pandas.read_excel #

pandas.read_excel(io,sheet_name=0,*,header=0,names=None,index_col=None,usecols=None,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=<no_default>,date_format=None,thousands=None,decimal='.',comment=None,skipfooter=0,storage_options=None,dtype_backend=<no_default>,engine_kwargs=None)[source]#

Read an Excel file into apandasDataFrame.

Supportsxls,xlsx,xlsm,xlsb,odf,ods andodt file extensionsread from a local filesystem or URL. Supports an option to reada single sheet or a list of sheets.

Parameters:

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

Any valid string path is acceptable. The string could be a URL. ValidURL schemes include http, ftp, s3, and file. For file URLs, a host isexpected. A local file could be:file://localhost/path/to/table.xlsx.

If you want to pass in a path object, pandas accepts anyos.PathLike.

By file-like object, we refer to objects with aread() method,such as a file handle (e.g. via builtinopen function)orStringIO.

Deprecated since version 2.1.0:Passing byte strings is deprecated. To read from abyte string, wrap it in aBytesIO object.

sheet_namestr, int, list, or None, default 0

Strings are used for sheet names. Integers are used in zero-indexedsheet positions (chart sheets do not count as a sheet position).Lists of strings/integers are used to request multiple sheets.SpecifyNone to get all worksheets.

Available cases:

Defaults to0: 1st sheet as aDataFrame
1: 2nd sheet as aDataFrame
"Sheet1": Load sheet with name “Sheet1”
[0,1,"Sheet5"]: Load first, second and sheet named “Sheet5”as a dict ofDataFrame
None: All worksheets.

headerint, list of int, default 0

Row (0-indexed) to use for the column labels of the parsedDataFrame. If a list of integers is passed those row positions willbe combined into aMultiIndex. 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, str, 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 aMultiIndex. If asubset of data is selected withusecols, index_colis based on the subset.

Missing values will be forward filled to allow roundtripping withto_excel formerged_cells=True. To avoid forward filling themissing values useset_index after reading the data instead ofindex_col.

usecolsstr, list-like, or callable, default None

If None, then parse all columns.
If str, then indicates comma separated list of Excel column lettersand column ranges (e.g. “A:E” or “A,C,E:F”). Ranges are inclusive ofboth sides.
If list of int, then indicates list of column numbers to be parsed(0-indexed).
If list of string, then indicates list of column names to be parsed.
If callable, then evaluate each column name against it and parse thecolumn if the callable returnsTrue.

Returns a subset of the columns according to behavior above.

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

Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32}Useobject to preserve data as stored in Excel and not interpret dtype,which will necessarily result inobject dtype.If converters are specified, they will be applied INSTEADof dtype conversion.If you useNone, it will infer the dtype of each column based on the data.

engine{‘openpyxl’, ‘calamine’, ‘odf’, ‘pyxlsb’, ‘xlrd’}, default None

If io is not a buffer or path, this must be set to identify io.Engine compatibility :

openpyxl supports newer Excel file formats.
calamine supports Excel (.xls, .xlsx, .xlsm, .xlsb)and OpenDocument (.ods) file formats.
odf supports OpenDocument file formats (.odf, .ods, .odt).
pyxlsb supports Binary Excel files.
xlrd supports old-style Excel files (.xls).

Whenengine=None, the following logic will be used to determine the engine:

Ifpath_or_buffer is an OpenDocument format (.odf, .ods, .odt),thenodf will be used.
Otherwise ifpath_or_buffer is an xls format,xlrd will be used.
Otherwise ifpath_or_buffer is in xlsb format,pyxlsb will be used.
Otherwiseopenpyxl will be used.

convertersdict, default None

Dict of functions for converting values in certain columns. Keys caneither be integers or column labels, values are functions that take oneinput argument, the Excel cell content, and return the transformedcontent.

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 thestart of the file. If callable, the callable function will be evaluatedagainst the row indices, returning True if the row should be skipped andFalse otherwise. An example of a valid callable argument would belambdax:xin[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, specificper-column NA values. By default the following values are interpretedas NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’,‘1.#IND’, ‘1.#QNAN’, ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘None’,‘n/a’, ‘nan’, ‘null’.

keep_default_nabool, default True

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

Ifkeep_default_na is True, andna_values are specified,na_values is appended to the default NaN values used for parsing.
Ifkeep_default_na is True, andna_values are not specified, onlythe default NaN values are used for parsing.
Ifkeep_default_na is False, andna_values are specified, onlythe NaN values specifiedna_values are used for parsing.
Ifkeep_default_na is False, andna_values are not specified, nostrings will be parsed as NaN.

Note that ifna_filter is passed in as False, thekeep_default_na andna_values parameters will be ignored.

na_filterbool, default True

Detect missing value markers (empty strings and the value of na_values). Indata without any NAs, passingna_filter=False can improve theperformance 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, 3each as a separate date column.
list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse asa single date column.
dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and callresult ‘foo’

If a column or index contains an unparsable date, the entire column orindex will be returned unaltered as an object data type. If you don`t want toparse some cells as date just change their type in Excel to “Text”.For non-standard datetime parsing, usepd.to_datetime afterpd.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 ofdatetime instances. The default usesdateutil.parser.parser to do theconversion. Pandas will try to calldate_parser in three different ways,advancing to the next if an exception occurs: 1) Pass one or more arrays(as defined byparse_dates) as arguments; 2) concatenate (row-wise) thestring values from the columns defined byparse_dates into a single arrayand pass that; and 3) calldate_parser once for each row using one ormore strings (corresponding to the columns defined byparse_dates) asarguments.

Deprecated since version 2.0.0:Usedate_format instead, or read in asobject and then applyto_datetime() as-needed.

date_formatstr or dict of column -> format, defaultNone

If used in conjunction withparse_dates, will parse dates according to thisformat. For anything more complex,please read in asobject and then applyto_datetime() as-needed.

Added in version 2.0.0.

thousandsstr, default None

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

decimalstr, default ‘.’

Character to recognize as decimal point 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 displayformat.(e.g. use ‘,’ for European data).

Added in version 1.4.0.

commentstr, default None

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

skipfooterint, default 0

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

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairsare forwarded tourllib.request.Request as header options. For otherURLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs areforwarded tofsspec.open. Please seefsspec andurllib for moredetails, and for more examples on storage options referhere.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultantDataFrame(still experimental). Behaviour is as follows:

"numpy_nullable": returns nullable-dtype-backedDataFrame(default).
"pyarrow": returns pyarrow-backed nullableArrowDtypeDataFrame.

Added in version 2.0.

engine_kwargsdict, optional

Arbitrary keyword arguments passed to excel engine.

Returns:

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

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pandas.read_excel#

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