I am working on this notebook. https://databricks.com/notebooks/simple-aws/petastorm-spark-converter-pytorch.html
I tried running the first line
df = spark.read.parquet("/databricks-datasets/flowers/parquet") \
.select(col("content"), col("label_index")) \
.limit(1000)
However I got this error
Path does not exist: dbfs:/databricks-datasets/flowers/parquet;
I am wondering where I can find the parquet version of the flowers dataset on databricks. FYI I am working on the community edition.
This dataset was converted into Delta format, so path right now is /databricks-datasets/flowers/delta, instead of /databricks-datasets/flowers/parquet, and you need to read it with the corresponding code:
df = spark.read.format('delta').load('/databricks-datasets/flowers/delta')
P.S. You can always use %fs ls path command to see what files are at given path
P.P.S. I'll ask to fix that notebook if it's possible
label_index is removed from the dataset. You can recreate is as followed
from pyspark.ml.feature import StringIndexer
indexer = StringIndexer(inputCol="label", outputCol="label_index")
indexed = indexer.fit(df).transform(df)
Related
I setup a standalone spark and a standalone HDFS.
I installed pyspark and was able to create spark session.
I uploaded one parquet file to HDFS under /data : hdfs://localhost:9000/data
I tried to create a dataframe out of this directory using PySpark
from pyspark.sql import SparkSession
spark = SparkSession.builder.master('local[*]').appName("test").getOrCreate()
df = spark.read.parquet("hdfs://localhost:9000/data").withColumnRenamed("Wafer ID", "Wafer_ID")
I am getting invalid column name even with withColumnRenamed.
I tried with the following code but I got same error for this as well
df = spark.read.parquet("hdfs://localhost:9000/data").select(col("Wafer ID").alias("Wafer_ID"))
I have means to change the column names manually (pandas) or use different file entirely but I want to know if there is a way to solve this problem.
What am I doing wrong?
Are there any method to write spark dataframe directly to xls/xlsx format ????
Most of the example in the web showing there is example for panda dataframes.
but I would like to use spark dataframe for working with my data. Any idea ?
I'm assuming that because you have the "databricks" tag you are wanting to create an .xlsx file within databricks file store and that you are running code within databricks notebooks. I'm also going to assume that your notebooks are running python.
There is no direct way to save an excel document from a spark dataframe. You can, however, convert a spark dataframe to a pandas dataframe then export from there. We'll need to start by installing the xlsxwriter package. You can do this for your notebook environment using a databricks utilites command:
dbutils.library.installPyPI('xlsxwriter')
dbutils.library.restartPython()
I was having a few permission issues saving an excel file directly to dbfs. A quick workaround was to save to the cluster's default directory then sudo move the file into dbfs. Here's some example code:
# Creating dummy spark dataframe
spark_df = spark.sql('SELECT * FROM default.test_delta LIMIT 100')
# Converting spark dataframe to pandas dataframe
pandas_df = spark_df.toPandas()
# Exporting pandas dataframe to xlsx file
pandas_df.to_excel('excel_test.xlsx', engine='xlsxwriter')
Then in a new command, specifying the command to run in shell with %sh:
%sh
sudo mv excel_test.xlsx /dbfs/mnt/data/
It is possible to generate an Excel file from pySpark.
df_spark.write.format("com.crealytics.spark.excel")\
.option("header", "true")\
.mode("overwrite")\
.save(path)
You need to install the com.crealytics:spark-excel_2.12:0.13.5 (or a more recent version of course) library though, for example in Azure Databricks by specifying it as a new Maven library in the libraries list of your cluster (one of the buttons on the left sidebar of the Databricks UI).
For more info see https://github.com/crealytics/spark-excel.
I believe you can do it like this.
sourcePropertySet.write
.format("com.databricks.spark.csv")
.option("header", "true")
.save("D:\\resultset.csv")
I'm not sure you can write directly to Excel, but Excel can definitely consume a CSV. This is almost certainly the easiest way of doing this kind of thing and the cleanest as well. In Excel you have all kinds of formatting, which can throw errors when used in some systems (think of merged cells).
