.DataFrame(
{"x": ["a", "b", "c", "d"], "y": [1, 2, 3, 4]}, index=[1.0, 2.0, 3.0, 4.0]
)
# appending a single partition to existing data
a = dd.from_pandas(df, 1)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data2")
a.to_hdf(fn, "/data*", mode="a")
out = dd.read_hdf(fn, "/data*")
assert_eq(dd.concat([df, df]), out)
# overwriting a file with a single partition
a = dd.from_pandas(df, 1)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data2")
a.to_hdf(fn, "/data*", mode="w")
out = dd.read_hdf(fn, "/data*")
assert_eq(df, out)
# appending two partitions to existing data
a = dd.from_pandas(df, 2)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data2")
a.to_hdf(fn, "/data*", mode="a")
out = dd.read_hdf(fn, "/data*")
assert_eq(dd.concat([df, df]), out)
# overwriting a file with two partitions
a = dd.from_pandas(df, 2)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data2")
a.to_hdf(fn, "/data*", mode="w")
out = dd.read_hdf(fn, "/data*")
assert_eq(df, out)
# overwriting a single partition, keeping other partitions
a = dd.from_pandas(df, 2)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data1")
a.to_hdf(fn, "/data2")
a.to_hdf(fn, "/data*", mode="a", append=False)
out = dd.read_hdf(fn, "/data*")
assert_eq(dd.concat([df, df]), out)
def par_mc_samples(df,
n,
reps,
replace=False,
random_state=8675309,
chunksize=100):
import dask
import dask.dataframe as dd
from copy import copy
import pandas as pd
cols = copy(df.columns)
cols = cols.insert(0, 'rep')
mc_samples_to_return = dd.from_pandas(pd.DataFrame().reindex(columns=cols),
chunksize=chunksize)
if type(df) != dask.dataframe.core.DataFrame:
dd_df = dd.from_pandas(df, chunksize=chunksize)
else:
dd_df = df
for i in range(0, reps):
frac = n / dd_df.shape[0].compute()
selected_samples_for_rep = dd_df.sample(
frac=frac, replace=replace, random_state=random_state).compute()
rep_number = []
for j in range(0, n):
rep_number.append([copy(i)])
rep_number = dd.from_pandas(pd.DataFrame(rep_number),
chunksize=chunksize)
rep_number.columns = ['rep_number']
selected_samples_for_rep.reset_index(drop=True, inplace=True)
selected_samples_for_rep = dd.from_pandas(selected_samples_for_rep,
chunksize=chunksize)
selected_samples_for_rep = dd.concat(
[copy(rep_number),
copy(selected_samples_for_rep)],
axis=1,
ignore_index=True,
sort=False)
selected_samples_for_rep.columns = cols
mc_samples_to_return = dd.concat(
[copy(mc_samples_to_return),
copy(selected_samples_for_rep)],
axis=0,
sort=False)
random_state += 1
mc_samples_to_return = mc_samples_to_return.groupby(by='rep')
return mc_samples_to_return
def make_data(n_keys, n_rows_l, n_rows_r):
left = dd.concat([
da.random.random(n_rows_l).to_dask_dataframe(columns='x'),
da.random.randint(0, n_keys,
size=n_rows_l).to_dask_dataframe(columns='id'),
],
axis=1)
right = dd.concat([
da.random.random(n_rows_r).to_dask_dataframe(columns='y'),
da.random.randint(0, n_keys,
size=n_rows_r).to_dask_dataframe(columns='id'),
],
axis=1)
gleft = left.map_partitions(cudf.from_pandas)
gright = right.map_partitions(cudf.from_pandas)
return gleft, gright
def join_instruments(self, inputs):
print('Is dask df', type(inputs[0].instrument_trades) is dd.DataFrame)
print('Is pandas df',
