def test_pyspark_roundtrip(tempdir, scheme, row_groups, comp, sql):
if comp in ['BROTLI', 'ZSTD', 'LZO', "LZ4"]:
pytest.xfail("spark doesn't support compression")
data = pd.DataFrame({'i32': np.random.randint(-2**17, 2**17, size=1001,
dtype=np.int32),
'i64': np.random.randint(-2**33, 2**33, size=1001,
dtype=np.int64),
'f': np.random.randn(1001),
'bhello': np.random.choice([b'hello', b'you',
b'people'], size=1001).astype("O"),
't': [datetime.datetime.now()]*1001})
data['t'] += pd.to_timedelta('1ns')
data['hello'] = data.bhello.str.decode('utf8')
data.loc[100, 'f'] = np.nan
data['bcat'] = data.bhello.astype('category')
data['cat'] = data.hello.astype('category')
fname = os.path.join(tempdir, 'test.parquet')
write(fname, data, file_scheme=scheme, row_group_offsets=row_groups,
compression=comp, times='int96', write_index=True)
df = sql.read.parquet(fname)
ddf = df.sort('index').toPandas()
for col in data:
if data[col].dtype.kind == "M":
# pyspark auto-converts timezones
offset = round((datetime.datetime.utcnow() -
datetime.datetime.now()).seconds / 3600)
ddf[col] + datetime.timedelta(hours=offset) == data[col]
else:
assert (ddf[col] == data[col])[~ddf[col].isnull()].all()
def test_floating_point_partition_name(tempdir):
df = pd.DataFrame({'x': [1e99, 5e-10, 2e+2, -0.1], 'y1': ['aa', 'aa', 'bb', 'aa']})
write(tempdir, df, file_scheme='hive', partition_on=['y1'])
pf = ParquetFile(tempdir)
out = pf.to_pandas()
assert out[out.y1 == 'aa'].x.tolist() == [1e99, 5e-10, -0.1]
assert out[out.y1 == 'bb'].x.tolist() == [200.0]
def time_text():
with tmpdir() as tempdir:
result = {}
fn = join_path(tempdir, 'temp.parq')
n = 1000000
d = pd.DataFrame({
'a': np.random.choice(['hi', 'you', 'people'], size=n),
'b': np.random.choice([b'hi', b'you', b'people'], size=n)})
for col in d.columns:
for fixed in [None, 6]:
df = d[[col]]
if isinstance(df.iloc[0, 0], bytes):
t = "bytes"
else:
t = 'utf8'
write(fn, df)
with measure('%s: write, fixed: %s' % (t, fixed), result):
write(fn, df, has_nulls=False, write_index=False,
fixed_text={col: fixed}, object_encoding=t)
pf = ParquetFile(fn)
pf.to_pandas() # warm-up
with measure('%s: read, fixed: %s' % (t, fixed), result):
pf.to_pandas()
return result
def test_roundtrip(tempdir, scheme, row_groups, comp):
data = pd.DataFrame(
{
"i32": np.arange(1000, dtype=np.int32),
"i64": np.arange(1000, dtype=np.int64),
"f": np.arange(1000, dtype=np.float64),
"bhello": np.random.choice([b"hello", b"you", b"people"], size=1000).astype("O"),
}
)
data["a"] = np.array([b"a", b"b", b"c", b"d", b"e"] * 200, dtype="S1")
data["aa"] = data["a"].map(lambda x: 2 * x).astype("S2")
data["hello"] = data.bhello.str.decode("utf8")
data["bcat"] = data.bhello.astype("category")
data["cat"] = data.hello.astype("category")
fname = os.path.join(tempdir, "test.parquet")
write(fname, data, file_scheme=scheme, row_group_offsets=row_groups, compression=comp)
r = ParquetFile(fname)
df = r.to_pandas()
assert data.cat.dtype == "category"
for col in r.columns:
assert (df[col] == data[col]).all()
def test_roundtrip_complex(tempdir, scheme):
import datetime
data = pd.DataFrame(
{
"ui32": np.arange(1000, dtype=np.uint32),
"i16": np.arange(1000, dtype=np.int16),
"ui8": np.array([1, 2, 3, 4] * 250, dtype=np.uint8),
"f16": np.arange(1000, dtype=np.float16),
"dicts": [{"oi": "you"}] * 1000,
"t": [datetime.datetime.now()] * 1000,
"td": [datetime.timedelta(seconds=1)] * 1000,
"bool": np.random.choice([True, False], size=1000),
}
)
data.loc[100, "t"] = None
fname = os.path.join(tempdir, "test.parquet")
write(fname, data, file_scheme=scheme)
r = ParquetFile(fname)
df = r.to_pandas()
for col in r.columns:
assert (df[col] == data[col])[~data[col].isnull()].all()
def test_numerical_partition_name(tempdir):
df = pd.DataFrame({'x': [1, 5, 2, 5], 'y1': ['aa', 'aa', 'bb', 'aa']})
write(tempdir, df, file_scheme='hive', partition_on=['y1'])
pf = ParquetFile(tempdir)
out = pf.to_pandas()
assert out[out.y1 == 'aa'].x.tolist() == [1, 5, 5]
assert out[out.y1 == 'bb'].x.tolist() == [2]
def test_merge(tempdir, dirs, row_groups):
fn = str(tempdir)
os.makedirs(os.path.join(fn, dirs[0]), exist_ok=True)
df0 = pd.DataFrame({"a": [1, 2, 3, 4]})
fn0 = os.sep.join([fn, dirs[0], "out0.parq"])
write(fn0, df0, row_group_offsets=row_groups)
os.makedirs(os.path.join(fn, dirs[1]), exist_ok=True)
df1 = pd.DataFrame({"a": [5, 6, 7, 8]})
fn1 = os.sep.join([fn, dirs[1], "out1.parq"])
