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test_pandas.py
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from time import sleep
import logging
import csv
from datetime import datetime, date
from decimal import Decimal
import warnings
import pytest
import boto3
import pandas as pd
import numpy as np
import awswrangler as wr
from awswrangler import Session, Pandas, Aurora
from awswrangler.exceptions import LineTerminatorNotFound, EmptyDataframe, InvalidSerDe, UndetectedType
logging.basicConfig(level=logging.INFO, format="[%(asctime)s][%(levelname)s][%(name)s][%(funcName)s] %(message)s")
logging.getLogger("awswrangler").setLevel(logging.DEBUG)
@pytest.fixture(scope="module")
def cloudformation_outputs():
response = boto3.client("cloudformation").describe_stacks(StackName="aws-data-wrangler-test")
outputs = {}
for output in response.get("Stacks")[0].get("Outputs"):
outputs[output.get("OutputKey")] = output.get("OutputValue")
yield outputs
@pytest.fixture(scope="module")
def bucket(session, cloudformation_outputs):
if "BucketName" in cloudformation_outputs:
bucket = cloudformation_outputs["BucketName"]
session.s3.delete_objects(path=f"s3://{bucket}/")
else:
raise Exception("You must deploy the test infrastructure using Cloudformation!")
yield bucket
session.s3.delete_objects(path=f"s3://{bucket}/")
@pytest.fixture(scope="module")
def database(cloudformation_outputs):
if "GlueDatabaseName" in cloudformation_outputs:
database = cloudformation_outputs["GlueDatabaseName"]
else:
raise Exception("You must deploy the test infrastructure using Cloudformation!")
yield database
tables = wr.glue.tables(database=database)["Table"].tolist()
for t in tables:
print(f"Dropping: {database}.{t}...")
wr.glue.delete_table_if_exists(database=database, table=t)
@pytest.fixture(scope="module")
def session(database):
yield Session(athena_database=database)
@pytest.fixture(scope="module")
def kms_key(cloudformation_outputs):
if "KmsKeyArn" in cloudformation_outputs:
database = cloudformation_outputs["KmsKeyArn"]
else:
raise Exception("You must deploy the test infrastructure using Cloudformation!")
yield database
@pytest.fixture(scope="module")
def loggroup(cloudformation_outputs):
if "LogGroupName" in cloudformation_outputs:
database = cloudformation_outputs["LogGroupName"]
else:
raise Exception("You must deploy the test infrastructure using Cloudformation!")
yield database
@pytest.fixture(scope="module")
def logstream(cloudformation_outputs, loggroup):
if "LogStream" in cloudformation_outputs:
logstream = cloudformation_outputs["LogStream"]
else:
raise Exception("You must deploy the test infrastructure using Cloudformation!")
client = boto3.client("logs")
response = client.describe_log_streams(logGroupName=loggroup, logStreamNamePrefix=logstream)
token = response["logStreams"][0].get("uploadSequenceToken")
events = []
for i in range(5):
events.append({"timestamp": int(1000 * datetime.utcnow().timestamp()), "message": str(i)})
args = {"logGroupName": loggroup, "logStreamName": logstream, "logEvents": events}
if token:
args["sequenceToken"] = token
client.put_log_events(**args)
yield logstream
@pytest.fixture(scope="module")
def postgres_parameters(cloudformation_outputs):
postgres_parameters = {}
if "PostgresAddress" in cloudformation_outputs:
postgres_parameters["PostgresAddress"] = cloudformation_outputs.get("PostgresAddress")
else:
raise Exception("You must deploy the test infrastructure using SAM!")
if "Password" in cloudformation_outputs:
postgres_parameters["Password"] = cloudformation_outputs.get("Password")
else:
raise Exception("You must deploy the test infrastructure using SAM!")
conn = Aurora.generate_connection(database="postgres",
host=postgres_parameters["PostgresAddress"],
port=3306,
user="test",
password=postgres_parameters["Password"],
engine="postgres")
with conn.cursor() as cursor:
sql = "CREATE EXTENSION IF NOT EXISTS aws_s3 CASCADE"
cursor.execute(sql)
conn.commit()
conn.close()
yield postgres_parameters
@pytest.fixture(scope="module")
def mysql_parameters(cloudformation_outputs):
mysql_parameters = {}
if "MysqlAddress" in cloudformation_outputs:
mysql_parameters["MysqlAddress"] = cloudformation_outputs.get("MysqlAddress")
else:
raise Exception("You must deploy the test infrastructure using SAM!")
if "Password" in cloudformation_outputs:
mysql_parameters["Password"] = cloudformation_outputs.get("Password")
else:
raise Exception("You must deploy the test infrastructure using SAM!")
