Pyspark Scenarios 3 : how to skip first few rows from data file in pyspark

Pyspark Scenarios 3 : how to skip first few rows from data file in pyspark
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Pyspark Scenario Based Questions
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Complete Pyspark Real Time Scenarios Videos.

Pyspark Scenarios 1: How to create partition by month and year in pyspark
pyspark scenarios 2 : how to read variable number of columns data in pyspark dataframe #pyspark
Pyspark Scenarios 3 : how to skip first few rows from data file in pyspark
Pyspark Scenarios 4 : how to remove duplicate rows in pyspark dataframe #pyspark #Databricks
Pyspark Scenarios 5 : how read all files from nested folder in pySpark dataframe
Pyspark Scenarios 6 How to Get no of rows from each file in pyspark dataframe
Pyspark Scenarios 7 : how to get no of rows at each partition in pyspark dataframe
Pyspark Scenarios 8: How to add Sequence generated surrogate key as a column in dataframe.
Pyspark Scenarios 9 : How to get Individual column wise null records count
Pyspark Scenarios 10:Why we should not use crc32 for Surrogate Keys Generation?
Pyspark Scenarios 11 : how to handle double delimiter or multi delimiters in pyspark
Pyspark Scenarios 12 : how to get 53 week number years in pyspark extract 53rd week number in spark
Pyspark Scenarios 13 : how to handle complex json data file in pyspark
Pyspark Scenarios 14 : How to implement Multiprocessing in Azure Databricks
Pyspark Scenarios 15 : how to take table ddl backup in databricks
Pyspark Scenarios 16: Convert pyspark string to date format issue dd-mm-yy old format
Pyspark Scenarios 17 : How to handle duplicate column errors in delta table
Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema
Pyspark Scenarios 19 : difference between #OrderBy #Sort and #sortWithinPartitions Transformations
Pyspark Scenarios 20 : difference between coalesce and repartition in pyspark #coalesce #repartition
Pyspark Scenarios 21 : Dynamically processing complex json file in pyspark #complexjson #databricks
Pyspark Scenarios 22 : How To create data files based on the number of rows in PySpark #pyspark

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[ravietl]

Superb Explanation.pls send more real time scenarios.its really helpful.Thank you so much

[Thulasisingala]

why we have used skipline (final_rdd.first())attribute, any way columns(collect()[0] attribute also having first row. we can directly use columns in lamda function

[svcc]

Can't we delete those 4 lines from Unix box and reprocess the file instead of the code change, usually code cannot be modified for this kind of issues

[BHARATHKUMARS-mrjc]

this is working for normal files.
but my csv file is encoded with utf-16. when I mentioned sc.textFile(path, use_unicode='utf-16') still not working.
Can you help me with this.

[rutulhatwar]

After writing zipwithIndex I am getting an error that file cant be found

[prabhakarsingh]

Great Explanation...It was very helpful....

I am stuck with one question, like I am unable to convert the RDD to DF

Below is the data of Contact.csv file:

id, name, address
101, Abhay, "Delhi, Banglore"
102, Nishant, "Delhi

, Ranchi"
103, Abhishek, Delhi

In the first row, address column is having delimiter comma ", ", so records are getting split.
In the second row, address column have two new line character after Delhi, so again records are getting split.

Due to the above problem unable to convert RDD to DF:

getting below exception:

Input row doesn't have expected number of values required by the schema. 3 fields are required while 1 values are provided.

[jasonbernard]

Nice Explanation. 2 questions. It appears that the file contents is loaded into memory...how to handle if the file is very large? Would window function be a better approach? Also, you are doing a simple split, what if the content uses quoting (1, "Smith, John", "somewhere, TX"). how would the approach change?

[meriangabra]

How to make it work with text data file instead of csv? because I tried it and gave me all columns as one one column.

[ranjansrivastava]

Hi Friend really a very good explanation. One suggestion can we use 'skiprows' properties instead of doing so many code. Please correct me.

[ruinmaster]

How to skip last 10 rows while reading csv in pyspark?
pls help

[meghanadhage]

I have a multiple csv files whose header starting position is varying w.r.t every file so to skip those unwanted rows dynamically.

Eg.
1. file1.csv:- Header is starting from 3rd row
2. File2.csv :- header is starting from 7th row

So every time header starting position is not constant. So how to skip rows for these kind of files

[chetanambi]

We can also try this method:

from pyspark.sql.functions import *

df.withColumn('index', \
.filter('index > 2') \
.drop('index') \
.show(5)

[raviyadav-dttb]

We can skip it very simple way but you make it very lengthy

[fansouzafrei]

Great solution!
By the way, can we use the pyspark API to skip this records or we can only use RDD?

Congratulations!

[tamizh]

Great, Thanks, I tried another one too, that too working fine
val schema2 = new StructType()
.add("id", IntegerType)
.add("name", StringType)
.add("gender", StringType)
.add("baddata", StringType)

df.show()
val fil_df = df.filter("id is not null").drop("baddata")
fil_df.show()

[SaikotRoy]

This can be the alternative solution:

Data set:

| value|

| line1 |
| line2 |
| line3 |
|id, name, sal|
| 1, abc, 1000|
| 2, cde, 1000|
| 3, xyz, 500 |


Solution:

q4_df =
q4_df.show()

df1 = q4_df.withColumn("index", monotonically_increasing_id())
df1.show()

df2 = df1.filter(df1.index >3).drop("index").withColumn("splitted", split("value", ", "))
df2.show()

for i in
df2 = df2.withColumn("col"+str(i), df2.splitted[i])

df2.drop("value", "splitted").toDF("EMPID", "EMPNAME", "SALARY").show()