larray.read_excel
- larray.read_excel(filepath, sheet=0, nb_axes=None, index_col=None, fill_value=nan, na=nan, sort_rows=False, sort_columns=False, wide=True, engine=None, range=slice(None, None, None), **kwargs) Array[source]
Read excel file from sheet name and returns an Array with the contents.
- Parameters
- filepathstr or Path
Path where the Excel file has to be read or use -1 to refer to the currently active workbook.
- sheetstr, Group or int, optional
Name or index of the Excel sheet containing the array to be read. By default the array is read from the first sheet.
- nb_axesint, optional
Number of axes of output array. The first
nb_axes- 1 columns and the header of the Excel sheet will be used to set the axes of the output array. If not specified, the number of axes is given by the position of the first column header including a\character plus one. If no column header includes a\character, the array is assumed to have one axis. Defaults to None.- index_collist, optional
Positions of columns for the n-1 first axes (ex. [0, 1, 2, 3]). Defaults to None (see nb_axes above).
- fill_valuescalar or Array, optional
Value used to fill cells corresponding to label combinations which are not present in the input. Defaults to NaN.
- sort_rowsbool, optional
Whether to sort the rows alphabetically (sorting is more efficient than not sorting). Defaults to False.
- sort_columnsbool, optional
Whether to sort the columns alphabetically (sorting is more efficient than not sorting). Defaults to False.
- widebool, optional
Whether to assume the array is stored in “wide” format. If False, the array is assumed to be stored in “narrow” format: one column per axis plus one value column. Defaults to True.
- engine{‘xlwings’, ‘openpyxl’, ‘xlrd’}, optional
Engine to use to read the Excel file. The ‘xlrd’ engine must be used to read Excel files with the old ‘.xls’ extension. Either ‘xlwings’ or ‘openpyxl’ can be used to read Excel files with the standard ‘.xlsx’ extension. Defaults to ‘xlwings’ if the module is installed, ‘openpyxl’ otherwise.
- rangestr, optional
Range to load the array from (only supported for the ‘xlwings’ engine). Defaults to slice(None) which loads the whole sheet, ignoring blank cells in the bottom right corner.
- **kwargs
- Returns
- Array
Examples
>>> fname = get_example_filepath('examples.xlsx')
Read array from first sheet
>>> # The data below is derived from a subset of the demo_pjan table from Eurostat >>> read_excel(fname) country gender\time 2013 2014 2015 Belgium Male 5472856 5493792 5524068 Belgium Female 5665118 5687048 5713206 France Male 31772665 32045129 32174258 France Female 33827685 34120851 34283895 Germany Male 39380976 39556923 39835457 Germany Female 41142770 41210540 41362080
Read array from a specific sheet
>>> # The data below is derived from a subset of the demo_fasec table from Eurostat >>> read_excel(fname, 'births') country gender\time 2013 2014 2015 Belgium Male 64371 64173 62561 Belgium Female 61235 60841 59713 France Male 415762 418721 409145 France Female 396581 400607 390526 Germany Male 349820 366835 378478 Germany Female 332249 348092 359097
Missing label combinations
Let us take a look inside the sheet ‘population_missing_values’. Note the missing label combinations: (Paris, male) and (New York, female):
country gender\time 2013 2014 2015 Belgium Male 5472856 5493792 5524068 Belgium Female 5665118 5687048 5713206 France Female 33827685 34120851 34283895 Germany Male 39380976 39556923 39835457
By default, cells associated with missing label combinations are filled with NaN. In that case, an int array is converted to a float array.
>>> read_excel(fname, sheet='population_missing_values') country gender\time 2013 2014 2015 Belgium Male 5472856.0 5493792.0 5524068.0 Belgium Female 5665118.0 5687048.0 5713206.0 France Male nan nan nan France Female 33827685.0 34120851.0 34283895.0 Germany Male 39380976.0 39556923.0 39835457.0 Germany Female nan nan nan
Using the
fill_valueargument, you can choose another value to use to fill missing cells.>>> read_excel(fname, sheet='population_missing_values', fill_value=0) country gender\time 2013 2014 2015 Belgium Male 5472856 5493792 5524068 Belgium Female 5665118 5687048 5713206 France Male 0 0 0 France Female 33827685 34120851 34283895 Germany Male 39380976 39556923 39835457 Germany Female 0 0 0
Specify the number of axes of the output array (useful when the name of the last axis is implicit)
The content of the sheet ‘missing_axis_name’ is:
country gender 2013 2014 2015 Belgium Male 5472856 5493792 5524068 Belgium Female 5665118 5687048 5713206 France Male 31772665 32045129 32174258 France Female 33827685 34120851 34283895 Germany Male 39380976 39556923 39835457 Germany Female 41142770 41210540 41362080
>>> # read the array stored in the sheet 'population_missing_axis_name' as is >>> arr = read_excel(fname, sheet='population_missing_axis_name') >>> # we expected a 3 x 2 x 3 array with data of type int >>> # but we got a 6 x 4 array with data of type object >>> arr.info 6 x 4 country [6]: 'Belgium' 'Belgium' 'France' 'France' 'Germany' 'Germany' {1} [4]: 'gender' '2013' '2014' '2015' dtype: object memory used: 192 bytes >>> # using argument 'nb_axes', you can force the number of axes of the output array >>> arr = read_excel(fname, sheet='population_missing_axis_name', nb_axes=3) >>> # as expected, we have a 3 x 2 x 3 array with data of type int >>> arr.info 3 x 2 x 3 country [3]: 'Belgium' 'France' 'Germany' gender [2]: 'Male' 'Female' {2} [3]: 2013 2014 2015 dtype: int64 memory used: 144 bytes
Read array saved in “narrow” format (wide=False)
Let us take a look inside the sheet ‘population_narrow’ where the data is stored in a ‘narrow’ format:
country time value Belgium 2013 11137974 Belgium 2014 11180840 Belgium 2015 11237274 France 2013 65600350 France 2014 66165980 France 2015 66458153
>>> # to read arrays stored in 'narrow' format, you must pass wide=False to read_excel >>> read_excel(fname, 'population_narrow_format', wide=False) country\time 2013 2014 2015 Belgium 11137974 11180840 11237274 France 65600350 66165980 66458153
Extract array from a given range (xlwings only)
>>> read_excel(fname, 'population_births_deaths', range='A9:E15') country gender\time 2013 2014 2015 Belgium Male 64371 64173 62561 Belgium Female 61235 60841 59713 France Male 415762 418721 409145 France Female 396581 400607 390526 Germany Male 349820 366835 378478 Germany Female 332249 348092 359097