conn = Aurora.generate_connection(database="mysql",
host=mysql_parameters["MysqlAddress"],
port=3306,
user="test",
password=mysql_parameters["Password"],
engine="mysql")
with conn.cursor() as cursor:
sql = "CREATE DATABASE IF NOT EXISTS test"
with warnings.catch_warnings():
warnings.filterwarnings(action="ignore", message=".*database exists.*")
cursor.execute(sql)
conn.commit()
conn.close()
yield mysql_parameters
@pytest.mark.parametrize("sample, row_num", [("data_samples/micro.csv", 30), ("data_samples/small.csv", 100)])
def test_read_csv(session, bucket, sample, row_num):
boto3.client("s3").upload_file(sample, bucket, sample)
path = f"s3://{bucket}/{sample}"
dataframe = session.pandas.read_csv(path=path)
session.s3.delete_objects(path=path)
assert len(dataframe.index) == row_num
@pytest.mark.parametrize("sample, row_num", [("data_samples/micro.csv", 30), ("data_samples/small.csv", 100)])
def test_read_csv_iterator(session, bucket, sample, row_num):
boto3.client("s3").upload_file(sample, bucket, sample)
path = f"s3://{bucket}/{sample}"
dataframe_iter = session.pandas.read_csv(path=path, max_result_size=200)
total_count = 0
for dataframe in dataframe_iter:
total_count += len(dataframe.index)
session.s3.delete_objects(path=path)
assert total_count == row_num
@pytest.mark.parametrize("sample, row_num", [("data_samples/micro.csv", 30), ("data_samples/small.csv", 100)])
def test_read_csv_usecols(session, bucket, sample, row_num):
boto3.client("s3").upload_file(sample, bucket, sample)
path = f"s3://{bucket}/{sample}"
dataframe = session.pandas.read_csv(path=path, usecols=["id", "name"])
session.s3.delete_objects(path=path)
assert len(dataframe.index) == row_num
assert len(dataframe.columns) == 2
@pytest.mark.parametrize("sample, row_num", [("data_samples/micro.csv", 30), ("data_samples/small.csv", 100)])
def test_read_csv_iterator_usecols(session, bucket, sample, row_num):
boto3.client("s3").upload_file(sample, bucket, sample)
path = f"s3://{bucket}/{sample}"
dataframe_iter = session.pandas.read_csv(path=path, usecols=[0, 1], max_result_size=200)
total_count = 0
for dataframe in dataframe_iter:
total_count += len(dataframe.index)
assert len(dataframe.columns) == 2
session.s3.delete_objects(path=path)
assert total_count == row_num
def test_read_csv_thousands_and_decimal(session, bucket):
text = "col1;col2\n1.000.000,00;2.000.000,00\n3.000.000,00;4.000.000,00"
filename = "test_read_csv_thousands_and_decimal/sample.txt"
boto3.resource("s3").Object(bucket, filename).put(Body=text)
path = f"s3://{bucket}/{filename}"
df = session.pandas.read_csv(path=path, sep=";", thousands=".", decimal=",")
assert len(df.index) == 2
assert len(df.columns) == 2
assert df.iloc[0].col1 == 1_000_000
assert df.iloc[0].col2 == 2_000_000
assert df.iloc[1].col1 == 3_000_000
assert df.iloc[1].col2 == 4_000_000
@pytest.mark.parametrize(
"mode, file_format, preserve_index, partition_cols, procs_cpu_bound, factor",
[
("overwrite", "csv", False, [], 1, 1),
("append", "csv", False, [], 1, 2),
("overwrite_partitions", "csv", False, [], 1, 1),
("overwrite", "csv", True, [], 1, 1),
("append", "csv", True, [], 1, 2),
("overwrite_partitions", "csv", True, [], 1, 1),
("overwrite", "csv", False, [], 5, 1),
("append", "csv", False, [], 5, 2),
("overwrite_partitions", "csv", False, [], 5, 1),
("overwrite", "csv", True, [], 5, 1),
("append", "csv", True, [], 5, 2),
("overwrite_partitions", "csv", True, [], 5, 1),
("overwrite", "csv", False, ["date"], 1, 1),
("append", "csv", False, ["date"], 1, 2),
("overwrite_partitions", "csv", False, ["date"], 1, 1),
("overwrite", "csv", True, ["date"], 1, 1),
("append", "csv", True, ["date"], 1, 2),
("overwrite_partitions", "csv", True, ["date"], 1, 1),
("overwrite", "csv", False, ["date"], 5, 1),
("append", "csv", False, ["date"], 5, 2),
("overwrite_partitions", "csv", False, ["date"], 5, 1),
("overwrite", "csv", True, ["date"], 5, 1),
("append", "csv", True, ["date"], 5, 2),
("overwrite_partitions", "csv", True, ["date"], 5, 1),
