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-null values and memory usage.
New 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.
- 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
andpandas.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'> Int64Index: 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'> Int64Index: 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.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