List of important Types in Amazon Cloud Formation tool:

IAM (User, Group, Policy, AccessKey, Role)
Athena (NamedQuery)
Route53 (RecordSetGroup)
EC2 (SecurityGroup::Id, KeyPair::KeyName, SpotFleet)
Lambda (Function, Permission)
Events (Rule)
ApiGateway (RestApi, Method, Deployment)
Logs (LogGroup)

Standard codes used for classification:
1 SC
2 OBC
3 SBC
4 VJNT
5 ST
6 OPEN
7 Orphan

Here are 8 important concepts of Shell scripting that everyone should know.

1) Exit Code Status $?
2) here_doc
3) if_else

if [[ $mystatus = 'No' ]];then
echo "slave down"
else
echo "slave ok"
fi

4) For loop

for i in ls
do
cp $path$i $new_path
done

5) Positional Parameters $1, $2, $#, $@

6) Variables

7) Functions
stop()
(
for socket in {2..9}
do
mysqladmin shutdown $socket
done
)

8) Globbing - t?est [ab] [a-c] [^ab] {b,c,test*}

This seems to be a useful tool to convert mysql or oracle schema to redshift. select queries can also be migrated easily using...

www.sqlines.com

In order to install packetbeat, you may need to change the windows security setting.

C:\packetbeat> Set-ExecutionPolicy

Supply values for the following parameters:
ExecutionPolicy: unrestricted

Now you can install packetbeat using...
C:\packetbeat> .\install-service-packetbeat.ps1

from itertools import groupby
s = 'abcccccbba'

for w, v in groupby(s):
print (''.join(w), ''.join(v))

a a
b b
c ccccc
b bb
a a

Here is the list of some of the modules used for audio-visual processing using deep learning.

chroma: Feature Learning for Chord Recognition: The Deep Chroma Extractor
MFCC: Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC
TCD-TIMIT: An Audio-Visual Corpus of Continuous Speech
librosa
RBF: Radial Basis Function
HMM: Hidden Markov Model
VGG: Resnet 50
voxceleb-luigi: Luigi pipeline to download VoxCeleb audio from YouTube and extract speaker segments
deepfakes
obamanet
yolo

module for google search

!pip install google

from googlesearch import search
for url in search('"Breaking Code" WordPress blog', stop=10):
print(url)

from datetime import datetime
date_string = "21 June, 2018"
date_object = datetime.strptime(date_string, "%d %B, %Y")

This will return the mixed datatypes but this can be slow.

df.applymap(type).nunique() > 1

Better approach pythonic approach would be...

from pandas.api.types import infer_dtype

infer_dtype(df.A)
'mixed-integer'

df.apply(lambda x: 'mixed' in infer_dtype(x))