3. Read CSV file in to Dataframe using PySpark
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- Опубліковано 29 вер 2022
- In this video, I discussed about reading csv files in to Dataframe using Pyspark.
Link for PySpark Playlist:
• 1. What is PySpark?
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#PySpark #Spark #DatabricksNotebook #PySparkcode #dataframe #WafaStudies #maheer - Наука та технологія
Your youtube playlist is an example how one should build a youtube playlist. Every video is sequenced in such a way that you need to go through the previous videos in order to understand the current video. Excellent work Maheer Sir. Thank you for all the hardwork ._/\_...
Thank you for your kind words 🙂
@@WafaStudies if any one follow your UA-cam channel .no need to join any course Bro ..such an excellent content you are providing in a sequence manner ..pls cover real time scenarios issues which you have faced while working in real time project ..Thanks
@@sivajip4482 thank you so much for your kind words 🙂
@@WafaStudies can you share ypur codes
yes bro he has explained better then most of the udemy courses.
Hi bhaii,
I can't explain how much it is useful for me.
working on same as Data engineer onAzure Databricks . Each and every topic from this playlist I am using... so helpful.
please continue....
Thanks a ton.
Thank you for your kind words 🙂
Very helpful series. Thank you for your efforts. You earned another subscriber!😄
I had one quick question which I faced during an interview, please make a short video if you get the time: if there are multiple tabs present in an excel file (.xlsx) how to load the data present in any one of the tabs in that file to a dataframe?
Thank you Anna for pyspark playlist
Please add more pyspark related classes
Thanks Maheer! Videos are very useful
Thank you so so much man, this is very helpful.
Good One .. Keep Creating Such Videos .
Thank you ☺️
Thanks Maheer
Good vedio. Very helpful
Great videos, excellent, thank you
Thank you ☺️
Big Thank you Brother ! 🤗
Thanks for the content
Very well explanation for beginners
Thank you ☺️
Very well explanation....Can you please share the code of all your videos ? it will help us to do practice on databricks
God bless you brother 🙏🏿🙏🏿
2 csv files in 2 paths like data, data1, but columns and schema is different ,then how can read this ,this can be possible to use in list these files to read
hi sir..one doubt ..which one i should learn at first? databricks or pyspark?
Nice explanation bro 👍 👌 👏
Thank you 😊
How to find the file is available in the path or not?
Is there anyone who is not able to create a folder inside 'Data' ? Any hack to do it?
So we can add folder while uploading files. Change the name of folder 'tables' to whatever name of folder you want to create. I will upload the files in customized folder.
hai sir..this play list have full content of pyspark
can you please some data file which you are showing in this video .
Thanks Maheer :)
Welcome
it would be helpful if you make ur worked notebook, and respective dataset into one repo and share here!
You can use also "recursiveFileLookup" to read all files (inside multiple folders) in one go.
How do you do that
Hi @@encryptedunlimited1094 you can use this sample code for reference.
# Read parquet files
df = spark.read.option("recursiveFileLookup", "true").parquet("file:///F:\Projects\Python\PySpark\data2")
print(df.schema)
df.show()
Completed
why don't you guys put the date field in your sample data..???
Hi Maheer ,
Can we get those files
I am ongoing with playlist as explanation is crystal clear. Thanks a lot for the list. Can you please help sharing the csv file as resource somewhere in description? Or else, pin your comment with csv files?
hii,when I created data folder in filestore it 's not shown.so where I uploaded employee data plz guide me.when I created dta folder in file store its created but not shown
Same issue
So we can add folder while uploading files. Change the name of folder 'tables' to whatever name of folder you want to create. I will upload the files in customized folder.
i am not able to create folder
it is showing create bt it is not creating any folder
same problem with me as well
schema=StructType().add('ID', IntegerType())\
.add('NAME', StringType())\
.add('GENDER', StringType())\
.add('SALARY', StringType())
schema1=StructType([StructField('ID',IntegerType()),
StructField('NAME',StringType()),
StructField('GENDER',StringType()),
StructField('SALARY',StringType())])
Which method is good schema vs schema1
schema1 is good in my opinion because we can't use MapType and nested AyyayType in add method i believe correct me if i am wrong.
Make same with different files
Yes. I will be doing for parquet, json and format files as well very soon.
Nice Explanation. Hello everyone I am planning to move to data engineer role and looking for real time support who can guide me in right direction. Kindly let me know. Thanks
Hi ,
Could you please create a video to combine below 3 csv data files into one data frame dynamically
File name: Class_01.csv
StudentID Student Name Gender Subject B Subject C Subject D
1 Balbinder Male 91 56 65
2 Sushma Female 90 60 70
3 Simon Male 75 67 89
4 Banita Female 52 65 73
5 Anita Female 78 92 57
File name: Class_02.csv
StudentID Student Name Gender Subject A Subject B Subject C Subject E
1 Richard Male 50 55 64 66
2 Sam Male 44 67 84 72
3 Rohan Male 67 54 75 96
4 Reshma Female 64 83 46 78
5 Kamal Male 78 89 91 90
File name: Class_03.csv
StudentID Student Name Gender Subject A Subject D Subject E
1 Mohan Male 70 39 45
2 Sohan Male 56 73 80
3 shyam Male 60 50 55
4 Radha Female 75 80 72
5 Kirthi Female 60 50 55
Hi @wafastudies, when i pass header in load function its working
df = spark.read.format('csv').option(key='header',value=True).load(path='emp1.csv')
display(df)
df.printSchema()
df.show()
output:
DataFrame[EMPLOYEE_ID: string, FIRST_NAME: string, LAST_NAME: string, SALARY: string, DEPARTMENT_ID: string, LOCATION_ID: string, HIRE_DATE: string]
root
|-- EMPLOYEE_ID: string (nullable = true)
|-- FIRST_NAME: string (nullable = true)
|-- LAST_NAME: string (nullable = true)
|-- SALARY: string (nullable = true)
|-- DEPARTMENT_ID: string (nullable = true)
|-- LOCATION_ID: string (nullable = true)
|-- HIRE_DATE: string (nullable = true)
+-----------+----------+---------+------+-------------+-----------+---------+
|EMPLOYEE_ID|FIRST_NAME|LAST_NAME|SALARY|DEPARTMENT_ID|LOCATION_ID|HIRE_DATE|
+-----------+----------+---------+------+-------------+-----------+---------+
| 101| Donald| null| 2600| 10| 1701|21-Jun-07|
| 102| Douglas| Grant| 2600| 20| 1702|13-Jan-08|
| 103| Jennifer| Whalen| 4400| 30| 1703|17-Sep-03|
+-----------+----------+---------+------+-------------+-----------+---------+