Egemen Zeytinci egemenzeytinci

## 23689767.py
class dotdict(dict):
    """
    A dictionary supporting dot notation.
    """
    __getattr__ = dict.get
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

## bank_marketing_label_only.py
# Label Encode instead of dummy variables

mappings = []

from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()

label_df = df.drop('y', axis=1)
for i, col in enumerate(label_df):

## swagger-yaml-to-html.py
#!/usr/bin/python
#
#  Copyright 2017 Otto Seiskari
#  Licensed under the Apache License, Version 2.0.
#  See http://www.apache.org/licenses/LICENSE-2.0 for the full text.
#
#  This file is based on
#  https://github.com/swagger-api/swagger-ui/blob/4f1772f6544699bc748299bd65f7ae2112777abc/dist/index.html
#  (Copyright 2017 SmartBear Software, Licensed under Apache 2.0)
#

## apply_df_by_multiprocessing.py
import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, num, kwargs = args
    return num, df.apply(func, **kwargs)

def apply_by_multiprocessing(df,func,**kwargs):
  workers=kwargs.pop('workers')

## aligning-images.md

      
              1 file
            
          
              59 forks
            
          
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              788 stars
            
          
                DavidWells
                / aligning-images.md
            
            
              Last active
              July 17, 2024 14:31
            
              
                Guide to aligning images in github readme.md files. https://davidwells.io/snippets/how-to-align-images-in-markdown
              
          
    Aligning images

This is a guide for aligning images.
See the full Advanced Markdown doc for more tips and tricks
left alignment


## neo4j_cypher_cheatsheet.md

      
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              357 stars
            
          
                DaniSancas
                / neo4j_cypher_cheatsheet.md
            
            
              Created
              June 14, 2016 23:52
            
              
                Neo4j's Cypher queries cheatsheet
              
          
    Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

Node: Graph data records
Relationship: Connect nodes (has direction and a type)
Property: Stores data in key-value pair in nodes and relationships
Label: Groups nodes and relationships (optional)


## imshow_grid.py
# in a courtesy of Caffe's filter visualization example
# http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

def imshow_grid(data, height=None, width=None, normalize=False, padsize=1, padval=0):
    '''
    Take an array of shape (N, H, W) or (N, H, W, C)
    and visualize each (H, W) image in a grid style (height x width).
    '''
    if normalize:
        data -= data.min()

## README.md

      
              4 files
            
          
              19 forks
            
          
              0 comments
            
          
              34 stars
            
          
                alexcasalboni
                / README.md
            
            
              Last active
              February 21, 2024 14:48
            
              
                Google Vision API - Examples and Python utilities
              
          
    Google Vision API Examples and Python utilities

Label Detection Examples
Face Detection Examples

  
## README-Template.md

      
              1 file
            
          
              4765 forks
            
          
              1703 comments
            
          
              14038 stars
            
          
                PurpleBooth
                / README-Template.md
            
            
              Last active
              July 28, 2024 03:34
            
              
                A template to make good README.md
              
          
    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


## gist:7360908

      
              1 file
            
          
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              1041 comments
            
          
              16973 stars
            
          
                rxaviers
                / gist:7360908
            
            
              Last active
              July 29, 2024 04:00
            
              
                Complete list of github markdown emoji markup
              
          
    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:
	class dotdict(dict):
	"""
	A dictionary supporting dot notation.
	"""
	__getattr__ = dict.get
	__setattr__ = dict.__setitem__
	__delattr__ = dict.__delitem__

	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)
	# Label Encode instead of dummy variables

	mappings = []

	from sklearn.preprocessing import LabelEncoder

	label_encoder = LabelEncoder()

	label_df = df.drop('y', axis=1)
	for i, col in enumerate(label_df):
	#!/usr/bin/python
	#
	# Copyright 2017 Otto Seiskari
	# Licensed under the Apache License, Version 2.0.
	# See http://www.apache.org/licenses/LICENSE-2.0 for the full text.
	#
	# This file is based on
	# https://github.com/swagger-api/swagger-ui/blob/4f1772f6544699bc748299bd65f7ae2112777abc/dist/index.html
	# (Copyright 2017 SmartBear Software, Licensed under Apache 2.0)
	#
	import multiprocessing
	import pandas as pd
	import numpy as np

	def _apply_df(args):
	df, func, num, kwargs = args
	return num, df.apply(func, **kwargs)

	def apply_by_multiprocessing(df,func,**kwargs):
	workers=kwargs.pop('workers')
	# in a courtesy of Caffe's filter visualization example
	# http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

	def imshow_grid(data, height=None, width=None, normalize=False, padsize=1, padval=0):
	'''
	Take an array of shape (N, H, W) or (N, H, W, C)
	and visualize each (H, W) image in a grid style (height x width).
	'''
	if normalize:
	data -= data.min()
`: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:`