write(fn1, df1, row_group_offsets=row_groups)
# with file-names
pf = writer.merge([fn0, fn1])
assert len(pf.row_groups) == 2 * len(row_groups)
out = pf.to_pandas().a.tolist()
assert out == [1, 2, 3, 4, 5, 6, 7, 8]
if "cat=1" in dirs:
assert "cat" in pf.cats
# with instances
pf = writer.merge([ParquetFile(fn0), ParquetFile(fn1)])
assert len(pf.row_groups) == 2 * len(row_groups)
out = pf.to_pandas().a.tolist()
assert out == [1, 2, 3, 4, 5, 6, 7, 8]
if "cat=1" in dirs:
assert "cat" in pf.cats
def test_input_column_list_not_mutated(tempdir):
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
write(tempdir, df, file_scheme='hive')
cols = ['a']
pf = ParquetFile(tempdir)
out = pf.to_pandas(columns=cols)
assert cols == ['a']
def test_filter_stats(tempdir):
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5, 6, 7],
})
write(tempdir, df, file_scheme='hive', row_group_offsets=[0, 4])
pf = ParquetFile(tempdir)
out = pf.to_pandas(filters=[('x', '>=', 5)])
assert out.x.tolist() == [5, 6, 7]
def test_in_filter(tempdir):
symbols = ['a', 'a', 'b', 'c', 'c', 'd']
values = [1, 2, 3, 4, 5, 6]
df = pd.DataFrame(data={'symbols': symbols, 'values': values})
write(tempdir, df, file_scheme='hive', partition_on=['symbols'])
pf = ParquetFile(tempdir)
out = pf.to_pandas(filters=[('symbols', 'in', ['a', 'c'])])
assert set(out.symbols) == {'a', 'c'}
def test_mixed_partition_types_warning(tempdir, partitions):
df = pd.DataFrame({
'partitions': partitions,
'x': [1, 2]
})
write(tempdir, df, file_scheme='hive', partition_on=['partitions'])
with pytest.warns(UserWarning, match=r'Partition names coerce to values of different types.*'):
ParquetFile(tempdir)
def test_datetime_partition_no_dupilcates(tempdir, partitions):
df = pd.DataFrame({
'partitions': partitions,
'x': [1, 2]
})
write(tempdir, df, file_scheme='hive', partition_on=['partitions'])
with pytest.raises(ValueError, match=r'Partition names map to the same value.*'):
ParquetFile(tempdir)
def test_index_not_in_columns(tempdir):
df = pd.DataFrame({'a': ['x', 'y', 'z'], 'b': [4, 5, 6]}).set_index('a')
write(tempdir, df, file_scheme='hive')
pf = ParquetFile(tempdir)
out = pf.to_pandas(columns=['b'])
assert out.index.tolist() == ['x', 'y', 'z']
out = pf.to_pandas(columns=['b'], index=False)
assert out.index.tolist() == [0, 1, 2]
def test_request_nonexistent_column(tempdir):
df = pd.DataFrame({'x': [1, 2, 3]})
fn = os.path.join(tempdir, 'foo.parquet')
write(fn, df)
pf = ParquetFile(fn)
with pytest.raises(ValueError):
pf.to_pandas(columns=['y'])
def test_grab_cats(tempdir):
s = pd.Series(['a', 'c', 'b']*20)
df = pd.DataFrame({'a': s, 'b': s.astype('category'),
'c': s.astype('category').cat.as_ordered()})
fastparquet.write(tempdir, df, file_scheme='hive')
pf = fastparquet.ParquetFile(tempdir)
cats = pf.grab_cats(['b', 'c'])
assert (cats['b'] == df.b.cat.categories).all()
assert (cats['c'] == df.c.cat.categories).all()
def test_2():
# to make and save a large-ish DataFrame
N = 10000000
df = pd.DataFrame({'ints': np.random.randint(0, 1000, size=N),
'floats': np.random.randn(N),
'times': pd.DatetimeIndex(start='1980', freq='s', periods=N)})
df.to_csv('test_2.csv')
fastparquet.write('test_2_UNCOMPRESSED.parq', df, compression='UNCOMPRESSED')
def test_append_simple(tempdir):
fn = os.path.join(str(tempdir), "test.parq")
df = pd.DataFrame({"a": [1, 2, 3, 0], "b": ["a", "a", "b", "b"]})
write(fn, df, write_index=False)
write(fn, df, append=True, write_index=False)
pf = ParquetFile(fn)
expected = pd.concat([df, df], ignore_index=True)
pd.util.testing.assert_frame_equal(pf.to_pandas(), expected, check_categorical=False)
def test_open_standard(tempdir):
df = pd.DataFrame({'x': [1, 2, 3, 4],
'y': [1.0, 2.0, 1.0, 2.0],
'z': ['a', 'b', 'c', 'd']})
fn = os.path.join(tempdir, 'foo.parquet')
write(fn, df, row_group_offsets=[0, 2], file_scheme='hive',
open_with=open)
pf = ParquetFile(fn, open_with=open)
d2 = pf.to_pandas()
pd.util.testing.assert_frame_equal(d2, df)
def test_filter_special(tempdir):
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5, 6, 7],
'symbol': ['NOW', 'OI', 'OI', 'OI', 'NOW', 'NOW', 'OI']
})
write(tempdir, df, file_scheme='hive', partition_on=['symbol'])
pf = ParquetFile(tempdir)
out = pf.to_pandas(filters=[('symbol', '==', 'NOW')])
assert out.x.tolist() == [1, 5, 6]
assert out.symbol.tolist() == ['NOW', 'NOW', 'NOW']
def test_datetime_category_no_dupilcates(tempdir, categories):
