80. Databricks | Pyspark | Tips: Write Dataframe into Single File with Specific File Name

Azure Databricks Learning: Pyspark Transformation and Tips
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How to write dataframe output into single file as well as with specific file name?

There is no direct solution in spark at the time of creating this video. The reason why it is not possible is explained with proper examples and code walk-through in this demo.
At the end of the demo, the workaround to achieve this solution is explained as well.

To get through understanding of this concept, please watch this video

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[code_nation]

As pandas is slow, we can use this function too, I changed the separator to pipe format but if you want it as comma only then remove the sep from options,
in path make sure to give file name with format at the end of path: Ex. path =

def to_single_file_csv(dataframe, path):
tmp_path = path.rsplit('/', 1)[0]+'/tmpdata'
= "True", sep = "|").csv(tmp_path)
file =
dbutils.fs.cp(file, path)
dbutils.fs.rm(tmp_path, True)

[lalithroy]

Hi Raja thank you for making videos in your own voice. Could you please make a videos on delta live tables as industry is moving towards it.

[nagulmeerashaik]

Thank🙏... Do more videos this series plssss....

[sachinjosethana]

Could you share the videos for Delta Live tables

[sabastineade]

This was really helpful, can we do the same when saving output into S3 Bucket in AWS?

[pankajshende]

Even I created folder before writing data from pandas df, I have getting error cannot save file in non-existent directory. could you please help why getting this error.

[nestam]

Hi Raja. Will there be any performance degradation while converting from spark df to pandas df?

[brahmendrakumarshukla]

Thanks Raja, Could you also help here to dataframe write in .xlsx file

[balajia]

Thanks Raja.. will it work for parquet format?

[kap]

Here is solution in spark: from pyspark.sql import SparkSession

# Create a SparkSession with the required configuration
spark = SparkSession.builder \
\
.config("spark.sql.sources.commitProtocolClass",
\
.getOrCreate()

# Read your data into a DataFrame (replace 'your_data' with the appropriate data source)
df =

# Perform your transformations on the DataFrame (if needed)

# Coalesce the DataFrame into a single partition
# This will ensure that the data is written to a single output file
df_single_partition = df.coalesce(1)

# Write the DataFrame to your output location
# (replace 'output_path' with the desired location)
df_single_partition.write.csv("output_path", header=True)

# Stop the SparkSession
spark.stop()