Pandas Cut Bin

Pandas Cut Bin

In this article, we will explore the pandas.cut function, which is used to segment and sort data values into bins or intervals. This is especially useful for converting continuous numerical data into categorical data, which can be beneficial for analysis and visualization purposes. We’ll cover the following aspects of pandas.cut:

1. Introduction to pandas.cut
2. Basic Usage of pandas.cut
3. Creating Custom Bins
4. Labeling Bins
5. Handling Outliers with Bins
6. Creating Equal-Width Bins
7. Creating Equal-Frequency Bins
8. Binning Based on Quantiles
9. Integration with DataFrames
10. Practical Examples

Let’s dive into each section and provide detailed code examples and explanations.

Introduction to pandas.cut

pandas.cut is a powerful function that allows us to bin data into discrete intervals. This can be particularly useful for statistical analysis and visualization, enabling us to categorize continuous data into manageable segments. Here’s a basic introduction:

import pandas as pd

data = pd.Series([1, 7, 5, 4, 6, 3, 8, 9, 2, 10])
bins = pd.cut(data, bins=3)
print(bins)

Output:

Explanation:

• Series: We create a pandas.Series with sample data.
• Cut Function: The pd.cut function is used to bin the data into 3 intervals.
• Output: This will print the bins each data point falls into.

Basic Usage of pandas.cut

Let’s start with the basic usage of pandas.cut. We’ll bin a simple array of numbers into specified intervals.

import pandas as pd

data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
bins = [0, 3, 6, 10]
binned_data = pd.cut(data, bins)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data from 1 to 10.
• Bins: We define bins with intervals [0, 3, 6, 10].
• Cut Function: We use pd.cut to bin the data according to the specified bins.
• Output: This will print which interval each data point belongs to.

Creating Custom Bins

We can create custom bins by defining the exact intervals we want to use for binning the data.

import pandas as pd

data = pd.Series([1, 15, 25, 35, 45, 55, 65, 75, 85, 95])
bins = [0, 20, 40, 60, 80, 100]
binned_data = pd.cut(data, bins)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data points ranging from 1 to 95.
• Bins: We define custom bins with intervals [0, 20, 40, 60, 80, 100].
• Cut Function: We use pd.cut to categorize the data into these bins.
• Output: This will print the bin each data point falls into.

Labeling Bins

pandas.cut allows us to label the bins for better readability and analysis.

import pandas as pd

data = pd.Series([5, 15, 25, 35, 45, 55, 65, 75, 85, 95])
bins = [0, 20, 40, 60, 80, 100]
labels = ['Very Low', 'Low', 'Medium', 'High', 'Very High']
binned_data = pd.cut(data, bins, labels=labels)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data points from 5 to 95.
• Bins: We define bins [0, 20, 40, 60, 80, 100].
• Labels: We assign labels to each bin for better clarity.
• Cut Function: We use pd.cut with the labels parameter.
• Output: This will print the labeled bins for each data point.

Handling Outliers with Bins

Sometimes data points may fall outside the specified bins. We can handle these outliers using the right parameter.

import pandas as pd

data = pd.Series([5, 15, 25, 35, 45, 55, 65, 75, 85, 105])
bins = [0, 20, 40, 60, 80, 100]
binned_data = pd.cut(data, bins, right=False)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data points from 5 to 105.
• Bins: We define bins [0, 20, 40, 60, 80, 100].
• Right Parameter: By setting right=False, we make intervals closed on the left.
• Cut Function: We use pd.cut with the right parameter.
• Output: This will print the bins, handling outliers by excluding the rightmost bin’s upper boundary.

Creating Equal-Width Bins

Equal-width bins divide the data range into equal-sized intervals.

import pandas as pd

data = pd.Series([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])
binned_data = pd.cut(data, bins=4)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data points from 1 to 19.
• Bins: We specify bins=4 to create 4 equal-width bins.
• Cut Function: We use pd.cut to bin the data into equal intervals.
• Output: This will print the equal-width bins each data point falls into.