# The purpose of this test is to ensure that the changes made for the previous test
# haven't broken categories in general.
df = pd.DataFrame({
'categories': categories,
'x': [1, 2]
}).astype({'categories': 'category'})
fn = os.path.join(tempdir, 'foo.parquet')
write(fn, df)
assert ParquetFile(fn).to_pandas().categories.tolist() == categories
def test_index(tempdir):
s = pd.Series(['a', 'c', 'b']*20)
df = pd.DataFrame({'a': s, 'b': s.astype('category'),
'c': range(60, 0, -1)})
for column in df:
d2 = df.set_index(column)
fastparquet.write(tempdir, d2, file_scheme='hive', write_index=True)
pf = fastparquet.ParquetFile(tempdir)
out = pf.to_pandas(index=column, categories=['b'])
pd.util.testing.assert_frame_equal(out, d2, check_categorical=False)
def test_read_multiple_no_metadata(tempdir):
df = pd.DataFrame({'x': [1, 5, 2, 5]})
write(tempdir, df, file_scheme='hive', row_group_offsets=[0, 2])
os.unlink(os.path.join(tempdir, '_metadata'))
os.unlink(os.path.join(tempdir, '_common_metadata'))
import glob
flist = list(sorted(glob.glob(os.path.join(tempdir, '*'))))
pf = ParquetFile(flist)
assert len(pf.row_groups) == 2
out = pf.to_pandas()
pd.util.testing.assert_frame_equal(out, df)
def test_logical_types(tempdir):
df = pd.util.testing.makeMixedDataFrame()
fn = os.path.join(tempdir, 'foo.parquet')
write(fn, df, row_group_offsets=[0, 2])
p = ParquetFile(fn)
s = statistics(p)
assert isinstance(s['min']['D'][0], (np.datetime64, pd.tslib.Timestamp))
def test_int96_stats(tempdir):
df = pd.util.testing.makeMixedDataFrame()
fn = os.path.join(tempdir, 'foo.parquet')
write(fn, df, row_group_offsets=[0, 2], times='int96')
p = ParquetFile(fn)
s = statistics(p)
assert isinstance(s['min']['D'][0], (np.datetime64, pd.tslib.Timestamp))
assert 'D' in sorted_partitioned_columns(p)
def test_zero_child_leaf(tempdir):
df = pd.DataFrame({'x': [1, 2, 3]})
fn = os.path.join(tempdir, 'foo.parquet')
write(fn, df)
pf = ParquetFile(fn)
assert pf.columns == ['x']
pf._schema[1].num_children = 0
assert pf.columns == ['x']
def test_datetime_partition_names(tempdir):
date_strings = ['2015-05-09', '2018-10-15', '2020-10-17', '2015-05-09']
df = pd.DataFrame({
'date': date_strings,
'x': [1, 5, 2, 5]
})
write(tempdir, df, file_scheme='hive', partition_on=['date'])
pf = ParquetFile(tempdir)
out = pf.to_pandas()
assert set(out.date.tolist()) == set(pd.to_datetime(date_strings).tolist())
assert out[out.date == '2015-05-09'].x.tolist() == [1, 5]
assert out[out.date == '2020-10-17'].x.tolist() == [2]
def test_filter_without_paths(tempdir):
fn = os.path.join(tempdir, 'test.parq')
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5, 6, 7],
'letter': ['a', 'b', 'c', 'd', 'e', 'f', 'g']
})
write(fn, df)
pf = ParquetFile(fn)
out = pf.to_pandas(filters=[['x', '>', 3]])
pd.util.testing.assert_frame_equal(out, df)
out = pf.to_pandas(filters=[['x', '>', 30]])
assert len(out) == 0
def test_multi_cat_fail(tempdir):
fn = os.path.join(tempdir, 'test.parq')
N = 200
df = pd.DataFrame(
{'a': np.random.randint(10, size=N),
'b': np.random.choice(['a', 'b', 'c'], size=N),
'c': np.arange(200)})
df = df.set_index(['a', 'b'])
write(fn, df, row_group_offsets=25)
pf = ParquetFile(fn)
with pytest.raises(RuntimeError):
pf.to_pandas()
def test_statistics(tempdir):
s = pd.Series([b'a', b'b', b'c']*20)
df = pd.DataFrame({'a': s, 'b': s.astype('category'),
'c': s.astype('category').cat.as_ordered()})
fastparquet.write(tempdir, df, file_scheme='hive')
pf = fastparquet.ParquetFile(tempdir)
stat = pf.statistics
assert stat['max']['a'] == [b'c']
assert stat['min']['a'] == [b'a']
assert stat['max']['b'] == [None]
assert stat['min']['b'] == [None]
assert stat['max']['c'] == [b'c']
assert stat['min']['c'] == [b'a']
def test_multi(tempdir):
fn = os.path.join(tempdir, 'test.parq')
N = 200
df = pd.DataFrame(
{'a': np.random.randint(10, size=N),
'b': np.random.choice(['a', 'b', 'c'], size=N),
'c': np.arange(200)})
df = df.set_index(['a', 'b'])
write(fn, df)
pf = ParquetFile(fn)
df1 = pf.to_pandas()
assert df1.equals(df)
assert df1.loc[1, 'a'].equals(df.loc[1, 'a'])
def test_multi_list(tempdir):
df = pd.DataFrame({'a': ['x', 'y', 'z'], 'b': [4, 5, 6]})
dir1 = os.path.join(tempdir, 'x')
write(dir1, df, file_scheme='hive')
dir2 = os.path.join(tempdir, 'y')
write(dir2, df, file_scheme='hive')
dir3 = os.path.join(tempdir, 'z', 'deep')
write(dir3, df, file_scheme='hive')
pf = ParquetFile([dir1, dir2])
out = pf.to_pandas() # this version may have extra column!
