def load_names():
"""Loads names used for renaming the banks
Returns
-------
Legate DataFrame
"""
cols = ["seller_name", "new"]
dtypes = OrderedDict([("seller_name", "category"), ("new", "category")])
out_pd = pd.read_csv(
col_names_path,
names=cols,
delimiter="|",
dtype=dtypes,
skiprows=1,
)
out_lp = lp.read_csv(
col_names_path,
names=cols,
delimiter="|",
dtype=dtypes,
skiprows=1,
)
assert equals(out_lp, out_pd)
def load_acquisition_csv():
"""Loads acquisition data
Returns
-------
Legate DataFrame
"""
cols = [
"loan_id",
"orig_channel",
"seller_name",
"orig_interest_rate",
"orig_upb",
"orig_loan_term",
"orig_date",
"first_pay_date",
"orig_ltv",
"orig_cltv",
"num_borrowers",
"dti",
"borrower_credit_score",
"first_home_buyer",
"loan_purpose",
"property_type",
"num_units",
"occupancy_status",
"property_state",
"zip",
"mortgage_insurance_percent",
"product_type",
"coborrow_credit_score",
"mortgage_insurance_type",
"relocation_mortgage_indicator",
"quarter",
]
dtypes = OrderedDict([
("loan_id", "int64"),
("orig_channel", "category"),
("seller_name", "category"),
("orig_interest_rate", "float64"),
("orig_upb", "int64"),
("orig_loan_term", "int64"),
("orig_date", "str"),
("first_pay_date", "str"),
("orig_ltv", "float64"),
("orig_cltv", "float64"),
("num_borrowers", "float64"),
("dti", "float64"),
("borrower_credit_score", "float64"),
("first_home_buyer", "category"),
("loan_purpose", "category"),
("property_type", "category"),
("num_units", "int64"),
("occupancy_status", "category"),
("property_state", "category"),
("zip", "int64"),
("mortgage_insurance_percent", "float64"),
("product_type", "category"),
("coborrow_credit_score", "float64"),
("mortgage_insurance_type", "float64"),
("relocation_mortgage_indicator", "category"),
("quarter", "int32"),
])
def dateparse(x):
return datetime.strptime(x, "%m/%Y")
out_pd = pd.read_csv(
acq_data_path,
names=cols,
delimiter="|",
index_col=False,
dtype=dtypes,
parse_dates=["orig_date", "first_pay_date"],
date_parser=dateparse,
)
out_lp = lp.read_csv(
acq_data_path,
names=cols,
delimiter="|",
index_col=False,
dtype=dtypes,
parse_dates=["orig_date", "first_pay_date"],
)
assert equals(out_lp, out_pd)
paths1 = [
os.path.join(base, "read_csv_datetime.csv"),
os.path.join(base, "read_csv_datetime.csv.gz"),
os.path.join(base, "read_csv_datetime.csv.bz2"),
]
paths2 = [
os.path.join(base, "read_csv_category.csv"),
os.path.join(base, "read_csv_category.csv.gz"),
os.path.join(base, "read_csv_category.csv.bz2"),
]
names = ["a", "b"]
dtypes = OrderedDict([("a", "int64"), ("b", str)])
for path1, path2 in itertools.product(paths1, paths2):
print(f"{path1} {path2}")
df = pd.concat(
[
pd.read_csv(path1, names=names, dtype=dtypes, index_col=False),
pd.read_csv(path2, names=names, dtype=dtypes, index_col=False),
],
ignore_index=True,
)
ldf = lp.read_csv([path1, path2],
names=names,
dtype=dtypes,
index_col=False)
assert ldf.equals(df)
def load_performance_csv():
"""Loads performance data
Returns
-------
Legate DataFrame
"""
cols = [
"loan_id",
"monthly_reporting_period",
"servicer",
"interest_rate",
"current_actual_upb",
"loan_age",
"remaining_months_to_legal_maturity",
"adj_remaining_months_to_maturity",
"maturity_date",
"msa",
"current_loan_delinquency_status",
"mod_flag",
"zero_balance_code",
"zero_balance_effective_date",
"last_paid_installment_date",
"foreclosed_after",
"disposition_date",
"foreclosure_costs",
"prop_preservation_and_repair_costs",
"asset_recovery_costs",
"misc_holding_expenses",
"holding_taxes",
"net_sale_proceeds",
"credit_enhancement_proceeds",
"repurchase_make_whole_proceeds",
"other_foreclosure_proceeds",
"non_interest_bearing_upb",
"principal_forgiveness_upb",
"repurchase_make_whole_proceeds_flag",
"foreclosure_principal_write_off_amount",
"servicing_activity_indicator",
]
dtypes = OrderedDict([
("loan_id", "int64"),
("monthly_reporting_period", "str"),
("servicer", "category"),
("interest_rate", "float64"),
("current_actual_upb", "float64"),
("loan_age", "float64"),
("remaining_months_to_legal_maturity", "float64"),
("adj_remaining_months_to_maturity", "float64"),
("maturity_date", "str"),
("msa", "float64"),
("current_loan_delinquency_status", "int32"),
("mod_flag", "category"),
("zero_balance_code", "category"),
("zero_balance_effective_date", "str"),
("last_paid_installment_date", "str"),
("foreclosed_after", "str"),
("disposition_date", "str"),
("foreclosure_costs", "float64"),
("prop_preservation_and_repair_costs", "float64"),
("asset_recovery_costs", "float64"),
("misc_holding_expenses", "float64"),
("holding_taxes", "float64"),
("net_sale_proceeds", "float64"),
("credit_enhancement_proceeds", "float64"),
("repurchase_make_whole_proceeds", "float64"),
("other_foreclosure_proceeds", "float64"),
("non_interest_bearing_upb", "float64"),
("principal_forgiveness_upb", "float64"),
