Я хотел бы знать, что означают следующие выражения, особенно значение ! и ?, в следующих примерах, связанных с запросом данных в Pandas DataFrame:

Восклицательный знак:

  • !cat olympics.csv

Вопросительный знак:

  • df.fillna?
  • import pandas as pd pd.Series?
  • copy_df.drop?
12
Yoel Zajac 27 Ноя 2018 в 14:02

1 ответ

Лучший ответ

Обе эти отметки будут работать в блокноте Jupyter .

Восклицательный знак {{X 0}} используется для выполнения команд из базовой операционной системы; вот пример использования WIndows dir:

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

 Directory of C:\Users\Root

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
[...]

Знак вопроса ? используется для справки в записной книжке:

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\root\anaconda3\lib\site-packages\pandas\core\frame.py
Type:      method  

И, как теперь должно быть ясно, ни одна из этих меток не связана с пандами:

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\root\anaconda3\lib\site-packages\numpy\core\fromnumeric.py
Type:      function
7
desertnaut 27 Ноя 2018 в 11:15