assert out.a.tolist() == ['x', 'y', 'z'] * 2
pf = ParquetFile([dir1, dir2, dir3])
out = pf.to_pandas()
assert out.a.tolist() == ['x', 'y', 'z'] * 3
def parquet_conv(filename, cwd=os.getcwd(), datasourceformat=".xlsx"):
"""Converts a file of .xlsx or .csv into .parquet and reads prints/returns the first column
:param filename: base filename to be converted to .parquet
:param cwd: current working directory
:param datasourceformat: what format the datasource comes in
:return: the requested column from pset instructions
"""
parquetfilename = filename + ".parquet"
data_wd = os.path.abspath(os.path.join(cwd, "data"))
data_source = os.path.join(data_wd, filename + datasourceformat)
try:
df = pd.read_csv(data_source)
except:
df = pd.read_excel(data_source)
atomic_write(fastparquet.write(parquetfilename, df, compression=None))
result = pd.read_parquet(parquetfilename,
engine="fastparquet",
columns=["hashed_id"])
print(result)
return result
def test_merge_fail(tempdir):
fn = str(tempdir)
df0 = pd.DataFrame({'a': [1, 2, 3, 4]})
fn0 = os.sep.join([fn, 'out0.parq'])
write(fn0, df0)
df1 = pd.DataFrame({'a': ['a', 'b', 'c']})
fn1 = os.sep.join([fn, 'out1.parq'])
write(fn1, df1)
with pytest.raises(ValueError) as e:
writer.merge([fn0, fn1])
assert 'schemas' in str(e)
os.remove(fn1)
write(fn1, df0, file_scheme='hive')
with pytest.raises(ValueError) as e:
writer.merge([fn0, fn1])
assert 'multi-file' in str(e)
def test_auto_null(tempdir):
tmp = str(tempdir)
df = pd.DataFrame({
'a': [1, 2, 3, 0],
'aa': [1, 2, 3, None],
'b': [1., 2., 3., np.nan],
'c': pd.to_timedelta([1, 2, 3, np.nan], unit='ms'),
'd': ['a', 'b', 'c', None],
'f': [True, False, True, True],
'ff': [True, False, None, True]
})
df['e'] = df['d'].astype('category')
fn = os.path.join(tmp, "test.parq")
with pytest.raises((TypeError, AttributeError)):
## TODO: this should be a nicer error?
write(fn, df, has_nulls=False)
write(fn, df, has_nulls=True)
pf = ParquetFile(fn)
for col in pf._schema[1:]:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.OPTIONAL
df2 = pf.to_pandas(categories=['e'])
cols = list(set(df) - {'ff'})
tm.assert_frame_equal(df[cols], df2[cols], check_categorical=False)
tm.assert_frame_equal(df[['ff']].astype('float16'), df2[['ff']])
write(fn, df, has_nulls=None)
pf = ParquetFile(fn)
for col in pf._schema[1:]:
if col.name in ['d', 'ff']:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.OPTIONAL
else:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.REQUIRED
df2 = pf.to_pandas()
tm.assert_frame_equal(df[cols], df2[cols], check_categorical=False)
tm.assert_frame_equal(df[['ff']].astype('float16'), df2[['ff']])
def test_only_partition_columns(tempdir):
df = pd.DataFrame({'a': np.random.rand(20),
'b': np.random.choice(['hi', 'ho'], size=20),
'c': np.random.choice(['a', 'b'], size=20)})
write(tempdir, df, file_scheme='hive', partition_on=['b'])
pf = ParquetFile(tempdir)
df2 = pf.to_pandas(columns=['b'])
df.b.value_counts().to_dict() == df2.b.value_counts().to_dict()
write(tempdir, df, file_scheme='hive', partition_on=['a', 'b'])
pf = ParquetFile(tempdir)
df2 = pf.to_pandas(columns=['a', 'b'])
df.b.value_counts().to_dict() == df2.b.value_counts().to_dict()
df2 = pf.to_pandas(columns=['b'])
df.b.value_counts().to_dict() == df2.b.value_counts().to_dict()
df2 = pf.to_pandas(columns=['b', 'c'])
df.b.value_counts().to_dict() == df2.b.value_counts().to_dict()
with pytest.raises(ValueError):
# because this leaves no data to write
write(tempdir, df[['b']], file_scheme='hive', partition_on=['b'])
def test_custom_metadata(tempdir):
df = pd.DataFrame({'a': [15]})
fn = os.path.join(tempdir, 'temp.parq')
write(fn, df, custom_metadata={"hello": "world"})
pf = ParquetFile(fn)
assert pf.key_value_metadata['hello'] == 'world'
def test_append_w_partitioning(tempdir):
fn = str(tempdir)
df = pd.DataFrame({'a': np.random.choice([1, 2, 3], size=50),
'b': np.random.choice(['hello', 'world'], size=50),
'c': np.random.randint(50, size=50)})
write(fn, df, file_scheme='hive', partition_on=['a', 'b'])
write(fn, df, file_scheme='hive', partition_on=['a', 'b'], append=True)
write(fn, df, file_scheme='hive', partition_on=['a', 'b'], append=True)
write(fn, df, file_scheme='hive', partition_on=['a', 'b'], append=True)
pf = ParquetFile(fn)
out = pf.to_pandas()
assert len(out) == 200
assert sorted(out.a)[::4] == sorted(df.a)
with pytest.raises(ValueError):
write(fn, df, file_scheme='hive', partition_on=['a'], append=True)
with pytest.raises(ValueError):
write(fn, df, file_scheme='hive', partition_on=['b', 'a'], append=True)
def test_duplicate_columns(tempdir):
fn = os.path.join(tempdir, 'tmp.parq')
