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id=\"适用人群\">适用人群\u003C\u002Fh2>\u003Cul>\n\u003Cli>数据分析师与数据科学家\u003C\u002Fli>\n\u003Cli>需要处理表格数据的工程师\u003C\u002Fli>\n\u003Cli>金融与统计分析人员\u003C\u002Fli>\n\u003Cli>机器学习前置的数据清洗工作\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"快速上手\">快速上手\u003C\u002Fh2>\u003Col>\n\u003Cli>安装：\u003Ccode>pip install pandas\u003C\u002Fcode> 或 \u003Ccode>conda install -c conda-forge pandas\u003C\u002Fcode>\u003C\u002Fli>\n\u003Cli>读入数据并开始分析：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-python\">import pandas as pd\n\ndf = pd.read_csv('data.csv')\nprint(df.describe())\nprint(df.groupby('category')['sales'].sum())\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>常用操作：\u003Ccode>df.head()\u003C\u002Fcode> 预览、\u003Ccode>df.dropna()\u003C\u002Fcode> 清洗、\u003Ccode>df.merge()\u003C\u002Fcode> 合并、\u003Ccode>df.pivot_table()\u003C\u002Fcode> 透视\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 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