defbobo/Cheatsheets_readme.md

## Cheatsheets_readme.md

      
    Raw
  

              Cheatsheets_readme.md
            
          
    Simple cheatsheets

Including git, vagrant, pandas


## git cheatsheets.md

      
    Raw
  

              git cheatsheets.md
            
          
    The sheet originaly forked from aleksey-bykov/git-cheat-list.md
Git cheat list


specifying revisions: http://schacon.github.io/git/git-rev-parse.html#_specifying_revisions


list of all affected files both tracked/untracked (for automation)
git status --porcelain


name of the current banch and nothing else (for automation)
git rev-parse --abbrev-ref HEAD


all commits that your branch have that are not yet in master
git log master..<HERE_COMES_YOUR_BRANCH_NAME>


setting up a character used for comments
git config core.commentchar <HERE_COMES_YOUR_COMMENT_CHAR>


fixing fatal: Could not parse object after unsuccessful revert
git revert --quit


view diff with inline changes (by lines)
git diff --word-diff=plain master


view diff of changes in a single line file (per char)
git diff --word-diff-regex=. master


view quick stat of a diff
git diff --shortstat master
git diff --numstat master
git diff --dirstat master


undo last just made commit
git reset HEAD~


list last 20 hashes in reverse
git log --format="%p..%h %cd %<(17)%an %s" --date=format:"%a %m/%d %H:%M" --reverse -n 20


list commits between dates
git log --format="%p..%h %cd %<(17)%an %s" --date=format:"%a %m/%d %H:%M" --reverse --after=2016-11-09T00:00:00-05:00 --before=2016-11-10T00:00:00-05:00


try a new output for diffing
git diff --compaction-heuristic ...
          --color-words ...


enable more thorough comparison
git config --global diff.algorithm patience


restoring a file from a certain commit relative to the latest
git checkout HEAD~<NUMBER> -- <RELATIVE_PATH_TO_FILE>


restoring a file from a certain commit relative to the given commit
git checkout <COMMIT_HASH>~<NUMBER> -- <RELATIVE_PATH_TO_FILE>


restoring a file from a certain commit
git checkout <COMMIT_HASH> -- <RELATIVE_PATH_TO_FILE>


creating a diff file from unstaged changes for a specific folder
git diff -- <RELATIVE_PATH_TO_FOLDER> changes.diff


applying a diff file

go to the root directory of your repository
run:
git apply changes.diff


show differences between last commit and currrent changes:
git difftool -d


referring to:

last commits ... HEAD~1 ...
last 3 commits ... HEAD~3 ...


show the history of changes of a file
git log -p -- ./Scripts/Libs/select2.js


ignoring whitespaces
git rebase --ignore-whitespace <BRANCH_NAME>


pulling for fast-forward only (eliminating a chance for unintended merging)
git pull --ff-only


list of all tags
git fetch
git tag -l


archive a branch using tags
git tag <TAG_NAME> <BRANCH_NAME>
git push origin --tags

you can delete your branch now


get a tagged branch
git checkout -b <BRANCH_NAME> <TAG_NAME>


list of all branches that haven't been merged to master
git branch --no-merge master


enable more elaborate diff algorithm by default
git config --global diff.algorithm histogram


list of all developers
git shortlog -s -n -e


display graph of branches
git log --decorate --graph --all --date=relative

or
git log --decorate --graph --all --oneline 


remembering the password
git config --global credential.helper store
git fetch

the first command tells git to remember the credentials that you are going to provide for the second command


path to the global config
C:\Users\Bykov\.gitconfig


example of a global config
[user]
    email = *****
    name = Aleksey Bykov
    password = *****
[merge]
    tool = p4merge
[mergetool "p4merge"]
    cmd = p4merge.exe \"$BASE\" \"$LOCAL\" \"$REMOTE\" \"$MERGED\"
    path = \"C:/Program Files/Perforce\"
    trustExitCode = false
[push]
    default = simple
[diff]
    tool = meld
    compactionHeuristic = true
[difftool "p4merge"]
    cmd = p4merge.exe \"$LOCAL\" \"$REMOTE\"
    path = C:/Program Files/Perforce/p4merge.exe
[difftool "meld"]
    cmd = \"C:/Program Files (x86)/Meld/Meld.exe\" \"$LOCAL\" \"$REMOTE\"
    path = C:/Program Files (x86)/Meld/Meld.exe


viewing differences between current and other branch
git difftool -d BRANCH_NAME


viewing differences between current and stash
git difftool -d stash


viewing differences between several commits in a diff tool
git difftool -d HEAD@{2}...HEAD@{0}