df = pd.DataFrame(np.arange(12).reshape(4, 3), columns=list('aaa'))
with pytest.raises(ValueError) as e:
write(fn, df)
assert 'duplicate' in str(e.value)
def test_bad_coltype(tempdir):
df = pd.DataFrame({'0': [1, 2], (0, 1): [3, 4]})
fn = os.path.join(tempdir, 'temp.parq')
with pytest.raises((ValueError, TypeError)) as e:
write(fn, df)
assert "tuple" in str(e.value)
df.body = df.body.apply(pre_processing)
word_grams = TfidfVectorizer(analyzer="word",
ngram_range=(1, 5),
stop_words="english",
max_features=10000)
word_vector = word_grams.fit_transform(df.body)
word_df = pd.DataFrame()
for i, col in enumerate(word_grams.get_feature_names()):
word_df[col] = pd.Series(word_vector[:, i].toarray().ravel())
df = pd.merge(df, word_df, left_index=True, right_index=True)
del word_df
# Note columns changed to _x for time
print(df.head())
df = df.drop(["body_x"], axis=1)
df = df.set_index("date_created")
print("write parquet")
fastparquet.write("processed_tweets.parquet", df)
print(df.shape)
def to_parquet(filename, prefix="maccdc2012"):
with open(filename) as f:
traffic = {}
nodes = set()
for line in f.readlines():
if "unreachable" in line:
continue
fields = line.split()
if not fields:
continue
if fields[1] != "IP":
continue
protocol = get_ip_protocol(line)
if protocol not in ("tcp", "udp", "eigrp", "icmp"):
continue
try:
addresses = []
# Extract source IP address and convert to integer
m = re.match(r'(?P<address>\d+\.\d+\.\d+\.\d+)', fields[2])
if not m:
continue
addresses.append(ip_to_integer(m.group('address')))
# Extract target IP address and convert to integer
m = re.match(r'(?P<address>\d+\.\d+\.\d+\.\d+)', fields[4])
if not m:
continue
addresses.append(ip_to_integer(m.group('address')))
nodes = nodes.union(addresses)
src, dst = sorted(addresses)
key = (protocol, src, dst)
# Extract packet size
nbytes = int(fields[-1])
if key in traffic:
traffic[key] += nbytes
else:
traffic[key] = nbytes
except:
pass
nodes = dict([(node, i) for i, node in enumerate(sorted(nodes))])
edges = []
for key in traffic:
edge = [nodes[key[1]], nodes[key[2]], key[0], traffic[key]]
edges.append(edge)
nodes_df = pd.DataFrame(np.arange(len(nodes)), columns=['id'])
nodes_df = nodes_df.set_index('id')
edges_df = pd.DataFrame(
np.array(edges),
columns=['source', 'target', 'protocol', 'weight'])
edges_df['source'] = pd.to_numeric(edges_df['source'])
edges_df['target'] = pd.to_numeric(edges_df['target'])
edges_df['weight'] = pd.to_numeric(edges_df['weight'])
edges_df['protocol'] = edges_df['protocol'].astype('category')
fp.write('{}_nodes.parq'.format(prefix), nodes_df)
fp.write('{}_edges.parq'.format(prefix), edges_df)
owner.append(ipinfo['autonomous_system_organization'])
asn.append(ipinfo['autonomous_system_number'])
ispname.append(ipinfo['isp'])
else:
print("error: for ip %s, ipinfo==None" % (ip))
owner.append('')
asn.append(0)
ispname.append('')
print("\n DONE getting ISP names")
# add IP_owner and IP_ASN columns to the dataframe
df["IP_owner"] = owner
df["IP_ASN"] = asn
# get company name from owner string
df["ISP_name"] = ispname
return df
#############################################################################
the_query = query_writer("06/15/14", "05/13/15", limit=999)
print(the_query)
project_id = 'mlab-185523'
df = acquire_mlab_data(project_id, "01/01/13", "02/01/13")
from fastparquet import write
write('mlab-test-data-0.parquet', df)
def write_parquet_gzip(df, file_name, num_of_samples):
print("Gzip Parquet writing started...")
filename = file_name + '.parq'
write(filename, df, num_of_samples, "GZIP")
def write_parquet_snappy(df, file_name, num_of_samples):
print("Snappy Parquet writing started...")
filename = file_name + '.parq'
write(filename, df, num_of_samples, "SNAPPY")
import pandas as pd
import numpy as np
from fastparquet import write
df = pd.read_csv('/etc/adult.data', names = ["Age", "Workclass", "fnlwgt", "Education", "Education_Num", "Martial_Status",
"Occupation", "Relationship", "Race", "Sex", "Capital_Gain", "Capital_Loss",
"Hours_per_week", "Country", "Target"])
write('adult.parq', df, compression='GZIP')
print("Remove constant cols")
train_df = train_df.drop(['ID', 'y'], axis=1)
test_df = test_df.drop(['ID'], axis=1)
print("Removed")
###########################################################
# Create interaction features
interactions2way = list(set(list(train_df)) - set(BASE_COLS))
interactions2way_list = list(combinations(interactions2way, 2))
for A, B in interactions2way_list:
feat = "_".join([A, B])
train_df[feat] = abs(train_df[A] - train_df[B])
test_df[feat] = abs(test_df[A] - test_df[B])
