sixit:: 11 “…with a good deal of work you may be able to coax exactly the same floating point results out of different compilers or different machine architectures” - Glenn Fiedler “If we are talking practicabilities simulations or replays will not possible [sic].” - Most upvoted answer on StackOverflow (by AttackingHobo)Out of the Box Solution. It should Just Work 1 What are We Trying to Achieve_ Try To Be Consistent Across screw-ups — proverb Assumption: ‘;’ prohibits reordering around it 2 Reality: FMA over ‘;’ under 2 out of 3 major compilers Analysis revealed that even within WG21 there is no obvious consensus about

WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. {{ header }} 3.2 pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy analysis toolkit, Release 0.25.3 In [5]: dates = pd.date_range('20130101', periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06']

* n, beta, A.get_data(), inc_x_y, A.get_data(), inc_x_y); } T First element of intermediate is out of cache1 1 1 1 1 1 1 1 1 1 a simple GER, our user can directly use these entations. er hand subsetsFern! 49Fern! 50 manya227 June 2024 Pipeline pipeline({ vadd(a, b, len, out_1), vadd(out_1, c, len, out_2), }); pipeline.constructPipeline(); pipeline = pipeline.finalize(); void my_fused_impl(const Array out_2, int64_t len){ int64_t x2 = 0; int64_t x0 = x2; int64_t len1 = len3; Array out_1_q = array_alloc(x0, len1); for(int64 t x2 = out 2 idx; x2 < out 2 idx + out 2 size;Fern

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 5.12 Getting Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 5.12.1 CSV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 7.9 Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450 Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854 19.13 Representing out-of-bounds spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855 19.14 Time

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 5.12 Getting Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 5.12.1 CSV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 7.9 Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857 19.13 Representing out-of-bounds spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 858 19.14 Time

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 5.12 Getting Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 5.12.1 CSV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478 7.9 Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 890 19.13 Representing Out-of-Bounds Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891 19.14 Time

apply (GH498) In [902]: df = DataFrame(randn(10, 4)) In [903]: df.apply(lambda x: x.describe()) Out[903]: 0 1 2 3 count 10.000000 10.000000 10.000000 10.000000 mean -0.556258 -0.268695 -0.215066 0 20, 2)) In [905]: s Out[905]: 0 0.324022 2 -0.983597 4 0.856254 6 1.396004 8 -0.374833 10 -0.883614 12 0.251235 14 0.280914 16 -1.374563 18 0.066904 In [906]: s[0] Out[906]: 0.32402202262891427 32402202262891427 In [907]: s[2] Out[907]: -0.98359726644878798 In [908]: s[4] Out[908]: 0.85625373985124742 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the

apply (GH498) In [903]: df = DataFrame(randn(10, 4)) In [904]: df.apply(lambda x: x.describe()) Out[904]: 0 1 2 3 count 10.000000 10.000000 10.000000 10.000000 mean -0.556258 -0.268695 -0.215066 0 indexing. Here is an example: In [905]: s = Series(randn(10), index=range(0, 20, 2)) In [906]: s Out[906]: 0 0.324022 2 -0.983597 1.3. v.0.7.0 (February 9, 2012) 5 pandas: powerful Python data analysis 0.280914 16 -1.374563 18 0.066904 In [907]: s[0] Out[907]: 0.32402202262891427 In [908]: s[2] Out[908]: -0.98359726644878798 In [909]: s[4] Out[909]: 0.85625373985124742 This is all exactly identical

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 6.12 Getting Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 6.13 Gotchas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 8.9 Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625 20.11 Representing out-of-bounds spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626 20.12 Time

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.2.1 Trying out pandas, no installation required! . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.2.2 Installing . . . . . . . . . . . . . . . . . . . . . 339 Running the vbench performance test suite (phasing out) . . . . . . . . . . . . . . . . . . . . 340 3.5.3 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 6.12 Getting Data In/Out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 6.12.1 CSV