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https://github.com/tfussell/xlnt.git
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Thomas Fussell
GitHub
commit
e043aeaff7
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## Examples
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## Examples
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### Simple - reading from an existing xlsx spread sheet.
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The following C plus plus code will read the values from an xlsx file and print the string values to the screen. This is a very simple example to get you started.
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```
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#include <iostream>
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#include <xlnt/xlnt.hpp>
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int main()
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{
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xlnt::workbook wb;
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wb.load("/home/timothymccallum/test.xlsx");
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auto ws = wb.active_sheet();
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std::clog << "Processing spread sheet" << std::endl;
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for (auto row : ws.rows(false))
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{
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for (auto cell : row)
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{
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std::clog << cell.to_string() << std::endl;
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}
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}
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std::clog << "Processing complete" << std::endl;
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return 0;
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}
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```
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Save the contents of the above file
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```
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/home/timothymccallum/process.cpp
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```
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Compile by typing the following command
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```
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g++ -std=c++14 -lxlnt process.cpp -o process
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```
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Excecute by typing the following command
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```
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./process
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```
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The output of the program, in my case, is as follows
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```
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Processing spread sheet
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This is cell A1.
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This is cell B1
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… and this is cell C1
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We are now on the second row at cell A2
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B2
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C2
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Processing complete
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```
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As you can see the process.cpp file simply walks through the spread sheet values row by row and column by column (A1, B1, C1, A2, B2, C2 and so on).
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### Simple - storing a spread sheet in a 2 dimensional C++ Vector for further processing
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Loading a spread sheet into a Vector provides oppourtunities for you to perform high performance processing. There will be more examples on performing fast look-ups, merging data, performing deduplication and more. For now, let's just learn how to get the spread sheet loaded into memory.
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```
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#include <iostream>
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#include <xlnt/xlnt.hpp>
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#include <vector>
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int main()
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{
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xlnt::workbook wb;
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wb.load("/home/timothymccallum/test.xlsx");
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auto ws = wb.active_sheet();
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std::clog << "Processing spread sheet" << std::endl;
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std::clog << "Creating a single vector which stores the whole spread sheet" << std::endl;
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std::vector< std::vector<std::string> > theWholeSpreadSheet;
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for (auto row : ws.rows(false))
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{
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std::clog << "Creating a fresh vector for just this row in the spread sheet" << std::endl;
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std::vector<std::string> aSingleRow;
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for (auto cell : row)
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{
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std::clog << "Adding this cell to the row" << std::endl;
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aSingleRow.push_back(cell.to_string());
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}
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std::clog << "Adding this entire row to the vector which stores the whole spread sheet" << std::endl;
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theWholeSpreadSheet.push_back(aSingleRow);
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}
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std::clog << "Processing complete" << std::endl;
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std::clog << "Reading the vector and printing output to the screen" << std::endl;
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for (int rowInt = 0; rowInt < theWholeSpreadSheet.size(); rowInt++)
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{
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for (int colInt = 0; colInt < theWholeSpreadSheet.at(rowInt).size(); colInt++)
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{
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std::cout << theWholeSpreadSheet.at(rowInt).at(colInt) << std::endl;
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}
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}
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return 0;
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}
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```
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Save the contents of the above file
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```
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/home/timothymccallum/process.cpp
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```
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Compile by typing the following command
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```
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g++ -std=c++14 -lxlnt process.cpp -o process
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```
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Excecute by typing the following command
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```
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./process
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```
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The output of the program, in my case, is as follows
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```
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Processing spread sheet
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Creating a single vector which stores the whole spread sheet
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Creating a fresh vector for just this row in the spread sheet
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Adding this cell to the row
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Adding this cell to the row
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Adding this cell to the row
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Adding this entire row to the vector which stores the whole spread sheet
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Creating a fresh vector for just this row in the spread sheet
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Adding this cell to the row
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Adding this cell to the row
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Adding this cell to the row
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Adding this entire row to the vector which stores the whole spread sheet
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Processing complete
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Reading the vector and printing output to the screen
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This is cell A1.
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This is cell B1
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… and this is cell C1
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We are now on the second row at cell A2
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B2
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C2
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```
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You will have noticed that this process is very fast. If you type the "time" as shown below, you can measure just how fast loading and retrieving your spread sheet is, using xlnt; In this case only a fraction of a second. More on this later.
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```
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time ./process
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...
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real 0m0.044s
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```
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### Simple - writing values to a new xlsx spread sheet.
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@ -8,6 +8,59 @@
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## vcpkg
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## Compiling from Source - Ubuntu 16.04 LTS (Xenial Xerus)
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Please run the following commands and read the comments as you go.
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You will notice that we update and upgrade quite frequently, this is to ensure that all dependancies are met at all times. You will also notice that we are installing some libraries from source, there are two reasons for this; firstly in the event that the library is not available through apt-get or secondly, because we want the very latest version (not the version available in apt-get).
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```
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sudo apt-get update
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sudo apt-get upgrade
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sudo apt-get install cmake
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sudo apt-get install zlibc
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sudo add-apt-repository ppa:ubuntu-toolchain-r/test
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sudo apt update
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sudo apt-get upgrade
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sudo apt-get install gcc-6 g++-6
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sudo apt update
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sudo apt-get upgrade
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```
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The following steps will set up some environment variables for this session
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```
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export CC=/usr/bin/gcc-6
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export CXX=/usr/bin/g++-6
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```
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The following steps will install the latest GCC compiler from source (you will notice that we installed GCC 6 using apt-get, this was a building block to the next step of getting GCC 6.3.0 - the latest compiler available at present).
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Download gcc 6.3.0 from https://gcc.gnu.org/mirrors.html I used the Japanese mirror as this is the closest location to me http://ftp.tsukuba.wide.ad.jp/software/gcc/releases/gcc-6.3.0/gcc-6.3.0.tar.gz
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```
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cd ~
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tar -zxvf Downloads/gcc-6.3.0.tar.gz
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cd gcc-6.3.0
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./contrib/download_prerequisites
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mkdir build
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cd build
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../configure --disable-multilib --enable-languages=c,c++
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make -j 2
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sudo make install
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```
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If the above make command fails please use "make clean pristine" and then remove and remake the build directory. This will clean up and prepare the environment for another attempt. A common reason for failure on virtual machines is lack of RAM (if you don't have enough RAM you may get an error like this "recipe for target 's-attrtab' failed"). Otherwise, if all goes well in about 30 to 60 minutes the compiler will be ready and we will move on to the next steps.
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The following steps will intall xlnt
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Download the zip file from the xlnt repository
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https://github.com/tfussell/xlnt/archive/master.zip
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```
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cd ~
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unzip Downloads/xlnt-master.zip
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cd xlnt-master
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cmake .
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make -j 2
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sudo make install
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```
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The following step will map the shared library names to the location of the corresponding shared library files
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```
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sudo ldconfig
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```
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xlnt will now be ready to use on your Ubuntu instance.
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## Compiling from Source
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Build configurations for Visual Studio, GNU Make, Ninja, and Xcode can be created using [cmake](https://cmake.org/) v3.2+. A full list of cmake generators can be found [here](https://cmake.org/cmake/help/v3.0/manual/cmake-generators.7.html). A basic build would look like (starting in the root xlnt directory):
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