diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 85bb47485a2e7..57c315af09e8d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2326,9 +2326,116 @@ def to_html( ) # ---------------------------------------------------------------------- + @Substitution( + klass="DataFrame", + type_sub=" and columns", + max_cols_sub=( + """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. + """ + ), + examples_sub=( + """ + >>> 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) + + 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) + + 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: # doctest: +SKIP + ... 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() + + 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') + + 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: 188.8 MB""" + ), + see_also_sub=( + """ + DataFrame.describe: Generate descriptive statistics of DataFrame + columns. + DataFrame.memory_usage: Memory usage of DataFrame columns.""" + ), + ) @doc(info) def info( - self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None + self, + verbose: Optional[bool] = None, + buf: Optional[IO[str]] = None, + max_cols: Optional[int] = None, + memory_usage: Optional[Union[bool, str]] = None, + null_counts: Optional[bool] = None, ) -> None: return info(self, verbose, buf, max_cols, memory_usage, null_counts) diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py index 7b5e553cf394e..d68a1fdde8da9 100644 --- a/pandas/io/formats/info.py +++ b/pandas/io/formats/info.py @@ -1,7 +1,10 @@ import sys +from typing import IO, Optional, Union from pandas._config import get_option +from pandas._typing import FrameOrSeries + from pandas.io.formats import format as fmt from pandas.io.formats.printing import pprint_thing @@ -11,18 +14,23 @@ def _put_str(s, space): def info( - data, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None + data: FrameOrSeries, + verbose: Optional[bool] = None, + buf: Optional[IO[str]] = None, + max_cols: Optional[int] = None, + memory_usage: Optional[Union[bool, str]] = None, + null_counts: Optional[bool] = None, ) -> None: """ - Print a concise summary of a DataFrame. + Print a concise summary of a %(klass)s. - This method prints information about a DataFrame including - the index dtype and column dtypes, non-null values and memory usage. + This method prints information about a %(klass)s including + the index dtype%(type_sub)s, non-null values and memory usage. Parameters ---------- - data : DataFrame - DataFrame to print information about. + data : %(klass)s + %(klass)s to print information about. verbose : bool, optional Whether to print the full summary. By default, the setting in ``pandas.options.display.max_info_columns`` is followed. @@ -30,13 +38,9 @@ def info( 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. + %(max_cols_sub)s memory_usage : bool, str, optional - Specifies whether total memory usage of the DataFrame + Specifies whether total memory usage of the %(klass)s elements (including the index) should be displayed. By default, this follows the ``pandas.options.display.memory_usage`` setting. @@ -50,7 +54,7 @@ def info( at the cost of computational resources. null_counts : bool, optional Whether to show the non-null counts. By default, this is shown - only if the frame is smaller than + only if the %(klass)s 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. @@ -58,97 +62,15 @@ def info( Returns ------- None - This method prints a summary of a DataFrame and returns None. + This method prints a summary of a %(klass)s and returns None. See Also -------- - DataFrame.describe: Generate descriptive statistics of DataFrame - columns. - DataFrame.memory_usage: Memory usage of DataFrame columns. + %(see_also_sub)s 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) - - 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) - - 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: # doctest: +SKIP - ... 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() - - 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') - - 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: 188.8 MB + %(examples_sub)s """ if buf is None: # pragma: no cover buf = sys.stdout