Minje Jeon minjejeon

## add-context-menu.reg
Windows Registry Editor Version 5.00

[HKEY_CLASSES_ROOT\SystemFileAssociations\.heic\Shell\Convert-to-Jpg\command]
@="\"C:\\Program Files\\ImageMagick-7.1.1-Q16-HDRI\\magick.exe\" mogrify -format jpg \"%1\""

## lotto.py
import numpy as np
for x in range(5):
    print(np.sort(np.random.choice(range(1,46),6,replace=False)))

## Dockerfile
FROM linuxserver/bookstack:latest

COPY ./install-weasyproxy.sh /tmp/
RUN sh /tmp/install-weasyproxy.sh

RUN mkdir /custom-cont-init.d/
COPY ./init-weasyproxy.sh /custom-cont-init.d/

## week2.ipynb

      
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                minjejeon
                / week2.ipynb
            
            
              Created
              March 15, 2021 14:10
            
              
                경영통계분석 week2 발표자료
              
          
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## spark-cluster.cmd
start /b spark-master.cmd
timeout 2 > NUL
start /b spark-worker.cmd

## gist:0e5562468f83274679e8bfa56fa1937e
from django.db import connection
from django.conf import settings
cursor = connection.cursor()
cursor.execute('SHOW TABLES')
results=[]
for row in cursor.fetchall():
    results.append(row)

for row in results:
    cursor.execute('ALTER TABLE %s CONVERT TO CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci;' % (row[0]))

## 1-import pandas

import pandas as pd
import numpy as np
from pandas import DataFrame, Series

## useful_pandas_snippets.py
# List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

# Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

# Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(valuelist)]

## matplot_hangul.ipynb

      
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                minjejeon
                / matplot_hangul.ipynb
            
            
              Last active
              September 17, 2016 14:06
            
              
                matplotlib 한글, inline svg 출력 세팅
              
          
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## instPkgPlusDeps.R
instPkgPlusDeps <- function(pkg, download = TRUE,
                            which = c("Depends", "Imports", "LinkingTo"),
                            inc.pkg = TRUE) {
  stopifnot(require("tools")) ## load tools
  ap <- available.packages() ## takes a minute on first use
  ## get dependencies for pkg recursively through all dependencies
  deps <- package_dependencies(pkg, db = ap, which = which, recursive = TRUE)
  ## the next line can generate warnings; I think these are harmless
  ## returns the Priority field. `NA` indicates not Base or Recommended
  pri <- sapply(deps[[1]], packageDescription, fields = "Priority")
	import numpy as np
	for x in range(5):
	print(np.sort(np.random.choice(range(1,46),6,replace=False)))
	FROM linuxserver/bookstack:latest

	COPY ./install-weasyproxy.sh /tmp/
	RUN sh /tmp/install-weasyproxy.sh

	RUN mkdir /custom-cont-init.d/
	COPY ./init-weasyproxy.sh /custom-cont-init.d/
	start /b spark-master.cmd
	timeout 2 > NUL
	start /b spark-worker.cmd
	from django.db import connection
	from django.conf import settings
	cursor = connection.cursor()
	cursor.execute('SHOW TABLES')
	results=[]
	for row in cursor.fetchall():
	results.append(row)

	for row in results:
	cursor.execute('ALTER TABLE %s CONVERT TO CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci;' % (row[0]))

	import pandas as pd
	import numpy as np
	from pandas import DataFrame, Series
	# List unique values in a DataFrame column
	pd.unique(df.column_name.ravel())

	# Convert Series datatype to numeric, getting rid of any non-numeric values
	df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

	# Grab DataFrame rows where column has certain values
	valuelist = ['value1', 'value2', 'value3']
	df = df[df.column.isin(valuelist)]
	instPkgPlusDeps <- function(pkg, download = TRUE,
	which = c("Depends", "Imports", "LinkingTo"),
	inc.pkg = TRUE) {
	stopifnot(require("tools")) ## load tools
	ap <- available.packages() ## takes a minute on first use
	## get dependencies for pkg recursively through all dependencies
	deps <- package_dependencies(pkg, db = ap, which = which, recursive = TRUE)
	## the next line can generate warnings; I think these are harmless
	## returns the Priority field. `NA` indicates not Base or Recommended
	pri <- sapply(deps[[1]], packageDescription, fields = "Priority")