PHP Ds / Pair toArray () function

PHP | toarray

Syntax:
array DsPair::toArray (void)
Parameters:this function takes no parameters.Return value:this function returns an associative array generated by transforming the Pair element.The following programs illustrate the Ds / Pair function::toArray() in PHP:Program 1:  
// Declare a pair $pair = new DsPair ( "a" , "GeeksforGeeks" );  
// Corresponding array echo "Array is:" ; print_r ( $pair -> toArray());  
?>
Exit:
Array is: Array ([key] = > a [value] = > GeeksforGeeks)
Program 2:  
// Declare a pair $pair = new DsPair ([1, 2], [ "GeeksforGeeks" , "Welcome" ]);  
// Corresponding array echo "Array is:" ; var_dump ( $pair -> toArray());  
?>
Exit:
Array is: array (2) {["key"] = > array (2) {[0] = > int (1) [1] = > int (2)} ["value"] = > array (2) {[0] = > string (13) "GeeksforGeeks" [1] = > string (7) "Welcome"}}
Link: https://www.php.net/manual/en/ds-pair.toarray.php



PHP Ds / Pair toArray () function: StackOverflow Questions

Answer #1

Approach 1: You can use pandas" pd.get_dummies.

Example 1:

import pandas as pd
s = pd.Series(list("abca"))
pd.get_dummies(s)
Out[]: 
     a    b    c
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0
3  1.0  0.0  0.0

Example 2:

The following will transform a given column into one hot. Use prefix to have multiple dummies.

import pandas as pd
        
df = pd.DataFrame({
          "A":["a","b","a"],
          "B":["b","a","c"]
        })
df
Out[]: 
   A  B
0  a  b
1  b  a
2  a  c

# Get one hot encoding of columns B
one_hot = pd.get_dummies(df["B"])
# Drop column B as it is now encoded
df = df.drop("B",axis = 1)
# Join the encoded df
df = df.join(one_hot)
df  
Out[]: 
       A  a  b  c
    0  a  0  1  0
    1  b  1  0  0
    2  a  0  0  1

Approach 2: Use Scikit-learn

Using a OneHotEncoder has the advantage of being able to fit on some training data and then transform on some other data using the same instance. We also have handle_unknown to further control what the encoder does with unseen data.

Given a dataset with three features and four samples, we let the encoder find the maximum value per feature and transform the data to a binary one-hot encoding.

>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])   
OneHotEncoder(categorical_features="all", dtype=<class "numpy.float64">,
   handle_unknown="error", n_values="auto", sparse=True)
>>> enc.n_values_
array([2, 3, 4])
>>> enc.feature_indices_
array([0, 2, 5, 9], dtype=int32)
>>> enc.transform([[0, 1, 1]]).toarray()
array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.]])

Here is the link for this example: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html

Get Solution for free from DataCamp guru