("repurchase_make_whole_proceeds_flag", "category"),
("foreclosure_principal_write_off_amount", "float64"),
("servicing_activity_indicator", "category"),
])
def dateparse(x):
if not isinstance(x, str):
return pd.NaT
try:
return datetime.strptime(x, "%m/%d/%Y")
except ValueError:
return datetime.strptime(x, "%m/%Y")
out_pd = pd.read_csv(
perf_data_path,
names=cols,
delimiter="|",
index_col=False,
dtype=dtypes,
parse_dates=[
"monthly_reporting_period",
"maturity_date",
"zero_balance_effective_date",
"last_paid_installment_date",
"foreclosed_after",
"disposition_date",
],
date_parser=dateparse,
)
out_lp = lp.read_csv(
perf_data_path,
names=cols,
delimiter="|",
index_col=False,
dtype=dtypes,
parse_dates=[
"monthly_reporting_period",
"maturity_date",
"zero_balance_effective_date",
"last_paid_installment_date",
"foreclosed_after",
"disposition_date",
],
)
assert equals(out_lp, out_pd)
import pandas as pd
from legate import pandas as lp
path = os.path.join(os.path.dirname(__file__), "files", "read_csv_index.csv")
names = ["__lvl1__", "__lvl2__", "a", "b"]
dtypes = OrderedDict([
("__lvl1__", "int64"),
("__lvl2__", "float64"),
("a", "int64"),
("b", "float64"),
])
df = pd.read_csv(path, names=names, dtype=dtypes, index_col=[1, 0])
ldf = lp.read_csv(path, names=names, dtype=dtypes, index_col=[1, 0])
assert ldf.equals(df)
df = pd.read_csv(path,
names=names,
dtype=dtypes,
index_col=["__lvl1__", "__lvl2__"])
ldf = lp.read_csv(path,
names=names,
dtype=dtypes,
index_col=["__lvl1__", "__lvl2__"])
assert ldf.equals(df)
path = os.path.join(os.path.dirname(__file__), "files",
"read_csv_index_and_header.csv")
# limitations under the License.
#
import os
from collections import OrderedDict
import pandas as pd
from legate import pandas as lp
path = os.path.join(os.path.dirname(__file__), "files",
"read_csv_datetime.csv")
names = ["a", "b"]
dtypes = OrderedDict([("a", "int64"), ("b", "str")])
df = pd.read_csv(
path,
names=names,
dtype=dtypes,
index_col=False,
)
ldf = lp.read_csv(
path,
names=names,
dtype=dtypes,
index_col=False,
)
assert ldf.equals(df)
# limitations under the License.
#
import os
from collections import OrderedDict
import pandas as pd
from legate import pandas as lp
path = os.path.join(os.path.dirname(__file__), "files", "read_csv_header.csv")
dtypes = OrderedDict([("a", "int64"), ("b", "float64")])
df = pd.read_csv(path)
ldf = lp.read_csv(path)
assert ldf.equals(df)
df = pd.read_csv(path, dtype=dtypes)
ldf = lp.read_csv(path, dtype=dtypes)
assert ldf.equals(df)
try:
path_another = os.path.join(os.path.dirname(__file__), "files",
"read_csv_header_another.csv")
ldf = lp.read_csv([path, path_another], verify_header=True)
raise ValueError("test failed")
except ValueError:
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
from collections import OrderedDict
import pandas as pd
from legate import pandas as lp
path = os.path.join(os.path.dirname(__file__), "files", "read_csv.csv")
names = ["a", "b"]
dtypes = OrderedDict([("a", "int64"), ("b", "float64")])
df = pd.read_csv(path, names=names, dtype=dtypes)
ldf = lp.read_csv(path, names=names, dtype=dtypes)
assert ldf.equals(df)
df = pd.read_csv(path, names=names, dtype=dtypes, nrows=1)
ldf = lp.read_csv(path, names=names, dtype=dtypes, nrows=1)
assert ldf.equals(df)
true_values = ["this is true", "this is also True"]
false_values = ["this is false", "this is also FALSE"]
na_values = ["this is null", "this is NA"]
df = pd.read_csv(
path,
names=names,
dtype=dtypes,
true_values=true_values,
false_values=false_values,
na_values=na_values,
)
ldf = lp.read_csv(
path,
names=names,
dtype=dtypes,
true_values=true_values,
false_values=false_values,
na_values=na_values,
)
assert ldf.equals(df)
df = pd.read_csv(
path,
names=names,
dtype=dtypes,
true_values=true_values,
false_values=false_values,
na_values=na_values,
nrows=3,
)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import pandas as pd
from legate import pandas as lp
path = os.path.join(os.path.dirname(__file__), "files",
"read_csv_datetime.csv")
df = pd.read_csv(path, parse_dates=[1])
ldf = lp.read_csv(path, parse_dates=[1])
assert ldf.equals(df)
df = pd.read_csv(path, names=["a", "b"], parse_dates=["b"])
ldf = lp.read_csv(path, names=["a", "b"], parse_dates=["b"])
assert ldf.equals(df)
# limitations under the License.
#
import os
import pandas as pd
from legate import pandas as lp
names = ["a", "b"]
paths = [
os.path.join(os.path.dirname(__file__), "files", "read_csv_quotes.csv"),
os.path.join(os.path.dirname(__file__), "files", "read_csv_colons.csv"),
]
quotechars = ['"', ":"]
for path, quotechar in zip(paths, quotechars):
df = pd.read_csv(
path,
names=names,
dtype="string",
quotechar=quotechar,
skipfooter=1,
engine="python",
)
ldf = lp.read_csv(path,
names=names,
dtype="string",
quotechar=quotechar,
skipfooter=1)
assert ldf.equals(df)
