Convert integer to categorical vector of fixed size in Python

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I have a set of N integers with the range of values to be from 1-6. I would like to produce the corresponding categorical vector of size 6 per each integer (therefore an array of size Nx6) which will be the categorical representation of my initial set. In the case that my integer will be 1 the result should be:

[1, 0, 0, 0, 0, 0]

While for 6:

[0, 0, 0, 0, 0, 1]

And etc..

You could use a simple list comprehension:

>>> x = 1
>>> [int(i+1 == x) for i in range(6)]
[1, 0, 0, 0, 0, 0]

>>> x = 6
>>> [int(i+1 == x) for i in range(6)]
[0, 0, 0, 0, 0, 1]

Likewise for an Nx6 list of lists:

>>> X = [4,1,5]
>>> [[int(i+1 == x) for i in range(6)] for x in X]
[[0, 0, 0, 1, 0, 0],
 [1, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 1, 0]]

Guide to Encoding Categorical Values in Python, Overview of multiple approaches to encoding categorical values In many practical Data Science activities, the data set will contain categorical variables. can be applied to transform the categorical data into suitable numeric values. object wheel_base float64 length float64 width float64 height float64  Data type of Is_Male column is integer . so let’s convert it into categorical. Method 1: Convert column to categorical in pandas python using categorical() function ## Typecast to Categorical column in pandas df1['Is_Male'] = pd.Categorical(df1.Is_Male) df1.dtypes now it has been converted to categorical which is shown below . Method 2:

If you are happy to use a 3rd party library, this can be achieved efficiently with NumPy:

import numpy as np

np.random.seed(0)

m, n = 6, 10
L = np.random.randint(1, m+1, n)  # construct array of 10 numbers between 1 and 6

A = np.zeros((n, m))              # initialize array of zeros
A[np.arange(n), L-1] = 1          # use advanced indexing to assign values

The result is a NumPy array which you can then index via A[0], A[1], etc.

print(A)

array([[ 0.,  0.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  1.],
       [ 1.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  1.,  0.,  0.,  0.]])

Using The Pandas Category Data Type, Introduction to pandas categorical data type and how to use it. many instances but internally is represented by an array of integers. This data set includes a 500MB+ csv file that has information about We've shown that the size of the dataframe is reduced by converting values to categorical data types. Converts a class vector (integers) to binary class matrix

You can use numpy.fill_diagonal to fill diagonals with value of your need:

import numpy as np

a = np.zeros((6, 6), int)
np.fill_diagonal(a, 1)

print(a)

Output:

[[1 0 0 0 0 0]
 [0 1 0 0 0 0]                                               
 [0 0 1 0 0 0]                                               
 [0 0 0 1 0 0]                                               
 [0 0 0 0 1 0]                                               
 [0 0 0 0 0 1]]

Now, if your integer is 1, use a[0],... for 6, use a[5].

Eg:

input_integer = 1
print(a[input_integer-1])

# [1 0 0 0 0 0]

(Tutorial) Handling Categorical Data in Python, Categorical features can only take on a limited, and usually fixed, number of These are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or a date/time. While some ML packages or libraries might transform categorical data to  Another approach to encoding categorical values is to use a technique called label encoding. Label encoding is simply converting each value in a column to a number. For example, the body_style column contains 5 different values. We could choose to encode it like this: convertible -> 0; hardtop -> 1; hatchback -> 2; sedan -> 3; wagon -> 4

Categorical data, A categorical variable takes on a limited, and usually fixed, number of possible As a signal to other Python libraries that this column should be treated as a categorical variable By converting an existing Series or column to a category dtype: Equality comparisons work with any list-like object of same length and scalars:. Categorical data¶ This is an introduction to pandas categorical data type, including a short comparison with R’s factor. Categoricals are a pandas data type corresponding to categorical variables in statistics. A categorical variable takes on a limited, and usually fixed, number of possible values (categories; levels in R). Examples are

How to One Hot Encode Sequence Data in Python, Categorical data must be converted to numbers. The vector will have a length of 2 for the 2 possible integer values. When a one hot encoding is used for the output variable, it may offer a more nuanced set of predictions  This first requires that the categorical values be mapped to integer values. Then, each integer value is represented as a binary vector that is all zero values except the index of the integer, which is marked with a 1. Worked Example of a One Hot Encoding. Let’s make this concrete with a worked example.

Test Run, The discretizer converts each of the six numeric data points to categorical data: 0, trouble refactoring the code to another language such as Python or Visual Basic. In its most basic form, for a given set of data points and a given number of The array named means has size k and holds the arithmetic means of the data  This approach is very simple and it involves converting each value in a column to a number. Consider a dataset of bridges having a column names bridge-types having below values. Though there will be many more columns in the dataset, to understand label-encoding, we will focus on one categorical column only.

Comments
  • What have you tried so far? Please publish any code you have.
  • You want what ? there is no set in your question - only two list's. Please show minimal reproducible example - input data and output data in sufficient amount so we can get what you want to do