type(inputs[0].instrument_trades) is pd.DataFrame)
self.results = dd.concat([input.instrument_trades
for input in inputs]).reset_index(drop=True)
print('Len df', len(self.results))
self.next(self.end)
def calc(self, df_input: dd.DataFrame, df_output: dd.DataFrame,
feature_set_list: List) -> dd.DataFrame:
for feature_set in feature_set_list:
df_output = dd.concat(
[df_output,
self.calc_feature_set(df_input, feature_set)],
axis=1)
return df_output
def concat(cls, datasets, dimensions, vdims):
dataframes = []
for key, ds in datasets:
data = ds.data.copy()
for d, k in zip(dimensions, key):
data[d.name] = k
dataframes.append(data)
return dd.concat(dataframes)
def test_concat2():
dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}),
('x', 1): pd.DataFrame({'a': [4, 5, 6], 'b': [3, 2, 1]}),
('x', 2): pd.DataFrame({'a': [7, 8, 9], 'b': [0, 0, 0]})}
a = dd.DataFrame(dsk, 'x', ['a', 'b'], [None, None])
dsk = {('y', 0): pd.DataFrame({'a': [10, 20, 30], 'b': [40, 50, 60]}),
('y', 1): pd.DataFrame({'a': [40, 50, 60], 'b': [30, 20, 10]}),
('y', 2): pd.DataFrame({'a': [70, 80, 90], 'b': [0, 0, 0]})}
b = dd.DataFrame(dsk, 'y', ['a', 'b'], [None, None])
c = dd.concat([a, b])
assert c.npartitions == a.npartitions + b.npartitions
assert eq(pd.concat([a.compute(), b.compute()]), c)
assert dd.concat([a, b]).dask == dd.concat([a, b]).dask
def concat(cls, datasets, dimensions, vdims):
import dask.dataframe as dd
dataframes = []
for key, ds in datasets:
data = ds.data.copy()
for d, k in zip(dimensions, key):
data[d.name] = k
dataframes.append(data)
return dd.concat(dataframes)
def _merge_two(left: Dict[str, Union[pd.DataFrame, dd.DataFrame]],
right: Dict[str, Union[pd.DataFrame, dd.DataFrame]],
index_col: Union[List, str],
dtype: str, deep=False) -> Dict[str, pd.DataFrame]:
"""merge 2 ingredient data."""
if len(left) == 0:
return right
res_data = {}
# for datapoints we use dask to help performance.
if dtype == 'datapoints':
res_data = dict([(k, v) for k, v in left.items()])
if deep:
for k, df in right.items():
if k in left.keys():
columns = left[k].columns.values
# res_data[k] = left[k].append(df[columns], interleave_partitions=True)
res_data[k] = dd.concat([left[k], df[columns]], axis=0, interleave_partitions=True)
res_data[k] = res_data[k].drop_duplicates(subset=index_col, keep='last')
# res_data[k] = res_data[k].sort_values(by=index_col)
else:
res_data[k] = df
else:
for k, df in right.items():
res_data[k] = df
# for concepts/entities, we don't need to use dask.
elif dtype == 'concepts':
left_df = pd.concat([x for x in left.values()], sort=False)
right_df = pd.concat([x for x in right.values()], sort=False)
if deep:
merged = left_df.append(right_df, sort=False)
res = merged.groupby(by=index_col).agg(__get_last_item)
res_data = {'concept': res.reset_index()}
else:
res_data = {'concept':
right_df.drop_duplicates(subset='concept',
keep='last')}
res_data = res_data
else: # entities
if deep:
for k, df in right.items():
if k in left.keys():
left[k] = left[k].append(df, ignore_index=True, sort=False)
left[k] = left[k].groupby(index_col).agg(__get_last_item).reset_index()
else:
left[k] = df
else:
for k, df in right.items():
left[k] = df
res_data = left
return res_data
def check_and_return(ddfs, dfs, join):