view all global settings
git config --global -l


delete tag
git tag -d my-tag
git push origin :refs/tags/my-tag


pushing tags
git push --tags


checking the history of a file or a folder
git log -- <FILE_OR_FOLDER>


disabling the scroller
git --no-pager <...>


who pushed last which branch
git for-each-ref --format="%(committerdate) %09 %(refname) %09 %(authorname)"


deleting remote branch
git push origin :<BRANCH_NAME>


deleting remote branch localy
git branch -r -D <BRANCH_NAME>

or to sync with the remote
git fetch --all --prune


deleting local branch
git branch -d <BRANCH_NAME>


list actual remote branchs
git ls-remote --heads origin


list all remote (fetched) branches
git branch -r


list all local branches
git branch -l


find to which branch a given commit belongs
git branch --contains <COMMIT>


updating from a forked repository
git remote add upstream https://github.com/Microsoft/TypeScript.git
git fetch upstream
git rebase upstream/master


## pandas
# Original forked from bsweger/useful_pandas_snippets.py

import pandas as pd
import numpy as np
from sqlalchemy import create_engine
engine = create_engine('mysql://root:xxx@127.0.0.1/lj?charset=utf8')
house = pd.read_sql_table('example', engine)
house_dd = pd.read_sql_query('select url, location, area, layout, size, buildtime, price, created from example group by url having count(url) > 1', engine)

def strip(text):
    try:
        return text.strip()
    except AttributeError:
        return text

def make_int(text):
    return int(text.strip('" '))

df = pd.DataFrame()
df1 = pd.read_csv('example.csv', sep=';')
df2 = pd.read_csv('new.csv', dtype=str, index_col=False,
                  usecols=['deal_num', 'acount', 'number'],
                  converters = {'deal_num' : strip,
                                    'acount' : strip,
                                    'number' : make_int})

# 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)]

# Grab DataFrame rows where column doesn't have certain values
value_list = ['value1', 'value2', 'value3']
df = df[~df.column.isin(value_list)]

# Delete column from DataFrame
del df['column']

# Select from DataFrame using criteria from multiple columns
# (use `|` instead of `&` to do an OR)
newdf = df[(df['column_one'] > 2004) & (df['column_two'] == 9)]

# Rename several DataFrame columns
df = df.rename(columns={
    'col1 old name': 'col1 new name',
    'col2 old name': 'col2 new name',
    'col3 old name': 'col3 new name',
})

# Lower-case all DataFrame column names
df.columns = map(str.lower, df.columns)

# Even more fancy DataFrame column re-naming
# lower-case all DataFrame column names (for example)
df.rename(columns=lambda x: x.split('.')[-1], inplace=True)

# Loop through rows in a DataFrame
# (if you must)
for index, row in df.iterrows():
    print(index, row['some column'])

# Much faster way to loop through DataFrame rows
# if you can work with tuples
# (h/t hughamacmullaniv)
for row in df.itertuples():
    print(row)

# Next few examples show how to work with text data in Pandas.
# Full list of .str functions: http://pandas.pydata.org/pandas-docs/stable/text.html

# Slice values in a DataFrame column (aka Series)
df.column.str[0:2]

# Lower-case everything in a DataFrame column
df.column_name = df.column_name.str.lower()

# Get length of data in a DataFrame column
df.column_name.str.len()

# Sort dataframe by multiple columns
df = df.sort(['col1', 'col2', 'col3'], ascending=[1, 1, 0])

# Get top n for each group of columns in a sorted dataframe
# (make sure dataframe is sorted first)
top5 = df.groupby(['groupingcol1', 'groupingcol2']).head(5)

# Grab DataFrame rows where specific column is null/notnull
newdf = df[df['column'].isnull()]

# Select from DataFrame using multiple keys of a hierarchical index
df.xs(('index level 1 value', 'index level 2 value'), level=('level 1', 'level 2'))

# Change all NaNs to None (useful before
# loading to a db)
df = df.where((pd.notnull(df)), None)

# More pre-db insert cleanup...make a pass through the dataframe, stripping whitespace
# from strings and changing any empty values to None
# (not especially recommended but including here b/c I had to do this in real life one time)
df = df.applymap(lambda x: str(x).strip() if len(str(x).strip()) else None)

