pandas.Series.info#

Series.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=True)[source]#

Print a concise summary of a Series.

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

Added in version 1.4.0.

Parameters:
verbosebool, optional

Whether to print the full summary. By default, the setting in pandas.options.display.max_info_columns is followed.

bufwritable 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.

max_colsint, optional

Unused, exists only for compatibility with DataFrame.info.

memory_usagebool, 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 Frequently Asked Questions for more details.

show_countsbool, 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.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.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:
...     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.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.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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