熊猫 - pandas.DataFrame.from_csv 与 pandas.read_csv
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Pandas - pandas.DataFrame.from_csv vs pandas.read_csv
提问by user3659451
What's the difference between:
有什么区别：
pandas.DataFrame.from_csv, doc link: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_csv.html
pandas.DataFrame.from_csv, 文档链接：http: //pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_csv.html
and
和
pandas.read_csv, doc link: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
pandas.read_csv, 文档链接：http: //pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
回答by joris
There is no real difference (both are based on the same underlying function), but as noted in the comments, they have some different default values(index_colis 0 or None, parse_datesis True or False for read_csvand DataFrame.from_csvrespectively) and read_csvsupports more arguments(in from_csvthey are just not passed through).
有没有真正的区别（两者都基于相同的底层函数），但在评论中指出，他们有一些不同的默认值（index_col0或无，parse_dates是真的还是假的read_csv，并DataFrame.from_csv分别），并read_csv支持更多的参数（在from_csv他们只是没有通过）。
Apart from that, it is recommended to use pd.read_csv.DataFrame.from_csvexists merely for historical reasons and to keep backwards compatibility (plans are to deprecate it, see here), but all new features are only added to read_csv(as you can see in the much longer list of keyword arguments). Actually, this should be made more clear in the docs.
除此之外，建议使用pd.read_csv. DataFrame.from_csv仅出于历史原因而存在并保持向后兼容性（计划弃用它，请参阅此处），但所有新功能仅添加到read_csv（如您在更长的关键字参数列表中所见）。实际上，这应该在文档中更清楚。
回答by ChaimG
Another difference is that pandas.read_csvis 46x to 490x as fast as pandas.DataFrame.from_csv(in my testing).
另一个区别是它的pandas.read_csv速度是pandas.DataFrame.from_csv（在我的测试中）的46 倍到 490 倍。
I tested it on Python 3.4.4 and pandas 0.19.2 on Windows on my proprietary csv file.
我在 Windows 上的 Python 3.4.4 和 pandas 0.19.2 上对我的专有 csv 文件进行了测试。
