def test_dataframe(self):
# ローカルのファイルを読んでS3に保存
repo = LocalFileRepository(
Path(os.path.dirname(__file__)) / Path("titanic.csv"))
data = DataFrameData.load(repo)
repo_s3 = S3FileRepository(self.access_key_id, self.secret_access_key,
self.s3file_url)
data.repository = repo_s3
data.save()
# S3からファイルを読み込み
data2 = DataFrameData.load(repo_s3)
self.assertTrue(len(data2.content) > 0)
def main(self, ds):
df = ds.get("titanic").content
df[self.column] = df[self.column].fillna(df[self.column].median())
rds = DataSet()
rds.put(f"fillna_{self.column}", DataFrameData(df[self.column]))
return rds
def main(self, ds):
df = ds.get("titanic").content
df.loc[df["Sex"] == "male", "Sex"] = 0
df.loc[df["Sex"] == "female", "Sex"] = 1
rds = DataSet()
rds.put("sex_to_code", DataFrameData(df["Sex"]))
return rds
def main(self, ds):
repo = LocalFileRepository(
Path(os.path.dirname(__file__)) / Path("../titanic.csv"))
titanic_data = DataFrameData.load(repo)
ds = DataSet()
ds.put("titanic", titanic_data)
return ds
def main(self, ds):
df = ds.get("titanic").content
df.loc[df["Sex"] == "male", "Sex"] = 0
df.loc[df["Sex"] == "female", "Sex"] = 1
ds = DataSet()
ds.put("titanic", DataFrameData(df))
return ds
def main(self, ds):
df = ds.get("titanic").content
df["Embarked"] = df["Embarked"].fillna("S")
df.loc[df["Embarked"] == "S", "Embarked"] = 0
df.loc[df["Embarked"] == "C", "Embarked"] = 1
df.loc[df["Embarked"] == "Q", "Embarked"] = 2
rds = DataSet()
rds.put("embarked_to_code", DataFrameData(df["Embarked"]))
return rds
def main(self, ds):
df = ds.get("titanic").content
df["Sex"][df["Sex"] == "male"] = 0
df["Sex"][df["Sex"] == "female"] = 1
rds = DataSet()
rds.put("sex_to_code", DataFrameData(df["Sex"]))
time.sleep(random.randint(3, 10))
return rds
def table_init(self):
repo = LocalFileRepository(
Path(os.path.dirname(__file__)) / Path("titanic.csv"))
data = DataFrameData.load(repo)
repo_s = SqlAlchemyRepository(self.engine)
md = SqlAlchemyModelData(repo_s, Titanic)
md.update_dataframe(data.content)
md.save()
def main(self, ds):
df = ds.get("titanic").content
df = df.drop(["Age", "Sex", "Embarked"], axis=1)
df = df.join(ds.get("fillna_Age").content)
df = df.join(ds.get("sex_to_code").content)
df = df.join(ds.get("embarked_to_code").content)
rds = DataSet()
rds.put("titanic_result", DataFrameData(df))
return rds
def main(self, ds):
df = ds.get("titanic").content
df["Embarked"] = df["Embarked"].fillna("S")
df["Embarked"][df["Embarked"] == "S"] = 0
df["Embarked"][df["Embarked"] == "C"] = 1
df["Embarked"][df["Embarked"] == "Q"] = 2
rds = DataSet()
rds.put("embarked_to_code", DataFrameData(df["Embarked"]))
time.sleep(random.randint(3, 10))
return rds
def prepare_db(engine):
Base.metadata.drop_all(engine)
Base.metadata.create_all(engine)
repo = LocalFileRepository(
Path(os.path.dirname(__file__)) / Path("../titanic.csv"))
titanic_data = DataFrameData.load(repo)
repo_s = SqlAlchemyRepository(engine)
md = SqlAlchemyModelData(repo_s, Titanic)
md.update_dataframe(titanic_data.content)
md.save()
def test_dataframe(self):
repo = LocalFileRepository(
Path(os.path.dirname(__file__)) / Path("titanic.csv"))
data = DataFrameData.load(repo)
tmp_path = Path(tempfile.gettempdir()) / Path(
next(tempfile._get_candidate_names()))
repo = LocalFileRepository(tmp_path)
data.repository = repo
data.save()
self.assertTrue(tmp_path.exists())
self.assertTrue(tmp_path.is_file())
def test_dataframe(self):
repo = LocalFileRepository(Path(os.path.dirname(__file__)) / Path("titanic.csv"))
data = DataFrameData.load(repo)
db_repo = PandasDbRepository(self.engine, "titanic")
dfd = DataFrameData(data.content, db_repo)
dfd.save()
dfd2 = DataFrameData.load(db_repo)
self.assertIsNotNone(dfd2.content)
self.assertTrue(data.content.equals(dfd2.content))
df.loc[df["Embarked"] == "C", "Embarked"] = 1
df.loc[df["Embarked"] == "Q", "Embarked"] = 2
ds = DataSet()
ds.put("titanic", DataFrameData(df))
return ds
if __name__ == "__main__":
basicConfig(level=DEBUG)
# データセットの読み込み
ds = DataSet()
repo = LocalFileRepository(
Path(os.path.dirname(__file__)) / Path("../titanic.csv"))
titanic_data = DataFrameData.load(repo)
ds.put("titanic", titanic_data)
#
print("## Original data")
print(ds.get("titanic").content)
# Graphで処理する
# Age欠損埋め -> 性別のコード化 -> 乗船した港 のコード化 の順で処理
graph = Graph()
fill_age = graph.append(FillNaMedian("Age"))
sex_to_code = graph.append(SexToCode(), [fill_age])
graph.append(EmbarkedToCode(), [sex_to_code])
ds = graph.run(ds)
print("## Processed data")