Use GroupBy Aggregations
GroupBy aggregations in Pandas is an efficient way to perform operations on subsets of data based on specific criteria rather than iterating through rows manually.
Example: Consider the following example of calculating the average value of each category.
Python3
#Without groupby import pandas as pd # Sample DataFrame data = {'Category': ['A', 'B', 'A', 'B', 'A', 'B'], 'Value': [10, 20, 15, 25, 12, 18]} df = pd.DataFrame(data) # Iterative approach to calculate average for each category categories = df['Category'].unique() for category in categories: avg_value = df[df['Category'] == category]['Value'].mean() print(f'Average Value for Category {category}: {avg_value}') |
In the above code, the number of categories are first retrieved. The average of each category is calculated by using for loop on each category.
Output:
Average Value for Category A: 12.333333333333334
Average Value for Category B: 21.0
Using Group by:
Instead of iterating through rows to perform aggregations, we can use the groupby function to group data and apply aggregate functions efficiently.
Python3
import pandas as pd # Sample DataFrame data = {'Category': ['A', 'B', 'A', 'B', 'A', 'B'], 'Value': [10, 20, 15, 25, 12, 18]} df = pd.DataFrame(data) # Using groupby to calculate average for each category result = df.groupby('Category')['Value'].mean() # Print the result print(result) |
Here, we group the DataFrame df by the ‘Category’ column using groupby (‘Category’). Then, we apply the mean() aggregate function to the ‘Value’ column within each group. The result is a Series with the average value for each category.
Output:
Category
A 12.333333
B 21.000000
Name: Value, dtype: float64
10 Python Pandas tips to make data analysis faster
Data analysis using Python’s Pandas library is a powerful process, and its efficiency can be enhanced with specific tricks and techniques. These Python tips will make our code concise, readable, and efficient. The adaptability of Pandas makes it an efficient tool for working with structured data. Whether you are a beginner or an experienced data scientist, mastering these Python tips can help you enhance your efficiency in data analysis tasks.
Pandas tips for Data Analysis
In this article we will explore about What are the various 10 python panads tips to make data analysis faster and that helps us to make our work more easier.
Table of Content
- Use Vectorized Operation
- Optimize Memory Usage
- Method Chaining
- Use GroupBy Aggregations
- Using describe() and Percentile
- Leverage the Power of pd.cut and pd.qcut
- Optimize DataFrame Merging
- Use isin for Filtering
- Profile Code with ydata_profiling
- Conclusion
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