# Now split into train_df and test_df and save the output of the processed dataset.
train_df['ID'] = id_train_df
train_df['y'] = y_train_df
test_df['ID'] = id_test_df
print('Writing Parquets')
# store
fastparquet.write('./data/processed/metalvl2/xtrain' + BUILD_NAME + '.parq',
train_df,
write_index=False)
fastparquet.write('./data/processed/metalvl2/xtest' + BUILD_NAME + '.parq',
test_df,
write_index=False)
print('Finished')
def main():
global baseCode
print("Connecting to IB Gateway")
print("Client ID: " + str(clientId))
print("Host ID: " + str(host))
ibConn = IBTrader.IBTrader()
time.sleep(3)
ibConn.connect(clientId=clientId, host=host, port=port)
time.sleep(5)
ibConn.contracts = {}
time.sleep(5)
ibConn.contracts = {}
ibConn.createCashContract(baseCode[:3], currency=baseCode[3:])
print("Adding: " + baseCode)
print("Contracts Processing: " + str(len(ibConn.contracts)))
for contract in ibConn.contracts:
#baseCode = ibConn.contract_details[contract]['m_summary']['m_localSymbol'].replace('.','')
print("Processing: " + baseCode)
print("Retrieving Hourly Data")
ibConn.requestHistoricalData(
ibConn.contracts[contract],
resolution="1 hour",
end_datetime='{} 22:00:00'.format(
(datetime.datetime.today()).strftime("%Y%m%d")),
lookback="1 M")
waiting = True
lastLen = 0
while waiting:
try:
if len(ibConn.historicalData[baseCode + '_CASH']) > lastLen:
lastLen = len(ibConn.historicalData[baseCode + '_CASH'])
time.sleep(2)
else:
waiting = False
except KeyError:
pass
time.sleep(5)
print("Saving Hourly Data")
hourlyData = ibConn.historicalData[baseCode + '_CASH']
hourlyData = hourlyData.drop(['V', 'OI', 'WAP'],
1).reset_index().sort_values('datetime')
filename = baseCode + '_H' + str(
(datetime.datetime.today()).strftime("%Y%m%d")) + '.parq'
write('/root/data/hour/' + filename, hourlyData)
bucket.upload_file('/root/data/hour/' + filename,
s3_StorageLocation + filename)
ibConn.historicalData = {}
years = [2019]
for year in years:
print("Retrieving Minute Data " + str(year))
for i in range(3, 0, -1):
d = datetime.datetime(year, i, calendar.monthrange(year, i)[1])
dateStr = d.strftime("%Y%m%d")
print("Month: " + str(d.strftime("%Y %m")))
for contract in ibConn.contracts:
#baseCode = ibConn.contract_details[contract]['m_summary']['m_localSymbol'].replace('.','')
print("\tProcessing: " + baseCode)
ibConn.requestHistoricalData(
ibConn.contracts[contract],
resolution="1 min",
end_datetime='{} 22:00:00'.format(dateStr),
lookback="1 M")
waiting = True
lastLen = 0
while waiting:
try:
if len(ibConn.historicalData[baseCode +
'_CASH']) > lastLen:
lastLen = len(ibConn.historicalData[baseCode +
'_CASH'])
#print("\tBars Received: "+str(lastLen))
time.sleep(10)
else:
waiting = False
except KeyError:
pass
time.sleep(5)
minuteData = ibConn.historicalData[baseCode + '_CASH']
minuteData = minuteData.drop(
['V', 'OI', 'WAP'],
1).reset_index().sort_values('datetime')
filename = baseCode + '_M' + str(
d.strftime("%Y_%m")) + '.parq'
write('/root/data/min/' + filename, minuteData)
bucket.upload_file('/root/data/min/' + filename,
s3_StorageLocation + "min/" + filename)
ibConn.historicalData = {}
ibConn.historicalData = {}
ibConn.cancelHistoricalData()
ibConn.cancelMarketData()
ibConn.contracts = {}
ibConn.disconnect()
print("Collection Complete")
exit()
import os
import pandas as pd
import datetime, time
from fastparquet import write
import urllib3
import json
import warnings
warnings.filterwarnings('ignore')
https = urllib3.PoolManager()
while True:
try:
now = datetime.datetime.now()
datetimeval = datetime.datetime.now().strftime("%Y%m%d%H%M")
parquet_file = "data.parquet"
if now.minute % 5 == 4 and now.second == 56:
r = https.request('GET',"https://www.purpleair.com/json?*")
if r.status != 200:
time.sleep(240)
continue
j = json.loads(r.data.decode('utf-8'))
data_df = pd.DataFrame(j)
write(parquet_file, data_df,compression='GZIP')
os.system("aws s3 cp data.parquet s3://utkarsh-midscapstone-whos-polluting-my-air/PurpleAir/{}.parquet".format(datetimeval))
time.sleep(120)
except:
pass
def test_columns_index_with_multi_index(tmpdir, engine):
fn = os.path.join(str(tmpdir), 'test.parquet')
index = pd.MultiIndex.from_arrays(
[np.arange(10), np.arange(10) + 1], names=['x0', 'x1'])
df = pd.DataFrame(np.random.randn(10, 2), columns=['a', 'b'], index=index)
df2 = df.reset_index(drop=False)
if engine == 'fastparquet':
fastparquet.write(fn, df, write_index=True)
# fastparquet doesn't support multi-index
with pytest.raises(ValueError):
ddf = dd.read_parquet(fn, engine=engine)
else:
import pyarrow as pa
pq.write_table(pa.Table.from_pandas(df), fn)
# Pyarrow supports multi-index reads
ddf = dd.read_parquet(fn, engine=engine)
assert_eq(ddf, df)
d = dd.read_parquet(fn, columns='a', engine=engine)
assert_eq(d, df['a'])
d = dd.read_parquet(fn,
index=['a', 'b'],
columns=['x0', 'x1'],
engine=engine)
assert_eq(d, df2.set_index(['a', 'b'])[['x0', 'x1']])
# Just index
d = dd.read_parquet(fn, index=False, engine=engine)
assert_eq(d, df2)
d = dd.read_parquet(fn, index=['a'], engine=engine)
assert_eq(d, df2.set_index('a')[['b']])
d = dd.read_parquet(fn, index=['x0'], engine=engine)
assert_eq(d, df2.set_index('x0')[['a', 'b']])
# Just columns
d = dd.read_parquet(fn, columns=['x0', 'a'], engine=engine)
assert_eq(d, df2.set_index('x1')[['x0', 'a']])
# Both index and columns
d = dd.read_parquet(fn, index=False, columns=['x0', 'b'], engine=engine)
assert_eq(d, df2[['x0', 'b']])
for index in ['x1', 'b']:
d = dd.read_parquet(fn,
index=index,
columns=['x0', 'a'],
engine=engine)
assert_eq(d, df2.set_index(index)[['x0', 'a']])
# Columns and index intersect
for index in ['a', 'x0']:
with pytest.raises(ValueError):
d = dd.read_parquet(fn,
index=index,
columns=['x0', 'a'],
engine=engine)
# Series output
for ind, col, sol_df in [(None, 'x0', df2.set_index('x1')),
(False, 'b', df2), (False, 'x0', df2),
('a', 'x0', df2.set_index('a')),
('a', 'b', df2.set_index('a'))]:
d = dd.read_parquet(fn, index=ind, columns=col, engine=engine)
assert_eq(d, sol_df[col])
extractor_data = pd.read_csv(
'/run/user/1000/gvfs/smb-share:server=nas01.local,share=rnd/data/date/date_extractions.csv'
)