# Get quick count of rows in a DataFrame
len(df.index)

# Pivot data (with flexibility about what what
# becomes a column and what stays a row).
# Syntax works on Pandas >= .14
pd.pivot_table(
  df,values='cell_value',
  index=['col1', 'col2', 'col3'], #these stay as columns; will fail silently if any of these cols have null values
  columns=['col4']) #data values in this column become their own column

# Change data type of DataFrame column
df.column_name = df.column_name.astype(np.int64)

# Get rid of non-numeric values throughout a DataFrame:
for col in refunds.columns.values:
  refunds[col] = refunds[col].replace('[^0-9]+.-', '', regex=True)

# Set DataFrame column values based on other column values (h/t: @mlevkov)
df.loc[(df['column1'] == some_value) & (df['column2'] == some_other_value), ['column_to_change']] = new_value

# Clean up missing values in multiple DataFrame columns
df = df.fillna({
    'col1': 'missing',
    'col2': '99.999',
    'col3': '999',
    'col4': 'missing',
    'col5': 'missing',
    'col6': '99'
})

# Concatenate two DataFrame columns into a new, single column
# (useful when dealing with composite keys, for example)
df['newcol'] = df['col1'].map(str) + df['col2'].map(str)

# Doing calculations with DataFrame columns that have missing values
# In example below, swap in 0 for df['col1'] cells that contain null
df['new_col'] = np.where(pd.isnull(df['col1']),0,df['col1']) + df['col2']

# Split delimited values in a DataFrame column into two new columns
df['new_col1'], df['new_col2'] = zip(*df['original_col'].apply(lambda x: x.split(': ', 1)))

# Collapse hierarchical column indexes
df.columns = df.columns.get_level_values(0)

# Convert Django queryset to DataFrame
qs = DjangoModelName.objects.all()
q = qs.values()
df = pd.DataFrame.from_records(q)

# Create a DataFrame from a Python dictionary
df = pd.DataFrame(list(a_dictionary.items()), columns = ['column1', 'column2'])

# Get a report of all duplicate records in a dataframe, based on specific columns
dupes = df[df.duplicated(['col1', 'col2', 'col3'], keep=False)]

# Set up formatting so larger numbers aren't displayed in scientific notation (h/t @thecapacity)
pd.set_option('display.float_format', lambda x: '%.3f' % x)

## vagrant.md

      
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              vagrant.md
            
          
    Vagrant 命令简介

vagrant init {boxname} 初始化
vagrant status  查看虚拟机运行状态
vagrant up 启动虚拟机
vagrant halt  关闭虚拟化开发环境
vagrant reload 修改配置文件后，重启虚拟化开发环境
vagrant port 查看端口映射
vagrant box add {target} --name box {boxname}
vagrant box list 查看当前可用的虚拟化开发环境
vagrant box remove boxname 删除指定的box环境
vagrant global-status 查看环境
vagrant destroy 销毁虚拟机
vagrant package 当前正在运行的VirtualBox虚拟环境打包成一个可重复使用的box
创建虚拟机

vagrant init phusion/ubuntu-14.04-amd64
vagrant up
vagrant ssh
vagrant halt
打包创建虚机

1、打包虚拟机
vagrant package
2、当前目录就会生成package.box，之后新建虚拟机则可使用这个box。
vagrant box add my_box ~/package.box
vagrant init my_box
vagrant up
	# Original forked from bsweger/useful_pandas_snippets.py

	import pandas as pd
	import numpy as np
	from sqlalchemy import create_engine
	engine = create_engine('mysql://root:xxx@127.0.0.1/lj?charset=utf8')
	house = pd.read_sql_table('example', engine)
	house_dd = pd.read_sql_query('select url, location, area, layout, size, buildtime, price, created from example group by url having count(url) > 1', engine)

	def strip(text):
	try:
	return text.strip()
	except AttributeError:
	return text

	def make_int(text):
	return int(text.strip('" '))

	df = pd.DataFrame()
	df1 = pd.read_csv('example.csv', sep=';')
	df2 = pd.read_csv('new.csv', dtype=str, index_col=False,
	usecols=['deal_num', 'acount', 'number'],
	converters = {'deal_num' : strip,
	'acount' : strip,
	'number' : make_int})

	# 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)]

	# Grab DataFrame rows where column doesn't have certain values
	value_list = ['value1', 'value2', 'value3']
	df = df[~df.column.isin(value_list)]

	# Delete column from DataFrame
	del df['column']

	# Select from DataFrame using criteria from multiple columns
	# (use `\|` instead of `&` to do an OR)
	newdf = df[(df['column_one'] > 2004) & (df['column_two'] == 9)]

