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# pandas.DataFrame.info

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

  Print a concise summary of a DataFrame.

  This method prints information about a DataFrame including the index dtype and columns, non-null 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

      When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used. By default, the setting in `pandas.options.display.max_info_columns` is used.

    memory_usage bool, str, optional

      Specifies whether total memory usage of the DataFrame 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 DataFrame and returns None.

  See also

  `DataFrame.describe`

    Generate descriptive statistics of DataFrame columns.

  `DataFrame.memory_usage`

    Memory usage of DataFrame columns.

  Examples

      >>> int_values = [1, 2, 3, 4, 5]
      >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
      >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
      >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
      ...                   "float_col": float_values})
      >>> df
          int_col text_col  float_col
      0        1    alpha       0.00
      1        2     beta       0.25
      2        3    gamma       0.50
      3        4    delta       0.75
      4        5  epsilon       1.00

  Prints information of all columns:

      >>> df.info(verbose=True)
      <class 'pandas.core.frame.DataFrame'>
      RangeIndex: 5 entries, 0 to 4
      Data columns (total 3 columns):
       #   Column     Non-Null Count  Dtype
      ---  ------     --------------  -----
       0   int_col    5 non-null      int64
       1   text_col   5 non-null      object
       2   float_col  5 non-null      float64
      dtypes: float64(1), int64(1), object(1)
      memory usage: 248.0+ bytes

  Prints a summary of columns count and its dtypes but not per column information:

      >>> df.info(verbose=False)
      <class 'pandas.core.frame.DataFrame'>
      RangeIndex: 5 entries, 0 to 4
      Columns: 3 entries, int_col to float_col
      dtypes: float64(1), int64(1), object(1)
      memory usage: 248.0+ bytes

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

      >>> import io
      >>> buffer = io.StringIO()
      >>> df.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 DataFrames and fine-tune memory optimization:

      >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
      >>> df = pd.DataFrame({
      ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
      ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
      ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
      ... })
      >>> df.info()
      <class 'pandas.core.frame.DataFrame'>
      RangeIndex: 1000000 entries, 0 to 999999
      Data columns (total 3 columns):
       #   Column    Non-Null Count    Dtype
      ---  ------    --------------    -----
       0   column_1  1000000 non-null  object
       1   column_2  1000000 non-null  object
       2   column_3  1000000 non-null  object
      dtypes: object(3)
      memory usage: 22.9+ MB

      >>> df.info(memory_usage='deep')
      <class 'pandas.core.frame.DataFrame'>
      RangeIndex: 1000000 entries, 0 to 999999
      Data columns (total 3 columns):
       #   Column    Non-Null Count    Dtype
      ---  ------    --------------    -----
       0   column_1  1000000 non-null  object
       1   column_2  1000000 non-null  object
       2   column_3  1000000 non-null  object
      dtypes: object(3)
      memory usage: 165.9 MB