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Classe « Series »

Méthode pandas.Series.info

Signature de la méthode info

def info(self, verbose: 'bool | None' = None, buf: 'IO[str] | None' = None, max_cols: 'int | None' = None, memory_usage: 'bool | str | None' = None, show_counts: 'bool' = True) -> 'None' 

Description

help(Series.info)

Print a concise summary of a Series.

This method prints information about a Series including
the index dtype, non-null values and memory usage.

.. versionadded:: 1.4.0

Parameters
----------
verbose : bool, optional
    Whether to print the full summary. By default, the setting in
    ``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
    Where to send the output. By default, the output is printed to
    sys.stdout. Pass a writable buffer if you need to further process
    the output.

memory_usage : bool, str, optional
    Specifies whether total memory usage of the Series
    elements (including the index) should be displayed. By default,
    this follows the ``pandas.options.display.memory_usage`` setting.

    True always show memory usage. False never shows memory usage.
    A value of 'deep' is equivalent to "True with deep introspection".
    Memory usage is shown in human-readable units (base-2
    representation). Without deep introspection a memory estimation is
    made based in column dtype and number of rows assuming values
    consume the same memory amount for corresponding dtypes. With deep
    memory introspection, a real memory usage calculation is performed
    at the cost of computational resources. See the
    :ref:`Frequently Asked Questions <df-memory-usage>` for more
    details.
show_counts : bool, optional
    Whether to show the non-null counts. By default, this is shown
    only if the DataFrame is smaller than
    ``pandas.options.display.max_info_rows`` and
    ``pandas.options.display.max_info_columns``. A value of True always
    shows the counts, and False never shows the counts.

Returns
-------
None
    This method prints a summary of a Series and returns None.

See Also
--------
Series.describe: Generate descriptive statistics of Series.
Series.memory_usage: Memory usage of Series.

Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> s = pd.Series(text_values, index=int_values)
>>> s.info()
<class 'pandas.core.series.Series'>
Index: 5 entries, 1 to 5
Series name: None
Non-Null Count  Dtype
--------------  -----
5 non-null      object
dtypes: object(1)
memory usage: 80.0+ bytes

Prints a summary excluding information about its values:

>>> s.info(verbose=False)
<class 'pandas.core.series.Series'>
Index: 5 entries, 1 to 5
dtypes: object(1)
memory usage: 80.0+ bytes

Pipe output of Series.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> s.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  # doctest: +SKIP
...     f.write(s)
260

The `memory_usage` parameter allows deep introspection mode, specially
useful for big Series and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6))
>>> s.info()
<class 'pandas.core.series.Series'>
RangeIndex: 1000000 entries, 0 to 999999
Series name: None
Non-Null Count    Dtype
--------------    -----
1000000 non-null  object
dtypes: object(1)
memory usage: 7.6+ MB

>>> s.info(memory_usage='deep')
<class 'pandas.core.series.Series'>
RangeIndex: 1000000 entries, 0 to 999999
Series name: None
Non-Null Count    Dtype
--------------    -----
1000000 non-null  object
dtypes: object(1)
memory usage: 55.3 MB

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