extractor_data['imaginary_id'] = extractor_data['croppedImageId_url'].map(
lambda x: x.split('/')[-1])
text = ParquetFile(
'/run/user/1000/gvfs/smb-share:server=nas01.local,share=rnd/data/parquet_data/text_extractions_temp.parq'
).to_pandas()
text.columns = ['imaginary_id', 'Text']
df = pd.merge(extractor_data, text, on='imaginary_id')
write(
'/run/user/1000/gvfs/smb-share:server=nas01.local,share=rnd/data/parquet_data/date_alg_results_and_ocr.parq',
df,
compression='GZIP',
file_scheme='hive')
print('rows in extractor data: ', len(extractor_data))
print('rows in text Parquet: ', len(text))
print('rows in merged df: ', len(df))
extractor_data.loc[extractor_data['conclusion'] == 'N\A',
'conclusion'] = np.nan
extractor_data.loc[extractor_data['conclusionConfidence'] == 'N\A',
'conclusionConfidence'] = np.nan
extractor_data.loc[:, 'conclusionConfidence'] = extractor_data[
'conclusionConfidence'].astype('float')
## sanity check
twitter_pred_df = pd.DataFrame({
"date_col":
twitter_date_col,
"twitter_pred":
twitter_pred.reshape(twitter_pred.shape[0], )
})
del twitter_test, twitter_train, twitter_data
wallstreet_test, wallstreet_train = get_wallstreet_data()
wallstreet_data = np.vstack((wallstreet_test, wallstreet_train))
wallstreet_data = np.expand_dims(wallstreet_data, axis=0)
wallstreet_pred = wallstreet_model.predict(wallstreet_data)
wallstreet_pred = y_scaler.inverse_transform(wallstreet_pred[0])
wallstreet_pred_df = pd.DataFrame({
"date_col":
non_twitter_dates,
"iex_pred":
wallstreet_pred.reshape(wallstreet_pred.shape[0], )
})
del wallstreet_test, wallstreet_train, wallstreet_data
ensamble_data = pd.merge(wallstreet_pred_df, iex_pred_df, on="date_col")
ensamble_data = pd.merge(ensamble_data, twitter_pred_df, on="date_col")
fastparquet.write("ensamble/ensamble_data.pq", ensamble_data)
def test_null_sizes(tempdir):
df = pd.DataFrame({'a': [True, None], 'b': [3000, np.nan]}, dtype="O")
fastparquet.write(tempdir, df, has_nulls=True, file_scheme='hive')
pf = fastparquet.ParquetFile(tempdir)
assert pf.dtypes['a'] == 'float16'
assert pf.dtypes['b'] == 'float64'
def test_bad_col(tempdir):
df = pd.DataFrame({'x': [1, 2]})
fn = os.path.join(tempdir, 'temp.parq')
with pytest.raises(ValueError) as e:
write(fn, df, has_nulls=['y'])
import numpy as np
from dateutil.parser import *
print("--Start--")
print("clean_iex_data")
# Get Data
df = pd.read_csv("./iex/mintue_trade_data.csv", na_values=[-1])
# Get date and minute to timestamps
df.date = df.date.astype(str) + " " + df.minute
df = df.drop(columns=["minute"])
df.date = df.date.apply(parse)
# Fill Na with values above
df = df.fillna(method="ffill")
df = df.dropna()
df.average = df.average.shift(-1)
df = df[:-1]
# Save all data to a pickle
fastparquet.write("iex_data/iex_clean.parquet", df)
# create a smaller dataframe to add to twitter and wallstreet journal
date_df = pd.DataFrame({"date_col": df.date, "stock_price_col" : df.average})
# save smaller df to a pickle
fastparquet.write("iex_data/date_iex_data.parquet", date_df)
print("--End--")
def test_auto_null_object(tempdir, pnull):
tmp = str(tempdir)
df = pd.DataFrame({'a': [1, 2, 3, 0],
'aa': pd.Series([1, 2, 3, None], dtype=object),
'b': [1., 2., 3., np.nan],
'c': pd.to_timedelta([1, 2, 3, np.nan], unit='ms'),
'd': ['a', 'b', 'c', None],
'f': [True, False, True, True],
'ff': [True, False, None, True]}) # object
df['e'] = df['d'].astype('category')
df['bb'] = df['b'].astype('object')
df['aaa'] = df['a'].astype('object')
object_cols = ['d', 'ff', 'bb', 'aaa', 'aa']
test_cols = list(set(df) - set(object_cols)) + ['d']
fn = os.path.join(tmp, "test.parq")
with pytest.raises(ValueError):
write(fn, df, has_nulls=False)
write(fn, df, has_nulls=True)
pf = ParquetFile(fn, pandas_nulls=pnull)
for col in pf._schema[1:]:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.OPTIONAL
df2 = pf.to_pandas(categories=['e'])
tm.assert_frame_equal(df[test_cols], df2[test_cols], check_categorical=False,
check_dtype=False)
tm.assert_frame_equal(df[['bb']].astype('float64'), df2[['bb']])
tm.assert_frame_equal(df[['aaa']].astype('int64'), df2[['aaa']])
if pnull:
tm.assert_frame_equal(df[['aa']].astype('Int64'), df2[['aa']])
tm.assert_frame_equal(df[['ff']].astype("boolean"), df2[['ff']])
else:
tm.assert_frame_equal(df[['aa']].astype('float'), df2[['aa']])
tm.assert_frame_equal(df[['ff']].astype("float"), df2[['ff']])
# not giving any value same as has_nulls=True
write(fn, df)
pf = ParquetFile(fn)
for col in pf._schema[1:]:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.OPTIONAL
df2 = pf.to_pandas(categories=['e'])
tm.assert_frame_equal(df[test_cols], df2[test_cols], check_categorical=False,
check_dtype=False)
tm.assert_frame_equal(df[['ff']].astype('boolean'), df2[['ff']])
tm.assert_frame_equal(df[['bb']].astype('float64'), df2[['bb']])
tm.assert_frame_equal(df[['aaa']].astype('int64'), df2[['aaa']])
# 'infer' is new recommended auto-null
write(fn, df, has_nulls='infer')
pf = ParquetFile(fn)
for col in pf._schema[1:]:
if col.name in object_cols:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.OPTIONAL
else:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.REQUIRED
df2 = pf.to_pandas()
tm.assert_frame_equal(df[test_cols], df2[test_cols], check_categorical=False)
tm.assert_frame_equal(df[['ff']].astype('boolean'), df2[['ff']])
tm.assert_frame_equal(df[['bb']].astype('float64'), df2[['bb']])
tm.assert_frame_equal(df[['aaa']].astype('int64'), df2[['aaa']])
# nut legacy None still works
write(fn, df, has_nulls=None)
pf = ParquetFile(fn)
for col in pf._schema[1:]:
if col.name in object_cols:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.OPTIONAL
else:
assert col.repetition_type == parquet_thrift.FieldRepetitionType.REQUIRED
df2 = pf.to_pandas()
tm.assert_frame_equal(df[test_cols], df2[test_cols], check_categorical=False)
tm.assert_frame_equal(df[['ff']].astype('boolean'), df2[['ff']])
tm.assert_frame_equal(df[['bb']].astype('float64'), df2[['bb']])
tm.assert_frame_equal(df[['aaa']].astype('int64'), df2[['aaa']])
def _write(refs, outpath, filetype=None):
types = {"json": "json", "parquet": "parquet", "zarr": "zarr"}
if filetype is None:
ext = os.path.splitext(outpath)[1].lstrip(".")