You can not save it directly but you can have it as its stored in temp location and move it to your directory. My code piece is:
import xlsxwriter import pandas as pd1
workbook = xlsxwriter.Workbook('data_checks_output.xlsx')
worksheet = workbook.add_worksheet('top_rows')
Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd1.ExcelWriter('data_checks_output.xlsx', engine='xlsxwriter')
output = dataset.limit(10)
output = output.toPandas()
output.to_excel(writer, sheet_name='top_rows',startrow=row_number)
writer.save()
Below code does the work of moving files.
%sh
sudo mv data_checks_output.xlsx /dbfs/mnt/fpmount/
Comment if anyone has new update or better way to do it.
Yet Pyspark does not offer any method to save excel file. But you can save csv file, then it can be read in Excel.
From pyspark.sql module version 2.3 you have write.csv:
df.write.csv('path/filename'))
Documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html?highlight=save
I have a bit of a strange one. I have loads of logs that I need to trawl. I have done that successfully in Spark & I am happy with it.
However, I need to add one more field to the dataframe, which is the data center.
The only place that the datacenter name can be derived is from the directory path.
For example:
/feedname/date/datacenter/another/logfile.txt
What would be the way to extract the log file path and inject it into the dataframe? From there, I can do some string splits & extract the bit I need.
My current code:
mpe_data = my_spark.read\
.option("header","false")\
.option("delimiter", "\t")\
.withColumn("Datacenter", input_file_name())\
.csv('hdfs://nameservice/data/feed/mpe/dt=20191013/*/*/*', final_structure)
mpe_data.printSchema()
mpe_data.createOrReplaceTempView("mpe")
You can get the file path using the _input_file_name_ in Spark 2.0+
from pyspark.sql.functions import input_file_name
df.withColumn("Datacenter", input_file_name())
Adding your piece of code as example, once you have read your file use the withcolumn to get the file_name.
mpe_data = my_spark.read\
.option("header","false")\
.option("delimiter", "\t")\
.csv('hdfs://nameservice/data/feed/mpe/dt=20191013/*/*/*', final_structure)
mpe_data.withColumn("Datacenter", input_file_name())
mpe_data.printSchema()
I have got a file in HDFS (/user/username/Project/data/file.xlsx) that I want to read into a DataFrame. (I do not care if it is a PySpark DataFrame or Pandas, but Pandas is preferred.)
I am using a Zeppelin Notebook to do my code.
Is it possible to get data from this file?
I have already tried the following commands, but none of them worked:
df = pd.read_excel("/user/username/Project/data/file.xlsx")
df = pd.read_excel("hdfs:///user/username/Project/data/file.xlsx")
df = pd.read_excel("hdfs://user/username/Project/data/file.xlsx")
I don't think you can read files stored in hdfs directly with pandas.
You probably have to either :
load the file into spark then use toPandas()
df = spark.read.format("excel").load("hdfs:xxx").toPandas()
use some alternative to enable pandas to read directly, as described here
It seems export and import commands in Python Interpreter in Apache Zeppellin can be only realised through "pd.read_csv" and "to_csv" modules.
My data on HDFS is in Sequence file format. I am using PySpark (Spark 1.6) and trying to achieve 2 things:
Data path contains a timestamp in yyyy/mm/dd/hh format that I would like to bring into the data itself. I tried SparkContext.wholeTextFiles but I think that might not support Sequence file format.
How do I deal with the point above if I want to crunch data for a day and want to bring in the date into the data? In this case I would be loading data like yyyy/mm/dd/* format.
Appreciate any pointers.
If stored types are compatible with SQL types and you use Spark 2.0 it is quite simple. Import input_file_name:
from pyspark.sql.functions import input_file_name
Read file and convert to a DataFrame:
df = sc.sequenceFile("/tmp/foo/").toDF()
Add file name:
df.withColumn("input", input_file_name())
If this solution is not applicable in your case then universal one is to list files directly (for HDFS you can use hdfs3 library):
files = ...
read one by one adding file name:
def read(f):
"""Just to avoid problems with late binding"""
return sc.sequenceFile(f).map(lambda x: (f, x))
rdds = [read(f) for f in files]
and union:
sc.union(rdds)