How to Create a Pivot table with multiple indexes from an excel sheet using Pandas in Python? Display the Pandas DataFrame in table style and border around the table and not around the rows. Article Contributed By :. Abhishek De.
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When we print the DataFrame object, the output is a two-dimensional table. It looks similar to an excel sheet record. If the first column in the Excel or CSV file has index values, then you can do the following to remove the Unnamed column in Pandas.
If the unnamed column is other than first, you can write the following line to remove it from any index. To get the list of column headers, use columns. You can see that we get the list of all the columns of DataFrame. To print the column data as a list, use the df.
The tolist function converts the specific column values to the list. If you want to act header as a specific row, you have to pass the header value as an integer.
If you have passed the 4th row as a header row, then the fourth row will be treated as the header row, and the values will be read from the next row onwards. To import an Excel file into Python using pandas, use the pd. How to import CSV files in Pandas. These rely on a server process running outside of Excel and VBA wrappers to call into that server. It has rudimentary support for user defined functions worksheet functions , but they run outside of the Excel process and only work if there is only one Excel process running.
DataNitro is no longer under active development and is not available to license any more, but it was included here for completeness. For some tasks you may need to read or write an Excel file directly.
For batch processing or tasks running on a server Excel may not be installed. The following packages allow you to read and write Excel files directly without needing to use Excel. Using OpenPyXL you can read and write xlsx, xlsm, xltx and xltm files.
The following code shows how an Excel workbook can be written as an xlsx file with a few lines of Python. The two are completely different and serve different purposes. OpenPyXL covers more advanced features of Excel such as charts, styles, number formatting and conditional formatting. It even includes a tokeniser for parsing Excel formulas! One really nice feature for writing reports is its built-in support for NumPy and Pandas data.
A possible downside of OpenPyXL is that it can be quite slow for handling large files. If you have to write reports with thousands of rows and your application is time-sensitive then XlsxWriter or PyExcelerate may be better choices. If you only need to write Excel workbooks and not read them then XlsxWriter is an easy to use package to use that works well.
If you are working with large files or are particularly concerned about speed then you may find XlsxWriter a better choice than OpenPyXL. It supports features such as formatting and many more, including:. Writing Excel workbooks using XlsxWriter is simple enough. Below is a basic example that shows creating a workbook, adding some data and saving it as an xlsx file.
It takes the hard work out of writing Pandas DataFrames to Excel, and even creating charts. When referencing the Pandas data in the worksheet as the formula in the chart above does , you have to figure out where the data will be in the worksheet so that the formulas point to the correct cells.
For reports involving a lot of formulas or charts this can become problematic as doing something as as simple as adding an extra row requires adjusting all affected formulas.
Rather than writing the workbook cell by cell or row by row, whole tables are added and can include formulas that reference other tables without having to know ahead of time where those tables will be. For more complex reports involving formulas xltable can be very useful.
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