Shantanu's Blog

Database Consultant

September 30, 2019


Create pandas dataframe using elastic beats data

Here are 5 steps to create a pandas dataframe using the packetbeat data

1) Start elastic container
2) Download and configure packetbeat config file
3) Start packetbeat container
4) Login to packetbeat container and start service
5) Import packetbeat data into pandas dataframe

# start elastic
docker run -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node"   --log-driver json-file  -d elasticsearch:7.3.1

# download packetbeat config file
curl -L -O

# add send_response parameter to mysql section of config file and change host address
vi packetbeat.docker.yml

  ports: [3306]
  send_response: true

  hosts: ''

# start a packetbeat container

docker run \
  --name pbeat \
  --disable-content-trust \
  --log-driver json-file \
  --user=packetbeat \
  --volume="$(pwd)/packetbeat.docker.yml:/usr/share/packetbeat/packetbeat.yml:ro" \
  --cap-add="NET_RAW" \
  --cap-add="NET_ADMIN" \
  --network=host \

# login to packetbeat container and start packetbeat service
docker exec -it pbeat bash
cd /usr/share/packetbeat/
./packetbeat -e

# download the packetbeat data to pandas dataframe:

import pandas as pd
import numpy as np

import elasticsearch
from elasticsearch import helpers

es_client = elasticsearch.Elasticsearch("")

mylist = list()

for r in helpers.scan(es_client, index="packetbeat-7.0.0-2019.10.01-000001"):
        mylist.append(({"ip": np.nan, "port": np.nan, "bytes": np.nan}))

df = pd.DataFrame(mylist)

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September 29, 2019


Connect to DocumentDB from EC2 instance

Here are 4 easy steps to connect to documentDB cluster from any EC2 instance that is part of the same VPC.

1) Download key file:
cd /tmp/

2) Create mongo container:
docker run --name mymongo  -p 27017:27017 -v /tmp/:/tmp/ -d mongo:3.4

3) Login to mongo container:
docker exec -it mymongo bash

4) connect to documentDB cluster:
mongo --ssl --host --sslCAFile /tmp/rds-combined-ca-bundle.pem --username abcd  --password xyz

Note that /tmp/ folder of host is mounted on container. All the files in that folder are available to "mymongo" container.

You can set up an SSH tunnel to the Amazon DocumentDB cluster by running the following command on your local computer. The -L flag is used for forwarding a local port.

> ssh -i "ec2Access.pem" -L -N

After the SSH tunnel is created, any commands that you issue to localhost:27017 are forwarded to the Amazon DocumentDB cluster sample-cluster running in the Amazon VPC.

Make sure that TLS is disabled.

Now, use this command to connect to documentDB...

> mongo --sslAllowInvalidHostnames --ssl --sslCAFile rds-combined-ca-bundle.pem --username --password  

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September 22, 2019


pandas case study 15

df = pd.DataFrame({"col1": [1, 3, 6, 10], "col2": ["A", "B", "A", "C"]})

col1    col2
 1        A
 3        B
 6        A
 10       C

How do I extend the dataframe by filling the missing values like this?

col1    col2
 1        A
 2        A
 3        B
 4        B
 5        B
 6        A
 7        A
 8        A
 9        A
 10       C

Reindex, forward fill and then reset the index will expand the dataframe:


And if col1 is a date column, then the answer is easier with:



# Pandas tips

## This is self join

df1 = df.reset_index()
df1.merge(df1, on="col1").query("index_x > index_y")

## And this is an example of groupby of all columns

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pandas case study 14

If we have this dataframe, how do I take the average of numeric values?

import pandas as pd

df = pd.DataFrame(
        "group": ["A", "A", "A", "B", "B"],
        "group_color": ["green", "green", "green", "blue", "blue"],
        "val1": [5, 2, 3, 4, 5],
        "val2": [4, 2, 8, 5, 7],

This can be resolved by pivot table or groupby method.

df.pivot_table(index=["group", "group_color"])

df.groupby("group").agg(lambda x: x.head(1) if x.dtype == "object" else x.mean())

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pandas case study 13

Let's assume we have a dictionary where values are stored as list.

dictionary_col2 = {"MOB": [1, 2], "ASP": [1, 2], "YIP": [1, 2]}

It is easy to import the data into a dataframe without any problem.


