\n",
+ "
pandas.core.frame.DataFrame.info
def info(verbose: bool | None=None, buf: WriteBuffer[str] | None=None, max_cols: int | None=None, memory_usage: bool | str | None=None, show_counts: bool | None=None, null_counts: bool | None=None) -> None
/usr/local/lib/python3.10/dist-packages/pandas/core/frame.pyPrint a concise summary of a DataFrame.\n",
+ "\n",
+ "This method prints information about a DataFrame including\n",
+ "the index dtype and columns, non-null values and memory usage.\n",
+ "\n",
+ "Parameters\n",
+ "----------\n",
+ "verbose : bool, optional\n",
+ " Whether to print the full summary. By default, the setting in\n",
+ " ``pandas.options.display.max_info_columns`` is followed.\n",
+ "buf : writable buffer, defaults to sys.stdout\n",
+ " Where to send the output. By default, the output is printed to\n",
+ " sys.stdout. Pass a writable buffer if you need to further process\n",
+ " the output. max_cols : int, optional\n",
+ " When to switch from the verbose to the truncated output. If the\n",
+ " DataFrame has more than `max_cols` columns, the truncated output\n",
+ " is used. By default, the setting in\n",
+ " ``pandas.options.display.max_info_columns`` is used.\n",
+ "memory_usage : bool, str, optional\n",
+ " Specifies whether total memory usage of the DataFrame\n",
+ " elements (including the index) should be displayed. By default,\n",
+ " this follows the ``pandas.options.display.memory_usage`` setting.\n",
+ "\n",
+ " True always show memory usage. False never shows memory usage.\n",
+ " A value of 'deep' is equivalent to "True with deep introspection".\n",
+ " Memory usage is shown in human-readable units (base-2\n",
+ " representation). Without deep introspection a memory estimation is\n",
+ " made based in column dtype and number of rows assuming values\n",
+ " consume the same memory amount for corresponding dtypes. With deep\n",
+ " memory introspection, a real memory usage calculation is performed\n",
+ " at the cost of computational resources. See the\n",
+ " :ref:`Frequently Asked Questions <df-memory-usage>` for more\n",
+ " details.\n",
+ "show_counts : bool, optional\n",
+ " Whether to show the non-null counts. By default, this is shown\n",
+ " only if the DataFrame is smaller than\n",
+ " ``pandas.options.display.max_info_rows`` and\n",
+ " ``pandas.options.display.max_info_columns``. A value of True always\n",
+ " shows the counts, and False never shows the counts.\n",
+ "null_counts : bool, optional\n",
+ " .. deprecated:: 1.2.0\n",
+ " Use show_counts instead.\n",
+ "\n",
+ "Returns\n",
+ "-------\n",
+ "None\n",
+ " This method prints a summary of a DataFrame and returns None.\n",
+ "\n",
+ "See Also\n",
+ "--------\n",
+ "DataFrame.describe: Generate descriptive statistics of DataFrame\n",
+ " columns.\n",
+ "DataFrame.memory_usage: Memory usage of DataFrame columns.\n",
+ "\n",
+ "Examples\n",
+ "--------\n",
+ ">>> int_values = [1, 2, 3, 4, 5]\n",
+ ">>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']\n",
+ ">>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]\n",
+ ">>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,\n",
+ "... "float_col": float_values})\n",
+ ">>> df\n",
+ " int_col text_col float_col\n",
+ "0 1 alpha 0.00\n",
+ "1 2 beta 0.25\n",
+ "2 3 gamma 0.50\n",
+ "3 4 delta 0.75\n",
+ "4 5 epsilon 1.00\n",
+ "\n",
+ "Prints information of all columns:\n",
+ "\n",
+ ">>> df.info(verbose=True)\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 5 entries, 0 to 4\n",
+ "Data columns (total 3 columns):\n",
+ " # Column Non-Null Count Dtype\n",
+ "--- ------ -------------- -----\n",
+ " 0 int_col 5 non-null int64\n",
+ " 1 text_col 5 non-null object\n",
+ " 2 float_col 5 non-null float64\n",
+ "dtypes: float64(1), int64(1), object(1)\n",
+ "memory usage: 248.0+ bytes\n",
+ "\n",
+ "Prints a summary of columns count and its dtypes but not per column\n",
+ "information:\n",
+ "\n",
+ ">>> df.info(verbose=False)\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 5 entries, 0 to 4\n",
+ "Columns: 3 entries, int_col to float_col\n",
+ "dtypes: float64(1), int64(1), object(1)\n",
+ "memory usage: 248.0+ bytes\n",
+ "\n",
+ "Pipe output of DataFrame.info to buffer instead of sys.stdout, get\n",
+ "buffer content and writes to a text file:\n",
+ "\n",
+ ">>> import io\n",
+ ">>> buffer = io.StringIO()\n",
+ ">>> df.info(buf=buffer)\n",
+ ">>> s = buffer.getvalue()\n",
+ ">>> with open("df_info.txt", "w",\n",
+ "... encoding="utf-8") as f: # doctest: +SKIP\n",
+ "... f.write(s)\n",
+ "260\n",
+ "\n",
+ "The `memory_usage` parameter allows deep introspection mode, specially\n",
+ "useful for big DataFrames and fine-tune memory optimization:\n",
+ "\n",
+ ">>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)\n",
+ ">>> df = pd.DataFrame({\n",
+ "... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),\n",
+ "... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),\n",
+ "... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)\n",
+ "... })\n",
+ ">>> df.info()\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 1000000 entries, 0 to 999999\n",
+ "Data columns (total 3 columns):\n",
+ " # Column Non-Null Count Dtype\n",
+ "--- ------ -------------- -----\n",
+ " 0 column_1 1000000 non-null object\n",
+ " 1 column_2 1000000 non-null object\n",
+ " 2 column_3 1000000 non-null object\n",
+ "dtypes: object(3)\n",
+ "memory usage: 22.9+ MB\n",
+ "\n",
+ ">>> df.info(memory_usage='deep')\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 1000000 entries, 0 to 999999\n",
+ "Data columns (total 3 columns):\n",
+ " # Column Non-Null Count Dtype\n",
+ "--- ------ -------------- -----\n",
+ " 0 column_1 1000000 non-null object\n",
+ " 1 column_2 1000000 non-null object\n",
+ " 2 column_3 1000000 non-null object\n",
+ "dtypes: object(3)\n",
+ "memory usage: 165.9 MB\n",
+ " \n",
+ "