Table of contents

  1. How to create an ec2 instance using boto3
  2. How to conditionally insert an item into a dynamodb table using boto3
  3. How to Create Dataframe from AWS Athena using Boto3 get_query_results method

How to create an ec2 instance using boto3

To create an Amazon EC2 instance using the boto3 library in Python, you need to follow these steps:

  1. Install boto3: If you haven't already, install the boto3 library using pip:

    pip install boto3
  2. Configure AWS Credentials: Make sure you have AWS credentials set up on your machine. You can set them up either by configuring the AWS CLI or by setting environment variables (AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY).

  3. Write Python Script: Create a Python script that uses the boto3 library to create an EC2 instance:

import boto3

# Set up AWS credentials
aws_access_key = 'your_access_key'
aws_secret_key = 'your_secret_key'
region = 'us-east-1'

# Initialize the EC2 resource
ec2 = boto3.resource('ec2', aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_key, region_name=region)

# Create an EC2 instance
instance = ec2.create_instances(
    ImageId='ami-12345678',  # Replace with a valid AMI ID
    InstanceType='t2.micro',  # Replace with the desired instance type
    KeyName='your-key-pair'  # Replace with your key pair name
)[0]  # Get the first instance in the list

print("Instance created:",

Replace the placeholders (your_access_key, your_secret_key, ami-12345678, t2.micro, your-key-pair) with your actual AWS credentials, desired AMI ID, instance type, and key pair name.

Keep in mind that managing AWS resources like EC2 instances involves security and cost considerations. Ensure that you follow best practices for security and resource management when working with AWS resources programmatically.

Also, note that directly embedding your AWS credentials in your code is not recommended for security reasons. Instead, consider using AWS Identity and Access Management (IAM) roles or a secure configuration mechanism to provide your credentials to your application.

How to conditionally insert an item into a dynamodb table using boto3

To conditionally insert an item into an Amazon DynamoDB table using the Boto3 library in Python, you can use the put_item() method with the ConditionExpression parameter. The ConditionExpression allows you to specify a condition that must be true for the item to be inserted. Here's how you can do it:

import boto3

# Initialize a DynamoDB resource
dynamodb = boto3.resource('dynamodb')

# Specify the table name
table_name = 'YourTableName'

# Get a reference to the DynamoDB table
table = dynamodb.Table(table_name)

# Define the item you want to insert
item_to_insert = {
    'primary_key': 'some_value',
    'attribute1': 'value1',
    'attribute2': 'value2'

# Define the condition expression
condition_expression = 'attribute_not_exists(primary_key)'  # Example condition: primary_key should not exist

# Insert the item conditionally
    response = table.put_item(
    print("Item inserted successfully.")
except Exception as e:
    print(f"Item not inserted: {e}")

In this example, the attribute_not_exists(primary_key) condition expression ensures that the item will only be inserted if there is no existing item with the same primary key value.

You can modify the condition_expression based on your specific needs. DynamoDB supports a variety of condition expressions that allow you to check for the existence of attributes, their values, and more.

Remember that when working with DynamoDB, proper error handling is essential due to the distributed nature of the database and the possibility of conflicts. The example above uses a try and except block to catch any exceptions that might occur during the insertion process.

How to Create Dataframe from AWS Athena using Boto3 get_query_results method

To create a DataFrame from AWS Athena query results using the Boto3 get_query_results method, you'll need to fetch the results, extract the column names and data, and then create a pandas DataFrame. Here's how you can do it:

  1. Install Required Libraries: Ensure you have the boto3 and pandas libraries installed. You can install them using the following command:

    pip install boto3 pandas
  2. Import Libraries: Import the required libraries at the beginning of your script:

    import boto3
    import pandas as pd
  3. Initialize Boto3 Client: Initialize a Boto3 client for Athena using your AWS credentials and region:

    client = boto3.client('athena', region_name='your-region', aws_access_key_id='your-access-key', aws_secret_access_key='your-secret-key')
  4. Execute Query: Use the start_query_execution method to execute your query and get the query execution ID:

    response = client.start_query_execution(
        QueryString='SELECT * FROM your_table',
            'Database': 'your_database'
            'OutputLocation': 's3://your-bucket/query-results/'
    query_execution_id = response['QueryExecutionId']
  5. Fetch and Process Query Results: Use the get_query_results method to fetch the query results and process them into a DataFrame:

    response = client.get_query_results(
    # Extract column names
    columns = [col['Name'] for col in response['ResultSet']['ResultSetMetadata']['ColumnInfo']]
    # Extract data rows
    data = []
    for row in response['ResultSet']['Rows']:
        data.append([cell['VarCharValue'] for cell in row['Data']])
    # Create a DataFrame
    df = pd.DataFrame(data, columns=columns)

Now you have a pandas DataFrame (df) containing the results of your Athena query. Make sure to replace the placeholders ('your-region', 'your-access-key', 'your-secret-key', 'your_table', 'your_database', 'your-bucket/query-results/') with your actual AWS and query details.

Keep in mind that fetching large query results directly into memory may have memory limitations. If you're dealing with large datasets, consider handling the results in smaller batches or storing them in Amazon S3 and reading them from there.

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