("overwrite", "csv", False, ["name", "date"], 1, 1),
("append", "csv", False, ["name", "date"], 1, 2),
("overwrite_partitions", "csv", False, ["name", "date"], 1, 1),
("overwrite", "csv", True, ["name", "date"], 1, 1),
("append", "csv", True, ["name", "date"], 1, 2),
("overwrite_partitions", "csv", True, ["name", "date"], 1, 1),
("overwrite", "csv", False, ["name", "date"], 5, 1),
("append", "csv", False, ["name", "date"], 5, 2),
("overwrite_partitions", "csv", False, ["name", "date"], 5, 1),
("overwrite", "csv", True, ["name", "date"], 5, 1),
("append", "csv", True, ["name", "date"], 5, 2),
("overwrite_partitions", "csv", True, ["name", "date"], 2, 1),
("overwrite", "parquet", False, [], 1, 1),
("append", "parquet", False, [], 1, 2),
("overwrite_partitions", "parquet", False, [], 1, 1),
("overwrite", "parquet", True, [], 1, 1),
("append", "parquet", True, [], 1, 2),
("overwrite_partitions", "parquet", True, [], 1, 1),
("overwrite", "parquet", False, [], 5, 1),
("append", "parquet", False, [], 5, 2),
("overwrite_partitions", "parquet", False, [], 5, 1),
("overwrite", "parquet", True, [], 5, 1),
("append", "parquet", True, [], 5, 2),
("overwrite_partitions", "parquet", True, [], 5, 1),
("overwrite", "parquet", False, ["date"], 1, 1),
("append", "parquet", False, ["date"], 1, 2),
("overwrite_partitions", "parquet", False, ["date"], 1, 1),
("overwrite", "parquet", True, ["date"], 1, 1),
("append", "parquet", True, ["date"], 1, 2),
("overwrite_partitions", "parquet", True, ["date"], 1, 1),
("overwrite", "parquet", False, ["date"], 5, 1),
("append", "parquet", False, ["date"], 5, 2),
("overwrite_partitions", "parquet", False, ["date"], 5, 1),
("overwrite", "parquet", True, ["date"], 5, 1),
("append", "parquet", True, ["date"], 5, 2),
("overwrite_partitions", "parquet", True, ["date"], 5, 1),
("overwrite", "parquet", False, ["name", "date"], 1, 1),
("append", "parquet", False, ["name", "date"], 1, 2),
("overwrite_partitions", "parquet", False, ["name", "date"], 1, 1),
("overwrite", "parquet", True, ["name", "date"], 1, 1),
("append", "parquet", True, ["name", "date"], 1, 2),
("overwrite_partitions", "parquet", True, ["name", "date"], 1, 1),
("overwrite", "parquet", False, ["name", "date"], 5, 1),
("append", "parquet", False, ["name", "date"], 5, 2),
("overwrite_partitions", "parquet", False, ["name", "date"], 5, 1),
("overwrite", "parquet", True, ["name", "date"], 5, 1),
("append", "parquet", True, ["name", "date"], 5, 2),
("overwrite_partitions", "parquet", True, ["name", "date"], 5, 1),
],
)
def test_to_s3(
session,
bucket,
database,
mode,
file_format,
preserve_index,
partition_cols,
procs_cpu_bound,
factor,
):
dataframe = pd.read_csv("data_samples/micro.csv")
func = session.pandas.to_csv if file_format == "csv" else session.pandas.to_parquet
path = f"s3://{bucket}/test/"
objects_paths = func(
dataframe=dataframe,
database=database,
path=path,
preserve_index=preserve_index,
mode=mode,
partition_cols=partition_cols,
procs_cpu_bound=procs_cpu_bound,
)
num_partitions = (len([keys for keys in dataframe.groupby(partition_cols)]) if partition_cols else 1)
assert len(objects_paths) >= num_partitions
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if factor * len(dataframe.index) == len(dataframe2.index):
break
assert factor * len(dataframe.index) == len(dataframe2.index)
if preserve_index:
assert (len(list(dataframe.columns)) + 1) == len(list(dataframe2.columns))
else:
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
def test_to_parquet_with_cast_int(
session,
bucket,
database,
):
dataframe = pd.read_csv("data_samples/nano.csv", dtype={"id": "Int64"}, parse_dates=["date", "time"])
path = f"s3://{bucket}/test/"
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=path,
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1,
cast_columns={"value": "int"})
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if len(dataframe.index) == len(dataframe2.index):
break
session.s3.delete_objects(path=path)
assert len(dataframe.index) == len(dataframe2.index)
