Use Vectorized Operation
Pandas is a library in Python supports vectorized operations. We can efficiently utilize these operations whenever possible instead of iterating through rows. For example, instead of using a for loop to perform calculations on each row, we can apply operations directly to entire columns.
Iterative Approach:
Python3
import pandas as pd # Sample DataFrame data = { 'old_column' : [ 1 , 2 , 3 , 4 , 5 ]} df = pd.DataFrame(data) # Looping through rows for index, row in df.iterrows(): df.at[index, 'new_column' ] = row[ 'old_column' ] * 2 print ( "DataFrame after looping through rows:" ) print (df) |
Output:
DataFrame after looping through rows:
old_column new_column
0 1 2.0
1 2 4.0
2 3 6.0
3 4 8.0
4 5 10.0
Vectorized Approach:
When a vectorized approach is used for the above operation, the entire calculation is applied at once. The entire old column values are multiplied by 2 and the result is assigned to the new column (‘new_column’).
Python3
import pandas as pd # Sample DataFrame data = { 'old_column' : [ 1 , 2 , 3 , 4 , 5 ]} df = pd.DataFrame(data) # Using vectorized operations df[ 'new_column' ] = df[ 'old_column' ] * 2 print ( "\nDataFrame after using vectorized operations:" ) print (df) |
Output:
DataFrame after using vectorized operations:
old_column new_column
0 1 2
1 2 4
2 3 6
3 4 8
4 5 10
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.
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