str, bytes, ExcelFile, xlrd.Book, path object, or file-like object

Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.xlsx.  If you want to pass in a path object, pandas accepts any os.PathLike.  By file-like object, we refer to objects with a read() method, such as a file handler (e.g. via builtin open function) or StringIO.

str, bytes, ExcelFile, xlrd.Book, path object, , or file-like object

任何有效的字符串路径。字符串可以是URL。有效的URL方案包括http、ftp、s3和file。对于文件url，需要一个主机。本地文件可以是:file://localhost/path/to/table.xlsx。如果您想传入一个path对象，pandas会接受任何类似os. path的东西。通过类文件对象，我们使用read()方法引用对象，比如文件处理程序(例如通过内置的open函数)或StringIO。

str, int, list, or None, default 0

Strings are used for sheet names. Integers are used in zero-indexed sheet positions. Lists of strings/integers are used to request multiple sheets. Specify None to get all sheets.

Available cases:

• Defaults to 0: 1st sheet as a DataFrame

• 1: 2nd sheet as a DataFrame

• "Sheet1": Load sheet with name “Sheet1”

• [0, 1, "Sheet5"]: Load first, second and sheet named “Sheet5” as a dict of DataFrame

• None: All sheets.

int, list of int, default 0

Row (0-indexed) to use for the column labels of the parsed DataFrame. If a list of integers is passed those row positions will be combined into a MultiIndex. Use None if there is no header.

array-like, default None

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

要使用的列名的列表。如果文件不包含头行，那么应该显式传递header=None。

使用names参数时，类似重命名。切记，要与原数据全部匹配：即不能多也不能少，多或者少都会抛出ValueError错误。

int, list of int, default None

Column (0-indexed) to use as the row labels of the DataFrame. Pass None if there is no such column. If a list is passed, those columns will be combined into a MultiIndex. If a subset of data is selected with usecols, index_col is based on the subset.

Column(0索引)用作数据格式的行标签。如果没有这样的列，则传递None。如果传递了一个列表，那么这些列将被合并到一个多索引中。如果使用usecols选择了数据子集，则index_col基于该子集。
index_col=0,      #不读取第一索引列

int, str, list-like, or callable default None

• If None, then parse all columns.

• If str, then indicates comma separated list of Excel column letters and column ranges (e.g. “A:E” or “A,C,E:F”). Ranges are inclusive of both sides.

• If list of int, then indicates list of column numbers to be parsed.

• If list of string, then indicates list of column names to be parsed.

  New in version 0.24.0.

• If callable, then evaluate each column name against it and parse the column if the callable returns True.

Returns a subset of the columns according to behavior above.

New in version 0.24.0.

• 如果没有，那么解析所有列。
• 如果str，则表示用逗号分隔的Excel列字母和列范围列表(如“A:E”或“A,C,E:F”)。范围包括两边。
• 如果list of int，则指示要解析的列号列表。
• 如果字符串列表，则指示要解析的列名列表。
• 新版本为0.24.0。

usecols=[1,2,7,8,14]
usecols参数，指定要读入列的索引ID

bool, default False

If the parsed data only contains one column then return a Series.

Type name or dict of column -> type, default None

Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} Use object to preserve data as stored in Excel and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.

数据或列的数据类型。例如{a: np。使用object保存存储在Excel中的数据，而不是解释dtype。如果指定了转换器，则将应用它们而不是dtype转换。

str, default None

If io is not a buffer or path, this must be set to identify io. Supported engines: “xlrd”, “openpyxl”, “odf”, “pyxlsb”, default “xlrd”. Engine compatibility : - “xlrd” supports most old/new Excel file formats. - “openpyxl” supports newer Excel file formats. - “odf” supports OpenDocument file formats (.odf, .ods, .odt). - “pyxlsb” supports Binary Excel files.

dict, default None

Dict of functions for converting values in certain columns. Keys can either be integers or column labels, values are functions that take one input argument, the Excel cell content, and return the transformed content.

list, default None

Values to consider as True.

list, default None

Values to consider as False.

list-like

Rows to skip at the beginning (0-indexed).

int, default None

Number of rows to parse.

New in version 0.23.0.

要解析的行数。

新版本0.23.0。

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: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.

bool, default True

Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:

• If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.

• If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.

• If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.

• If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.

Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored.

解析数据时是否包含默认的NaN值。根据是否传入na_values，行为如下:

如果keep_default_na为真，并且指定了na_values，那么na_values将附加到用于解析的缺省NaN值中。

如果keep_default_na为真，并且没有指定na_values，则只使用默认的NaN值进行解析。

如果keep_default_na为False，并且指定了na_values，则仅使用指定na_values的NaN值进行解析。

如果keep_default_na为False，并且没有指定na_values，则不会将任何字符串解析为NaN。

注意，如果将na_filter作为False传入，则keep_default_na和na_values参数将被忽略。

bool, default True

Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.

bool, default False

Indicate number of NA values placed in non-numeric columns.

bool, list-like, or dict, default False

The behavior is as follows:

• bool. If True -> try parsing the index.

• list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.

• list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.

• dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’

If a column or index contains an unparseable date, the entire column or index will be returned unaltered as an object data type. If you don`t want to parse some cells as date just change their type in Excel to “Text”. For non-standard datetime parsing, use pd.to_datetime after pd.read_excel.

Note: A fast-path exists for iso8601-formatted dates.

其行为如下:

• bool类型：如果为真——>尝试解析索引。
• int或名称的列表。例如，If[1,2,3] ->尝试将1,2,3列分别解析为一个单独的日期列。
• list类型：例如，If[[1,3]] ->组合列1和3并解析为单个日期列。
• dict类型：例如{' foo ':[1,3]} ->解析列1,3作为日期并调用结果' foo '

如果列或索引包含不可解析的日期，则整个列或索引将作为对象数据类型不变地返回。如果你不想把一些单元格解析为date，那就把它们在Excel中的类型改为Text。对于非标准的日期时间解析，在pd.read_excel后面使用pd.to_datetime。

注意:有一个用于iso8601格式的日期的快速路径。

function, optional

Function to use for converting a sequence of string columns to an array of datetime instances. The default uses dateutil.parser.parser to do the conversion. Pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and pass that; and 3) call date_parser once for each row using one or more strings (corresponding to the columns defined by parse_dates) as arguments.

str, default None

Thousands separator for parsing string columns to numeric. Note that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format.

数以千计的分隔符用于将字符串列解析为数字。请注意，此参数仅对存储为文本的列在Excel中是必要的，任何数值列都将自动解析，无论显示格式如何。

str, default None

Comments out remainder of line. Pass a character or characters to this argument to indicate comments in the input file. Any data between the comment string and the end of the current line is ignored.

int, default 0

Rows at the end to skip (0-indexed).

convert_float

bool, default True

Convert integral floats to int (i.e., 1.0 –> 1). If False, all numeric data will be read in as floats: Excel stores all numbers as floats internally.

bool, default True

Duplicate columns will be specified as ‘X’, ‘X.1’, …’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.

DataFrame or dict of DataFrames

DataFrame from the passed in Excel file. See notes in sheet_name argument for more information on when a dict of DataFrames is returned.