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
assert dataframe[dataframe["id"] == 0].iloc[0]["name"] == dataframe2[dataframe2["id"] == 0].iloc[0]["name"]
@pytest.mark.parametrize("sample, row_num, max_result_size", [
("data_samples/nano.csv", 5, 5000),
("data_samples/micro.csv", 30, 100),
("data_samples/small.csv", 100, 100),
("data_samples/micro.csv", 30, 500),
("data_samples/small.csv", 100, 500),
("data_samples/micro.csv", 30, 3000),
("data_samples/small.csv", 100, 3000),
("data_samples/micro.csv", 30, 700),
])
def test_read_sql_athena_iterator(session, bucket, database, sample, row_num, max_result_size):
parse_dates = []
if sample == "data_samples/nano.csv":
parse_dates.append("time")
parse_dates.append("date")
dataframe_sample = pd.read_csv(sample, parse_dates=parse_dates)
path = f"s3://{bucket}/test/"
session.pandas.to_parquet(dataframe=dataframe_sample,
database=database,
path=path,
preserve_index=False,
mode="overwrite")
total_count = 0
for counter in range(10):
sleep(1)
dataframe_iter = session.pandas.read_sql_athena(ctas_approach=False,
sql="select * from test",
database=database,
max_result_size=max_result_size)
total_count = 0
for dataframe in dataframe_iter:
total_count += len(dataframe.index)
assert len(list(dataframe.columns)) == len(list(dataframe_sample.columns))
if total_count == row_num:
break
session.s3.delete_objects(path=path)
assert total_count == row_num
@pytest.mark.parametrize("body, sep, quotechar, lineterminator, last_index, last_terminator_suspect_index,"
"first_non_special_byte_index, sep_counter, quote_counter", [
(b'"foo","boo"\n', ",", '"', "\n", None, 11, 9, 0, 1),
(b'"foo","boo"\n"bar', ",", '"', "\n", None, 11, 9, 0, 1),
(b'!foo!;!boo!@', ";", '!', "@", None, 11, 9, 0, 1),
(b'"foo","boo"\n"bar\n', ",", '"', "\n", 16, 11, 9, 0, 1),
])
def test_extract_terminator_profile(body, sep, quotechar, lineterminator, last_index, last_terminator_suspect_index,
first_non_special_byte_index, sep_counter, quote_counter):
profile = Pandas._extract_terminator_profile(body=body,
sep=sep,
quotechar=quotechar,
lineterminator=lineterminator,
last_index=last_index)
assert profile["last_terminator_suspect_index"] == last_terminator_suspect_index
assert profile["first_non_special_byte_index"] == first_non_special_byte_index
assert profile["sep_counter"] == sep_counter
assert profile["quote_counter"] == quote_counter
@pytest.mark.parametrize("body, sep, quoting, quotechar, lineterminator, ret", [
(b"012\njawdnkjawnd", ",", csv.QUOTE_MINIMAL, '"', "\n", 3),
(b"012\n456\njawdnkjawnd", ",", csv.QUOTE_MINIMAL, '"', "\n", 7),
(b'012",\n"foo', ",", csv.QUOTE_ALL, '"', "\n", 5),
(b'012",\n', ",", csv.QUOTE_ALL, '"', "\n", 5),
(b'012",\n"012,\n', ",", csv.QUOTE_ALL, '"', "\n", 5),
(b'012",\n,,,,,,,,"012,\n', ",", csv.QUOTE_ALL, '"', "\n", 5),
(b'012",,,,\n"012,\n', ",", csv.QUOTE_ALL, '"', "\n", 8),
(b'012",,,,\n,,,,,,""012,\n', ",", csv.QUOTE_ALL, '"', "\n", 8),
(b'012",,,,\n,,,,,,""012"\n,', ",", csv.QUOTE_ALL, '"', "\n", 21),
(b'012",,,,\n,,,,,,""01"2""\n,"a', ",", csv.QUOTE_ALL, '"', "\n", 8),
(b'"foo","boo"\n"\n","bar"', ",", csv.QUOTE_ALL, '"', "\n", 11),
(b'"foo"\n"boo","\n","\n","\n","\n","\n",,,,,,"\n",,,,', ",", csv.QUOTE_ALL, '"', "\n", 5),
(b'012",\n"foo","\n\n\n\n","\n', ",", csv.QUOTE_ALL, '"', "\n", 5),
])
def test_find_terminator(body, sep, quoting, quotechar, lineterminator, ret):
assert Pandas._find_terminator(body=body,
sep=sep,
quoting=quoting,
quotechar=quotechar,
lineterminator=lineterminator) == ret
@pytest.mark.parametrize("body, sep, quoting, quotechar, lineterminator",
[(b"jawdnkjawnd", ",", csv.QUOTE_MINIMAL, '"', "\n"),
(b"jawdnkjawnd", ",", csv.QUOTE_ALL, '"', "\n"),
(b"jawdnkj\nawnd", ",", csv.QUOTE_ALL, '"', "\n"),
(b'jawdnkj"x\n\n"awnd', ",", csv.QUOTE_ALL, '"', "\n"),
(b'jawdnkj""\n,,,,,,,,,,awnd', ",", csv.QUOTE_ALL, '"', "\n"),
(b'jawdnkj,""""""\nawnd', ",", csv.QUOTE_ALL, '"', "\n")])
def test_find_terminator_exception(body, sep, quoting, quotechar, lineterminator):
