I'm experiencing a very weird behavior in pyspark (databricks).
In my initial dataframe (df_original) I have multiple columns (id, text and some_others) and I add a new column 'detected_language'. The new column is added using a join with another dataframe df_detections (with columns id and detected_language). The ids in the two dataframes correspond to each other).
df_detections is created like this:
ids = [125, ...] # length x
detections = ['ko', ...] # length x
detections_with_id = list(zip(ids, detections))
df_detections = spark.createDataFrame(detections_with_id, ["id", "detected_language"])
df = df_original.join(df_detections, on='id', how='left)
Here is the weird part. Whenever I display the dataframe using a select statement I get the correct detected_language value. However, using only display I get a totally different value (e.g. 'fr' or any other language code) for the same entry (see the statements and their corresponding results below).
How is that possible? Can anybody think of a reason why this is? And how would I solve something like this?
Displaying correct value with select:
display(df.select(['id', 'text', 'detected_language']))
id
text
detected_language
125
내 한국어 텍스트
ko
...
...
...
Displaying wrong value without select:
display(df)
id
text
other_columns...
detected_language
125
내 한국어 텍스트
...
fr
...
...
...
...
I appreciate any hints or ideas! Thank you!
Related
As in the title. I have a list of columns and need to replace a certain string with 0 in these columns. I can do that using select statement with nested when function but I want to preserve my original dataframe and only change the columns in question. df.replace(string, 0, list_of_columns) doesn't work as there is a data type mismatch.
So I ended up with something like this which worked for me:
for column in column_list:
df = df.withColumn(column, F.when((F.col(column) == "string"), "0").otherwise(F.col(column)))
I have a dataframe which contains months and will change quite frequently. I am saving this dataframe values as list e.g. months = ['202111', '202112', '202201']. Using a for loop to to iterate through all list elements and trying to provide dynamic column values with following code:
for i in months:
df = (
adjustment_1_prepared_df.select("product", "mnth", "col1", "col2")
.groupBy("product")
.agg(
f.min(f.when(condition, f.col("col1")).otherwise(9999999)).alias(
concat("col3_"), f.lit(i.col)
)
)
)
So basically in alias I am trying to give column name as a combination of constant (minInv_) and a variable (e.g. 202111) but I am getting error. How can I give a column name as combination of fixed string and a variable.
Thanks in advance!
.alias("col3_"+str(i.col))
I have a dataframe with column having values like "COR//xxxxxx-xx-xxxx" or "xxxxxx-xx-xxxx"
I need to compare this column with another column in a different dataframe based on the column value.
If column value have "COR//xxxxx-xx-xxxx", I need to use substring("column", 4, length($"column")
If the column value have "xxxxx-xx-xxxx", I can compare directly without using substring.
For example:
val DF1 = DF2.join(DF3, upper(trim($"column1".substr(4, length($"column1")))) === upper(trim(DF3("column1"))))
I am not sure how to add the condition while joining. Could anyone please let me know how can we achieve this in Spark dataframe?
You can try adding a new column based on the conditions and join on the new column. Something like this.
val data = List("COR//xxxxx-xx-xxxx", "xxxxx-xx-xxxx")
val DF2 = ps.sparkSession.sparkContext.parallelize(data).toDF("column1")
val DF4 = DF2.withColumn("joinCol", when(col("column1").like("%COR%"),
expr("substring(column1, 6, length(column1)-1)")).otherwise(col("column1")) )
DF4.show(false)
The new column will have values like this.
+------------------+-------------+
|column1 |joinCol |
+------------------+-------------+
|COR//xxxxx-xx-xxxx|xxxxx-xx-xxxx|
|xxxxx-xx-xxxx |xxxxx-xx-xxxx|
+------------------+-------------+
You can now join based on the new column added.
val DF1 = DF4.join(DF3, upper(trim(DF4("joinCol"))) === upper(trim(DF3("column1"))))
Hope this helps.
Simply create a new column to use in the join:
DF2.withColumn("column2",
when($"column1" rlike "COR//.*",
$"column1".substr(lit(4), length($"column1")).
otherwise($"column1"))
Then use column2 in the join. It is also possible to add the whole when clause directly in the join but it would look very messy.
Note that to use a constant value in substr you need to use lit. And if you want to remove the whole "COR//" part, use 6 instead of 4.
I need to iterate rows of a pyspark.sql.dataframe.DataFrame.DataFrame.
I have done it in pandas in the past with the function iterrows() but I need to find something similar for pyspark without using pandas.
If I do for row in myDF: it iterates columns.DataFrame
Thanks
You can use select method to operate on your dataframe using a user defined function something like this :
columns = header.columns
my_udf = F.udf(lambda data: "do what ever you want here " , StringType())
myDF.select(*[my_udf(col(c)) for c in columns])
then inside the select you can choose what you want to do with each column .
Given a table of the following format in MATLAB:
userid | itemid | keywords
A = [ 3 10 'book'
3 10 'briefcase'
3 10 'boat'
12 20 'windows'
12 20 'picture'
12 35 'love'
4 10 'day'
12 10 'working day'
... ... ... ];
where A is a table of size (58000*3), I want to write the data in a csv file with the following format:
csv.file
itemid keywords
10 book, briefcase, boat, day, working day, ...
20 windows, picture, ...
35 love, ...
where we the list of itemids is stored in Iids = [10,20,35,...]
I would like to avoide using loops for this as you can imagine the matrix is big-sized. Any idea is appreciated.
I wasn't able to think of a solution without loops. But you can optimize your loop by:
using logical indexing
running such loop only M times (if M is the number of unique itemid elements) instead of N times (if N is the number of elements in your table).
The solution I come up with is this.
First of all, create your table
A=table([3;3;3;12;12;12;4;12], [10;10;10;20;20;35;10;10],{'book','briefcase','boat','windows','picture','love','day','working day'}','VariableNames',{'userid','itemid','keywords'});
which looks like
Select the unique values for column itemid (your Iids):
Iids=unique(A.itemid);
which looks like
Create a new, empty, table which will contain the results:
NewTable=table();
And now the minimal loop I've come up with:
for id=Iids'
% select rows with given itemid value
RowsWithGivenId=A(A.itemid==id,:);
% create new row in NewTable with the id and the (joined together) keywords from the selected rows
NewTable=[NewTable; table(id,{strjoin(RowsWithGivenId.keywords,', ')})];
end
Also, append the new column names in NewTable
NewTable.Properties.VariableNames = {'itemid','keywords'};
And now NewTable looks like:
Please note: due to the fact that the keywords in the new table are separated by comma, a csv file is not the format I recommend. By using writetable() as writetable(NewTable,'myfile.csv');
what you'll get is
As instead, by replacing ; instead of a separating comma (in strjoin()), you'll get a nicer format: