closes #16645 tests added / passed passes git diff upstream/master -u -- "*.py" | flake8 --diff whatsnew entry I was working on this a few months back, but got busy. data = pd.read_excel("C:\\Users\\firstname.lastname\\Desktop\\variable+".xlsx", sheet_name=variable2, na_values='', usecols="A,B", skiprows=range(2)) This works fine to load the file however in column B there is a string that is precisely equal to "NA", in the resulting dataframe this … Parameters data ndarray (structured or homogeneous), Iterable, dict, or DataFrame. If we, for some reason, don’t want to parse all columns in the Excel file, we can use the parameter usecols. Returns Equivalent to read_excel(ExcelFile, …) See the read_excel docstring for more info on accepted parameters. The string could be a URL. This should work, where skipendrows is … The primary pandas data structure. I'm free until January so thought I'd pick this back up. Then the third row will be treated as the header row and the values will be read from the next row onwards.
pandas.DataFrame ¶ class pandas. For file URLs, a host is expected. Strings are used for sheet names. pandas.read_excel ¶ pandas.read_excel ... Parameters: io: str, file descriptor, pathlib.Path, ExcelFile or xlrd.Book. When using Pandas read_excel we will automatically get all columns from an Excel files.
For instance, a local file could be /path/to/workbook.xlsx. df = pd.read_excel('MLBPlayerSalaries.xlsx', sheet_names='MLBPlayerSalaries', index_col='Player') Reading Specific Columns using read_excel.
You can use pandas read_excel which has skip_footer argument. See the read_excel docstring for more info on accepted parameters. Valid URL schemes include http, ftp, s3, gcs, and file. sheet_name: str, int, list, or None, default 0. excel_data_df = pandas.read_excel('records.xlsx', sheet_name='Numbers', header=None) If you pass the header value as an integer, let’s say 3. This should work, where skipendrows is number of end rows you want to skip. Apologies. Returns a DataFrame corresponding to the result set of the query string. Dict can contain Series, arrays, constants, or list-like objects. pandas.read_sql_query (sql, con, index_col = None, coerce_float = True, params = None, parse_dates = None, chunksize = None) [source] ¶ Read SQL query into a DataFrame. Changed in version 0.23.0: If data is a dict, column order follows insertion-order for Python 3.6 and later.