3D Graphics for Dummies Significant content “borrowed” from Dan Chang @ Nintendo NTD “with permission” Chris Ryan CppCon 2021 github.com/ChrisR98008/CppCon20213 3D Graphics for Dummies4 3D Graphics for Dummies5 3D Graphics for Dummies6 3D Graphics for Dummies7 3D Graphics for Dummies8 3D Graphics for Dummies9 3D Graphics for Dummies10 3D Graphics for Dummies11 3D Graphics for Dummies12 3D 3D Graphics for Dummies13 3D Graphics for Dummies14 3D Graphics for Dummies15 3D Graphics for Dummies Winding / Right Hand Rule Fingers curled in the order of the points, thumb points up Counter clockwise

Fine-grained numpy errstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 1.6.1.10 get_dummies now returns integer dtypes . . . . . . . . . . . . . . . . . . . . . 62 1.6.1.11 Downcast values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1234 34.2.1.11 pandas.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1237 34.2.1.12 pandas.factorize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1400 34.3.14.52pandas.Series.str.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1400 34.3.15 Categorical . . . . . . .

Fine-grained numpy errstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 1.5.1.10 get_dummies now returns integer dtypes . . . . . . . . . . . . . . . . . . . . . 60 1.5.1.11 Downcast values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1232 34.2.1.11 pandas.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235 34.2.1.12 pandas.factorize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1380 34.3.14.52pandas.Series.str.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1381 34.3.15 Categorical . . . . . . .

Fine-grained numpy errstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 get_dummies now returns integer dtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Downcast values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131 pandas.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1132 pandas.factorize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1281 pandas.Series.str.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1281 35.3.15 Categorical .

Fine-grained numpy errstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 get_dummies now returns integer dtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Downcast values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134 pandas.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 pandas.factorize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1284 pandas.Series.str.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1284 35.3.15 Categorical .

Fine-grained numpy errstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 1.8.1.10 get_dummies now returns integer dtypes . . . . . . . . . . . . . . . . . . . . . 91 1.8.1.11 Downcast values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1275 34.2.1.11 pandas.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1278 34.2.1.12 pandas.factorize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1459 34.3.14.52pandas.Series.str.get_dummies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1460 34.3.15 Categorical . . . . . . .

DataFrame containing k columns of 1s and 0s using get_dummies(): In [84]: df = pd.DataFrame({'key': list('bbacab'), 'data1': range(6)}) In [85]: pd.get_dummies(df['key']) Out[85]: a b c 0 0 1 0 1 0 1 0 2 In [86]: dummies = pd.get_dummies(df['key'], prefix='key') In [87]: dummies Out[87]: key_a key_b key_c 0 0 1 0 1 0 1 0 2 1 0 0 3 0 0 1 4 1 0 0 5 0 1 0 In [88]: df[['data1']].join(dummies) Out[88]: data1 powerful Python data analysis toolkit, Release 0.25.3 (continued from previous page) In [92]: pd.get_dummies(pd.cut(values, bins)) Out[92]: (0.0, 0.2] (0.2, 0.4] (0.4, 0.6] (0.6, 0.8] (0.8, 1.0] 0 0 0 0 0

every non-int64 type would raise an erroneous MergeError (GH28870). • Better error message in get_dummies() when columns isn’t a list-like value (GH28383) • Bug in Index.join() that caused infinite recursion DataFrame containing k columns of 1s and 0s using get_dummies(): In [84]: df = pd.DataFrame({'key': list('bbacab'), 'data1': range(6)}) In [85]: pd.get_dummies(df['key']) Out[85]: a b c 0 0 1 0 1 0 1 0 2 In [86]: dummies = pd.get_dummies(df['key'], prefix='key') In [87]: dummies Out[87]: key_a key_b key_c 0 0 1 0 1 0 1 0 2 1 0 0 3 0 0 1 4 1 0 0 5 0 1 0 In [88]: df[['data1']].join(dummies) Out[88]:

instances of SparseDataFrame. This change also affects routines using concat() internally, like get_dummies(), which now returns a DataFrame in all cases (previously a SparseDataFrame was returned if all the DataFrame containing k columns of 1s and 0s using get_dummies(): In [84]: df = pd.DataFrame({'key': list('bbacab'), 'data1': range(6)}) In [85]: pd.get_dummies(df['key']) Out[85]: a b c 0 0 1 0 1 0 1 0 2 example when merging the result with the original DataFrame: In [86]: dummies = pd.get_dummies(df['key'], prefix='key') In [87]: dummies Out[87]: key_a key_b key_c 0 0 1 0 1 0 1 0 (continues on next page)

instances of SparseDataFrame. This change also affects routines using concat() internally, like get_dummies(), which now returns a DataFrame in all cases (previously a SparseDataFrame was returned if all the DataFrame containing k columns of 1s and 0s using get_dummies(): In [84]: df = pd.DataFrame({'key': list('bbacab'), 'data1': range(6)}) In [85]: pd.get_dummies(df['key']) Out[85]: a b c 0 0 1 0 1 0 1 0 2 example when merging the result with the original DataFrame: In [86]: dummies = pd.get_dummies(df['key'], prefix='key') In [87]: dummies Out[87]: key_a key_b key_c 0 0 1 0 1 0 1 0 (continues on next page)