pandas.Series.info — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

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.

Parameters:

verbosebool, optional

Whether to print the full summary. By default, the setting inpandas.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 theFrequently 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 thanpandas.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.

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"], 106) s = pd.Series(np.random.choice(["a", "b", "c"], 106)) 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