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:
io: str, 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_name: str, 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. When None, will return a dictionary containing DataFrames for each sheet.
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: Returns a dictionary containing DataFrames for each sheet.
header: 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.
names: array-like, default None
List of column names to use. If file contains no header row, then you should explicitly pass header=None.
index_col: int, 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 with usecols, index_col is based on the subset.
Missing values will be forward filled to allow roundtripping with to_excel for merged_cells=True. To avoid forward filling the missing values use set_index after reading the data instead of index_col.
usecols: 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 (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.
dtype: 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, which will necessarily result in object dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. If you use None, it will infer the dtype of each column based on the data.
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.
converters: 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.
true_values: list, default None
Values to consider as True.
false_values: list, default None
Values to consider as False.
skiprows: list-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].
nrows: int, default None
Number of rows to parse. Does not include header rows.
na_values: 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’, ‘’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘None’, ‘n/a’, ‘nan’, ‘null’.
keep_default_na: 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.
na_filter: 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.
verbose: bool, default False
Indicate number of NA values placed in non-numeric columns.
parse_dates: 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 unparsable date, the entire column or index will be returned unaltered as an object data type. If you dont want to parse some cells as date just change their type in Excel to “Text”. For non-standard datetime parsing, use pd.to_datetimeafterpd.read_excel`.
Note: A fast-path exists for iso8601-formatted dates.
date_format: str 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 as object and then apply to_datetime() as-needed.
Added in version 2.0.0.
thousands: 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.
decimal: str, 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).
comment: 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.
skipfooter: int, default 0
Rows at the end to skip (0-indexed).
storage_options: dict, 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 to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
dtype_backend: {‘numpy_nullable’, ‘pyarrow’}
Back-end data type applied to the resultant DataFrame (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows:
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 Value0 NaN 11 NaN 22 #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 Value0 string1 1.01 string2 2.02 None NaN