\n",
475 | "
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) -> None
/usr/local/lib/python3.10/dist-packages/pandas/core/frame.pyPrint a concise summary of a DataFrame.\n",
478 | "\n",
479 | "This method prints information about a DataFrame including\n",
480 | "the index dtype and columns, non-null values and memory usage.\n",
481 | "\n",
482 | "Parameters\n",
483 | "----------\n",
484 | "verbose : bool, optional\n",
485 | " Whether to print the full summary. By default, the setting in\n",
486 | " ``pandas.options.display.max_info_columns`` is followed.\n",
487 | "buf : writable buffer, defaults to sys.stdout\n",
488 | " Where to send the output. By default, the output is printed to\n",
489 | " sys.stdout. Pass a writable buffer if you need to further process\n",
490 | " the output.\n",
491 | "max_cols : int, optional\n",
492 | " When to switch from the verbose to the truncated output. If the\n",
493 | " DataFrame has more than `max_cols` columns, the truncated output\n",
494 | " is used. By default, the setting in\n",
495 | " ``pandas.options.display.max_info_columns`` is used.\n",
496 | "memory_usage : bool, str, optional\n",
497 | " Specifies whether total memory usage of the DataFrame\n",
498 | " elements (including the index) should be displayed. By default,\n",
499 | " this follows the ``pandas.options.display.memory_usage`` setting.\n",
500 | "\n",
501 | " True always show memory usage. False never shows memory usage.\n",
502 | " A value of 'deep' is equivalent to "True with deep introspection".\n",
503 | " Memory usage is shown in human-readable units (base-2\n",
504 | " representation). Without deep introspection a memory estimation is\n",
505 | " made based in column dtype and number of rows assuming values\n",
506 | " consume the same memory amount for corresponding dtypes. With deep\n",
507 | " memory introspection, a real memory usage calculation is performed\n",
508 | " at the cost of computational resources. See the\n",
509 | " :ref:`Frequently Asked Questions <df-memory-usage>` for more\n",
510 | " details.\n",
511 | "show_counts : bool, optional\n",
512 | " Whether to show the non-null counts. By default, this is shown\n",
513 | " only if the DataFrame is smaller than\n",
514 | " ``pandas.options.display.max_info_rows`` and\n",
515 | " ``pandas.options.display.max_info_columns``. A value of True always\n",
516 | " shows the counts, and False never shows the counts.\n",
517 | "\n",
518 | "Returns\n",
519 | "-------\n",
520 | "None\n",
521 | " This method prints a summary of a DataFrame and returns None.\n",
522 | "\n",
523 | "See Also\n",
524 | "--------\n",
525 | "DataFrame.describe: Generate descriptive statistics of DataFrame\n",
526 | " columns.\n",
527 | "DataFrame.memory_usage: Memory usage of DataFrame columns.\n",
528 | "\n",
529 | "Examples\n",
530 | "--------\n",
531 | ">>> int_values = [1, 2, 3, 4, 5]\n",
532 | ">>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']\n",
533 | ">>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]\n",
534 | ">>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,\n",
535 | "... "float_col": float_values})\n",
536 | ">>> df\n",
537 | " int_col text_col float_col\n",
538 | "0 1 alpha 0.00\n",
539 | "1 2 beta 0.25\n",
540 | "2 3 gamma 0.50\n",
541 | "3 4 delta 0.75\n",
542 | "4 5 epsilon 1.00\n",
543 | "\n",
544 | "Prints information of all columns:\n",
545 | "\n",
546 | ">>> df.info(verbose=True)\n",
547 | "<class 'pandas.core.frame.DataFrame'>\n",
548 | "RangeIndex: 5 entries, 0 to 4\n",
549 | "Data columns (total 3 columns):\n",
550 | " # Column Non-Null Count Dtype\n",
551 | "--- ------ -------------- -----\n",
552 | " 0 int_col 5 non-null int64\n",
553 | " 1 text_col 5 non-null object\n",
554 | " 2 float_col 5 non-null float64\n",
555 | "dtypes: float64(1), int64(1), object(1)\n",
556 | "memory usage: 248.0+ bytes\n",
557 | "\n",
558 | "Prints a summary of columns count and its dtypes but not per column\n",
559 | "information:\n",
560 | "\n",
561 | ">>> df.info(verbose=False)\n",
562 | "<class 'pandas.core.frame.DataFrame'>\n",
563 | "RangeIndex: 5 entries, 0 to 4\n",
564 | "Columns: 3 entries, int_col to float_col\n",
565 | "dtypes: float64(1), int64(1), object(1)\n",
566 | "memory usage: 248.0+ bytes\n",
567 | "\n",
568 | "Pipe output of DataFrame.info to buffer instead of sys.stdout, get\n",
569 | "buffer content and writes to a text file:\n",
570 | "\n",
571 | ">>> import io\n",
572 | ">>> buffer = io.StringIO()\n",
573 | ">>> df.info(buf=buffer)\n",
574 | ">>> s = buffer.getvalue()\n",
575 | ">>> with open("df_info.txt", "w",\n",
576 | "... encoding="utf-8") as f: # doctest: +SKIP\n",
577 | "... f.write(s)\n",
578 | "260\n",
579 | "\n",
580 | "The `memory_usage` parameter allows deep introspection mode, specially\n",
581 | "useful for big DataFrames and fine-tune memory optimization:\n",
582 | "\n",
583 | ">>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)\n",
584 | ">>> df = pd.DataFrame({\n",
585 | "... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),\n",
586 | "... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),\n",
587 | "... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)\n",
588 | "... })\n",
589 | ">>> df.info()\n",
590 | "<class 'pandas.core.frame.DataFrame'>\n",
591 | "RangeIndex: 1000000 entries, 0 to 999999\n",
592 | "Data columns (total 3 columns):\n",
593 | " # Column Non-Null Count Dtype\n",
594 | "--- ------ -------------- -----\n",
595 | " 0 column_1 1000000 non-null object\n",
596 | " 1 column_2 1000000 non-null object\n",
597 | " 2 column_3 1000000 non-null object\n",
598 | "dtypes: object(3)\n",
599 | "memory usage: 22.9+ MB\n",
600 | "\n",
601 | ">>> df.info(memory_usage='deep')\n",
602 | "<class 'pandas.core.frame.DataFrame'>\n",
603 | "RangeIndex: 1000000 entries, 0 to 999999\n",
604 | "Data columns (total 3 columns):\n",
605 | " # Column Non-Null Count Dtype\n",
606 | "--- ------ -------------- -----\n",
607 | " 0 column_1 1000000 non-null object\n",
608 | " 1 column_2 1000000 non-null object\n",
609 | " 2 column_3 1000000 non-null object\n",
610 | "dtypes: object(3)\n",
611 | "memory usage: 165.9 MB\n",
612 | " \n",
631 | "