pandas.read_csv

原型：

pandas.read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)

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參數很多，下面就常用的參數給出說明，詳細請參考panda文檔?：

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filepath_or_buffer :?要讀取的csv文件的URL，本地或者遠程文件均可。

header?: int or list of ints, default ‘infer’?

Row number(s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no?names?passed, otherwise?None. Explicitly pass?header=0?to be able to replace existing? names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if?skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.

names?: array-like, default None

List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list are not allowed unless mangle_dupe_cols=True, which is the default.

mangle_dupe_cols?: boolean, default True

Duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.

skip_blank_lines?: boolean, default True

If True, skip over blank lines rather than interpreting as NaN values

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返回值：Read CSV (comma-separated) file into DataFrame

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轉載于:https://www.cnblogs.com/zhuangliu/p/6374853.html

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