	# Rename several DataFrame columns
	df = df.rename(columns={
	'col1 old name': 'col1 new name',
	'col2 old name': 'col2 new name',
	'col3 old name': 'col3 new name',
	})

	# Lower-case all DataFrame column names
	df.columns = map(str.lower, df.columns)

	# Even more fancy DataFrame column re-naming
	# lower-case all DataFrame column names (for example)
	df.rename(columns=lambda x: x.split('.')[-1], inplace=True)

	# Loop through rows in a DataFrame
	# (if you must)
	for index, row in df.iterrows():
	print(index, row['some column'])

	# Much faster way to loop through DataFrame rows
	# if you can work with tuples
	# (h/t hughamacmullaniv)
	for row in df.itertuples():
	print(row)

	# Next few examples show how to work with text data in Pandas.
	# Full list of .str functions: http://pandas.pydata.org/pandas-docs/stable/text.html

	# Slice values in a DataFrame column (aka Series)
	df.column.str[0:2]

	# Lower-case everything in a DataFrame column
	df.column_name = df.column_name.str.lower()

	# Get length of data in a DataFrame column
	df.column_name.str.len()

	# Sort dataframe by multiple columns
	df = df.sort(['col1', 'col2', 'col3'], ascending=[1, 1, 0])

	# Get top n for each group of columns in a sorted dataframe
	# (make sure dataframe is sorted first)
	top5 = df.groupby(['groupingcol1', 'groupingcol2']).head(5)

	# Grab DataFrame rows where specific column is null/notnull
	newdf = df[df['column'].isnull()]

	# Select from DataFrame using multiple keys of a hierarchical index
	df.xs(('index level 1 value', 'index level 2 value'), level=('level 1', 'level 2'))

	# Change all NaNs to None (useful before
	# loading to a db)
	df = df.where((pd.notnull(df)), None)

	# More pre-db insert cleanup...make a pass through the dataframe, stripping whitespace
	# from strings and changing any empty values to None
	# (not especially recommended but including here b/c I had to do this in real life one time)
	df = df.applymap(lambda x: str(x).strip() if len(str(x).strip()) else None)

	# Get quick count of rows in a DataFrame
	len(df.index)

	# Pivot data (with flexibility about what what
	# becomes a column and what stays a row).
	# Syntax works on Pandas >= .14
	pd.pivot_table(
	df,values='cell_value',
	index=['col1', 'col2', 'col3'], #these stay as columns; will fail silently if any of these cols have null values
	columns=['col4']) #data values in this column become their own column

	# Change data type of DataFrame column
	df.column_name = df.column_name.astype(np.int64)

	# Get rid of non-numeric values throughout a DataFrame:
	for col in refunds.columns.values:
	refunds[col] = refunds[col].replace('[^0-9]+.-', '', regex=True)

	# Set DataFrame column values based on other column values (h/t: @mlevkov)
	df.loc[(df['column1'] == some_value) & (df['column2'] == some_other_value), ['column_to_change']] = new_value

	# Clean up missing values in multiple DataFrame columns
	df = df.fillna({
	'col1': 'missing',
	'col2': '99.999',
	'col3': '999',
	'col4': 'missing',
	'col5': 'missing',
	'col6': '99'
	})

	# Concatenate two DataFrame columns into a new, single column
	# (useful when dealing with composite keys, for example)
	df['newcol'] = df['col1'].map(str) + df['col2'].map(str)

	# Doing calculations with DataFrame columns that have missing values
	# In example below, swap in 0 for df['col1'] cells that contain null
	df['new_col'] = np.where(pd.isnull(df['col1']),0,df['col1']) + df['col2']

	# Split delimited values in a DataFrame column into two new columns
	df['new_col1'], df['new_col2'] = zip(*df['original_col'].apply(lambda x: x.split(': ', 1)))

	# Collapse hierarchical column indexes
	df.columns = df.columns.get_level_values(0)

	# Convert Django queryset to DataFrame
	qs = DjangoModelName.objects.all()
	q = qs.values()
	df = pd.DataFrame.from_records(q)

	# Create a DataFrame from a Python dictionary
	df = pd.DataFrame(list(a_dictionary.items()), columns = ['column1', 'column2'])

	# Get a report of all duplicate records in a dataframe, based on specific columns
	dupes = df[df.duplicated(['col1', 'col2', 'col3'], keep=False)]

	# Set up formatting so larger numbers aren't displayed in scientific notation (h/t @thecapacity)
	pd.set_option('display.float_format', lambda x: '%.3f' % x)