I am trying to get a concrete understanding on how the pandas UDF grouped map is working. While looking at the code here[1], i see that first the arrow object is converted into pandas series and then pd.concat is applied to create the full data frame.
What is confusing for me is , since arrow has support for Tabular format[2] and an API exist for converting table format to pandas in pyarrow[3] , why is that not being used.
I am pretty sure i am missing out on something very basic, any pointers would be useful ?
[1] https://github.com/apache/spark/blob/0494dc90af48ce7da0625485a4dc6917a244d580/python/pyspark/sql/pandas/serializers.py#L234-L276
[2] https://arrow.apache.org/docs/cpp/tables.html
[3] https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_pandas
Related
I'm trying to import a CSV into an existing PostgreSQL table using DBeaver Import Tool and I need to transform a numeric value multiplying it by 100.
Someone knows the syntax to be used in the expression?
The official documentation talks about JEXL expressions, but i can't find any example.
I can't post images, but the one in this question is exactly where i need to put the expression.
I was expecting something like:
${column}*100
It seems the columns are exposed as variables so a simple(r) column * 100 should do.
Polars has a pl.Binary datatype with very little documentation. I'd like to get the binary representation of values in a DataFrame, but it appears that the data type and the resulting binary casts are independent. For example:
import polars as pl
pl.Series([1], dtype=pl.UInt16).cast(pl.Binary)[0].hex()
yields b'\x31' which corresponds to the character '1'. So it appears pl.Binary first casts as Utf8. Is there any way to convert the values to a series of Byte arrays representing the underlying dtypes?
Rationale: I have a pandas tool that I use to write dataframes into native BCP native format for SQL Server and bulk-uploads them using bcp.exe. The result is table uploads that are typically 300x faster than pandas.DataFrame.to_sql.
I'd like to implement something similar to polars without having to go through numpy (which my pandas implementation currently does), whereby I can cast to np.void() and perform a sum (fold) across columns to concatenate binary arrays. I'm looking to do something similar wtih polars.
I have some timeseries data in the form of a pl.DataFrame object with a datetime col and a data col. I would like to correct an error in the data that occurs during a distinct time range by overwriting it with a value.
Now in pandas, one would use the datetimes as index and slice that timerange and assign to it, like so
df.loc[start_dt_string:end_dt_string, column_name] = some_val
Being completely new to polars I have a hard time figuring out how to express this. I tried selecting rows with .filter and .is_between but of course this doesn't support assignment. How would one go about doing this with polars?
Apparently I missed this in the docs, so RTFM to the rescue. In the corresponding section of the Coming from Pandas guide, this case is covered almost verbatim:
df.with_column(
pl.when(pl.col("c") == 2)
.then(pl.col("b"))
.otherwise(pl.col("a")).alias("a")
)
The above pandas example uses timerange slicing, so for the sake of completeness I'm going to add polars code that does exactly the same:
df.with_column(
pl.when(pl.col(dt_column_name).is_between(
datetime(start_dt_string),
datetime(end_dt_string),
include_bounds=True
).then(pl.lit(some_val))
.otherwise(pl.col(column_name))
.alias(column_name)
)
I have spark dataframe
Here it is
I would like to fetch the values of a column one by one and need to assign it to some variable?How can it be done in pyspark.Sorry I am a newbie to spark as well as stackoverflow.Please forgive the lack of clarity in question
col1=df.select(df.column_of_df).collect()
list1=[str(i[0]) for i in col1]
#after this we can iterate through list (list1 in this case)
I don't understand exactly what you are asking, but if you want to store them in a variable outside of the dataframes that spark offers, the best option is to select the column you want and store it as a panda series (if they are not a lot, because your memory is limited).
from pyspark.sql import functions as F
var = df.select(F.col('column_you_want')).toPandas()
Then you can iterate on it like a normal pandas series.
In Spark with scala, is there any easy way to automatically turn the variable or column into an object from imported data and therefore we can use column_a.contains("something") per se inside .map( )?
It looks like you are coming from R. Spark is row oriented and not column oriented. If you want to do a contains for example you would first filter the rows and than apply a map to it, or use collect and do both operations at once but this is a bit harder to get right.