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This script looks for all files starting with protected_WAG_CNVR_FY26_Immz_2yr_1Vax_Ages and print row counts in each file

for f in $(ls -1 | grep 'protected_WAG_CNVR_FY26_Immz_2yr_1Vax_Ages');do
count=$(wc -l < "$f")
echo "$f : $count"
done

The output :

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This approach leads to the duplications in df_holdout and df_target

• subtract() is row-based and requires exact row match

• If df has duplicate rows, subtract() doesn't guarantee it removes just one instance.

# Filter 20% of the data for the holdout group
df_holdout = df.sample(fraction=0.2, seed=42)
    

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Write count record to .ok file

This code is functionally correct, but it's inefficient and overly complex for what it does: writing a single integer (record count) to a file.

def write_table_to_file(df, container, storage_account, out_name = None, delimiter = "|", audit_prefix = ".ok"):

  ok_output_path  = f"abfss://{container}@{storage_account}.dfs.core.windows.net/tmp_ok_output/"

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Check version of GPU Mac

1. Check the GPU info

system_profiler SPDisplaysDataType

This will display something like:

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Check version of Mac Chip

1. Check the Chip Type

sysctl -n machdep.cpu.brand_string

This will display something like:

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Insert text every beginning or ending of all lines in sublime

Context :

I have 1000 line following like structure texts.

f54g
f5g546
2122v

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Generate .pem File from .p12 Certificate

This guide explains how to extract a .pem file from a .p12 file using OpenSSL and troubleshoot common errors encountered during the process.

1. Generate the .pem File

Run the following command to extract the .pem file:

openssl pkcs12 -in /Users/dvuiw/Desktop/customer.p12 -nokeys -out /Users/dvuiw/Desktop/certicate.pem -nodes -password pass:123456789

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Divide the column containing JSON string into separate columns in PySpark

• create Dataframe

import pyspark.sql.functions as F
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType

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 window_spec = Window.partitionBy("rxa_claim_id", "ITEM_PRODUCT_CODE").orderBy(df_idi["EVENT_TIMESTAMP"].desc())
 df_valid_claim = df_valid_claim.withColumn("row_number", F.row_number().over(window_spec))
 df_valid_claim.display()

window_spec_2 = Window.partitionBy("rxa_claim_id", "ITEM_PRODUCT_CODE")
valid_claim =  df_valid_claim.withColumn("row_number_count", F.count("row_number").over(window_spec_2))
df_valid_claim.display()

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Draw Diagrams With Markdown

• https://mermaid.live

• Excalidraw : An open source virtual hand-drawn style whiteboard. Collaborative and end-to-end encrypted.

• Technology Radar

Sequence Diagrams