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| PRODUCT_CATALOG_PATH = "mnt/PRODGEN2/OUTPUT/RUSSIA_DATA_FOUNDATION/UNIVERSALCATALOG/MASTERDATA/MARS_UNIVERSAL_PETCARE_MATERIALS.csv" | |
| # product_catalog = spark.read.csv(PRODUCT_CATALOG_PATH, header = "True", sep=chr(1), schema = product_catalogSchema) | |
| # Убрал привязку к схеме (если схема меняется... а такое случается, то надо ее обновлять, т.к. перестает загружать новые записи) | |
| product_catalog = spark.read.csv(PRODUCT_CATALOG_PATH, header = "True", sep=chr(1)) | |
| product_catalog = ( | |
| product_catalog | |
| .withColumn("VMSTD", F.to_date(col("VMSTD"),'yyyy.MM.dd')) # Cahnge date format | |
| .withColumn("START_DATE", F.to_date(col("START_DATE"),'yyyy-MM-dd')) # Cahnge date format |
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| CALENDAR_PATH = "mnt/PRODGEN2/OUTPUT/RUSSIA_DATA_FOUNDATION/UNIVERSALCATALOG/MASTERDATA/MARS_UNIVERSAL_CALENDAR.csv" | |
| calendarSchema = StructType([ | |
| StructField("OriginalDate", DateType(), False), | |
| StructField("MarsYear", IntegerType(), False), | |
| StructField("MarsPeriod", IntegerType(), False), | |
| StructField("MarsWeek", IntegerType(), False), | |
| StructField("MarsDay", IntegerType(), False), | |
| StructField("MarsPeriodName", StringType(), False), | |
| StructField("MarsPeriodFullName", StringType(), False), |
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| import numpy as np | |
| EPSILON = 1e-10 | |
| def _error(actual: np.ndarray, predicted: np.ndarray): | |
| """ Simple error """ | |
| return actual - predicted |
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| def show_pandas_DF(pd_DF): | |
| displayHTML(pd_DF.to_html()) | |
| show_pandas_DF( DATAFRAME.head(3) ) |
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| import pymssql | |
| import pandas as pd | |
| ## instance a python db connection object- same form as psycopg2/python-mysql drivers also | |
| conn = pymssql.connect(server="172.0.0.1", user="howens",password="some_fake_password", port=63642) # You can lookup the port number inside SQL server. | |
| ## Hey Look, college data | |
| stmt = "SELECT * FROM AlumniMirror..someTable" | |
| # Excute Query here | |
| df = pd.read_sql(stmt,conn) |