dropna参见https://blog.csdn.net/roamer314/article/details/84816171

df[~(df['col'].isnull())] #删掉空行

df.dropna(axis=0) #删除有空值的行，使用参数axis=0

df.dropna(axis=1) #删除有空值的列，使用参数axis=1

drop解释

Ipython:dataframe.drop?
Signature:
dataframe.drop(
    labels=None,
    axis=0,
    index=None,
    columns=None,
    level=None,
    inplace=False,
    errors='raise',
)
Docstring:
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
axis, or by specifying directly index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level.
Parameters
----------
labels : single label or list-like
    Index or column labels to drop.
axis : {0 or 'index', 1 or 'columns'}, default 0
    Whether to drop labels from the index (0 or 'index') or
    columns (1 or 'columns').
index, columns : single label or list-like
    Alternative to specifying axis (``labels, axis=1``
    is equivalent to ``columns=labels``).
    .. versionadded:: 0.21.0
level : int or level name, optional
    For MultiIndex, level from which the labels will be removed.
inplace : bool, default False
    If True, do operation inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
    If 'ignore', suppress error and only existing labels are
    dropped.
Returns
-------
dropped : pandas.DataFrame
Raises
------
KeyError
    If none of the labels are found in the selected axis
See Also
--------
DataFrame.loc : Label-location based indexer for selection by label.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
    where (all or any) data are missing.
DataFrame.drop_duplicates : Return DataFrame with duplicate rows
    removed, optionally only considering certain columns.
Series.drop : Return Series with specified index labels removed.

Examples
--------
 df = pd.DataFrame(np.arange(12).reshape(3,4),
                   columns=['A', 'B', 'C', 'D'])
 df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
Drop columns
 df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
 df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11
Drop a row by index
 df.drop([0, 1])
   A  B   C   D
2  8  9  10  11
Drop columns and/or rows of MultiIndex DataFrame
 midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
                              ['speed', 'weight', 'length']],
                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
 df = pd.DataFrame(index=midx, columns=['big', 'small'],
                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
                         [250, 150], [1.5, 0.8], [320, 250],
                         [1, 0.8], [0.3,0.2]])
 df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2
 df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3
 df.drop(index='length', level=1)
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8