\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",
"