I want to convert a pcap file to asc or blf file. I am new to the field and I appreciate your
answers.
Thanks in advance.
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I have a folder that contains zip files in subfolders. I want to unzip all the files using this python code. code shows no error but the files are not extracted can't figure out the problem. Thanks in Advance.
from zipfile import ZipFile
from pathlib import Path
entries = Path('E:\\Bootcamp')
for entry in entries.rglob('*.zip'):
with ZipFile(entry, 'r') as zip:
print('Check1')
zip.extractall()
print('check2')
The extracted files will be located in the folder where your python file has been saved
I am trying to read a text file delimited by |. I am trying this
spark.read.format("com.databricks.spark.csv").option("header","true").option("delimiter", "|").option("inferSchema","true").csv("/tmp/file.txt").show()
I am only reading/seeing only the header but no data.
When I try the same with textFile, I am getting data but all in one column
spark.read.format("com.databricks.spark.csv").option("header","true").option("delimiter", "|").option("inferSchema","true").textFile("/tmp/file.txt").show()
Is there a way to read data via csv? I am using spark 2.4.4
The reason for the issue was the file is in UTF16 so I had to convert it and do run dostounix on it. Thanks for your advice. Apologies I really did not know that
I want to read a specific line in a csv file in pandas on python.
Here is the structure of the file :
file :
example
how would be the best way to fill the values into a dataframe, with the correct name of the parameters?
thanks for help
Possible methods:
pandas.read_table method seems to be a good way to read (also in chunks) a tabular data file
doc: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_table.html
pandas has a good fast (compiled) csv reader pandas.read_csv (may be more than one).
doc: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
Ref Link: https://codereview.stackexchange.com/questions/152194/reading-from-a-txt-file-to-a-pandas-dataframe
I would like to save a huge pyspark dataframe as a Hive table. How can I do this efficiently? I am looking to use saveAsTable(name, format=None, mode=None, partitionBy=None, **options) from pyspark.sql.DataFrameWriter.saveAsTable.
# Let's say I have my dataframe, my_df
# Am I able to do the following?
my_df.saveAsTable('my_table')
My question is which formats are available for me to use and where can I find this information for myself? Is OrcSerDe an option? I am still learning about this. Thank you.
Following file formats are supported.
text
csv
ldap
json
parquet
orc
Referece: https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
So I was able to write the pyspark dataframe to a compressed Hive table by using a pyspark.sql.DataFrameWriter. To do this I had to do something like the following:
my_df.write.orc('my_file_path')
That did the trick.
https://spark.apache.org/docs/1.6.0/api/python/pyspark.sql.html#pyspark.sql.DataFrame.write
I am using pyspark 1.6.0 btw
script that reads CSV files and gets headers and filter by specific column, I have tried researching on it but nothing of quality I have managed to get.
Please any help will be deeply appreciated
There's a standard csv library included with Python.
https://docs.python.org/3/library/csv.html
It will automatically create a dictionary of arrays where the first row in the CSV determines the keys in the dict.
You can also follow pandas.read_csv for the same.