[英]Python: Data match wrongly in Azure Databricks
我正在嘗試為位於'/dbfs/FileStore/tables/FirstRate30mins'
中的所有文件創建增量湖,但數據 map 錯誤。
AAL最早正確的數據應該是:
AAL_date/time AAL_adjOpen AAL_adjHigh AAL_adjLow AAL_adjClose AAL_adjVolume
2013-12-09 08:30:00 22.8049 22.8049 21.7868 22.2016 141800
但是當我display(df_30mins_['AAL'])
時,有一個錯誤的價格output。有2005年的數據。
AAL_date/time AAL_adjOpen AAL_adjHigh AAL_adjLow AAL_adjClose AAL_adjVolume
2005-01-03 08:00:00 0.9939 0.9985 0.9863 0.9955 1711416
當我嘗試顯示其他數據時,例如display(df_30mins_['A'])
,數據也是相同的,並且 map 錯誤。
A_date/time A_adjOpen A_adjHigh A_adjLow A_adjClose A_adjVolume
2005-01-03 08:00:00 0.9939 0.9985 0.9863 0.9955 1711416
這是源代碼:
import os
import numpy as np
import pandas as pd
from pyspark import SparkFiles
from pyspark import SparkContext
from pyspark.sql import functions
import pyspark.sql.functions #import avg, col, udf
from pyspark.sql import SQLContext
from pyspark.sql import DataFrame
from pyspark.sql.types import *
import json
#LIST, RENAME, AND SAVE ALL FILES AS DELTA LAKE AUTOMATICALLY
path = '/dbfs/FileStore/tables/FirstRate30mins'
filename_lists = os.listdir(path)
df_30mins_ = {}
_delta ={}
for filename in os.listdir(path):
#split file name
name = filename.split('_')[0]
#create clolumn header names
temp = StructType([StructField(name+"_date/time", StringType(), True),StructField(name+"_adjOpen", FloatType(), True),StructField(name+"_adjHigh", FloatType(), True),StructField(name+"_adjLow", FloatType(), True),StructField(name+"_adjClose", FloatType(), True),StructField(name+"_adjVolume", IntegerType(), True)])
#list and create csv dataframes
temp_df = spark.read.format("csv").option("header", "false").schema(temp).load("/FileStore/tables/FirstRate30mins/")
#name each dataframes
df_30mins_[name] = temp_df
#name each table
table_name = name+'_30mins_delta'
#create delta lake for each dataframes
df_30mins_[name].write.format("delta").mode("overwrite").saveAsTable(table_name)
display(df_30mins_['AAL'])
display(df_30mins_['A'])
display(df_30mins_['AAPL'])
#display(spark.sql('SELECT * FROM aal_30mins_delta'))
#display(spark.sql('SELECT * FROM a_30mins_delta'))
#display(spark.sql('SELECT * FROM aapl_30mins_delta'))
對不起。 只是想念+filename
這是正確的代碼。
import os
import numpy as np
import pandas as pd
from pyspark import SparkFiles
from pyspark import SparkContext
from pyspark.sql import functions
import pyspark.sql.functions #import avg, col, udf
from pyspark.sql import SQLContext
from pyspark.sql import DataFrame
from pyspark.sql.types import *
import json
#LIST, RENAME, AND SAVE ALL FILES AS DELTA LAKE AUTOMATICALLY
path = '/dbfs/FileStore/tables/FirstRate30mins'
filename_lists = os.listdir(path)
df_30mins_ = {}
_delta ={}
for filename in os.listdir(path):
#split file name
name = filename.split('_')[0]
#create clolumn header names
temp = StructType([StructField(name+"_date/time", StringType(), True),StructField(name+"_adjOpen", FloatType(), True),StructField(name+"_adjHigh", FloatType(), True),StructField(name+"_adjLow", FloatType(), True),StructField(name+"_adjClose", FloatType(), True),StructField(name+"_adjVolume", IntegerType(), True)])
#list and create csv dataframes
temp_df = spark.read.format("csv").option("header", "false").schema(temp).load("/FileStore/tables/FirstRate30mins/"+filename)
#name each dataframes
df_30mins_[name] = temp_df
#name each table
table_name = name+'_30mins_delta'
#create delta lake for each dataframes
df_30mins_[name].write.format("delta").mode("overwrite").saveAsTable(table_name)
display(df_30mins_['AAL'])
display(df_30mins_['AAPL'])
display(df_30mins_['A'])
display(spark.sql('SELECT * FROM aal_30mins_delta'))
display(spark.sql('SELECT * FROM aapl_30mins_delta'))
display(spark.sql('SELECT * FROM a_30mins_delta'))
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