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Saving databricks pyspark df to blob storage as one file that is named and overwrites

As stated, we are trying to save out a pyspark df to blob storage as a single csv file. We have tried with coalesce. However, with the out of memory errors this can produce, we are reluctant to stick with it in a larger env.
Using .toPandas().to_csv(f"/dbfs/filePath.csv") produces an error that the file doesn't exist. Using toPandas().to_csv(f"abfss://filePath.csv") produces an error that abfss is unrecognised.
Anyone got any ways to save the csv out without the need to go down the coalesce, file rename, moving, and deleting route?
Thanks in advance!:)

Anyone got any ways to save the csv out without the need to go down
the coalesce, file rename, moving, and deleting route?
Using the pandas can be a good option like you mentioned if you have small data. spark by default will give the extra files when writing to a file.
Un mount the mount point and mount it again. Sometimes, the mount point credentials may expire, and this can happen.
Unmounting:
dbutils.fs.unmount("/mnt/mountpoint")
Result:

How to write in CSV file without creating folder in pyspark?

While writing in CSV file, automatically folder is created and then csv file with cryptic name is created, how to create this CSV with any specific name but without creating folder in pyspark not in pandas.

That's just the way Spark works with the parallelizing mechanism. Spark application meant to have one or more workers to read your data and to write into a location. When you write a CSV file, having a directory with multiple files is the way multiple workers can write at the same time.
If you're using HDFS, you can consider writing another bash script to move or reorganize files the way you want
If you're using Databricks, you can use dbutils.ls to interact with DBFS files in the same way.

This is the way spark is designed to write out multiple files in parallel. Writing out many files at the same time is faster for big datasets. But still you can achieve by using of coalesce(1,true).saveAsTextFile() .You can refer here

In PySpark, the following code helped me to directly write data into CSV file
df.toPandas().to_csv('FileName.csv')

open and read a Sigmaplot .JNB file in Python

My lab has a very large directory of Sigmaplot files, saved as .JNB . I would like to process the data in these files using Python. However, I have thus far been unable to read the files into anything interpretable.
I've already tried pretty much every numpy read function and most the panda read functions, and am getting nothing but gibberish.
Does anyone have any advice about reading these files short of exporting them all to excel one by one?

PySpark: spit out single file when writing instead of multiple part files

Is there a way to prevent PySpark from creating several small files when writing a DataFrame to JSON file?
If I run:
df.write.format('json').save('myfile.json')
or
df1.write.json('myfile.json')
it creates the folder named myfile and within it I find several small files named part-***, the HDFS way. Is it by any means possible to have it spit out a single file instead?

Well, the answer to your exact question is coalesce function. But as already mentioned it is not efficient at all as it will force one worker to fetch all data and write it sequentially.
df.coalesce(1).write.format('json').save('myfile.json')
P.S. Btw, the result file is not a valid json file. It is a file with a json object per line.

This was a better solution for me.
rdd.map(json.dumps)
.saveAsTextFile(json_lines_file_name)

df1.rdd.repartition(1).write.json('myfile.json')
Would be nice, but isn't available. Check this related question. https://stackoverflow.com/a/33311467/2843520

How to_csv in Bluemix

We have a dataframe we are working it in a ipython notebook. Granted, if one could save a dataframe in such a way that the whole group could have access to it through their notebooks, would be ideal, and I'd love to know how to do that. However could you help with the following specific problem?
When we do df.to_csv("Csv file name") it appears that it is located in the exact same place as the files we placed in object storage to utilize in the ipython notebook. However, when one goes to Manage Files, it's nowhere to be found.
When one runs pd.DataFrame.to_csv(df), text of the csv file is apparently given. However when one copies that into a text editor (ex- Sublime text), saves it at a csv, and attempts to read it in to a dataframe, the expected dataframe is not yielded.
How does one export a dataframe to csv format, and then access it?

I'm not familiar with bluemix, but it sounds like you're trying to save a pandas dataframe in a way that all of your collaborators can access and it look the same way for everyone.
Maybe saving and reading from CSVs is messing up the formatting of your dataframe. Have you tried using pickling? Since pickling is based around python, it should give consistent results.
Try this:
import pandas as pd
pd.to_pickle(df, "/path/to/pickle/My_pickle")
and on the read side:
df_read = pd.read_pickle("/path/to/pickle/My_pickle")