with pytest.raises(LineTerminatorNotFound):
assert Pandas._find_terminator(body=body,
sep=sep,
quoting=quoting,
quotechar=quotechar,
lineterminator=lineterminator)
@pytest.mark.parametrize("max_result_size", [400, 700, 1000, 10000])
def test_etl_complex(session, bucket, database, max_result_size):
dataframe = pd.read_csv("data_samples/complex.csv",
dtype={"my_int_with_null": "Int64"},
parse_dates=["my_timestamp", "my_date"])
path = f"s3://{bucket}/test/"
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=path,
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1)
sleep(1)
df_iter = session.pandas.read_sql_athena(ctas_approach=False,
sql="select * from test",
database=database,
max_result_size=max_result_size)
count = 0
for df in df_iter:
count += len(df.index)
for row in df.itertuples():
assert len(list(dataframe.columns)) == len(list(df.columns))
assert isinstance(row.my_timestamp, datetime)
assert isinstance(row.my_date, date)
assert isinstance(row.my_float, float)
assert isinstance(row.my_int, np.int64)
assert isinstance(row.my_string, str)
assert str(row.my_timestamp) == "2018-01-01 04:03:02.001000"
assert str(row.my_date) == "2019-02-02 00:00:00"
assert str(row.my_float) == "12345.6789"
assert str(row.my_int) == "123456789"
assert str(
row.my_string
) == "foo\nboo\nbar\nFOO\nBOO\nBAR\nxxxxx\nÁÃÀÂÇ\n汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå"
session.s3.delete_objects(path=path)
assert count == len(dataframe.index)
def test_etl_complex_ctas(session, bucket, database):
dataframe = pd.read_csv("data_samples/complex.csv",
dtype={"my_int_with_null": "Int64"},
parse_dates=["my_timestamp", "my_date"])
path = f"s3://{bucket}/test/"
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=path,
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1)
sleep(1)
df = session.pandas.read_sql_athena(ctas_approach=True, sql="select * from test", database=database)
for row in df.itertuples():
assert isinstance(row.my_timestamp, datetime)
assert isinstance(row.my_date, date)
assert isinstance(row.my_float, float)
assert isinstance(row.my_int, int)
assert isinstance(row.my_string, str)
assert str(row.my_int_with_null) in ("1", "nan")
assert str(row.my_timestamp) == "2018-01-01 04:03:02.001000"
assert str(row.my_date) == "2019-02-02 00:00:00"
assert str(row.my_float) == "12345.6789"
assert row.my_int == 123456789
assert str(
row.my_string
) == "foo\nboo\nbar\nFOO\nBOO\nBAR\nxxxxx\nÁÃÀÂÇ\n汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå"
session.s3.delete_objects(path=path)
assert len(list(dataframe.columns)) == len(list(df.columns))
assert len(df.index) == len(dataframe.index)
def test_to_parquet_with_kms(
bucket,
database,
kms_key,
):
extra_args = {"ServerSideEncryption": "aws:kms", "SSEKMSKeyId": kms_key}
session_inner = Session(s3_additional_kwargs=extra_args)
dataframe = pd.read_csv("data_samples/nano.csv")
path = f"s3://{bucket}/test/"
session_inner.pandas.to_parquet(dataframe=dataframe,
database=database,
path=path,
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1)
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session_inner.pandas.read_sql_athena(ctas_approach=False,
sql="select * from test",
database=database)
if len(dataframe.index) == len(dataframe2.index):
break
session_inner.s3.delete_objects(path=path)
assert len(dataframe.index) == len(dataframe2.index)
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
assert dataframe[dataframe["id"] == 0].iloc[0]["name"] == dataframe2[dataframe2["id"] == 0].iloc[0]["name"]
def test_to_parquet_with_empty_dataframe(session, bucket, database):
dataframe = pd.DataFrame()
with pytest.raises(EmptyDataframe):
assert session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1)
def test_read_log_query(session, loggroup):
dataframe = session.pandas.read_log_query(
log_group_names=[loggroup],
query="fields @timestamp, @message | sort @timestamp desc | limit 5",
)
assert len(dataframe.index) == 5
assert len(dataframe.columns) == 3
