Currently, I am using df.show(2) or df.show(truncate=0) to see all results of a dataframe. But I am getting data in a scrambled manner. How to neatly align ?
I have close to 15 columns to display and column names, data are getting chopped of as below screen shot
The show method takes Boolean True or False for truncate param, so you can try doing:
df.show(truncate=False)
It might be irrelevant, but if you are using Databricks platform, then it has a display method built in, that renders the whole DataFrame in easy to use visual format - more here.
Otherwise, you will have to export the data to some third-party tool, like Excel.
Related
I have registered a dataset after an Azure Databricks ETL operation. When it is registered as an AzureML Dataset, one of the columns is rendered as a timestamp. I know the schema has been inferred properly as the Dataset->Explore blade renders it properly:
However, when using Dataset.get_by_name(ws,<name>).to_pandas_dataframe(), the timestamp column is rendered as all None:
How do I mention the schema so that it is rendered properly while Getting the Dataset.get_by_name()
This may be a bug, but my guess is that the Explore tab is only looking at the first 1000 rows, which have no issues, but that there may be a malformatted value in a row after the 1st 1000.
Can you:
confirm the column complete for all rows in Databricks?
what is the source file format?
what is the column type for that column in the registered Tabular Dataset, can you confirm that it isn't a string?
I am new to databricks notebooks and dataframes. I have a requirement to load few columns(out of many) in a table of around 14million records into a dataframe. once the table is loaded, I need to create a new column based on values present in two columns.
I want to write the logic for the new column along with the select command while loading the table into dataframe.
Ex:
df = spark.read.table(tableName)
.select(columnsList)
.withColumn('newColumnName', 'logic')
will it have any performance impact? is it better to first load the table for the few columns into the df and then perform the column manipulation on the loaded df?
does the table data gets loaded all at once or row by row into the df? if row by row, then by including column manipulation logic while reading the table, am I causing any performance degradation?
Thanks in advance!!
This really depends on the underlying format of the table - is it backed by Parquet or Delta, or it's an interface to the actual database, etc. In general, Spark is trying to read only necessary data, and if, for example, Parquet is used (or Delta), then it's easier because it's column-oriented file format, so data for each column is placed together.
Regarding the question on the reading - Spark is lazy by default, so even if you put df = spark.read.table(....) as separate variable, then add .select, and then add .withColumn, it won't do anything until you call some action, for example .count, or write your results. Until that time, Spark will just check that table exists, your operations are correct, etc. You can always call .explain on the resulting dataframe to see how Spark will perform operations.
P.S. I recommend to grab a free copy of the Learning Spark, 2ed that is provided by Databricks - it will provide you a foundation for development of the code for Spark/Databricks
I have a dataframe that I am working with in a Python based Jupyter notebook. I want to add an additional column based on the content of an existing column, where the content of the new column is derived from running an external API call on the original column.
The solution I attempted was to use a Python based UDF. The first cell contains something like this:
def analysis(old_column):
new_column = myapi.analyze(text=old_column)
return(new_column)
analysis_udf = udf(analysis)
and the second cell this:
df2 = df1.withColumn("col2",analysis_udf('col1'))
df2.select('col2').show(n=5)
My dataframe is relatively large, with some 70000 rows, and where col1 can have a 100 to 10000+ characters of text. When I ran the code above in cell 2, it actually seemed to run fairly quickly (minutes), and dumped out the 5 rows of the df2 dataframe. So I thought I was in business. However, my next cell had the following code:
df2.cache()
df2.filter(col('col2').isNull()).count()
The intent of this code is to cache the contents of the new dataframe to improve access time to the DF, and then count how many of the entries in the dataframe have null values generated by the UDF. This surprisingly (to me) took many hours to run, and eventually provided an output of 6. Its not clear to me why the second cell ran quickly and the third was slow. I would have though that the df2.select('col2').show(n=5) call would have caused the UDF to run on all of the rows, and that one would have been slow, and then subsequent calls to access the new column of the dataframe would be quick. But that wasn't the case, so I supposed then that the cache call was the one that was actually causing the UDF to run on all of the rows so any subsequent calls now should be quick. So added another cell with:
df2.show(n=5)
Assuming it would run quickly, but again, it was taking much longer than I expected and it seems like perhaps the UDF was running again. (?)
My questions are
Which Spark api calls actually cause the udf to run (or re-run), and how to structure the calls to run the UDF only once so that the new column is created with the text output by the UDF's python function.
I have read that Python UDFs should be avoided because they are slow (seems correct) so what alternatives do I have when I need to use an API call to generate the new column?
I would have though that the df2.select('col2').show(n=5) call would have caused the UDF to run on
It is not a correct assumption. Spark will evaluate as little data as possible, given limitations of the API. Because you use Python udf it will evaluate minimum number of partitions required to collect 5 rows.
Which spark api calls actually cause the udf to run (or re-run), and how to structure the calls to run the UDF only once so that the new column is created with the text output by the UDF's python function.
Any evaluation, if data is no longer cached (evicted from memory).
Possibly any usage of the resulting column, unless udf is marked as non-deterministic.
I have read that Python UDFs should be avoided because they are slow (seems correct) so what alternatives do I have when I need to use an API call to generate the new column?
Unless you want to switch to Scala or RDD API, the only alternative is pandas_udf, which is somewhat more efficient, but supports only a limited subset of types.
I have a parquet file with 400+ columns, when I read it, the default datatypes attached to a lot of columns is String (may be due to the schema specified by someone else).
I was not able to find a parameter similar to
inferSchema=True' #for spark.read.parquet, present for spark.read.csv
I tried changing
mergeSchema=True #but it doesn't improve the results
To manually cast columns as float, I used
df_temp.select(*(col(c).cast("float").alias(c) for c in df_temp.columns))
this runs without error, but converts all the actual string column values to Null. I can't wrap this in a try, catch block as its not throwing any error.
Is there a way where i can check whether the columns contains only 'integer/ float' values and selectively cast those columns to float?
Parquet columns are typed, so there is no such thing as schema inference when loading Parquet files.
Is there a way where i can check whether the columns contains only 'integer/ float' values and selectively cast those columns to float?
You can use the same logic as Spark - define preferred type hierarchy and attempt to cast, until you get to the point, where you find the most selective type, that parses all values in the column.
How to force inferSchema for CSV to consider integers as dates (with "dateFormat" option)?
Spark data type guesser UDAF
There's no easy way currently,
there's a Github issue already existing which can be referred
https://github.com/databricks/spark-csv/issues/264
somthing like https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
existing for scala this can be created for pyspark
I 'm trying to fill missing values in spark dataframe using PySpark. But there is not any proper way to do it. My task is to fill the missing values of some rows with respect to their previous or following rows. Concretely , I would change the 0.0 value of one row to the value of the previous row, while doing nothing on a none-zero row . I did see the Window function in spark, but it only supports some simple operation like max, min, mean, which are not suitable for my case. It would be optimal if we could have a user defined function sliding over the given Window.
Does anybody have a good idea ?
Use Spark window API to access previous row data. If you work on time series data, see also this package for missing data imputation.