Creating Equal-Frequency Bins

Equal-frequency bins ensure each bin has the same number of data points.

import pandas as pd

data = pd.Series([1, 2, 2, 3, 3, 3, 4, 4, 5, 6])
binned_data = pd.qcut(data, q=4)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data points.
• Q-Cut Function: We use pd.qcut with q=4 to create 4 equal-frequency bins.
• Output: This will print the equal-frequency bins each data point falls into.

Binning Based on Quantiles

We can also bin data based on quantiles.

import pandas as pd

data = pd.Series([1, 2, 2, 3, 3, 3, 4, 4, 5, 6])
quantiles = [0, 0.25, 0.5, 0.75, 1.0]
binned_data = pd.qcut(data, q=quantiles)
print(binned_data)

Output:

Explanation:

• Series: We create a pandas.Series with data points.
• Quantiles: We define quantiles [0, 0.25, 0.5, 0.75, 1.0].
• Q-Cut Function: We use pd.qcut with the specified quantiles.
• Output: This will print the bins based on quantiles for each data point.

Integration with DataFrames

pandas.cut can be integrated with DataFrames to bin data in columns.

import pandas as pd

data = {'Values': [1, 7, 5, 4, 6, 3, 8, 9, 2, 10]}
df = pd.DataFrame(data)
bins = [0, 3, 6, 10]
df['Binned'] = pd.cut(df['Values'], bins)
print(df)

Output:

Explanation:

• DataFrame: We create a pandas.DataFrame with a column ‘Values’.
• Bins: We define bins [0, 3, 6, 10].
• Cut Function: We use pd.cut to bin the ‘Values’ column.
• Output: This will print the DataFrame with a new column ‘Binned’ showing the bins.

Practical Examples

Let’s go through some practical examples to solidify our understanding of pandas.cut.

Example 1: Binning Age Data

import pandas as pd

ages = pd.Series([25, 35, 45, 55, 65, 75, 85])
bins = [0, 30, 50, 70, 100]
labels = ['Young', 'Middle-Aged', 'Senior', 'Elderly']
age_bins = pd.cut(ages, bins, labels=labels)
print(age_bins)

Output:

Explanation:

• Series: We create a pandas.Series with age data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the age data.
• Output: This will print the age categories.

Example 2: Binning Salary Data

import pandas as pd

salaries = pd.Series([2500, 4000, 6000, 8000, 12000, 15000, 20000])
bins = [0, 5000, 10000, 20000]
labels = ['Low', 'Medium', 'High']
salary_bins = pd.cut(salaries, bins, labels=labels)
print(salary_bins)

Output:

Explanation:

• Series: We create a pandas.Series with salary data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the salary data.
• Output: This will print the salary categories.

Example 3: Binning Temperature Data

import pandas as pd

temperatures = pd.Series([-10, 0, 10, 20, 30, 40, 50])
bins = [-20, 0, 10, 20, 30, 40, 60]
labels = ['Freezing', 'Cold', 'Cool', 'Warm', 'Hot', 'Very Hot']
temp_bins = pd.cut(temperatures, bins, labels=labels)
print(temp_bins)

Output:

Explanation:

• Series: We create a pandas.Series with temperature data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the temperature data.
• Output: This will print the temperature categories.

Example 4: Binning Exam Scores

import pandas as pd

scores = pd.Series([55, 65, 75, 85, 95])
bins = [0, 60, 70, 80, 90, 100]
labels = ['F', 'D', 'C', 'B', 'A']
score_bins = pd.cut(scores, bins, labels=labels)
print(score_bins)

Output:

Explanation:

• Series: We create a pandas.Series with exam scores.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the scores.
• Output: This will print the grade categories.

Example 5: Binning Weight Data

import pandas as pd

weights = pd.Series([50, 60, 70, 80, 90, 100, 110])
bins = [40, 60, 80, 100, 120]
labels = ['Underweight', 'Normal', 'Overweight', 'Obese']
weight_bins = pd.cut(weights, bins, labels=labels)
print(weight_bins)

Output:

Explanation:

• Series: We create a pandas.Series with weight data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the weight data.
• Output: This will print the weight categories.

