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):
        int_col      5 non-null int64
        text_col     5 non-null object
        float_col    5 non-null float64
        dtypes: float64(1), int64(1), object(1)
        memory usage: 200.0+ 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: 200.0+ 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_1    1000000 non-null object
        column_2    1000000 non-null object
        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_1    1000000 non-null object
        column_2    1000000 non-null object
        column_3    1000000 non-null object
        dtypes: object(3)
        memory usage: 188.8 MB

def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
             null_counts=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):
        int_col      5 non-null int64
        text_col     5 non-null object
        float_col    5 non-null float64
        dtypes: float64(1), int64(1), object(1)
        memory usage: 200.0+ 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: 200.0+ 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_1    1000000 non-null object
        column_2    1000000 non-null object
        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_1    1000000 non-null object
        column_2    1000000 non-null object
        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('Empty {name}'.format(name=type(self).__name__))
            fmt.buffer_put_lines(buf, lines)
            return

        cols = 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 = ((len(self.columns) <= max_cols) and
                           (len(self) < max_rows))
        else:
            show_counts = null_counts
        exceeds_info_cols = len(self.columns) > max_cols

        def _verbose_repr():
            lines.append('Data columns (total %d columns):' %
                         len(self.columns))
            space = max(len(pprint_thing(k)) for k in self.columns) + 4
            counts = None

            tmpl = "{count}{dtype}"
            if show_counts:
                counts = self.count()
                if len(cols) != len(counts):  # pragma: no cover
                    raise AssertionError(
                        'Columns must equal counts '
                        '({cols:d} != {counts:d})'.format(
                            cols=len(cols), counts=len(counts)))
                tmpl = "{count} non-null {dtype}"

            dtypes = self.dtypes
            for i, col in enumerate(self.columns):
                dtype = dtypes.iloc[i]
                col = pprint_thing(col)

                count = ""
                if show_counts:
                    count = counts.iloc[i]

                lines.append(_put_str(col, space) + tmpl.format(count=count,
                                                                dtype=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 ("{num:3.1f}{size_q} "
                            "{x}".format(num=num, size_q=size_qualifier, x=x))
                num /= 1024.0
            return "{num:3.1f}{size_q} {pb}".format(num=num,
                                                    size_q=size_qualifier,
                                                    pb='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.get_dtype_counts()
        dtypes = ['{k}({kk:d})'.format(k=k[0], kk=k[1]) for k
                  in sorted(counts.items())]
        lines.append('dtypes: {types}'.format(types=', '.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("memory usage: {mem}\n".format(
                mem=_sizeof_fmt(mem_usage, size_qualifier)))

        fmt.buffer_put_lines(buf, lines)