### Question :

What is the meaning of exclamation and question marks in Jupyter notebook?

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:

**Exclamation mark:**

`!cat olympics.csv`

**Question Mark(s):**

`df.fillna?`

`import pandas as pd`

pd.Series?`copy_df.drop?`

##
Answer #1:

Both of these marks will work in a **Jupyter notebook**.

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
[...]
```

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:usersrootanaconda3libsite-packagespandascoreframe.py
Type: method
```

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:usersrootanaconda3libsite-packagesnumpycorefromnumeric.py
Type: function
```