filetype = types[ext]
elif filetype not in types:
raise KeyError
if filetype == "json":
with open(outpath, "w") as f:
json.dump(refs, f)
return
import pandas as pd
references2 = {
k: {
"data": v.encode('ascii') if not isinstance(v, list) else None,
"url": v[0] if isinstance(v, list) else None,
"offset": v[1] if isinstance(v, list) else None,
"size": v[2] if isinstance(v, list) else None
}
for k, v in refs['refs'].items()
}
# use pandas for sorting
df = pd.DataFrame(references2.values(),
index=list(references2)).sort_values("offset")
if filetype == "zarr":
# compression should be NONE, if intent is to store in single zip
g = zarr.open_group(outpath, mode='w')
g.attrs.update({
k: v
for k, v in refs.items()
if k in ['version', "templates", "gen"]
})
g.array(name="key",
data=df.index.values,
dtype="object",
compression="zstd",
object_codec=numcodecs.VLenUTF8())
g.array(name="offset",
data=df.offset.values,
dtype="uint32",
compression="zstd")
g.array(name="size",
data=df['size'].values,
dtype="uint32",
compression="zstd")
g.array(name="data",
data=df.data.values,
dtype="object",
object_codec=numcodecs.VLenBytes(),
compression="gzip")
# may be better as fixed length
g.array(name="url",
data=df.url.values,
dtype="object",
object_codec=numcodecs.VLenUTF8(),
compression='gzip')
if filetype == "parquet":
import fastparquet
metadata = {
k: v
for k, v in refs.items()
if k in ['version', "templates", "gen"]
}
fastparquet.write(outpath,
df,
custom_metadata=metadata,
compression="ZSTD")
def test_bad_object_encoding(tempdir):
df = pd.DataFrame({'x': ['a', 'ab']})
with pytest.raises(ValueError) as e:
write(str(tempdir), df, object_encoding='utf-8')
assert "utf-8" in str(e.value)
JSON_FILE = 'Parquet/output/nodes.json'
nodes = []
tree = ET.parse(open(SOURCE_FILE))
for node in tree.iterfind('node'):
nodes.append({
'id': int(node.get('id')),
'longitude': float(node.get('lon')),
'latitude': float(node.get('lat')),
'username': node.get('user')
})
df = pd.DataFrame.from_records(nodes)
# Write nodes dictionary in an parquet file
write(PARQ_FILE, df)
# Write nodes dictionary in an avro file and use snappy compression algorithm
write(PARQ_SNAPPY_FILE, df, compression='snappy')
# Write nodes dictionary in an avro file and use GZIP compression algorithm
write(PARQ_GZIP_FILE, df, compression='GZIP')
# do the same with JSON format (for comparison)
df.to_json(JSON_FILE)
# Compare the size of the file formats
def print_file_size(file_path):
file_stats = os.stat(file_path)
print(f'Size of file {file_path} is {file_stats.st_size}')
#!/usr/bin/env python
"""
An example of writing parquet files with 'fastparquet'.
References:
- https://github.com/dask/fastparquet
"""
import pandas
from fastparquet import write
df = pandas.read_csv("/etc/passwd", sep=":")
# this is uncompressed write (I think!)
write('/tmp/file.parq', df)
# this is compressed write
write('/tmp/file_compressed.parq', df, compression='GZIP', file_scheme='hive')
verbose_eval=False,
params=params6,
early_stopping_rounds=50):
'XGB6' + BUILD_NAME
}
merc = GeneralisedStacking(base_estimators_dict=estimators,
estimator_type='regression',
feval=r2_score,
stack_type='s',
folds_strategy=skf)
merc.fit(train, y_train)
lvl1meta_train_regressor = merc.meta_train
lvl1meta_test_regressor = merc.predict(test)
lvl1meta_train_regressor['ID'] = id_train
lvl1meta_train_regressor['y'] = y_train
lvl1meta_test_regressor['ID'] = id_test
print('Writing Parquets')
# store
fastparquet.write('./data/processed/metalvl1/xtrain_metalvl1' + BUILD_NAME +
'.parq',
lvl1meta_train_regressor,
write_index=False)
fastparquet.write('./data/processed/metalvl1/xtest_metalvl1' + BUILD_NAME +
'.parq',
lvl1meta_test_regressor,
write_index=False)
print('Finished')
