Leverage the Power of pd.cut and pd.qcut
The pd.cut and pd.qcut functions in Pandas are used for binning numerical data into discrete intervals or quantiles, respectively. These functions are useful for various data analysis and machine learning tasks. Letâs discuss this in detail.
1. pd.cut:
The pd.cut function is used for binning continuous data into discrete intervals (bins). Further, this can be used to convert continuous variables to categorical variables. We can analyze various patterns from this.
Example: In this example, the numerical column âValuesâ is divided into three bins (Low, Medium, High) using pd.cut. Each value is assigned to the appropriate bin based on the specified intervals.
Python3
import pandas as pd # Sample DataFrame with a numerical column data = { 'Values' : [ 5 , 12 , 18 , 25 , 32 , 40 , 50 , 60 ]} df = pd.DataFrame(data) # Binning values into discrete intervals using pd.cut bins = [ 0 , 20 , 40 , 60 ] labels = [ 'Low' , 'Medium' , 'High' ] df[ 'Binned_Values' ] = pd.cut(df[ 'Values' ], bins = bins, labels = labels) # Print the DataFrame print (df) |
Output:
Values Binned_Values
0 5 Low
1 12 Low
2 18 Low
3 25 Medium
4 32 Medium
5 40 Medium
6 50 High
7 60 High
2. pd.qcut:
The pd.qcut function does bin based on quantiles. This is used when we need to bin similar distribution values together. It divides the data into discrete intervals based on the given quantiles. This is particularly useful when you want to ensure that each bin contains a similar distribution of values.
Example: In this example, the numerical column âValuesâ is divided into four quantile-based bins (Q1, Q2, Q3, Q4) using pd.qcut.
Python3
# Sample DataFrame with a numerical column data = { 'Values' : [ 5 , 12 , 18 , 25 , 32 , 40 , 50 , 60 ]} df = pd.DataFrame(data) # Binning values into quantiles using pd.qcut df[ 'Quantile_Binned' ] = pd.qcut(df[ 'Values' ], q = [ 0 , 0.25 , 0.5 , 0.75 , 1 ], labels = [ 'Q1' , 'Q2' , 'Q3' , 'Q4' ]) # Print the DataFrame print (df) |
Output:
Values Quantile_Binned
0 5 Q1
1 12 Q1
2 18 Q2
3 25 Q2
4 32 Q3
5 40 Q3
6 50 Q4
7 60 Q4
10 Python Pandas tips to make data analysis faster
Data analysis using Pythonâs 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
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