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421Published 2019Subjects: “...Data mining Computer programs....”
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434by Sun, QimingTable of Contents: “...1.5.3 Magics -- 1.5.4 Remote execution -- Summary -- References -- 2 Data processing -- 2.1 Vectorized data processing with NumPy -- 2.1.1 The basics of NumPy -- 2.1.2 Universal functions (ufunc) -- 2.1.3 Inplace operations -- 2.1.4 Broadcasting -- 2.1.5 Fancy indexing -- 2.1.6 Mask array -- Indexing a dense array -- Indexing a sparse array -- Randomly distributed mask array -- Indexing a high-dimensional array -- 2.1.7 Data structure of NumPy ndarray -- 2.1.7.1 The strides attribute for flexible array structure -- 2.1.7.2 C-contiguous and F-contiguous storage -- 2.1.8 Array views -- Slicing an array -- NumPy functions that return a view of the input array -- Calling np.ndarray() with the keyword argument buffer -- 2.1.9 The reshape function -- 2.2 Data types in NumPy -- 2.2.1 Type casting -- 2.2.2 Scalar type and zero-dimensional array -- 2.2.3 Infinity ( inf ) and not-a-number ( nan ) -- 2.2.4 Data with high precision -- 2.2.5 Structured array -- 2.3 Data with labels: Pandas -- 2.3.1 Pandas data objects -- 2.3.1.1 Series -- 2.3.1.2 DataFrame -- 2.3.2 Broadcasting -- 2.3.3 Indexing -- 2.3.3.1 Location-based indexing -- 2.3.3.2 Label-based indexing -- 2.3.3.3 Attribute-based indexing -- 2.3.3.4 Indexing elements in DataFrame -- 2.3.4 query and eval methods -- 2.3.5 Altering structure of DataFrame -- 2.3.5.1 Changing axes labels -- 2.3.5.2 Inserting or removing rows and columns -- 2.3.5.3 Reorganizing rows and columns -- 2.3.6 Data types -- 2.3.7 Missing data -- 2.3.8 Grouping and aggregation -- 2.3.9 View and copy -- Summary -- References -- 3 Visualization -- 3.1 Matplotlib -- 3.2 Pandas visualization -- 3.3 Mayavi for 3D plotting -- 3.4 Quantum chemistry visualization -- 3.4.1 Jinja template -- Expression -- Control statements -- Whitespace control -- 3.4.2 Molden format -- 3.4.3 Cube format -- Summary -- References -- 4 Scientific computing tools....”
Published 2025
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435by Sun, QimingTable of Contents: “...1.5.3 Magics -- 1.5.4 Remote execution -- Summary -- References -- 2 Data processing -- 2.1 Vectorized data processing with NumPy -- 2.1.1 The basics of NumPy -- 2.1.2 Universal functions (ufunc) -- 2.1.3 Inplace operations -- 2.1.4 Broadcasting -- 2.1.5 Fancy indexing -- 2.1.6 Mask array -- Indexing a dense array -- Indexing a sparse array -- Randomly distributed mask array -- Indexing a high-dimensional array -- 2.1.7 Data structure of NumPy ndarray -- 2.1.7.1 The strides attribute for flexible array structure -- 2.1.7.2 C-contiguous and F-contiguous storage -- 2.1.8 Array views -- Slicing an array -- NumPy functions that return a view of the input array -- Calling np.ndarray() with the keyword argument buffer -- 2.1.9 The reshape function -- 2.2 Data types in NumPy -- 2.2.1 Type casting -- 2.2.2 Scalar type and zero-dimensional array -- 2.2.3 Infinity ( inf ) and not-a-number ( nan ) -- 2.2.4 Data with high precision -- 2.2.5 Structured array -- 2.3 Data with labels: Pandas -- 2.3.1 Pandas data objects -- 2.3.1.1 Series -- 2.3.1.2 DataFrame -- 2.3.2 Broadcasting -- 2.3.3 Indexing -- 2.3.3.1 Location-based indexing -- 2.3.3.2 Label-based indexing -- 2.3.3.3 Attribute-based indexing -- 2.3.3.4 Indexing elements in DataFrame -- 2.3.4 query and eval methods -- 2.3.5 Altering structure of DataFrame -- 2.3.5.1 Changing axes labels -- 2.3.5.2 Inserting or removing rows and columns -- 2.3.5.3 Reorganizing rows and columns -- 2.3.6 Data types -- 2.3.7 Missing data -- 2.3.8 Grouping and aggregation -- 2.3.9 View and copy -- Summary -- References -- 3 Visualization -- 3.1 Matplotlib -- 3.2 Pandas visualization -- 3.3 Mayavi for 3D plotting -- 3.4 Quantum chemistry visualization -- 3.4.1 Jinja template -- Expression -- Control statements -- Whitespace control -- 3.4.2 Molden format -- 3.4.3 Cube format -- Summary -- References -- 4 Scientific computing tools....”
Published 2025
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