pandas.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, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, storage_options=None)[source]

Read an Excel file into a pandas DataFrame.

Supports xls, xlsx, xlsm, xlsb, odf, ods and odt file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets.

iostr, 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 handle (e.g. via builtin open function) or StringIO.

sheet_namestr, 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.

headerint, 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.

namesarray-like, default None

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

index_colint, 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.

usecolsint, 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.

squeezebool, default False

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

dtypeType 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.

enginestr, default None

If io is not a buffer or path, this must be set to identify io. Supported engines: “xlrd”, “openpyxl”, “odf”, “pyxlsb”. Engine compatibility :

  • “xlrd” supports old-style Excel files (.xls).

  • “openpyxl” supports newer Excel file formats.

  • “odf” supports OpenDocument file formats (.odf, .ods, .odt).

  • “pyxlsb” supports Binary Excel files.

Changed in version 1.2.0: The engine xlrd now only supports old-style .xls files. When engine=None, the following logic will be used to determine the engine:

  • If path_or_buffer is an OpenDocument format (.odf, .ods, .odt), then odf will be used.

  • Otherwise if path_or_buffer is an xls format, xlrd will be used.

  • Otherwise if openpyxl is installed, then openpyxl will be used.

  • Otherwise if xlrd >= 2.0 is installed, a ValueError will be raised.

  • Otherwise xlrd will be used and a FutureWarning will be raised. This case will raise a ValueError in a future version of pandas.

convertersdict, 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.

true_valueslist, default None

Values to consider as True.

false_valueslist, default None

Values to consider as False.

skiprowslist-like, int, or callable, optional

Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be lambda x: x in [0, 2].

nrowsint, default None

Number of rows to parse.

na_valuesscalar, 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’.

keep_default_nabool, 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.

na_filterbool, 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.

verbosebool, default False

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

parse_datesbool, 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.

date_parserfunction, 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.

thousandsstr, 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.

commentstr, 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.

skipfooterint, default 0

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

convert_floatbool, 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.

mangle_dupe_colsbool, 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.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a local path or a file-like buffer. See the fsspec and backend storage implementation docs for the set of allowed keys and values.

New in version 1.2.0.

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.

See also


Write DataFrame to an Excel file.


Write DataFrame to a comma-separated values (csv) file.


Read a comma-separated values (csv) file into DataFrame.


Read a table of fixed-width formatted lines into DataFrame.


The file can be read using the file name as string or an open file object:

>>> pd.read_excel('tmp.xlsx', index_col=0)  
       Name  Value
0   string1      1
1   string2      2
2  #Comment      3
>>> pd.read_excel(open('tmp.xlsx', 'rb'),
...               sheet_name='Sheet3')  
   Unnamed: 0      Name  Value
0           0   string1      1
1           1   string2      2
2           2  #Comment      3

Index and header can be specified via the index_col and header arguments

>>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  
     0         1      2
0  NaN      Name  Value
1  0.0   string1      1
2  1.0   string2      2
3  2.0  #Comment      3

Column types are inferred but can be explicitly specified

>>> pd.read_excel('tmp.xlsx', index_col=0,
...               dtype={'Name': str, 'Value': float})  
       Name  Value
0   string1    1.0
1   string2    2.0
2  #Comment    3.0

True, False, and NA values, and thousands separators have defaults, but can be explicitly specified, too. Supply the values you would like as strings or lists of strings!

>>> pd.read_excel('tmp.xlsx', index_col=0,
...               na_values=['string1', 'string2'])  
       Name  Value
0       NaN      1
1       NaN      2
2  #Comment      3

Comment lines in the excel input file can be skipped using the comment kwarg

>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  
      Name  Value
0  string1    1.0
1  string2    2.0
2     None    NaN