jupyter实用tips

numpy lookfor()函数,查询函数文档，且不限于numpy模块

import numpy as np
np.lookfor("average")

Search results for 'average'
----------------------------
numpy.average
    Compute the weighted average along the specified axis.
numpy.mean
    Compute the arithmetic mean along the specified axis.
numpy.nanmean
    Compute the arithmetic mean along the specified axis, ignoring NaNs.
numpy.ma.average
    Return the weighted average of array over the given axis.
numpy.ma.mean
    Returns the average of the array elements along given axis.
numpy.matrix.mean
    Returns the average of the matrix elements along the given axis.
numpy.chararray.mean
    Returns the average of the array elements along given axis.
numpy.ma.MaskedArray.mean
    Returns the average of the array elements along given axis.
numpy.cov
    Estimate a covariance matrix, given data and weights.
numpy.irr
    Return the Internal Rate of Return (IRR).
numpy.std
    Compute the standard deviation along the specified axis.
numpy.sum
    Sum of array elements over a given axis.
numpy.var
    Compute the variance along the specified axis.
numpy.sort
    Return a sorted copy of an array.
numpy.median
    Compute the median along the specified axis.
numpy.nanstd
    Compute the standard deviation along the specified axis, while
numpy.nanvar
    Compute the variance along the specified axis, while ignoring NaNs.
numpy.nanmedian
    Compute the median along the specified axis, while ignoring NaNs.
numpy.partition
    Return a partitioned copy of an array.
numpy.ma.var
    Compute the variance along the specified axis.
numpy.apply_along_axis
    Apply a function to 1-D slices along the given axis.
numpy.ma.apply_along_axis
    Apply a function to 1-D slices along the given axis.
numpy.ma.MaskedArray.var
    Compute the variance along the specified axis.
numpy.random.PCG64.advance
    advance(delta)
numpy.random.Philox.advance
    advance(delta)
numpy.random.Generator.dirichlet
    Draw samples from the Dirichlet distribution.
numpy.random.RandomState.dirichlet
    Draw samples from the Dirichlet distribution.
numpy.random.Generator.multivariate_normal
    Draw random samples from a multivariate normal distribution.
numpy.random.RandomState.multivariate_normal
    Draw random samples from a multivariate normal distribution.

jupyter 的特殊功能：将光标移到函数末尾，shift+tab可以查看函数文档，再次shift+tab显示更详细的内容

例如下面：
np.max()

Signature:
np.max(
    a,
    axis=None,
    out=None,
    keepdims=<no value>,
    initial=<no value>,
    where=<no value>,
)
Docstring:
Return the maximum of an array or maximum along an axis.

Parameters
----------
a : array_like
    Input data.
axis : None or int or tuple of ints, optional
    Axis or axes along which to operate.  By default, flattened input is
    used.

    .. versionadded:: 1.7.0

    If this is a tuple of ints, the maximum is selected over multiple axes,
    instead of a single axis or all the axes as before.
out : ndarray, optional
    Alternative output array in which to place the result.  Must
    be of the same shape and buffer length as the expected output.
    See `ufuncs-output-type` for more details.

keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

    If the default value is passed, then `keepdims` will not be
    passed through to the `amax` method of sub-classes of
    `ndarray`, however any non-default value will be.  If the
    sub-class' method does not implement `keepdims` any
    exceptions will be raised.

批量注释，取消：选中要注释的段落后 ctr+/
批量右移：tab
批量左移：shift+tab