Jupyter notebook中的感叹号和问号是什么意思? [英] What is the meaning of exclamation and question marks in Jupyter notebook?

问题描述

我想知道下面的表达式是什么意思，特别是!和?的意思，在下面的例子中，与查询Pandas中的数据有关数据框:

I would like to know what's the meaning of the following expressions, especially the meaning of ! and ?, in the following examples, related to querying data in a Pandas DataFrame:

感叹号:

• !cat olympics.csv

问号:

• df.fillna?
• 将pandas导入为pdpd.Series?
• copy_df.drop?

推荐答案

这两个标记都适用于 Jupyter notebook.

Both of these marks will work in a Jupyter notebook.

感叹号! 用于执行来自底层操作系统的命令；这是一个使用 WIndows dir 的例子:

The exclamation mark ! is used for executing commands from the uderlying operating system; here is an example using WIndows dir:

!dir
# result:
Volume in drive C has no label.
 Volume Serial Number is 52EA-B90C

 Directory of C:UsersRoot

27/11/2018  13:08    <DIR>          .
27/11/2018  13:08    <DIR>          ..
23/08/2016  11:00             2,258 .adalcache
12/09/2016  18:06    <DIR>          .anaconda
[...]

问题 ? 标记用于提供笔记本内帮助:

The question ? mark is used to provide in-notebook help:

import pandas as pd
import numpy as np

df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [np.nan, np.nan, np.nan, 5],
                   [np.nan, 3, np.nan, 4]],
                   columns=list('ABCD'))

df.fillna?
# result:

Signature: df.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
Docstring:
Fill NA/NaN values using the specified method

Parameters
----------
value : scalar, dict, Series, or DataFrame
    Value to use to fill holes (e.g. 0), alternately a
    dict/Series/DataFrame of values specifying which value to use for
    each index (for a Series) or column (for a DataFrame). (values not
    in the dict/Series/DataFrame will not be filled). This value cannot
    be a list.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
    Method to use for filling holes in reindexed Series
    pad / ffill: propagate last valid observation forward to next valid
    backfill / bfill: use NEXT valid observation to fill gap
axis : {0, 1, 'index', 'columns'}
inplace : boolean, default False
    If True, fill in place. Note: this will modify any
    other views on this object, (e.g. a no-copy slice for a column in a
    DataFrame).
limit : int, default None
    If method is specified, this is the maximum number of consecutive
    NaN values to forward/backward fill. In other words, if there is
    a gap with more than this number of consecutive NaNs, it will only
    be partially filled. If method is not specified, this is the
    maximum number of entries along the entire axis where NaNs will be
    filled.
downcast : dict, default is None
    a dict of item->dtype of what to downcast if possible,
    or the string 'infer' which will try to downcast to an appropriate
    equal type (e.g. float64 to int64 if possible)

See Also
--------
reindex, asfreq

Returns
-------
filled : DataFrame
File:      c:users
ootanaconda3libsite-packagespandascoreframe.py
Type:      method  

现在应该很清楚了，这些标记都不是熊猫特有的:

And as it should be clear by now, none of these marks is pandas-specific:

np.argmax?
# result:

Signature: np.argmax(a, axis=None, out=None)
Docstring:
Returns the indices of the maximum values along an axis.

Parameters
----------
a : array_like
    Input array.
axis : int, optional
    By default, the index is into the flattened array, otherwise
    along the specified axis.
out : array, optional
    If provided, the result will be inserted into this array. It should
    be of the appropriate shape and dtype.

Returns
-------
index_array : ndarray of ints
    Array of indices into the array. It has the same shape as `a.shape`
    with the dimension along `axis` removed.

See Also
--------
ndarray.argmax, argmin
amax : The maximum value along a given axis.
unravel_index : Convert a flat index into an index tuple.

Notes
-----
In case of multiple occurrences of the maximum values, the indices
corresponding to the first occurrence are returned.

Examples
--------
>>> a = np.arange(6).reshape(2,3)
>>> a
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.argmax(a)
5
>>> np.argmax(a, axis=0)
array([1, 1, 1])
>>> np.argmax(a, axis=1)
array([2, 2])

>>> b = np.arange(6)
>>> b[1] = 5
>>> b
array([0, 5, 2, 3, 4, 5])
>>> np.argmax(b) # Only the first occurrence is returned.
1
File:      c:users
ootanaconda3libsite-packages
umpycorefromnumeric.py
Type:      function

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