@pytest.mark.parametrize("file_format, serde, index, partition_cols",
[("csv", "OpenCSVSerDe", None, []), ("csv", "OpenCSVSerDe", "default", []),
("csv", "OpenCSVSerDe", "my_date", []), ("csv", "OpenCSVSerDe", "my_timestamp", []),
("csv", "OpenCSVSerDe", "my_timestamp", []),
("csv", "LazySimpleSerDe", "my_date", ["my_timestamp", "my_float"]),
("csv", "LazySimpleSerDe", None, []), ("csv", "LazySimpleSerDe", "default", []),
("csv", "LazySimpleSerDe", "my_date", []), ("csv", "LazySimpleSerDe", "my_timestamp", []),
("csv", "LazySimpleSerDe", "my_timestamp", ["my_date", "my_int"]),
("parquet", None, None, []), ("parquet", None, "default", []),
("parquet", None, "my_date", []), ("parquet", None, "my_timestamp", []),
("parquet", None, None, ["my_int"]), ("parquet", None, "default", ["my_int"]),
("parquet", None, "my_date", ["my_int"]), ("parquet", None, "my_timestamp", ["my_int"]),
("parquet", None, None, ["my_float"]), ("parquet", None, "default", ["my_float"]),
("parquet", None, "my_date", ["my_float"]), ("parquet", None, "my_timestamp", ["my_float"]),
("parquet", None, None, ["my_date"]), ("parquet", None, "default", ["my_date"]),
("parquet", None, "my_date", ["my_date"]), ("parquet", None, "my_timestamp", ["my_date"]),
("parquet", None, None, ["my_timestamp"]), ("parquet", None, "default", ["my_timestamp"]),
("parquet", None, "my_date", ["my_timestamp"]),
("parquet", None, "my_timestamp", ["my_timestamp"]),
("parquet", None, None, ["my_timestamp", "my_date"]),
("parquet", None, "default", ["my_date", "my_timestamp"]),
("parquet", None, "my_date", ["my_timestamp", "my_date"]),
("parquet", None, "my_timestamp", ["my_date", "my_timestamp"]),
("parquet", None, "default", ["my_date", "my_timestamp", "my_int"]),
("parquet", None, "my_date", ["my_timestamp", "my_float", "my_date"])])
def test_to_s3_types(session, bucket, database, file_format, serde, index, partition_cols):
dataframe = pd.read_csv("data_samples/complex.csv",
dtype={"my_int_with_null": "Int64"},
parse_dates=["my_timestamp", "my_date"])
dataframe["my_date"] = dataframe["my_date"].dt.date
dataframe["my_bool"] = True
preserve_index = True
if not index:
preserve_index = False
elif index != "default":
dataframe["new_index"] = dataframe[index]
dataframe = dataframe.set_index("new_index")
args = {
"dataframe": dataframe,
"database": database,
"path": f"s3://{bucket}/test/",
"preserve_index": preserve_index,
"mode": "overwrite",
"procs_cpu_bound": 1,
"partition_cols": partition_cols
}
if file_format == "csv":
func = session.pandas.to_csv
args["serde"] = serde
del dataframe["my_string"]
else:
func = session.pandas.to_parquet
objects_paths = func(**args)
assert len(objects_paths) == 1
sleep(2)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
for row in dataframe2.itertuples():
if file_format == "parquet":
if index:
if index == "my_date":
assert isinstance(row.new_index, date)
elif index == "my_timestamp":
assert isinstance(row.new_index, datetime)
assert isinstance(row.my_timestamp, datetime)
assert type(row.my_date) == date
assert isinstance(row.my_float, float)
assert isinstance(row.my_int, np.int64)
assert isinstance(row.my_string, str)
assert isinstance(row.my_bool, bool)
assert str(
row.my_string
) == "foo\nboo\nbar\nFOO\nBOO\nBAR\nxxxxx\nÁÃÀÂÇ\n汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå"
elif file_format == "csv":
if serde == "LazySimpleSerDe":
assert isinstance(row.my_float, float)
assert isinstance(row.my_int, np.int64)
assert str(row.my_timestamp).startswith("2018-01-01 04:03:02.001")
assert str(row.my_date) == "2019-02-02"
assert str(row.my_float) == "12345.6789"
assert str(row.my_int) == "123456789"
assert str(row.my_bool) == "True"
assert len(dataframe.index) == len(dataframe2.index)
if index:
assert (len(list(dataframe.columns)) + 1) == len(list(dataframe2.columns))
else:
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
def test_to_csv_with_sep(
session,
bucket,
database,
):
dataframe = pd.read_csv("data_samples/nano.csv")
session.pandas.to_csv(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
