the petting zoo. If we revisit the plot in Figure 4-1 with the newly assigned labels in the third column of Table 4-2, we can see a pattern. It is possible to linearly separate3 the data points belonging with manual embeddings. One example of an automated embedding generation technique is the word2vec family of algorithms6 (apart from others like GloVe7) which can learn embeddings for word tokens for NLP result for any other permutation of the words in the context. Hence the name Bag of Words for this family of model architectures. In practice, you need not be limited to this architecture for solving the

IGuestOSType::familyId Guest OS family identifier string. 6.100.1.2 familyDescription (read-only) wstring IGuestOSType::familyDescription Human readable description of the guest OS family. 6.100.1.3 id (read-only) vdi) NOT ... CurState (D3->B.vdi) The first column is the virtual machine configuration before the base hard disk B.vdi is at- tached, the second column shows the machine after this hard disk is attached (read-only) wstring IVirtualBox::guestOSFamilies[] Array of guest OS family identifiers known to this VirtualBox installation. The guest OS family identifier corrresponds to the IGuestOSType::familyId attribute

Figure 3-11: The image on the left is a cut-mix of turtle (3%) and tortoise (97%) images in the center column and the top-right image is their average mix. Bottom-right is a mixup of turtle 8 When the samples For example, the top-right image is an average mix of turtle and tortoise images in the center column. The average mixing is a label mixing technique that averages the sample images to produce the mixed when making a big decision (a big purchase or an important life event). We discuss with friends and family to decide whether it is a good decision. We rely on their perspectives and life experiences to guide

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.20 Looking up values by index/column labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.21 Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 2.8.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574 2.9 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.20 Looking up values by index/column labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.21 Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 2.8.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574 2.9 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 2.9 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 2.9 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 2.5.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 2.6 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 2.5.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 2.6 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 3.5.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 3.6 Working with text data array) (GH31441) • Fixed regression in DataFrame.groupby() whereby taking the minimum or maximum of a column with period dtype would raise a TypeError. (GH31471) • Fixed regression in DataFrame.groupby() with DataFrame.loc() and DataFrame.iloc() when selecting a row containing a single datetime64 or timedelta64 column (GH31649) • Fixed regression where setting pd.options.display.max_colwidth was not accepting negative