Mastering JSON and XML Data Handling in Pandas 📄🔍
👋 Hello! I'm passionate about DevOps and have over 1+ years of experience in the field. I'm proficient in a variety of cutting-edge technologies and always motivated to expand my knowledge and skills. Let's connect and grow together!
SKILLS:
🔹 Languages & Runtimes: Python, Shell Scripting, HCL, YAML 🔹 Cloud Technologies: AWS, Microsoft Azure, GCP 🔹 Infrastructure Tools: Docker, Terraform, AWS CloudFormation 🔹 Other Tools: Linux, Git and GitHub Actions, Jenkins, Jira, GitLab (beginner), Docker, AWS DevOps 🔹 Web Development: HTML, CSS, Bootstrap, Python, SQL
Job & Responsibilities:
🚀 Improved development efficiency by implementing CI/CD pipelines, resulting in a 30% reduction in deployment time on the test server. 🔒 Strengthened deployment and testing reliability by utilizing Docker containers and optimizing Dockerfile, reducing development issues on the test server by 20%. ⚙️ Automated S3 bucket log creation with Shell scripting, eliminating 100% of manual search and saving 2 hours per week. 📅 Scheduled EC2 instance start/stop using Lambda functions and Event Bridge, leading to a 25% decrease in infrastructure costs. 🔧 Utilized AWS, Linux, Python, Docker, Shell scripting, Terraform, Jenkins Pipelines, and automation to streamline workflows and improve overall system performance.
I'm very detail-oriented and possess strong written and verbal communication skills. As a high performer with a possibility mindset, I strive to solve problems using efficient approaches.
Let's Connect & Grow:
If you find my profile suitable for the role you are searching for, please feel free to reach out to me at sumanprasad9766@gmail.com.
Navigating the World of JSON and XML Data in Pandas 🌐🚀
Handling JSON and XML data is a crucial aspect of data manipulation, and Pandas provides convenient tools for reading, parsing, converting, and exploring data in these formats.
Reading and Parsing JSON Data 📖🔍
Pandas simplifies the process of reading and parsing JSON data, making it easy to work with structured data.
Use Case: Reading and Parsing JSON Data
# Example
import pandas as pd
# JSON data as a string
json_data = '{"name": "John", "age": 30, "city": "New York"}'
# Read JSON data into a Pandas DataFrame
df = pd.read_json(json_data, orient='index')
# Display the DataFrame with parsed JSON data
print(df)
Converting Data to JSON and XML 🔄📄
Pandas allows you to effortlessly convert DataFrame data to JSON and XML formats.
Use Case: Converting Data to JSON
# Example
import pandas as pd
# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'San Francisco', 'Los Angeles']}
df = pd.DataFrame(data)
# Convert DataFrame to JSON
json_data = df.to_json(orient='records')
# Display the JSON data
print(json_data)
Use Case: Converting Data to XML
# Example
import pandas as pd
# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'San Francisco', 'Los Angeles']}
df = pd.DataFrame(data)
# Convert DataFrame to XML
xml_data = df.to_xml()
# Display the XML data
print(xml_data)
Handling JSON and XML data in Pandas is seamless, allowing you to easily read, parse, and convert data between different formats. Whether you're dealing with JSON strings, converting DataFrames to JSON or XML, Pandas provides a user-friendly interface for effective data manipulation. 🌐🚀
