Python: Data match wrongly in Azure Databricks

Question

I am trying to create delta lake for all files located in '/dbfs/FileStore/tables/FirstRate30mins', but the data map wrongly.

The earliest correct data of AAL should be:

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

but when I display(df_30mins_['AAL']), there is a wrong price output. There are 2005 data.

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

when I try to display other data, such as display(df_30mins_['A']), the data are also the same and map wrongly.

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

Here is the source code:

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'))