0 1 1 1
1 2 2 2

We can traspose the data:


0 1
MOB 1 2
ASP 1 2
YIP 1 2

# stack and unstack creates a series object in this case.


0  MOB    1
   ASP    1
   YIP    1
1  MOB    2
   ASP    2
   YIP    2
dtype: int64


MOB  0    1
     1    2
ASP  0    1
     1    2
YIP  0    1
     1    2
dtype: int64

explode is the series method that will do similar transformation:


MOB    1
MOB    2
ASP    1
ASP    2
YIP    1
YIP    2

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Check EC2 security group for open ports

Here is a lambda function that will check if there is open port in a given security group.
It will send a message to sns topic if is found anywhere in that security group.

def lambda_handler(event, context):
    import boto3, json
    ec2 = boto3.client('ec2' , region_name='us-east-1' )
    security_group = ec2.describe_security_groups(GroupIds=['sg-12345'])
    for i in range(100):
          for k, v in security_group['SecurityGroups'][0]['IpPermissions'][i]['IpRanges'][0].items():
            if '' in v:
              print (k, v)
              message = {"alert": "open port found "}
              sns_client = boto3.client("sns", region_name="us-east-1")
              response = sns_client.publish(TargetArn='arn:aws:sns:us-east-1:12345:NotifyMe', Message=json.dumps({'default': json.dumps(message)}), MessageStructure='json')

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September 20, 2019


Using category encoders instead of One Hot

We use "One hot encoding" all the time. Right? It will convert the categorical values into new binary columns populated with sparse data. It means there will be a lot of 0 values and only one column will have "True" value of 1. Let's see an example.

Let us create a sample dataframe with X features and y as a target. load_boston is the function available in sklearn.datasets class.

import pandas as pd
from sklearn.datasets import load_boston
bunch = load_boston()
y =
X = pd.DataFrame(, columns=bunch.feature_names)

Pandas method called "get_dummies" makes it very easy to quickly transform the data.

ndf=X.join(pd.get_dummies(X['RAD'], prefix='RAD'))

CRIM ZN INDUS CHA ... RAD_1.0 RAD_2.0 RAD_3.0 RAD_4.0 RAD_5.0 RAD_6.0 RAD_7.0 RAD_8.0 RAD_24.0

As you can see all the unique values in "RAD" column are provided with their own columns. If pandas find a value "24.0", it will create a new column with default value of 0 and change it to 1 only if found "True".  This is really wonderful. The only problem is that if there are thousand unique values in this column, we will have to deal with one thousand columns!

In order to reduce the number of columns we will use "category_encoders" module instead of "get_dummies" like this...

import category_encoders as ce
enc = ce.BinaryEncoder(cols=['RAD']).fit_transform(X)


As you can see there are now only 5 additional columns and the 9 unique values of  original column are distributed among themselves. Unlike one hot encoding where only 1 column is populated as "True", here there are 2 or 3 columns those may have "1" in it. This will reduce the number of column and allow us to use "hot encodings" even if there are too many unique values in a given column.

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September 16, 2019


Secure FTP access to your S3 bucket in 4 easy steps

1) Visit SFTP transfer home page and create a new server:

Endpoint configuration: Public
Identity provider: Service managed

2) Create required role (my_sftp_role) and policy (my_sftp_policy) using this documentation:

3) Create required SSH keys using this guide:

4) Create a new user:
a) Provide a name like 'test'
b) access role (my_sftp_role) and select policy (my_sftp_policy) that we created in step 2
c) Choose the same S3 bucket as home directory that we mentioned in the policy
d) Upload the SSH public key, we created in step 3

You can now connect to your SFTP server using private key that was created in step 3:

sftp -i /home/ec2-user/transfer-key

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September 10, 2019


import aws command line output to pandas

Here is how we can get the API gateway report into pandas dataframe. The first command will download the api gateway REST apis in JSON format. Python pandas module has json_normalize function that will convert the json data into dataframe. json.load is used to read the document from file.

# !aws --region=us-east-1 apigateway get-rest-apis > /tmp/to_file.json

import pandas as pd
import json
from import json_normalize

with open("/tmp/to_file.json") as f:
    data = json.load(f)

df = json_normalize(data, "items")

df["createdDate"] = pd.to_datetime(df["createdDate"], unit="s")
df["type"] = df["endpointConfiguration.types"].str[0]

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Cloudformation template of 3 lines

These 3 lines of cloudformation code will create a SNS topic. Since the name is not defined in the template, a new name will be created automatically.

    Type: AWS::SNS::Topic

When you remove this template, the topic is also removed. If you want to keep the resources even after the template is deleted, then update the stack with the following template...

    DeletionPolicy: Retain
      Type: AWS::SNS::Topic

Once the stack is updated, your resources will not be removed even if you delete the stack that created it.

