Permit any Mapping as source of DataFrame

[mrkn]

This is a proof-of-concept patch to accept a Mapping to create a DataFrame.
Applying this change makes pd.DataFrame(Dict("a" => [1, 2, 3], "b" => [4, 5, 6])) in Julia work.

julia> df = pd.DataFrame(Dict("a" => [1, 2, 3], "b" => [4, 5, 6]))
Python:
   b  a
0  4  1
1  5  2
2  6  3

There is no performance degradation.

$ asv continuous -E virtualenv -b ^frame_ctor.FromDict origin/main HEAD
· Creating environments
· Discovering benchmarks
·· Uninstalling from virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
·· Installing 684a22fb <support_mapping_in_dataframe> into virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
· Running 16 total benchmarks (2 commits * 1 environments * 8 benchmarks)
[ 0.00%] · For pandas commit 2aa155ae <main> (round 1/2):
[ 0.00%] ·· Building for virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
[ 0.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[ 3.12%] ··· Running (frame_ctor.FromDicts.time_dict_of_categoricals--)........
[25.00%] · For pandas commit 684a22fb <support_mapping_in_dataframe> (round 1/2):
[25.00%] ·· Building for virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
[25.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[28.12%] ··· Running (frame_ctor.FromDicts.time_dict_of_categoricals--)........
[50.00%] · For pandas commit 684a22fb <support_mapping_in_dataframe> (round 2/2):
[50.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[53.12%] ··· frame_ctor.FromDicts.time_dict_of_categoricals                                327±8μs
[56.25%] ··· frame_ctor.FromDicts.time_list_of_dict                                    16.8±0.09ms
[59.38%] ··· frame_ctor.FromDicts.time_nested_dict                                      16.1±0.2ms
[62.50%] ··· frame_ctor.FromDicts.time_nested_dict_columns                              16.3±0.3ms
[65.62%] ··· frame_ctor.FromDicts.time_nested_dict_index                                13.2±0.1ms
[68.75%] ··· frame_ctor.FromDicts.time_nested_dict_index_columns                       12.9±0.08ms
[71.88%] ··· frame_ctor.FromDicts.time_nested_dict_int64                                29.1±0.1ms
[75.00%] ··· frame_ctor.FromDictwithTimestamp.time_dict_with_timestamp_offsets                  ok
[75.00%] ··· ======== ============
              offset
             -------- ------------
              <Nano>   7.97±0.1ms
              <Hour>   10.9±0.2ms
             ======== ============

[75.00%] · For pandas commit 2aa155ae <main> (round 2/2):
[75.00%] ·· Building for virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
[75.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[78.12%] ··· frame_ctor.FromDicts.time_dict_of_categoricals                                330±2μs
[81.25%] ··· frame_ctor.FromDicts.time_list_of_dict                                     17.2±0.3ms
[84.38%] ··· frame_ctor.FromDicts.time_nested_dict                                     16.2±0.08ms
[87.50%] ··· frame_ctor.FromDicts.time_nested_dict_columns                              16.2±0.1ms
[90.62%] ··· frame_ctor.FromDicts.time_nested_dict_index                                13.2±0.2ms
[93.75%] ··· frame_ctor.FromDicts.time_nested_dict_index_columns                        13.5±0.2ms
[96.88%] ··· frame_ctor.FromDicts.time_nested_dict_int64                                29.7±0.3ms
[100.00%] ··· frame_ctor.FromDictwithTimestamp.time_dict_with_timestamp_offsets                  ok
[100.00%] ··· ======== =============
               offset
              -------- -------------
               <Nano>   8.16±0.07ms
               <Hour>   11.2±0.06ms
              ======== =============


BENCHMARKS NOT SIGNIFICANTLY CHANGED.