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# Discretize

Transform continuous data into discrete categories or bins to simplify analysis.

## Data discretization definition:

Discretization is the process of converting continuous data into a set of discrete intervals or categories. This technique can be used for data reduction, simplification, or to make the data more suitable for analysis and it typically applied to very large datasets.

## Data discretization examples using Python

Please note that you need to have the necessary Python libraries installed in your Python environment to run the code samples below.

### using pandas `cut()`

One practical example of discretization in Python is using the pandas `cut()` function. The `cut()` function allows you to specify the number of bins you want to use and the range of the data, and it returns a new column with the values binned into those intervals. For example:

``````import pandas as pd

# create a sample dataset
data = pd.DataFrame({'values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})

# bin the values into three intervals
data['bins'] = pd.cut(data['values'], bins=3, labels=['Low', 'Medium', 'High'])

# view the binned data
print(data)
``````

This will create a new column called 'bins' with the values binned into three intervals, 'Low', 'Medium', and 'High'. The output will be:

``````   values    bins
0       1     Low
1       2     Low
2       3  Medium
3       4  Medium
4       5  Medium
5       6    High
6       7    High
7       8    High
8       9    High
9      10    High
``````

Another example of using using the pandas `cut()` function, if we have a DataFrame `df` containing a column `ages` , we can create bins and labels as follows:

``````import pandas as pd

# create a sample dataset
data = {'ages': [21, 32, 45, 18, 52, 28, 38, 50]}
df = pd.DataFrame(data)

bins = [0, 18, 35, 50, float('inf')]
labels = ['child', 'young adult', 'middle-aged', 'senior']

df['age_group'] = pd.cut(df['ages'], bins=bins, labels=labels)

print(df)
``````

This will create a new column `age_group` in `df` containing the categorical labels based on the binning and print out the result:

``````   ages    age_group
2    45  middle-aged
3    18        child
4    52       senior
6    38  middle-aged
7    50  middle-aged
``````

### using scikit-learn

Another example of discretization in Python is using the KBinsDiscretizer class from the scikit-learn library. This class allows you to specify the number of bins, the strategy for dividing the data, and whether to encode the intervals as integers or one-hot vectors. For example:

``````from sklearn.preprocessing import KBinsDiscretizer

# create a sample dataset
data = [, , , , , , , , , ]

# bin the values into three intervals
discretizer = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
data_bins = discretizer.fit_transform(data)

# view the binned data
print(data_bins)
``````

This will create a new array with the values binned into three intervals, and will yield the following:

``````[[0.]
[0.]
[1.]
[1.]
[1.]
[2.]
[2.]
[2.]
[2.]
[2.]]
``````

In this case, the `encode` parameter is set to `ordinal`, which means that the intervals are represented as integers. If it were set to `onehot`, the intervals would be represented as one-hot vectors.

Other data engineering terms related to
Data Transformation: