Use isin for Filtering
The isin method in Pandas is used to filer a DataFrame based on multiple values. This method is useful when we want to select rows where a specific column matches any of the given values. Let’s discuss with an example.
1. Without isin Method:
Example: In this example, we have a DataFrame df with columns ‘ID’, ‘Category’, and ‘Value’. We want to filter rows where the ‘Category’ column matches any value in the list [‘A’, ‘B’].
Python3
import pandas as pd # Sample DataFrame data = {'ID': [1, 2, 3, 4, 5], 'Category': ['A', 'B', 'A', 'C', 'B'], 'Value': [10, 20, 15, 25, 30]} df = pd.DataFrame(data) # Filtering using traditional method categories_to_filter = ['A', 'B'] filtered_df = df[df['Category'].apply(lambda x: x in categories_to_filter)] # Print the filtered DataFrame print("Filtered DataFrame (Without isin):") print(filtered_df) |
Output:
Filtered DataFrame (Without isin):
ID Category Value
0 1 A 10
1 2 B 20
2 3 A 15
4 5 B 30
Take Advantage of .loc for Conditional Updates:
We can use the .loc for conditional updates of DataFrame values. This method is more efficient than using loops. The .loc accessor allows us to select and modify data based on conditions without the need for iteration over rows. Let’s discuss this with an example.
With .loc:
Python3
import pandas as pd # Sample DataFrame data = {'ID': [1, 2, 3, 4, 5], 'Category': ['A', 'B', 'A', 'C', 'B'], 'Value': [10, 20, 15, 25, 30]} df = pd.DataFrame(data) # Updating values using .loc for conditional updates df.loc[df['Value'] > 20, 'Category'] = 'High'df.loc[df['Value'] <= 20, 'Category'] = 'Low'# Print the updated DataFrame using .loc print("\nUpdated DataFrame (With .loc):") print(df) |
Output:
Updated DataFrame (With .loc):
ID Category Value
0 1 Low 10
1 2 Low 20
2 3 Low 15
3 4 High 25
4 5 High 30
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
Contact Us