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+ .ipynb_checkpoints
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+ * .swp
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+ {
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+ "cells" : [
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+ {
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+ "cell_type" : " markdown" ,
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+ "metadata" : {},
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+ "source" : [
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+ " # 第1章 准备工作"
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+ ]
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+ },
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+ {
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+ "cell_type" : " markdown" ,
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+ "metadata" : {},
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+ "source" : [
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+ " ## 重要的Python库"
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+ ]
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+ },
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+ {
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+ "cell_type" : " markdown" ,
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+ "metadata" : {},
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+ "source" : [
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+ " 1. NumPy\n " ,
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+ " - NumPy在数据分析方面有一个主要作用,即作为算法之间传递数据的容器,对于数值型数据,NumPy数组在存储和处理数据时要比内置的Python数据结构高效的多,此外,由低级语言(C、Fortran等)编写的库可以直接操作NumPy数组中的数据,无需进行任何数据复制工作\n " ,
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+ " 2. pandas\n " ,
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+ " - 兼具NumPy高性能的数组计算功能以及电子表格和关系型数据库(SQL等)灵活的数据处理功能\n " ,
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+ " 3. matplotlib\n " ,
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+ " 4. IPython\n " ,
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+ " 5. SciPy"
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+ ]
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+ }
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+ ],
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+ "metadata" : {
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+ "kernelspec" : {
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+ "display_name" : " Python 2" ,
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+ "language" : " python" ,
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+ "name" : " python2"
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+ },
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+ "language_info" : {
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+ "codemirror_mode" : {
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+ "name" : " ipython" ,
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+ "version" : 2
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+ },
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+ "file_extension" : " .py" ,
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+ "mimetype" : " text/x-python" ,
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+ "name" : " python" ,
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+ "nbconvert_exporter" : " python" ,
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+ "pygments_lexer" : " ipython2" ,
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+ "version" : " 2.7.10"
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+ }
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+ },
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+ "nbformat" : 4 ,
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+ "nbformat_minor" : 2
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+ }
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