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Classe « DataFrame »

Méthode pandas.DataFrame.dropna

Signature de la méthode dropna

def dropna(self, *, axis: 'Axis' = 0, how: 'AnyAll | lib.NoDefault' = <no_default>, thresh: 'int | lib.NoDefault' = <no_default>, subset: 'IndexLabel | None' = None, inplace: 'bool' = False, ignore_index: 'bool' = False) -> 'DataFrame | None' 

Description

help(DataFrame.dropna)

Remove missing values.

See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    Determine if rows or columns which contain missing values are
    removed.

    * 0, or 'index' : Drop rows which contain missing values.
    * 1, or 'columns' : Drop columns which contain missing value.

    Only a single axis is allowed.

how : {'any', 'all'}, default 'any'
    Determine if row or column is removed from DataFrame, when we have
    at least one NA or all NA.

    * 'any' : If any NA values are present, drop that row or column.
    * 'all' : If all values are NA, drop that row or column.

thresh : int, optional
    Require that many non-NA values. Cannot be combined with how.
subset : column label or sequence of labels, optional
    Labels along other axis to consider, e.g. if you are dropping rows
    these would be a list of columns to include.
inplace : bool, default False
    Whether to modify the DataFrame rather than creating a new one.
ignore_index : bool, default ``False``
    If ``True``, the resulting axis will be labeled 0, 1, ..., n - 1.

    .. versionadded:: 2.0.0

Returns
-------
DataFrame or None
    DataFrame with NA entries dropped from it or None if ``inplace=True``.

See Also
--------
DataFrame.isna: Indicate missing values.
DataFrame.notna : Indicate existing (non-missing) values.
DataFrame.fillna : Replace missing values.
Series.dropna : Drop missing values.
Index.dropna : Drop missing indices.

Examples
--------
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

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et Keras et Tensorflow Voir le programme détaillé