sol = concat(dfs, join=join)
res = dd.concat(ddfs, join=join, interleave_partitions=divisions)
assert_eq(res, sol)
if known:
parts = compute_as_if_collection(dd.DataFrame, res.dask,
res.__dask_keys__())
for p in [i.iloc[:0] for i in parts]:
res._meta == p # will error if schemas don't align
assert not cat_index or has_known_categories(res.index) == known
return res
def test_concat2():
dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}),
('x', 1): pd.DataFrame({'a': [4, 5, 6], 'b': [3, 2, 1]}),
('x', 2): pd.DataFrame({'a': [7, 8, 9], 'b': [0, 0, 0]})}
meta = make_meta({'a': 'i8', 'b': 'i8'})
a = dd.DataFrame(dsk, 'x', meta, [None, None])
dsk = {('y', 0): pd.DataFrame({'a': [10, 20, 30], 'b': [40, 50, 60]}),
('y', 1): pd.DataFrame({'a': [40, 50, 60], 'b': [30, 20, 10]}),
('y', 2): pd.DataFrame({'a': [70, 80, 90], 'b': [0, 0, 0]})}
b = dd.DataFrame(dsk, 'y', meta, [None, None])
dsk = {('y', 0): pd.DataFrame({'b': [10, 20, 30], 'c': [40, 50, 60]}),
('y', 1): pd.DataFrame({'b': [40, 50, 60], 'c': [30, 20, 10]})}
meta = make_meta({'b': 'i8', 'c': 'i8'})
c = dd.DataFrame(dsk, 'y', meta, [None, None])
dsk = {('y', 0): pd.DataFrame({'b': [10, 20, 30], 'c': [40, 50, 60],
'd': [70, 80, 90]}),
('y', 1): pd.DataFrame({'b': [40, 50, 60], 'c': [30, 20, 10],
'd': [90, 80, 70]},
index=[3, 4, 5])}
meta = make_meta({'b': 'i8', 'c': 'i8', 'd': 'i8'},
index=pd.Index([], 'i8'))
d = dd.DataFrame(dsk, 'y', meta, [0, 3, 5])
cases = [[a, b], [a, c], [a, d]]
assert dd.concat([a]) is a
for case in cases:
result = dd.concat(case)
pdcase = [_c.compute() for _c in case]
assert result.npartitions == case[0].npartitions + case[1].npartitions
assert result.divisions == (None, ) * (result.npartitions + 1)
assert_eq(pd.concat(pdcase), result)
assert set(result.dask) == set(dd.concat(case).dask)
result = dd.concat(case, join='inner')
assert result.npartitions == case[0].npartitions + case[1].npartitions
assert result.divisions == (None, ) * (result.npartitions + 1)
assert_eq(pd.concat(pdcase, join='inner'), result)
assert set(result.dask) == set(dd.concat(case, join='inner').dask)
def test_concat4_interleave_partitions():
pdf1 = pd.DataFrame(np.random.randn(10, 5),
columns=list('ABCDE'), index=list('abcdefghij'))
pdf2 = pd.DataFrame(np.random.randn(13, 5),
columns=list('ABCDE'), index=list('fghijklmnopqr'))
pdf3 = pd.DataFrame(np.random.randn(13, 6),
columns=list('CDEXYZ'), index=list('fghijklmnopqr'))
ddf1 = dd.from_pandas(pdf1, 2)
ddf2 = dd.from_pandas(pdf2, 3)
ddf3 = dd.from_pandas(pdf3, 2)
msg = ('All inputs have known divisions which cannot be '
'concatenated in order. Specify '
'interleave_partitions=True to ignore order')
cases = [[ddf1, ddf1], [ddf1, ddf2], [ddf1, ddf3], [ddf2, ddf1],
[ddf2, ddf3], [ddf3, ddf1], [ddf3, ddf2]]
for case in cases:
pdcase = [c.compute() for c in case]
with pytest.raises(ValueError) as err:
dd.concat(case)
assert msg in str(err.value)
assert_eq(dd.concat(case, interleave_partitions=True),
pd.concat(pdcase))
assert_eq(dd.concat(case, join='inner', interleave_partitions=True),
pd.concat(pdcase, join='inner'))
msg = "'join' must be 'inner' or 'outer'"
with pytest.raises(ValueError) as err:
dd.concat([ddf1, ddf1], join='invalid', interleave_partitions=True)
assert msg in str(err.value)
