Minimum Ver- sion Notes SciPy 1.7.1 Miscellaneous statistical functions numba 0.53.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.19.0 pandas-like API for N-dimensional numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

Minimum Ver- sion Notes SciPy 1.4.1 Miscellaneous statistical functions numba 0.50.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional analysis toolkit, Release 1.4.2 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

Minimum Ver- sion Notes SciPy 1.4.1 Miscellaneous statistical functions numba 0.50.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional analysis toolkit, Release 1.4.4 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

Minimum Ver- sion Notes SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional analysis toolkit, Release 1.3.2 (continued from previous page) Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

Minimum Ver- sion Notes SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional analysis toolkit, Release 1.3.3 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

Minimum Ver- sion Notes SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional analysis toolkit, Release 1.3.4 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

HTML parser for read_html (see note) matplotlib 2.2.3 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.6.0 Reading / writing for xlsx files pandas-gbq 0.12 analysis toolkit, Release 1.2.0 (continued from previous page) Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

HTML parser for read_html (see note) matplotlib 2.2.3 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.6.0 Reading / writing for xlsx files pandas-gbq 0.12 analysis toolkit, Release 1.2.3 (continued from previous page) Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.12 numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.12 numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38