Aggregating and Summarizing Data
When working with large datasets in automation, you often need to extract meaningful insights by aggregating and summarizing data. Aggregation refers to the process of combining data based on shared characteristics, such as grouping sensor readings by device or summarizing results for each day. This is especially useful in automation analytics, where you might want to analyze trends, monitor performance, or generate summary reports for different devices, time periods, or locations. By grouping data, you can quickly identify patterns or outliers that might require further investigation or prompt automated responses.
123456789101112import pandas as pd # Example dataset: sensor readings from multiple devices data = { "device": ["A", "A", "B", "B", "C", "C"], "reading": [10.5, 12.3, 9.8, 11.2, 13.6, 14.1] } df = pd.DataFrame(data) # Group by 'device' and calculate average reading per device avg_readings = df.groupby("device")["reading"].mean() print(avg_readings)
To use aggregation in pandas, start by selecting the column you want to group by—such as device in your sensor log. The groupby method splits the data into groups based on the values in this column. After grouping, you can apply aggregation functions like mean(), sum(), or count() to calculate summary statistics for each group. The steps are as follows:
- Create or load your DataFrame with relevant columns;
- Use
groupby("column_name")to split the data into groups; - Select the column(s) you want to summarize;
- Apply an aggregation function, such as
mean(), to each group; - Store the result in a new variable or DataFrame for further analysis or reporting.
123# Summarize multiple statistics for each device and store in a new DataFrame summary = df.groupby("device")["reading"].agg(["mean", "min", "max", "count"]) print(summary)
1. What does the groupby method do in pandas?
2. Why is data aggregation useful for automation engineers?
3. Fill in the blank to compute summary statistics by device using pandas groupby.
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When working with large datasets in automation, you often need to extract meaningful insights by aggregating and summarizing data. Aggregation refers to the process of combining data based on shared characteristics, such as grouping sensor readings by device or summarizing results for each day. This is especially useful in automation analytics, where you might want to analyze trends, monitor performance, or generate summary reports for different devices, time periods, or locations. By grouping data, you can quickly identify patterns or outliers that might require further investigation or prompt automated responses.
123456789101112import pandas as pd # Example dataset: sensor readings from multiple devices data = { "device": ["A", "A", "B", "B", "C", "C"], "reading": [10.5, 12.3, 9.8, 11.2, 13.6, 14.1] } df = pd.DataFrame(data) # Group by 'device' and calculate average reading per device avg_readings = df.groupby("device")["reading"].mean() print(avg_readings)
To use aggregation in pandas, start by selecting the column you want to group by—such as device in your sensor log. The groupby method splits the data into groups based on the values in this column. After grouping, you can apply aggregation functions like mean(), sum(), or count() to calculate summary statistics for each group. The steps are as follows:
- Create or load your DataFrame with relevant columns;
- Use
groupby("column_name")to split the data into groups; - Select the column(s) you want to summarize;
- Apply an aggregation function, such as
mean(), to each group; - Store the result in a new variable or DataFrame for further analysis or reporting.
123# Summarize multiple statistics for each device and store in a new DataFrame summary = df.groupby("device")["reading"].agg(["mean", "min", "max", "count"]) print(summary)
1. What does the groupby method do in pandas?
2. Why is data aggregation useful for automation engineers?
3. Fill in the blank to compute summary statistics by device using pandas groupby.
Obrigado pelo seu feedback!
