pandas.read_excel

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, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, **kwds)[source]

Read an Excel table into a pandas DataFrame

Parameters:

io : string, path object (pathlib.Path or py._path.local.LocalPath),

file-like object, pandas ExcelFile, or xlrd workbook. The string could be a URL. Valid URL schemes include http, ftp, s3, gcs, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/workbook.xlsx

sheet_name : string, int, mixed list of strings/ints, 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.

str|int -> DataFrame is returned. list|None -> Dict of DataFrames is returned, with keys representing sheets.

Available Cases

  • Defaults to 0 -> 1st sheet as a DataFrame
  • 1 -> 2nd sheet as a DataFrame
  • “Sheet1” -> 1st sheet as a DataFrame
  • [0,1,”Sheet5”] -> 1st, 2nd & 5th sheet as a dictionary of DataFrames
  • None -> All sheets as a dictionary of DataFrames

sheetname : string, int, mixed list of strings/ints, or None, default 0

Deprecated since version 0.21.0: Use sheet_name instead

header : int, list of ints, 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, list of ints, 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.

parse_cols : int or list, default None

Deprecated since version 0.21.0: Pass in usecols instead.

usecols : int, str, list-like, or callable default None

  • If None, then parse all columns,
  • If int, then indicates last column to be parsed

Deprecated since version 0.24.0: Pass in a list of ints instead from 0 to usecols inclusive.

  • If string, 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 ints, then indicates list of column numbers to be parsed.
  • If list of strings, 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.

New in version 0.24.0.

squeeze : boolean, default False

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

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. If converters are specified, they will be applied INSTEAD of dtype conversion.

New in version 0.20.0.

engine : string, default None

If io is not a buffer or path, this must be set to identify io. Acceptable values are None or xlrd

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

New in version 0.19.0.

false_values : list, default None

Values to consider as False

New in version 0.19.0.

skiprows : list-like

Rows to skip at the beginning (0-indexed)

nrows : int, default None

Number of rows to parse

New in version 0.23.0.

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’, ‘n/a’, ‘nan’, ‘null’.

keep_default_na : bool, default True

If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to.

verbose : boolean, default False

Indicate number of NA values placed in non-numeric columns

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.

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.

skip_footer : int, default 0

Deprecated since version 0.23.0: Pass in skipfooter instead.

skipfooter : int, default 0

Rows at the end to skip (0-indexed)

convert_float : boolean, 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_cols : boolean, 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.

Returns:

parsed : 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.

Examples

An example DataFrame written to a local file

>>> df_out = pd.DataFrame([('string1', 1),
...                        ('string2', 2),
...                        ('string3', 3)],
...                       columns=['Name', 'Value'])
>>> df_out
      Name  Value
0  string1      1
1  string2      2
2  string3      3
>>> df_out.to_excel('tmp.xlsx')

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

>>> pd.read_excel('tmp.xlsx')
      Name  Value
0  string1      1
1  string2      2
2  string3      3
>>> pd.read_excel(open('tmp.xlsx','rb'))
      Name  Value
0  string1      1
1  string2      2
2  string3      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  string3      3

Column types are inferred but can be explicitly specified

>>> pd.read_excel('tmp.xlsx', dtype={'Name':str, 'Value':float})
      Name  Value
0  string1    1.0
1  string2    2.0
2  string3    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',
...               na_values=['string1', 'string2'])
      Name  Value
0      NaN      1
1      NaN      2
2  string3      3

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

>>> df = pd.DataFrame({'a': ['1', '#2'], 'b': ['2', '3']})
>>> df.to_excel('tmp.xlsx', index=False)
>>> pd.read_excel('tmp.xlsx')
    a  b
0   1  2
1  #2  3
>>> pd.read_excel('tmp.xlsx', comment='#')
   a  b
0  1  2
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