Introduction to DataFrames for DevOps
In DevOps, you often need to analyze operational data such as server metrics, logs, or deployment results. Handling this data efficiently is crucial for making informed decisions and improving processes. pandas is a powerful Python library that introduces the DataFrame—a flexible, table-like data structure designed for organizing and analyzing data. DataFrames make it easy to handle complex datasets, filter and aggregate information, and extract actionable insights. For DevOps professionals, learning to use DataFrames enables you to quickly assess system health, identify trends, and automate reporting tasks.
12345678910111213import pandas as pd # Create a DataFrame with hardcoded server metrics data = { "server": ["web-01", "web-02", "db-01", "cache-01"], "cpu_usage": [55, 87, 43, 76], "memory_usage": [70, 92, 65, 60], "status": ["healthy", "warning", "healthy", "warning"] } df = pd.DataFrame(data) print(df)
A DataFrame is structured like a spreadsheet, with rows and columns. Each column represents a variable (such as cpu_usage or status), and each row represents an observation (like a specific server). You can easily select columns to focus on particular metrics, or filter rows to highlight servers with issues. For example, to select only the server and cpu_usage columns, you would use df[["server", "cpu_usage"]]. To filter for servers with high CPU usage, you can apply conditions directly to the DataFrame, making it simple to identify and address operational concerns.
123# Filter servers with CPU usage greater than 80% high_cpu = df[df["cpu_usage"] > 80] print(high_cpu)
1. What is a pandas DataFrame?
2. How can DataFrames help in DevOps?
3. What is a common operation performed on DataFrames?
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In DevOps, you often need to analyze operational data such as server metrics, logs, or deployment results. Handling this data efficiently is crucial for making informed decisions and improving processes. pandas is a powerful Python library that introduces the DataFrame—a flexible, table-like data structure designed for organizing and analyzing data. DataFrames make it easy to handle complex datasets, filter and aggregate information, and extract actionable insights. For DevOps professionals, learning to use DataFrames enables you to quickly assess system health, identify trends, and automate reporting tasks.
12345678910111213import pandas as pd # Create a DataFrame with hardcoded server metrics data = { "server": ["web-01", "web-02", "db-01", "cache-01"], "cpu_usage": [55, 87, 43, 76], "memory_usage": [70, 92, 65, 60], "status": ["healthy", "warning", "healthy", "warning"] } df = pd.DataFrame(data) print(df)
A DataFrame is structured like a spreadsheet, with rows and columns. Each column represents a variable (such as cpu_usage or status), and each row represents an observation (like a specific server). You can easily select columns to focus on particular metrics, or filter rows to highlight servers with issues. For example, to select only the server and cpu_usage columns, you would use df[["server", "cpu_usage"]]. To filter for servers with high CPU usage, you can apply conditions directly to the DataFrame, making it simple to identify and address operational concerns.
123# Filter servers with CPU usage greater than 80% high_cpu = df[df["cpu_usage"] > 80] print(high_cpu)
1. What is a pandas DataFrame?
2. How can DataFrames help in DevOps?
3. What is a common operation performed on DataFrames?
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