Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé

Classe « DataFrame »

Méthode pandas.DataFrame.update

Signature de la méthode update

def update(self, other, join: 'UpdateJoin' = 'left', overwrite: 'bool' = True, filter_func=None, errors: 'IgnoreRaise' = 'ignore') -> 'None' 

Description

help(DataFrame.update)

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters
----------
other : DataFrame, or object coercible into a DataFrame
    Should have at least one matching index/column label
    with the original DataFrame. If a Series is passed,
    its name attribute must be set, and that will be
    used as the column name to align with the original DataFrame.
join : {'left'}, default 'left'
    Only left join is implemented, keeping the index and columns of the
    original object.
overwrite : bool, default True
    How to handle non-NA values for overlapping keys:

    * True: overwrite original DataFrame's values
      with values from `other`.
    * False: only update values that are NA in
      the original DataFrame.

filter_func : callable(1d-array) -> bool 1d-array, optional
    Can choose to replace values other than NA. Return True for values
    that should be updated.
errors : {'raise', 'ignore'}, default 'ignore'
    If 'raise', will raise a ValueError if the DataFrame and `other`
    both contain non-NA data in the same place.

Returns
-------
None
    This method directly changes calling object.

Raises
------
ValueError
    * When `errors='raise'` and there's overlapping non-NA data.
    * When `errors` is not either `'ignore'` or `'raise'`
NotImplementedError
    * If `join != 'left'`

See Also
--------
dict.update : Similar method for dictionaries.
DataFrame.merge : For column(s)-on-column(s) operations.

Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame's length does not increase as a result of the update,
only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  y
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e', 'f'], name='B')
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

If `other` contains NaNs the corresponding values are not updated
in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400., 500., 600.]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0

Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé