8 examples of 'one hot encoding pandas' in Python

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def one_hot_encode(X, K):
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    # input is N x D
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    # output is N x D x K
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    N, D = X.shape
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    Y = np.zeros((N, D, K))
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    for n, d in zip(*X.nonzero()):
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        # 0.5...5 --> 1..10 --> 0..9
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        k = int(X[n,d]*2 - 1)
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        Y[n,d,k] = 1
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    return Y

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def one_hot_encoder(a):
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    length = len(a)
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    b = np.zeros( (length, 10) )
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    b[np.arange(length), a] = 1
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    return b

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def onehot(self,arr):
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    n, w, h = arr.shape
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    arr = arr.reshape(n, -1)
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    arr = self.onehotencoder.fit_transform(arr)
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    arr = arr.reshape(n, w, h, self.k)
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    arr = arr.transpose(0, 3, 1, 2)
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    return arr

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def to_onehot(seq):
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    x = np.zeros((seq.shape[0], 4), dtype=np.float32)
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    alphabet = ["A", "C", "G", "T"]
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    for i in range(len(alphabet)):
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        sel = np.where(seq == alphabet[i])
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        x[sel[0], i] = 1
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    return x

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def encode_one_hot(s):
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    all = []
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    for c in s:
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        if c not in characters:
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            continue
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        x = np.zeros((INPUT_VOCAB_SIZE)) 
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        index = char_indices[c]
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        x[index] = 1 
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        all.append(x)
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    return all

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def one_hot_encode_y(y, n_classes=135):
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    y = np.asarray(y)
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    if n_classes == 1:
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        return reshape_add_axis(y, len(y.shape)-1)
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    else:
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        from keras.utils import to_categorical
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        shape = y.shape
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        y = to_categorical(y, num_classes=n_classes).astype(np.uint8)
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        y = y.reshape(shape + (n_classes,))
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    return y

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def one_hot(*,
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            index: Union[None, int, Sequence[int]] = None,
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            shape: Union[int, Sequence[int]],
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            value: Any = 1,
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            dtype: Type[np.number]) -> np.ndarray:
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    """Returns a numpy array with all 0s and a single non-zero entry(default 1).
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    Args:
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        index: The index that should store the `value` argument instead of 0.
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            If not specified, defaults to the start of the array.
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        shape: The shape of the array.
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        value: The hot value to place at `index` in the result.
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        dtype: The dtype of the array.
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    Returns:
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        The created numpy array.
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    """
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    if index is None:
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        index = 0 if isinstance(shape, int) else (0,) * len(shape)
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    result = np.zeros(shape=shape, dtype=dtype)
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    result[index] = value
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    return result

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def decode_one_hot(x):
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    s = []
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    for onehot in x:
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        one_index = np.argmax(onehot) 
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        s.append(indices_char[one_index]) 
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    return ''.join(s)