bthaman

def df_to_sql_fast(df, table_name, numeric_columns, date_columns, append_or_replace, conn):
    """
    Appends or overwrites a SQL Server table 
    using data from a Pandas DataFrame.
    Submits df records at once for faster performance
    compared to df_to_sql.

    Parameters:
        df (DataFrame): df used to create/append table
        table_name (str): Name of existing SQL Server table

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"""
 Demonstrate how to keep your SQL Server database consistent when performing a series of updates using pyodbc, and something goes wrong
 somewhere in the middle of it all.
 
 Transactions are managed at the connection level (not the cursor level). When creating the connection, set autocommit=False.
 When a command (e.g., an update) is executed against the connection, it will not be committed automatically.
 
 If you are executing multiple commands against the connection, and an error is raised before all the commands are complete,
 your database may not be consistent. But, since the commands were not committed, it's ok.
 

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SELECT T.name AS Table_Name ,
       C.name AS Column_Name ,
       P.name AS Data_Type ,
       P.max_length AS Size ,
       CAST(P.precision AS VARCHAR) + '/' + CAST(P.scale AS VARCHAR) AS Precision_Scale
FROM   sys.objects AS T
       JOIN sys.columns AS C ON T.object_id = C.object_id
       JOIN sys.types AS P ON C.system_type_id = P.system_type_id
WHERE  T.type_desc = 'USER_TABLE';

bthaman

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

bthaman

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

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# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# For each unique value in a DataFrame column, get a frequency count
df['Column Name'].value_counts()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

bthaman

import sys

''' 
    list paths that Python searches for packages.
    see https://leemendelowitz.github.io/blog/how-does-python-find-packages.html for the whole story. 
    an example of a path Python searches are the paths listed in the PYTHONPATH environment variable.
'''
print('\n'.join(sys.path))

bthaman

import os


def get_download_path():
    """Returns the default downloads path for linux or windows"""
    if os.name == 'nt':
        import winreg
        sub_key = r'SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders'
        downloads_guid = '{374DE290-123F-4565-9164-39C4925E467B}'
        with winreg.OpenKey(winreg.HKEY_CURRENT_USER, sub_key) as key:

bthaman

import requests


def download_file(url, out_file_name):
    try:
        pdf_url = url
        r = requests.get(pdf_url)

        with open(out_file_name, 'wb') as f:
            f.write(r.content)

bthaman

"""
author:      Bill Thaman
description: Uses a genetic algorithm, with selective pressure, to find the optimal combination
             of projects where the sum of costs is less than or equal to a total. Inspired by the knapsack problem.
"""
from random import randint, random
from operator import add
from functools import reduce
import roulette
import project