Move info and tests to their own files in IO/formats

Author: Marco Gorelli

pandas/conftest.py

@@ -112,6 +112,35 @@ def axis_series(request):
     return request.param
 
 
+@pytest.fixture
+def datetime_frame():
+    """
+    Fixture for DataFrame of floats with DatetimeIndex
+
+    Columns are ['A', 'B', 'C', 'D']
+
+                       A         B         C         D
+    2000-01-03 -1.122153  0.468535  0.122226  1.693711
+    2000-01-04  0.189378  0.486100  0.007864 -1.216052
+    2000-01-05  0.041401 -0.835752 -0.035279 -0.414357
+    2000-01-06  0.430050  0.894352  0.090719  0.036939
+    2000-01-07 -0.620982 -0.668211 -0.706153  1.466335
+    2000-01-10 -0.752633  0.328434 -0.815325  0.699674
+    2000-01-11 -2.236969  0.615737 -0.829076 -1.196106
+    ...              ...       ...       ...       ...
+    2000-02-03  1.642618 -0.579288  0.046005  1.385249
+    2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
+    2000-02-07 -2.656149 -0.601387  1.410148  0.444150
+    2000-02-08 -1.201881 -1.289040  0.772992 -1.445300
+    2000-02-09  1.377373  0.398619  1.008453 -0.928207
+    2000-02-10  0.473194 -0.636677  0.984058  0.511519
+    2000-02-11 -0.965556  0.408313 -1.312844 -0.381948
+
+    [30 rows x 4 columns]
+    """
+    return DataFrame(tm.getTimeSeriesData())
+
+
 @pytest.fixture
 def ip():
     """

pandas/core/frame.py

@@ -14,7 +14,6 @@
 import datetime
 from io import StringIO
 import itertools
-import sys
 from textwrap import dedent
 from typing import (
     IO,
@@ -131,7 +130,7 @@
 
 from pandas.io.common import get_filepath_or_buffer
 from pandas.io.formats import console, format as fmt
-from pandas.io.formats.printing import pprint_thing
+from pandas.io.formats.info import info
 import pandas.plotting
 
 if TYPE_CHECKING:
@@ -2225,282 +2224,10 @@ def to_html(
         )
 
