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徑。字符串可以是URL。有效的URL方案包括http、ftp、s3和file。對于文件url，需要一個主機。本地文件可以是:file://localhost/path/to/table.xlsx。如果您想傳入一個path對象，pandas會接受任何類似os. path的東西。通過類文件對象，我們使用read()方法引用對象，比如文件處理程序(例如通過內置的open函數)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。如果文件不包含頭行，那么應該顯式傳遞header=None。

使用names參數時，類似重命名。切記，要與原數據全部匹配：即不能多也不能少，多或者少都會拋出ValueError錯誤。

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)用作數據格式的行標簽。如果沒有這樣的列，則傳遞None。如果傳遞了一個列表，那么這些列將被合并到一個多索引中。如果使用usecols選擇了數據子集，則index_col基于該子集。
index_col=0,? ? ? #不讀取第一索引列

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.

• 如果沒有，那么解析所有列。
• 如果str，則表示用逗號分隔的Excel列字母和列范圍列表(如“A:E”或“A,C,E:F”)。范圍包括兩邊。
• 如果list of int，則指示要解析的列號列表。
• 如果字符串列表，則指示要解析的列名列表。
• 新版本為0.24.0。

usecols=[1,2,7,8,14]
usecols參數，指定要讀入列的索引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.

數據或列的數據類型。例如{a: np。使用object保存存儲在Excel中的數據，而不是解釋dtype。如果指定了轉換器，則將應用它們而不是dtype轉換。

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.

要解析的行數。

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.

解析數據時是否包含默認的NaN值。根據是否傳入na_values，行為如下:

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

如果keep_default_na為真，并且沒有指定na_values，則只使用默認的NaN值進行解析。

如果keep_default_na為False，并且指定了na_values，則僅使用指定na_values的NaN值進行解析。

如果keep_default_na為False，并且沒有指定na_values，則不會將任何字符串解析為NaN。

注意，如果將na_filter作為False傳入，則keep_default_na和na_values參數將被忽略。

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.

其行為如下:

• bool類型：如果為真——>嘗試解析索引。
• int或名稱的列表。例如，If[1,2,3] ->嘗試將1,2,3列分別解析為一個單獨的日期列。
• list類型：例如，If[[1,3]] ->組合列1和3并解析為單個日期列。
• dict類型：例如{' foo ':[1,3]} ->解析列1,3作為日期并調用結果' foo '

如果列或索引包含不可解析的日期，則整個列或索引將作為對象數據類型不變地返回。如果你不想把一些單元格解析為date，那就把它們在Excel中的類型改為Text。對于非標準的日期時間解析，在pd.read_excel后面使用pd.to_datetime。

注意:有一個用于iso8601格式的日期的快速路徑。

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.

數以千計的分隔符用于將字符串列解析為數字。請注意，此參數僅對存儲為文本的列在Excel中是必要的，任何數值列都將自動解析，無論顯示格式如何。

?

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.