EXL
class
rackio_AI.EXL()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.
read(self, pathname, **exl_options)Read an Excel file into a pandas DataFrame.
Parameters
- :param pathname: (str, path object, file-like object or directory path) 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.
- :param sheet_name: (str, int, list, 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.
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: All sheets.
- :param 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.
- :param names: (array-like, default None) List of column names to use. If file contains no header row, then you should explicitly pass header=None.
- :param index_col: (int, 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.
- :param usecols: (int, 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.
- 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.
- :param squeeze: (bool, default False) If the parsed data only contains one column then return a Series.
- :param 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.
- :param mangle_dupe_cols: (bool, 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.
- :param engine: (str, default None)
If io is not a buffer or path, this must be set to identify io. Supported engines:
“xlrd”, “openpyxl”, “odf”, “pyxlsb”. Engine compatibility :
- “xlrd” supports old-style Excel files (.xls).
- “openpyxl” supports newer Excel file formats.
- “odf” supports OpenDocument file formats (.odf, .ods, .odt).
- “pyxlsb” supports Binary Excel files. Changed in version 1.2.0: The engine xlrd now only supports old-style .xls files. 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 openpyxl is installed, then openpyxl will be used.
- Otherwise if xlrd >= 2.0 is installed, a ValueError will be raised.
- Otherwise xlrd will be used and a FutureWarning will be raised. This case will raise a ValueError in a future version of pandas.
- :param 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.
- :param true_values: (list, default None) Values to consider as True.
- :param false_values: (list, default None) Values to consider as False.
- :param 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].
- :param nrows: (int, default None) Number of rows to parse.
- :param 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’. - :param 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.
- :param 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.
- :param verbose: (bool, default False) Indicate number of NA values placed in non-numeric columns.
- :param 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 unparseable 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 after pd.read_excel. Note: A fast-path exists for iso8601-formatted dates.
- :param date_parser: (function, 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.
- :param 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.
- :param decimal: (str, default ‘.’) Character to recognize as decimal point (e.g. use ‘,’ for European data).
- :param skipfooter: (int, default 0) Rows at the end to skip (0-indexed). Character to break file into lines. Only valid with C parser.
- :param 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.
- :param mangle_dupe_cols: (bool, 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.
- :param storage_options: (dict, optional) Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a local path or a file-like buffer. See the fsspec and backend storage implementation docs for the set of allowed keys and values
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.
Snippet Code
>>> import os
>>> from rackio_AI import RackioAI, get_directory
>>> directory = os.path.join(get_directory('excel'))
>>> RackioAI.load(directory, ext=".xlsx", header=0, sheet_name="SalesOrders")
OrderDate Region Rep Item Units Unit Cost Total
0 2019-01-06 East Jones Pencil 95 1.99 189.05
1 2019-01-23 Central Kivell Binder 50 19.99 999.50
2 2019-02-09 Central Jardine Pencil 36 4.99 179.64
3 2019-02-26 Central Gill Pen 27 19.99 539.73
4 2019-03-15 West Sorvino Pencil 56 2.99 167.44
5 2019-04-01 East Jones Binder 60 4.99 299.40
6 2019-04-18 Central Andrews Pencil 75 1.99 149.25
7 2019-05-05 Central Jardine Pencil 90 4.99 449.10
8 2019-05-22 West Thompson Pencil 32 1.99 63.68
9 2019-06-08 East Jones Binder 60 8.99 539.40
10 2019-06-25 Central Morgan Pencil 90 4.99 449.10
11 2019-07-12 East Howard Binder 29 1.99 57.71
12 2019-07-29 East Parent Binder 81 19.99 1619.19
13 2019-08-15 East Jones Pencil 35 4.99 174.65
14 2019-09-01 Central Smith Desk 2 125.00 250.00
15 2019-09-18 East Jones Pen Set 16 15.99 255.84
16 2019-10-05 Central Morgan Binder 28 8.99 251.72
17 2019-10-22 East Jones Pen 64 8.99 575.36
18 2019-11-08 East Parent Pen 15 19.99 299.85
19 2019-11-25 Central Kivell Pen Set 96 4.99 479.04
20 2019-12-12 Central Smith Pencil 67 1.29 86.43
21 2019-12-29 East Parent Pen Set 74 15.99 1183.26
22 2020-01-15 Central Gill Binder 46 8.99 413.54
23 2020-02-01 Central Smith Binder 87 15.00 1305.00
24 2020-02-18 East Jones Binder 4 4.99 19.96
25 2020-03-07 West Sorvino Binder 7 19.99 139.93
26 2020-03-24 Central Jardine Pen Set 50 4.99 249.50
27 2020-04-10 Central Andrews Pencil 66 1.99 131.34
28 2020-04-27 East Howard Pen 96 4.99 479.04
29 2020-05-14 Central Gill Pencil 53 1.29 68.37
30 2020-05-31 Central Gill Binder 80 8.99 719.20
31 2020-06-17 Central Kivell Desk 5 125.00 625.00
32 2020-07-04 East Jones Pen Set 62 4.99 309.38
33 2020-07-21 Central Morgan Pen Set 55 12.49 686.95
34 2020-08-07 Central Kivell Pen Set 42 23.95 1005.90
35 2020-08-24 West Sorvino Desk 3 275.00 825.00
36 2020-09-10 Central Gill Pencil 7 1.29 9.03
37 2020-09-27 West Sorvino Pen 76 1.99 151.24
38 2020-10-14 West Thompson Binder 57 19.99 1139.43
39 2020-10-31 Central Andrews Pencil 14 1.29 18.06
40 2020-11-17 Central Jardine Binder 11 4.99 54.89
41 2020-12-04 Central Jardine Binder 94 19.99 1879.06
42 2020-12-21 Central Andrews Binder 28 4.99 139.72