     # ----------------------------------------------------------------------
-
     def info(
         self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None
-    ) -> None:
-        """
-        Print a concise summary of a DataFrame.
-
-        This method prints information about a DataFrame including
-        the index dtype and column dtypes, 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.
-        null_counts : bool, optional
-            Whether to show the non-null counts. By default, this is shown
-            only if the frame 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:  # 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()
-        <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: 188.8 MB
-        """
-        if buf is None:  # pragma: no cover
-            buf = sys.stdout
-
-        lines = []
-
-        lines.append(str(type(self)))
-        lines.append(self.index._summary())
-
-        if len(self.columns) == 0:
-            lines.append(f"Empty {type(self).__name__}")
-            fmt.buffer_put_lines(buf, lines)
-            return
-
-        cols = self.columns
-        col_count = len(self.columns)
-
-        # hack
-        if max_cols is None:
-            max_cols = get_option("display.max_info_columns", len(self.columns) + 1)
-
-        max_rows = get_option("display.max_info_rows", len(self) + 1)
-
-        if null_counts is None:
-            show_counts = (col_count <= max_cols) and (len(self) < max_rows)
-        else:
-            show_counts = null_counts
-        exceeds_info_cols = col_count > max_cols
-
-        def _verbose_repr():
-            lines.append(f"Data columns (total {len(self.columns)} columns):")
-
-            id_head = " # "
-            column_head = "Column"
-            col_space = 2
-
-            max_col = max(len(pprint_thing(k)) for k in cols)
-            len_column = len(pprint_thing(column_head))
-            space = max(max_col, len_column) + col_space
-
-            max_id = len(pprint_thing(col_count))
-            len_id = len(pprint_thing(id_head))
-            space_num = max(max_id, len_id) + col_space
-            counts = None
-
-            header = _put_str(id_head, space_num) + _put_str(column_head, space)
-            if show_counts:
-                counts = self.count()
-                if len(cols) != len(counts):  # pragma: no cover
-                    raise AssertionError(
-                        f"Columns must equal counts ({len(cols)} != {len(counts)})"
-                    )
-                count_header = "Non-Null Count"
-                len_count = len(count_header)
-                non_null = " non-null"
-                max_count = max(len(pprint_thing(k)) for k in counts) + len(non_null)
-                space_count = max(len_count, max_count) + col_space
-                count_temp = "{count}" + non_null
-            else:
-                count_header = ""
-                space_count = len(count_header)
-                len_count = space_count
-                count_temp = "{count}"
-
-            dtype_header = "Dtype"
-            len_dtype = len(dtype_header)
-            max_dtypes = max(len(pprint_thing(k)) for k in self.dtypes)
-            space_dtype = max(len_dtype, max_dtypes)
-            header += _put_str(count_header, space_count) + _put_str(
-                dtype_header, space_dtype
-            )
-
-            lines.append(header)
-            lines.append(
-                _put_str("-" * len_id, space_num)
-                + _put_str("-" * len_column, space)
-                + _put_str("-" * len_count, space_count)
-                + _put_str("-" * len_dtype, space_dtype)
-            )
-
-            for i, col in enumerate(self.columns):
-                dtype = self.dtypes.iloc[i]
-                col = pprint_thing(col)
-
-                line_no = _put_str(f" {i}", space_num)
-                count = ""
-                if show_counts:
-                    count = counts.iloc[i]
-
-                lines.append(
-                    line_no
-                    + _put_str(col, space)
-                    + _put_str(count_temp.format(count=count), space_count)
-                    + _put_str(dtype, space_dtype)
-                )
-
-        def _non_verbose_repr():
-            lines.append(self.columns._summary(name="Columns"))
-
-        def _sizeof_fmt(num, size_qualifier):
-            # returns size in human readable format
-            for x in ["bytes", "KB", "MB", "GB", "TB"]:
-                if num < 1024.0:
-                    return f"{num:3.1f}{size_qualifier} {x}"
-                num /= 1024.0
-            return f"{num:3.1f}{size_qualifier} PB"
-
-        if verbose:
-            _verbose_repr()
-        elif verbose is False:  # specifically set to False, not nesc None
-            _non_verbose_repr()
-        else:
-            if exceeds_info_cols:
-                _non_verbose_repr()
-            else:
-                _verbose_repr()
-
-        counts = self._data.get_dtype_counts()
-        dtypes = [f"{k[0]}({k[1]:d})" for k in sorted(counts.items())]
-        lines.append(f"dtypes: {', '.join(dtypes)}")
-
-        if memory_usage is None:
-            memory_usage = get_option("display.memory_usage")
-        if memory_usage:
-            # append memory usage of df to display
-            size_qualifier = ""
-            if memory_usage == "deep":
-                deep = True
-            else:
-                # size_qualifier is just a best effort; not guaranteed to catch
-                # all cases (e.g., it misses categorical data even with object
-                # categories)
-                deep = False
-                if "object" in counts or self.index._is_memory_usage_qualified():
-                    size_qualifier = "+"
-            mem_usage = self.memory_usage(index=True, deep=deep).sum()
-            lines.append(f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n")
-        fmt.buffer_put_lines(buf, lines)
+    ):
+        return info(self, verbose, buf, max_cols, memory_usage, null_counts)
 
     def memory_usage(self, index=True, deep=False) -> Series:
         """
@@ -8623,7 +8350,3 @@ def _from_nested_dict(data):
             new_data[col] = new_data.get(col, {})
             new_data[col][index] = v
     return new_data
-
-
-def _put_str(s, space):
-    return str(s)[:space].ljust(space)