pandas 之2: pd.read_excel 读取excel

def read_excel(io,
               sheet_name=0,
               header=0,
               names=None,
               index_col=None,
               usecols=None,
               squeeze=False,
               dtype=None,
               engine=None,
               converters=None,
               true_values=None,
               false_values=None,
               skiprows=None,
               nrows=None,
               na_values=None,
               parse_dates=False,
               date_parser=None,
               thousands=None,
               comment=None,
               skipfooter=0,
               convert_float=True,
               **kwds)

io：excel文件路径

sheet_name：string, int, mixed list of strings/ints, or None, default 0，sheet表名

* Defaults to 0 -> 1st sheet as a DataFrame
* 1 -> 2nd sheet as a DataFrame
* "Sheet1" -> 1st sheet as a DataFrame
* [0,1,"Sheet5"] -> 1st, 2nd & 5th sheet as a dictionary of DataFrames
* None -> All sheets as a dictionary of DataFrames

header：指定作为列名的行，默认为0，即取第一行作为列名；若数据不含列名，则设定 header = None

names：可用列表等参数指定列名序列，如果没有列名，则需要先设置 header=None；如果只有一列，需要设置为列表形式，例如：['第一列']，否则会出现错误：TypeError: Index(...) must be called with a collection of some kind

names : array-like, default None
    List of column names to use. If file contains no header row,
    then you should explicitly pass header=None

index_col：以某一列作为行标签，也就是行索引

skiprows：从头开始跳过的行数，可以传列表

skipfooter：省略从末尾开始的行数

na_values：识别NA/NaN数据，并替换为该值

na_values : scalar, str, list-like, or dict, default None
    Additional strings to recognize as NA/NaN. If dict passed, specific
    per-column NA values. By default the following values are interpreted
    as NaN: '""" + fill("', '".join(sorted(_NA_VALUES)), 70, subsequent_indent="    ") + """'.

squeeze：当传入数据只有一列时，返回序列Series，而不是Dataframe数据

squeeze : boolean, default False
    If the parsed data only contains one column then return a Series

nrows：要解析的行数

nrows : int, default None
    Number of rows to parse

.. versionadded:: 0.23.0

引用网址：https://www.cnblogs.com/qi-yuan-008/archive/2019/10/14/11672761.html