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# pandas.Series.info

Series.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=True)

  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:
    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.

    max_cols int, optional

      Unused, exists only for compatibility with DataFrame.info.

    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 Frequently Asked Questions 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.Series'>
      Index: 5 entries, 1 to 5
      Series name: None
      Non-Null Count  Dtype
      --------------  -----
      5 non-null      str
      dtypes: str(1)
      memory usage: 106.0 bytes

  Prints a summary excluding information about its values:

      >>> s.info(verbose=False)
      <class 'pandas.Series'>
      Index: 5 entries, 1 to 5
      dtypes: str(1)
      memory usage: 106.0 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  str
      dtypes: str(1)
      memory usage: 8.6 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  str
      dtypes: str(1)
      memory usage: 8.6 MB