4 examples of 'pandas replace nan with 0' in Python

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def replace_nan(value, default=0):
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    if math.isnan(value):
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        return default
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    return value

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def _replace_nan(a, val):
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    """
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    If `a` is of inexact type, make a copy of `a`, replace NaNs with
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    the `val` value, and return the copy together with a boolean mask
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    marking the locations where NaNs were present. If `a` is not of
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    inexact type, do nothing and return `a` together with a mask of None.
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    Note that scalars will end up as array scalars, which is important
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    for using the result as the value of the out argument in some
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    operations.
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    Parameters
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    ----------
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    a : array-like
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        Input array.
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    val : float
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        NaN values are set to val before doing the operation.
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    Returns
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    -------
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    y : ndarray
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        If `a` is of inexact type, return a copy of `a` with the NaNs
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        replaced by the fill value, otherwise return `a`.
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    mask: {bool, None}
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        If `a` is of inexact type, return a boolean mask marking locations of
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        NaNs, otherwise return None.
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    """
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    a = np.array(a, subok=True, copy=True)
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    if a.dtype == np.object_:
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        # object arrays do not support `isnan` (gh-9009), so make a guess
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        mask = a != a
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    elif issubclass(a.dtype.type, np.inexact):
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        mask = np.isnan(a)
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    else:
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        mask = None
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    if mask is not None:
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        np.copyto(a, val, where=mask)
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    return a, mask

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def fix_nans(mat):
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    """
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    returns the matrix with average over models if a model, sample, chromosome had nan in it.
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    :param mat: ndarray (model, sample, chromosome)
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    :return: mat ndarray (model, sample, chromosome)
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    """
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    mat = np.nan_to_num(mat)
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    idx, idy, idz = np.where(mat == 0)
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    for x, y, z in zip(idx, idy, idz):
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        mat[x, y, z] = mat[:, y, z].mean()
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    return mat

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def filter_nan2none(value):
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    """Convert the NaN value to None, leaving everything else unchanged.
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    This function is meant to be used as a Django template filter. It
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    is useful in combination with filters that handle None (or any
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    false value) specially, such as the 'default' filter, when one
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    wants special treatment for the NaN value. It is also useful
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    before the 'format' filter to avoid the NaN value being formatted.
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    """
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    if is_nan(value):
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        return None
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    return value