Example 6: Binning Height Data

import pandas as pd

heights = pd.Series([150, 160, 170, 180, 190, 200])
bins = [140, 160, 180, 200]
labels = ['Short', 'Average', 'Tall']
height_bins = pd.cut(heights, bins, labels=labels)
print(height_bins)

Output:

Explanation:

• Series: We create a pandas.Series with height data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the height data.
• Output: This will print the height categories.

Example 7: Binning Speed Data

import pandas as pd

speeds = pd.Series([30, 40, 50, 60, 70, 80, 90])
bins = [20, 40, 60, 80, 100]
labels = ['Slow', 'Moderate', 'Fast', 'Very Fast']
speed_bins = pd.cut(speeds, bins, labels=labels)
print(speed_bins)

Output:

Explanation:

• Series: We create a pandas.Series with speed data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the speed data.
• Output: This will print the speed categories.

Example 8: Binning Income Data

import pandas as pd

incomes = pd.Series([2000, 3000, 4000, 5000, 6000, 7000, 8000])
bins = [1000, 3000, 5000, 7000, 9000]
labels = ['Low Income', 'Middle Income', 'High Income', 'Very High Income']
income_bins = pd.cut(incomes, bins, labels=labels)
print(income_bins)

Output:

Explanation:

• Series: We create a pandas.Series with income data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the income data.
• Output: This will print the income categories.

Example 9: Binning Distance Data

import pandas as pd

distances = pd.Series([5, 15, 25, 35, 45, 55, 65])
bins = [0, 20, 40, 60, 80]
labels = ['Very Close', 'Close', 'Far', 'Very Far']
distance_bins = pd.cut(distances, bins, labels=labels)
print(distance_bins)

Output:

Explanation:

• Series: We create a pandas.Series with distance data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the distance data.
• Output: This will print the distance categories.

Example 10: Binning Monthly Revenue

import pandas as pd

revenue = pd.Series([2000, 4000, 6000, 8000, 10000, 12000, 14000])
bins = [0, 3000, 6000, 9000, 15000]
labels = ['Low', 'Moderate', 'High', 'Very High']
revenue_bins = pd.cut(revenue, bins, labels=labels)
print(revenue_bins)

Output:

Explanation:

• Series: We create a pandas.Series with monthly revenue.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the revenue with labels.
• Output: This will print the labeled revenue bins.

Example 11: Binning Speed Data

import pandas as pd

speeds = pd.Series([30, 50, 70, 90, 110, 130, 150])
bins = [0, 60, 120, 180]
labels = ['Slow', 'Average', 'Fast']
speed_bins = pd.cut(speeds, bins, labels=labels)
print(speed_bins)

Output:

Explanation:

• Series: We create a pandas.Series with speed data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the speeds with labels.
• Output: This will print the labeled speed bins.

Example 12: Binning Energy Consumption

import pandas as pd

energy = pd.Series([100, 200, 300, 400, 500, 600, 700])
bins = [0, 250, 500, 750]
labels = ['Low', 'Moderate', 'High']
energy_bins = pd.cut(energy, bins, labels=labels)
print(energy_bins)

Output:

Explanation:

• Series: We create a pandas.Series with energy consumption data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the energy consumption with labels.
• Output: This will print the labeled energy consumption bins.

Example 13: Binning Profit Data

import pandas as pd

profits = pd.Series([10000, 20000, 30000, 40000, 50000, 60000, 70000])
bins = [0, 25000, 50000, 75000]
labels = ['Low', 'Moderate', 'High']
profit_bins = pd.cut(profits, bins, labels=labels)
print(profit_bins)

Output:

Explanation:

• Series: We create a pandas.Series with profit data.
• Bins and Labels: We define bins and corresponding labels.
• Cut Function: We use pd.cut to bin the profits with labels.
• Output: This will print the labeled profit bins.