def test_concat4_interleave_partitions():
pdf1 = pd.DataFrame(np.random.randn(10, 5), columns=list("ABCDE"), index=list("abcdefghij"))
pdf2 = pd.DataFrame(np.random.randn(13, 5), columns=list("ABCDE"), index=list("fghijklmnopqr"))
pdf3 = pd.DataFrame(np.random.randn(13, 6), columns=list("CDEXYZ"), index=list("fghijklmnopqr"))
ddf1 = dd.from_pandas(pdf1, 2)
ddf2 = dd.from_pandas(pdf2, 3)
ddf3 = dd.from_pandas(pdf3, 2)
msg = (
"All inputs have known divisions which cannnot be "
"concatenated in order. Specify "
"interleave_partitions=True to ignore order"
)
cases = [[ddf1, ddf1], [ddf1, ddf2], [ddf1, ddf3], [ddf2, ddf1], [ddf2, ddf3], [ddf3, ddf1], [ddf3, ddf2]]
for case in cases:
pdcase = [c.compute() for c in case]
with tm.assertRaisesRegexp(ValueError, msg):
dd.concat(case)
assert eq(dd.concat(case, interleave_partitions=True), pd.concat(pdcase))
assert eq(dd.concat(case, join="inner", interleave_partitions=True), pd.concat(pdcase, join="inner"))
msg = "'join' must be 'inner' or 'outer'"
with tm.assertRaisesRegexp(ValueError, msg):
dd.concat([ddf1, ddf1], join="invalid", interleave_partitions=True)
def test_concat2():
dsk = {
("x", 0): pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}),
("x", 1): pd.DataFrame({"a": [4, 5, 6], "b": [3, 2, 1]}),
("x", 2): pd.DataFrame({"a": [7, 8, 9], "b": [0, 0, 0]}),
}
a = dd.DataFrame(dsk, "x", ["a", "b"], [None, None])
dsk = {
("y", 0): pd.DataFrame({"a": [10, 20, 30], "b": [40, 50, 60]}),
("y", 1): pd.DataFrame({"a": [40, 50, 60], "b": [30, 20, 10]}),
("y", 2): pd.DataFrame({"a": [70, 80, 90], "b": [0, 0, 0]}),
}
b = dd.DataFrame(dsk, "y", ["a", "b"], [None, None])
dsk = {
("y", 0): pd.DataFrame({"b": [10, 20, 30], "c": [40, 50, 60]}),
("y", 1): pd.DataFrame({"b": [40, 50, 60], "c": [30, 20, 10]}),
}
c = dd.DataFrame(dsk, "y", ["b", "c"], [None, None])
dsk = {
("y", 0): pd.DataFrame({"b": [10, 20, 30], "c": [40, 50, 60], "d": [70, 80, 90]}),
("y", 1): pd.DataFrame({"b": [40, 50, 60], "c": [30, 20, 10], "d": [90, 80, 70]}, index=[3, 4, 5]),
}
d = dd.DataFrame(dsk, "y", ["b", "c", "d"], [0, 3, 5])
cases = [[a, b], [a, c], [a, d]]
assert dd.concat([a]) is a
for case in cases:
result = dd.concat(case)
pdcase = [c.compute() for c in case]
assert result.npartitions == case[0].npartitions + case[1].npartitions
assert result.divisions == (None,) * (result.npartitions + 1)
assert eq(pd.concat(pdcase), result)
assert result.dask == dd.concat(case).dask
result = dd.concat(case, join="inner")
assert result.npartitions == case[0].npartitions + case[1].npartitions
assert result.divisions == (None,) * (result.npartitions + 1)
assert eq(pd.concat(pdcase, join="inner"), result)
assert result.dask == dd.concat(case, join="inner").dask
msg = "Unable to concatenate DataFrame with unknown division " "specifying axis=1"
with tm.assertRaisesRegexp(ValueError, msg):
dd.concat(case, axis=1)
def test_set_index_empty_partition():
test_vals = [1, 2, 3]
converters = [
int,
float,
str,
lambda x: pd.to_datetime(x, unit='ns'),
]
for conv in converters:
df = pd.DataFrame([{'x': conv(i), 'y': i} for i in test_vals], columns=['x', 'y'])
ddf = dd.concat([
dd.from_pandas(df, npartitions=1),
dd.from_pandas(df[df.y > df.y.max()], npartitions=1),
])
