Python pandas._libs.hashtable.value_count_int64() Examples

The following are 5 code examples of pandas._libs.hashtable.value_count_int64(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas._libs.hashtable , or try the search function .
Example #1
Source File: algorithms.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _value_counts_arraylike(values, dropna):
    """
    Parameters
    ----------
    values : arraylike
    dropna : boolean

    Returns
    -------
    (uniques, counts)

    """
    values = _ensure_arraylike(values)
    original = values
    values, dtype, ndtype = _ensure_data(values)

    if needs_i8_conversion(dtype):
        # i8

        keys, counts = htable.value_count_int64(values, dropna)

        if dropna:
            msk = keys != iNaT
            keys, counts = keys[msk], counts[msk]

    else:
        # ndarray like

        # TODO: handle uint8
        f = getattr(htable, "value_count_{dtype}".format(dtype=ndtype))
        keys, counts = f(values, dropna)

        mask = isna(values)
        if not dropna and mask.any():
            if not isna(keys).any():
                keys = np.insert(keys, 0, np.NaN)
                counts = np.insert(counts, 0, mask.sum())

    keys = _reconstruct_data(keys, original.dtype, original)

    return keys, counts
Example #2
Source File: algorithms.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def _value_counts_arraylike(values, dropna):
    """
    Parameters
    ----------
    values : arraylike
    dropna : boolean

    Returns
    -------
    (uniques, counts)

    """
    values = _ensure_arraylike(values)
    original = values
    values, dtype, ndtype = _ensure_data(values)

    if needs_i8_conversion(dtype):
        # i8

        keys, counts = htable.value_count_int64(values, dropna)

        if dropna:
            msk = keys != iNaT
            keys, counts = keys[msk], counts[msk]

    else:
        # ndarray like

        # TODO: handle uint8
        f = getattr(htable, "value_count_{dtype}".format(dtype=ndtype))
        keys, counts = f(values, dropna)

        mask = isna(values)
        if not dropna and mask.any():
            if not isna(keys).any():
                keys = np.insert(keys, 0, np.NaN)
                counts = np.insert(counts, 0, mask.sum())

    keys = _reconstruct_data(keys, original.dtype, original)

    return keys, counts
Example #3
Source File: algorithms.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _value_counts_arraylike(values, dropna):
    """
    Parameters
    ----------
    values : arraylike
    dropna : boolean

    Returns
    -------
    (uniques, counts)

    """
    values = _ensure_arraylike(values)
    original = values
    values, dtype, ndtype = _ensure_data(values)

    if needs_i8_conversion(dtype):
        # i8

        keys, counts = htable.value_count_int64(values, dropna)

        if dropna:
            msk = keys != iNaT
            keys, counts = keys[msk], counts[msk]

    else:
        # ndarray like

        # TODO: handle uint8
        f = getattr(htable, "value_count_{dtype}".format(dtype=ndtype))
        keys, counts = f(values, dropna)

        mask = isna(values)
        if not dropna and mask.any():
            if not isna(keys).any():
                keys = np.insert(keys, 0, np.NaN)
                counts = np.insert(counts, 0, mask.sum())

    keys = _reconstruct_data(keys, original.dtype, original)

    return keys, counts
Example #4
Source File: algorithms.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _value_counts_arraylike(values, dropna):
    """
    Parameters
    ----------
    values : arraylike
    dropna : boolean

    Returns
    -------
    (uniques, counts)

    """
    values = _ensure_arraylike(values)
    original = values
    values, dtype, ndtype = _ensure_data(values)

    if needs_i8_conversion(dtype):
        # i8

        keys, counts = htable.value_count_int64(values, dropna)

        if dropna:
            msk = keys != iNaT
            keys, counts = keys[msk], counts[msk]

    else:
        # ndarray like

        # TODO: handle uint8
        f = getattr(htable, "value_count_{dtype}".format(dtype=ndtype))
        keys, counts = f(values, dropna)

        mask = isna(values)
        if not dropna and mask.any():
            if not isna(keys).any():
                keys = np.insert(keys, 0, np.NaN)
                counts = np.insert(counts, 0, mask.sum())

    keys = _reconstruct_data(keys, original.dtype, original)

    return keys, counts
Example #5
Source File: algorithms.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _value_counts_arraylike(values, dropna):
    """
    Parameters
    ----------
    values : arraylike
    dropna : boolean

    Returns
    -------
    (uniques, counts)

    """
    values = _ensure_arraylike(values)
    original = values
    values, dtype, ndtype = _ensure_data(values)

    if needs_i8_conversion(dtype):
        # i8

        keys, counts = htable.value_count_int64(values, dropna)

        if dropna:
            msk = keys != iNaT
            keys, counts = keys[msk], counts[msk]

    else:
        # ndarray like

        # TODO: handle uint8
        f = getattr(htable, "value_count_{dtype}".format(dtype=ndtype))
        keys, counts = f(values, dropna)

        mask = isna(values)
        if not dropna and mask.any():
            if not isna(keys).any():
                keys = np.insert(keys, 0, np.NaN)
                counts = np.insert(counts, 0, mask.sum())

    keys = _reconstruct_data(keys, original.dtype, original)

    return keys, counts