preserve_index=False,
mode="overwrite",
sep="|")
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
def test_to_csv_serde_exception(
session,
bucket,
database,
):
dataframe = pd.read_csv("data_samples/nano.csv")
with pytest.raises(InvalidSerDe):
assert session.pandas.to_csv(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
preserve_index=False,
mode="overwrite",
serde="foo")
@pytest.mark.parametrize("compression", [None, "snappy", "gzip"])
def test_to_parquet_compressed(session, bucket, database, compression):
dataframe = pd.read_csv("data_samples/small.csv")
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
preserve_index=False,
mode="overwrite",
compression=compression,
procs_cpu_bound=1)
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
assert dataframe[dataframe["id"] == 1].iloc[0]["name"] == dataframe2[dataframe2["id"] == 1].iloc[0]["name"]
def test_to_parquet_lists(session, bucket, database):
dataframe = pd.DataFrame({
"id": [0, 1],
"col_int": [[1, 2], [3, 4, 5]],
"col_float": [[1.0, 2.0, 3.0], [4.0, 5.0]],
"col_string": [["foo"], ["boo", "bar"]],
"col_timestamp": [[datetime(2019, 1, 1), datetime(2019, 1, 2)], [datetime(2019, 1, 3)]],
"col_date": [[date(2019, 1, 1), date(2019, 1, 2)], [date(2019, 1, 3)]],
"col_list_int": [[[1]], [[2, 3], [4, 5, 6]]],
"col_list_list_string": [[[["foo"]]], [[["boo", "bar"]]]],
})
paths = session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1)
assert len(paths) == 1
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False,
sql="select id, col_int, col_float, col_list_int from test",
database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert 4 == len(list(dataframe2.columns))
val = dataframe[dataframe["id"] == 0].iloc[0]["col_list_int"]
val2 = dataframe2[dataframe2["id"] == 0].iloc[0]["col_list_int"]
assert val == val2
def test_to_parquet_with_cast_null(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"id": [0, 1],
"col_null_tinyint": [None, None],
"col_null_smallint": [None, None],
"col_null_int": [None, None],
"col_null_bigint": [None, None],
"col_null_float": [None, None],
"col_null_double": [None, None],
"col_null_string": [None, None],
"col_null_date": [None, None],
"col_null_timestamp": [None, None],
})
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
preserve_index=False,
mode="overwrite",
procs_cpu_bound=1,
cast_columns={
"col_null_tinyint": "tinyint",
"col_null_smallint": "smallint",
"col_null_int": "int",
"col_null_bigint": "bigint",
"col_null_float": "float",
"col_null_double": "double",
"col_null_string": "string",
"col_null_date": "date",
"col_null_timestamp": "timestamp",
})
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
def test_read_sql_athena_with_time_zone(session, database):
query = "select current_timestamp as value, typeof(current_timestamp) as type"
dataframe = session.pandas.read_sql_athena(ctas_approach=False, sql=query, database=database)
assert len(dataframe.index) == 1
assert len(dataframe.columns) == 2
assert dataframe["type"][0] == "timestamp with time zone"
assert dataframe["value"][0].year == datetime.utcnow().year
def test_normalize_columns_names_athena():
dataframe = pd.DataFrame({
"CamelCase": [1, 2, 3],
"With Spaces": [4, 5, 6],
"With-Dash": [7, 8, 9],
"Ãccént": [10, 11, 12],
"with.dot": [10, 11, 12],
"Camel_Case2": [13, 14, 15],
"Camel___Case3": [16, 17, 18]
})
Pandas.normalize_columns_names_athena(dataframe=dataframe, inplace=True)
assert dataframe.columns[0] == "camel_case"
assert dataframe.columns[1] == "with_spaces"
assert dataframe.columns[2] == "with_dash"
assert dataframe.columns[3] == "accent"
assert dataframe.columns[4] == "with_dot"
assert dataframe.columns[5] == "camel_case2"
assert dataframe.columns[6] == "camel_case3"
def test_to_parquet_with_normalize(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"CamelCase": [1, 2, 3],
"With Spaces": [4, 5, 6],
"With-Dash": [7, 8, 9],
"Ãccént": [10, 11, 12],
"with.dot": [10, 11, 12],
"Camel_Case2": [13, 14, 15],
