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 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.
- Parameters:
- 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 builtinopen
function) orStringIO
.Deprecated since version 2.1.0: Passing byte strings is deprecated. To read from a byte string, wrap it in a
BytesIO
object.- sheet_namestr, int, list, or None, default 0
Strings are used for sheet names. Integers are used in zero-indexed sheet positions (chart sheets do not count as a sheet position). Lists of strings/integers are used to request multiple sheets. Specify
None
to get all worksheets.Available cases:
Defaults to
0
: 1st sheet as a DataFrame1
: 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 DataFrameNone
: All worksheets.
- 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, 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 a
MultiIndex
. If a subset of data is selected withusecols
, index_col is based on the subset.Missing values will be forward filled to allow roundtripping with
to_excel
formerged_cells=True
. To avoid forward filling the missing 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 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 (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 the column if the callable returns
True
.
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} Use
object
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 INSTEAD of 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).
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
path_or_buffer
is in xlsb format,pyxlsb
will be used.Otherwise
openpyxl
will be used.
- 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’, ‘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 whether
na_values
is passed in, the behavior is as follows:If
keep_default_na
is True, andna_values
are specified,na_values
is appended to the default NaN values used for parsing.If
keep_default_na
is True, andna_values
are not specified, only the default NaN values are used for parsing.If
keep_default_na
is False, andna_values
are specified, only the NaN values specifiedna_values
are used for parsing.If
keep_default_na
is False, andna_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
andna_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 unparsable 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
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 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.Deprecated since version 2.0.0: Use
date_format
instead, or read in asobject
and then applyto_datetime()
as-needed.- date_formatstr or dict of column -> format, default
None
If used in conjunction with
parse_dates
, will parse dates according to this format. 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 that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format.
- 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 display format.(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 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).
- 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 pairs are forwarded to
urllib.request.Request
as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.- dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’
Back-end data type applied to the resultant
DataFrame
(still experimental). Behaviour is as follows:"numpy_nullable"
: returns nullable-dtype-backedDataFrame
(default)."pyarrow"
: returns pyarrow-backed nullableArrowDtype
DataFrame.
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_name argument for more information on when a dict of DataFrames is returned.
See also
DataFrame.to_excel
Write DataFrame to an Excel file.
DataFrame.to_csv
Write DataFrame to a comma-separated values (csv) file.
read_csv
Read a comma-separated values (csv) file into DataFrame.
read_fwf
Read a table of fixed-width formatted lines into DataFrame.
Notes
For specific information on the methods used for each Excel engine, refer to the pandas user guide
Examples
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