assert any(ddf.get_partition(p).compute().empty for p in range(ddf.npartitions))
assert assert_eq(ddf.set_index('x'), df.set_index('x'))
def test_concat_datetimeindex():
# https://github.com/dask/dask/issues/2932
b2 = pd.DataFrame({'x': ['a']},
index=pd.DatetimeIndex(['2015-03-24 00:00:16'],
dtype='datetime64[ns]'))
b3 = pd.DataFrame({'x': ['c']},
index=pd.DatetimeIndex(['2015-03-29 00:00:44'],
dtype='datetime64[ns]'))
b2['x'] = b2.x.astype('category').cat.set_categories(['a', 'c'])
b3['x'] = b3.x.astype('category').cat.set_categories(['a', 'c'])
db2 = dd.from_pandas(b2, 1)
db3 = dd.from_pandas(b3, 1)
result = concat([b2.iloc[:0], b3.iloc[:0]])
assert result.index.dtype == '<M8[ns]'
result = dd.concat([db2, db3])
expected = pd.concat([b2, b3])
assert_eq(result, expected)
def test_orc_multiple(orc_files):
d = read_orc(orc_files[0])
d2 = read_orc(orc_files)
assert_eq(d2[columns], dd.concat([d, d])[columns], check_index=False)
d2 = read_orc(os.path.dirname(orc_files[0]) + '/*.orc')
assert_eq(d2[columns], dd.concat([d, d])[columns], check_index=False)
def concat(cls, columns_objs):
cast_objs = cls.cast(columns_objs)
return dd.concat([col.data for col in cast_objs])
def to_dd(self) -> dd.DataFrame:
dfs = []
for group in self.groups:
df = dd.from_dask_array(self.conn[group], columns=[group])
dfs.append(df)
return dd.concat(dfs, axis=1)
def test_concat5():
pdf1 = pd.DataFrame(np.random.randn(7, 5), columns=list("ABCDE"), index=list("abcdefg"))
pdf2 = pd.DataFrame(np.random.randn(7, 6), columns=list("FGHIJK"), index=list("abcdefg"))
pdf3 = pd.DataFrame(np.random.randn(7, 6), columns=list("FGHIJK"), index=list("cdefghi"))
pdf4 = pd.DataFrame(np.random.randn(7, 5), columns=list("FGHAB"), index=list("cdefghi"))
pdf5 = pd.DataFrame(np.random.randn(7, 5), columns=list("FGHAB"), index=list("fklmnop"))
ddf1 = dd.from_pandas(pdf1, 2)
ddf2 = dd.from_pandas(pdf2, 3)
ddf3 = dd.from_pandas(pdf3, 2)
ddf4 = dd.from_pandas(pdf4, 2)
ddf5 = dd.from_pandas(pdf5, 3)
cases = [
[ddf1, ddf2],
[ddf1, ddf3],
[ddf1, ddf4],
[ddf1, ddf5],
[ddf3, ddf4],
[ddf3, ddf5],
[ddf5, ddf1, ddf4],
[ddf5, ddf3],
[ddf1.A, ddf4.A],
[ddf2.F, ddf3.F],
[ddf4.A, ddf5.A],
[ddf1.A, ddf4.F],
[ddf2.F, ddf3.H],
[ddf4.A, ddf5.B],
[ddf1, ddf4.A],
[ddf3.F, ddf2],
[ddf5, ddf1.A, ddf2],
]
for case in cases:
pdcase = [c.compute() for c in case]
assert eq(dd.concat(case, interleave_partitions=True), pd.concat(pdcase))
assert eq(dd.concat(case, join="inner", interleave_partitions=True), pd.concat(pdcase, join="inner"))
assert eq(dd.concat(case, axis=1), pd.concat(pdcase, axis=1))
assert eq(dd.concat(case, axis=1, join="inner"), pd.concat(pdcase, axis=1, join="inner"))
# Dask + pandas
cases = [
[ddf1, pdf2],
[ddf1, pdf3],
[pdf1, ddf4],
[pdf1.A, ddf4.A],
[ddf2.F, pdf3.F],
[ddf1, pdf4.A],
[ddf3.F, pdf2],
[ddf2, pdf1, ddf3.F],
]
for case in cases:
pdcase = [c.compute() if isinstance(c, _Frame) else c for c in case]
assert eq(dd.concat(case, interleave_partitions=True), pd.concat(pdcase))
assert eq(dd.concat(case, join="inner", interleave_partitions=True), pd.concat(pdcase, join="inner"))
assert eq(dd.concat(case, axis=1), pd.concat(pdcase, axis=1))
assert eq(dd.concat(case, axis=1, join="inner"), pd.concat(pdcase, axis=1, join="inner"))