"Camel___Case3": [16, 17, 18]
})
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/TestTable-with.dot/",
mode="overwrite")
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False,
sql="select * from test_table_with_dot",
database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert (len(list(dataframe.columns)) + 1) == len(list(dataframe2.columns))
assert dataframe2.columns[0] == "camel_case"
assert dataframe2.columns[1] == "with_spaces"
assert dataframe2.columns[2] == "with_dash"
assert dataframe2.columns[3] == "accent"
assert dataframe2.columns[4] == "with_dot"
assert dataframe2.columns[5] == "camel_case2"
assert dataframe2.columns[6] == "camel_case3"
def test_to_parquet_with_normalize_and_cast(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"CamelCase": [1, 2, 3],
"With Spaces": [4, 5, 6],
"With-Dash": [7, 8, 9],
"Ãccént": [10, 11, 12],
"with.dot": [10, 11, 12],
"Camel_Case2": [13, 14, 15],
"Camel___Case3": [16, 17, 18]
})
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/TestTable-with.dot/",
mode="overwrite",
partition_cols=["CamelCase"],
cast_columns={
"Camel_Case2": "double",
"Camel___Case3": "float"
})
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False,
sql="select * from test_table_with_dot",
database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert (len(list(dataframe.columns)) + 1) == len(list(dataframe2.columns))
assert dataframe2.columns[0] == "with_spaces"
assert dataframe2.columns[1] == "with_dash"
assert dataframe2.columns[2] == "accent"
assert dataframe2.columns[3] == "with_dot"
assert dataframe2.columns[4] == "camel_case2"
assert dataframe2.columns[5] == "camel_case3"
assert dataframe2.columns[6] == "__index_level_0__"
assert dataframe2.columns[7] == "camel_case"
assert dataframe2[dataframe2.columns[4]].dtype == "float64"
assert dataframe2[dataframe2.columns[5]].dtype == "float64"
def test_drop_duplicated_columns():
dataframe = pd.DataFrame({
"a": [1, 2, 3],
"b": [4, 5, 6],
"c": [7, 8, 9],
})
dataframe.columns = ["a", "a", "c"]
dataframe = Pandas.drop_duplicated_columns(dataframe=dataframe)
assert dataframe.columns[0] == "a"
assert dataframe.columns[1] == "c"
def test_to_parquet_duplicated_columns(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"a": [1, 2, 3],
"b": [4, 5, 6],
"c": [7, 8, 9],
})
dataframe.columns = ["a", "a", "c"]
session.pandas.to_parquet(dataframe=dataframe, database=database, path=f"s3://{bucket}/test/", mode="overwrite")
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
assert dataframe2.columns[0] == "a"
assert dataframe2.columns[1] == "c"
def test_to_parquet_with_pyarrow_null_type(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"a": [1, 2, 3],
"b": [4, 5, 6],
"col_null": [None, None, None],
"c": [7, 8, 9],
})
with pytest.raises(UndetectedType):
assert session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
mode="overwrite")
def test_to_parquet_casting_to_string(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"a": [1, 2, 3],
"col_string_null": [None, None, None],
"c": [7, 8, 9],
})
session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
mode="overwrite",
cast_columns={"col_string_null": "string"})
dataframe2 = None
for counter in range(10):
sleep(1)
dataframe2 = session.pandas.read_sql_athena(ctas_approach=False, sql="select * from test", database=database)
if len(dataframe.index) == len(dataframe2.index):
break
assert len(dataframe.index) == len(dataframe2.index)
assert (len(list(dataframe.columns)) + 1) == len(list(dataframe2.columns))
def test_to_parquet_casting_with_null_object(
session,
bucket,
database,
):
dataframe = pd.DataFrame({
"a": [1, 2, 3],
"b": [4, 5, 6],
"col_null": [None, None, None],
})
with pytest.raises(UndetectedType):
assert session.pandas.to_parquet(dataframe=dataframe,
database=database,
path=f"s3://{bucket}/test/",
mode="overwrite")
def test_read_sql_athena_with_nulls(session, bucket, database):
df = pd.DataFrame({"col_int": [1, None, 3], "col_bool": [True, False, False], "col_bool_null": [True, None, False]})