Please continue reading and stay tuned for my Excel in Python series if it sounds great!<\/p>\n
This tutorial is all about saving data to Excel. <\/p>\n
Concretely, I will first introduce Excel\u2019s data structure and lingos. <\/p>\n
Then, you will learn the difference between four popular Python ways to save data to excel, including Finally, I will focus on the You can find all datasets and codes in this tutorial here<\/a>. The data in our example datasets are not actual data and are only used for educational purposes.<\/p>\n Before jumping into reading data from Excel, let\u2019s look at how data is stored in Excel and get ourselves familiar with some Excel lingos.<\/p>\n Simply put, data is stored in cells in Excel, and each cell can be identified with its unique row and column number pair. <\/p>\n Columns in Excel are labeled in alphabets, starting from \u2018 Yes, you got the idea! It is just like the DataFrame data structure in our friends, pandas<\/a>.<\/p>\n And if you want to select an area to dump data into Excel, you can imagine drawing a rectangle data region based on the upper-left cell of the part.<\/p>\n On top of that, other commonly seen Excel lingos include worksheets and workbooks. <\/p>\n Tip<\/strong>: More file extensions supported by Excel can be found in the Microsoft official doc <\/em>here<\/em><\/a>.<\/em><\/p>\n Now, you are ready to know how to read data from Excel!<\/p>\n Here is the summary of popular Python ways to save data to excel:<\/p>\n One of In summary, you can use However, I recommend using Therefore, let\u2019s see how to use Since Before heading over to the When you are trying to write to multiple sheets<\/a>, you need to create an To create an pandas<\/code> and openpyxl<\/code>. <\/p>\npandas.DataFrame.to_excel()<\/strong><\/code> method. I will guide you through four actual use cases in pandas.DataFrame.to_excel()<\/code>, ranging from one Excel worksheet, multiple Excel worksheets, multiple Excel workbooks<\/a>, and dealing with index cases. <\/p>\nQuick Introduction to Excel<\/h2>\n
A<\/code>\u2019, and rows in Excel are labeled in roman numbers, starting from \u20181<\/code>\u2019. For example, in the following Excel picture, A1<\/code> is the cell in the intersection of the first column and first row, \u2018ID\u2019.<\/p>\n![\"\"](\"https:\/\/blog.finxter.com\/wp-content\/uploads\/2022\/04\/image-28.png\")
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.xlsx<\/code> and .xls<\/code>. <\/li>\n<\/ul>\nPopular Python ways to Save Data to Excel<\/h2>\n
![\"save](\"https:\/\/blog.finxter.com\/wp-content\/uploads\/2022\/04\/image-29-1024x495.png\")
<\/figure>\n<\/div>\npandas.DataFrame.to_excel()<\/code>\u2019s advantages is engine compatibility. It uses multiple external engines for different Excel extensions, including openpyxl<\/code>, xlwt<\/code>, and xlsxwriter<\/code>.<\/p>\n\n
xlwt<\/code>\u201d supports old-style Microsoft Excel versions 95 to 2003 files (.xls<\/code>).<\/li>\nopenpyxl<\/code>\u201d supports newer Excel 2010 file formats (.xlsx, .xlsm, .xltx, .xltm<\/code>).<\/li>\nxlsxwriter<\/code>\u201d supports Excel 2007+ file format (.xlsx<\/code>).<\/li>\n<\/ul>\nopenpyxl<\/code> and other libraries for specific Excel file formats and basic data processing. And please remember that openpyxl<\/code> and xlsxwriter<\/code> support newer Excel file formats<\/strong>. <\/p>\npandas.DataFrame.to_excel()<\/code> for data science and analytics applications<\/strong> because it supports most Excel file formats and accepts a DataFrame object with powerful methods.<\/p>\npandas.DataFrame.to_excel()<\/code> to save data to Excel!<\/p>\nSaving Data to Excel – pandas.DataFrame.to_excel()<\/h2>\n
pandas.DataFrame.to_excel()<\/code> is the most powerful and inclusive way to save data from Excel files with different extensions, I will first introduce its syntax and walk you through three use cases with real Python codes below using pandas.DataFrame.to_excel()<\/code>.<\/p>\nMeet pandas.ExcelWriter<\/h3>\n
pandas.DataFrame.to_excel<\/code> method, we need to know a new friend, pandas.ExcelWriter<\/code>. It is a class for writing pandas.DataFrame<\/code> objects into excel sheets.<\/p>\nExcelWriter<\/code> object and pass it to pandas.DataFrame.to_excel()<\/code> as the first parameter.<\/p>\nExcelWriter<\/code> object, we pass something to it according to its syntax:<\/p>\n# Syntax for pandas.ExcelWriter\npandas.ExcelWriter(path[, engine=None[, date_format=None[, datetime_format=None[, mode='w'[, storage_options=None[, if_sheet_exists=None[, engine_kwargs=None[, **kwargs]]]]]])\n<\/pre>\n
![\"\"](\"https:\/\/blog.finxter.com\/wp-content\/uploads\/2022\/04\/image-30-1024x260.png\")
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![\"\ud83d\udca1\"](\"https:\/\/s.w.org\/images\/core\/emoji\/13.1.0\/72x72\/1f4a1.png\")
Tip<\/strong>: Generally, you only need to use the four parameters in bold format. The default engine is automatically chosen depending on the file extension.<\/p>\n![\"\"](\"https:\/\/blog.finxter.com\/wp-content\/uploads\/2022\/04\/image-31-1024x417.png\")
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