datavudeja

import pandas as pd
import hashlib
from datetime import datetime

def detect_dataframe_changes(source_df: pd.DataFrame, target_df: pd.DataFrame, primary_key: str, ignored_columns: list):
    """
    Detects inserted, updated, and deleted rows between two DataFrames.

    Args:
        source_df (pd.DataFrame): The source DataFrame.

datavudeja

import pandas as pd
import ray
from ray.data import Dataset
from ray.data.context import DataContext, ShuffleStrategy
from typing import List
import time
from ray.data.aggregate import Count, Mean, Min, Max, Quantile, Std, Unique, AggregateFnV2
from ray.data.block import BlockAccessor, Block
from typing import List, Tuple, Optional
from ray.data import Dataset

datavudeja

from hashlib import md5
import pandas as pd
from typing import Optional, Iterable

def get_md5_from_series(input_iterable: Iterable) -> str:
    """
    Create a MD5 hash from an Iterable, typically a row from a Pandas ``DataFrame``, but can be any
    Iterable object instance such as a list, tuple or Pandas ``Series``.
    
    Args:

datavudeja

# conda activate bert-github

#%%
import pandas as pd
import numpy as np
import os
import glob
import pathlib
from tqdm import tqdm
from bs4 import BeautifulSoup

datavudeja

# Splits the values and expands them in multiple numbered columns
temp_df = df[column].str.split("|", expand=True).fillna('')

# One-Hot encodes all the values for each column
temp_df = pd.get_dummies(temp_df).astype('uint8')

# Removes the "N_" prefixe for each column to expose duplicates
temp_df = remove_prefixes(temp_df)

# Merges the duplicate columns

datavudeja

#! /usr/bin/env python3

# Usage:
#  $ python3 this.py > output.csv
#  $ nkf --overwrite --oc=UTF-8-BOM output.csv

import glob
import hashlib
import os
import sys

datavudeja

import pandas as pd
import hashlib

def hash_dataframe(df):
    """
    Generate a hash for a DataFrame using the SHA-256 algorithm.
    
    This function creates a hash for each row of the DataFrame using pandas' `hash_pandas_object` 
    and then hashes the resulting array of row hashes using `hashlib.sha256`.
    

datavudeja

import pandas as pd
import json

def readJson(filename,) -> dict:
    """ 
    Reads a json file and returns a dictionary
    """
    file = open(filename, 'r')
    data = file.read()
    file.close()

datavudeja

#2022-07-25 Argparse Parse from Command Line

"C:\<Entwicklung>\WORK_JUPYTER\root\tools\argparse_template.py"

""" template code for argparse """

datavudeja

import pandas as pd
import pyodbc

class DataImporter:
    """
    This Python class takes in a pandas DataFrame from a CSV file and a SQL Server connection string, as 
    well as a name for the SQL table where the data will be imported. The class compares the columns of 
    the DataFrame to those in the SQL table and ensures that at least 50% of the fields match. If there 
    are missing fields, the class adds them to the SQL table using the same naming convention as existing 
    fields. All new fields are created as varchar fields by default. Once the SQL table has all the fields