. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3023 5 Release notes 3025 5.1 Version 1.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3025 1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa (continues on next page) 2.2. Guides 191 pandas: 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 In the example above, we inserted

3.10 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.5 v0.20.1 (May 5, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (GH16014) 1.4.3.10 Other • Bug in DataFrame.drop() with an empty-list with non-unique indices (GH16270) 1.5 v0.20.1 (May 5, 2017) This is a major release from 0.19.2 and includes a number of API changes, deprecations Partial String Indexing Changes – Concat of different float dtypes will not automatically upcast 1.5. v0.20.1 (May 5, 2017) 37 pandas: powerful Python data analysis toolkit, Release 0.21.1 – Pandas

What’s new in 1.0.0 (January 29, 2020) pandas: powerful Python data analysis toolkit, Release 1.0.0 1.5 Backwards incompatible API changes 1.5.1 Avoid using names from MultiIndex.levels As part of a larger 3)]) Out[2]: IntervalArray([(0, 1], (2, 3]], closed='right', dtype='interval[int64]') pandas 1.0.0 1.5. Backwards incompatible API changes 9 pandas: powerful Python data analysis toolkit, Release 1.0.0 -------------- ----- 0 int_col 3 non-null int64 1 text_col 3 non-null object (continues on next page) 1.5. Backwards incompatible API changes 11 pandas: powerful Python data analysis toolkit, Release 1.0

the previously deprecated convert keyword argument in Series.take() and DataFrame. take() (GH17352) 1.5 Performance improvements • Significant speedup in SparseArray initialization that benefits most operations are preserved across columns for DataFrames). For example, In [252]: df_orig = pd.DataFrame([[1, 1.5]], columns=['int', 'float']) In [253]: df_orig.dtypes Out[253]: int int64 float float64 dtype: object object In [254]: row = next(df_orig.iterrows())[1] In [255]: row Out[255]: int 1.0 float 1.5 Name: 0, dtype: float64 All values in row, returned as a Series, are now upcasted to floats, also the original

the previously deprecated convert keyword argument in Series.take() and DataFrame. take() (GH17352) 1.5 Performance improvements • Significant speedup in SparseArray initialization that benefits most operations are preserved across columns for DataFrames). For example, In [252]: df_orig = pd.DataFrame([[1, 1.5]], columns=['int', 'float']) In [253]: df_orig.dtypes Out[253]: int int64 float float64 dtype: object object In [254]: row = next(df_orig.iterrows())[1] In [255]: row Out[255]: int 1.0 float 1.5 Name: 0, dtype: float64 All values in row, returned as a Series, are now upcasted to floats, also the original

1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa In [79]: (iris.assign(sepal_ratio=iris['SepalWidth'] 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 In the example above, we inserted 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 assign always returns a copy of the

1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa In [84]: iris.assign(sepal_ratio=iris["SepalWidth"] 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 In the example above, we inserted 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 assign always returns a copy of the

1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa In [84]: iris.assign(sepal_ratio=iris["SepalWidth"] 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 In the example above, we inserted 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 assign always returns a copy of the

1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa In [84]: iris.assign(sepal_ratio=iris["SepalWidth"] 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 In the example above, we inserted 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 assign always returns a copy of the

1 3.5 1.4 0.2 Iris-setosa 1 4.9 3.0 1.4 0.2 Iris-setosa 2 4.7 3.2 1.3 0.2 Iris-setosa 3 4.6 3.1 1.5 0.2 Iris-setosa 4 5.0 3.6 1.4 0.2 Iris-setosa In [84]: iris.assign(sepal_ratio=iris["SepalWidth"] 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 In the example above, we inserted 686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3 4.6 3.1 1.5 0.2 Iris-setosa 0.673913 4 5.0 3.6 1.4 0.2 Iris-setosa 0.720000 assign always returns a copy of the