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Whether a decision to perform a gastrectomy in patients with CLD should be based on the development of HCC or the presence of CLD depends on the patient’s overall liver function. A previous study showed that percutaneous liver biopsy could be helpful in the evaluation of HCC in patients with CLD.^[@R31]^ However, a major complication, such as intrahepatic hemorrhage, is associated with liver biopsy. In this study, we demonstrated that HBsAg positivity was associated with the presence of HCC and PN. As the number of patients with CLD who underwent gastrectomy at our institute was not enough to evaluate the diagnostic ability of HBeAg positivity or negativity for the presence of HCC and PN in patients with CLD, further studies with a larger number of patients will be needed.

HBV reactivation is a major problem after cessation of NUCs,^[@R32]–[@R34]^ and treatment with PEG-IFN is recommended.^[@R35]^ In this study, HBV reactivation occurred in 6 of 59 patients. However, liver dysfunction did not occur in any patient. This finding suggests that HBV reactivation is an important issue for patients with CLD who undergo PEG-IFN therapy. One-year HBsAg seroclearance with PEG-IFN therapy was observed in 1 of 4 patients with HBeAg-negative CHB. Although there was no available data on the risk of HBV reactivation in HBsAg-positive CHB,^[@R36]^ this

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DataFrame shuffle three columns from a data frame

I have the following dataframe, which I want to shuffle the values in three columns, namely ‘Col1’, ‘Col2’ & ‘Col3’, into five different columns in a separate dataframe:
Col1 Col2 Col3
1 3 7
1 4 8
2 5 3
5 6 7
5 8 3

The desired dataframe after shuffle is:
Col1 Col2 Col3 Col4 Col5
1 3 7 5 8
1 4 8 2 3
2 5 3 1 7
5 6 7 6 3
5 8 3 4 7

I am looking for a highly efficient way to do this. Any help would be much appreciated.

We can use the sample function to shuffle the columns.
from pyspark.sql.functions import col, split
df = spark.createDataFrame([[1, 3, 7], [1, 4
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