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Using pre-trained resnet model after modifying layers

pytorch is the package developed by facebook to help computer vision and Natural Language Processing. TorchVision package consists of popular datasets, model architectures, and common image transformations for computer vision. We use the "models" class and import pre-trained model from resnet group.

from torchvision import models
import torch
res_mod = models.resnet34(pretrained=True)

It is possible to print the layers that the pre-trained model is using. The numpy array data passes through all this trouble to give birth to final dataset that will represent the given class.

for name, child in res_mod.named_children():

In some cases we may want to selectively unfreeze layers and have the gradients computed for just a few chosen layers.
for e.g. in this case layer3 and layer4 should be made available for training while re-using rest of the slabs.

for name, child in res_mod.named_children():
    if name in ["layer3", "layer4"]:
        print(name + " has been unfrozen.")
        for param in child.parameters():
            param.requires_grad = True
        for param in child.parameters():
            param.requires_grad = False

The change in the sequence should be communicated back to torch so that it will be used for the training.

optimizer_conv = torch.optim.SGD(
    filter(lambda x: x.requires_grad, res_mod.parameters()), lr=0.001, momentum=0.9

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Autonormalize using featuretools

This is how a typical pandas data frame look like. How do I know the relations between the columns? Is it possible to normalize the data into 2 or 3 tables?

import pandas as pd
rows = [['tigers', 'boston', 'MA', 20],
       ['elephants', 'chicago', 'IL', 21],
       ['foxes', 'miami', 'FL', 20],
       ['snakes', 'austin', 'TX', 20],
       ['dolphins', 'honolulu', 'HI', 19],
       ['eagles', 'houston', 'TX', 21]]
df = pd.DataFrame(rows, columns=['team', 'city', 'state', 'roster_size'])

This is just 2 lines of code that will show the relations and build complex relations automatically.

from featuretools.autonormalize import autonormalize as an
print (an.find_dependencies(df))

featuretools module has auto normalize class that will do all this and more!

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September 09, 2019


Pandas dataframe to athena

Here are 5 steps to save your pandas dataframe to Athena table.

1) Create a sample dataframe.

from io import StringIO
import pandas as pd

u_cols = ["page_id", "web_id"]
audit_trail = StringIO(

df = pd.read_csv(audit_trail, sep="|", names=u_cols)

2) Convert all columns to string.

df = df.astype(str)

3) Create a new bucket. You can also use rb --force to empty the bucket before re-creating.

!aws s3 mb s3://todel162/

4) Save the pandas dataframe as parquet files to S3

import awswrangler
session = awswrangler.Session()
session.pandas.to_parquet(dataframe=df, path="s3://todel162")

5) Login to console and create a new table in Athena.

   `page_id` string,
  `web_id` string
  'serialization.format' = '1'
) LOCATION 's3://todel162/'
TBLPROPERTIES ('has_encrypted_data'='false');

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Using pre-trained models

Here is how you can use pre-trained model in just 5 or 6 lines of code using imageAI library.

#!pip install -q opencv-python tensorflow keras imageAI


from imageai.Detection import ObjectDetection
detector = ObjectDetection()
detector.detectObjectsFromImage(input_image="test.jpeg", output_image_path="out.jpeg")


from imageai.Prediction import ImagePrediction
predictor = ImagePrediction()

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September 08, 2019


Pandas transform inconsistent behavior for list

There is a serious bug in pandas aggregation using transform method.

df = pd.DataFrame(data={'label': ['a', 'b', 'b', 'c'], 'wave': [1, 2, 3, 4], 'y': [0,0,0,0]})

The following does not return a list as we would expect.

df['new'] = df.groupby(['label'])[['wave']].transform(list)

I can use tuple instead of list to get the correct results. But that is a work-around. The bug looks very annoying because we do not know if any other functions will also misbehave.

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September 07, 2019


Docker security check

Running the security check on docker server, is easy.

git clone
cd docker-bench-security
sudo sh

You may get a few warnings like this...

[WARN] 1.2.4  - Ensure auditing is configured for Docker files and directories - /var/lib/docker

Open this file and add the log files paths. Do not forget to restart audit deamon.

# vi  /etc/audit/audit.rules

-w /usr/bin/docker -p wa
-w /var/lib/docker -p wa
-w /etc/docker -p wa
-w /etc/default/docker -p wa
-w /etc/docker/daemon.json -p wa
-w /usr/bin/docker-containerd -p wa
-w /usr/bin/docker-runc -p wa
-w /etc/sysconfig/docker -p wa

# restart auditd service

Another file to be added for security purpose:

vi /etc/docker/daemon.json

    "icc": false,
    "log-driver": "syslog",
    "disable-legacy-registry": true,
    "live-restore": true,
    "userland-proxy": false,
    "no-new-privileges": true

Add this environment variable:

echo "DOCKER_CONTENT_TRUST=1" | sudo tee -a /etc/environment

# restart docker



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