/*
Copyright (c) 2019-2022, Hossein Moein
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of Hossein Moein and/or the DataFrame nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL Hossein Moein BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include <DataFrame/DataFrame.h> // Main DataFrame header
#include <DataFrame/DataFrameFinancialVisitors.h> // Financial algorithms
#include <DataFrame/DataFrameMLVisitors.h> // Machine-learning algorithms
#include <DataFrame/DataFrameStatsVisitors.h> // Statistical algorithms
#include <iostream>
// -----------------------------------------------------------------------------
// DataFrame library is entirely under hmdf name-space
//
using namespace hmdf;
// A DataFrame with ulong index type
//
using ULDataFrame = StdDataFrame<unsigned long>;
// A DataFrame with string index type
//
using StrDataFrame = StdDataFrame<std::string>;
// This is just some arbitrary type to show how any type could be in DataFrame
//
struct MyData {
int i { 10 };
double d { 5.5 };
std::string s { "Boo" };
MyData() = default;
};
// -----------------------------------------------------------------------------
// The main purpose of this file is to introduce the basic operations of
// of DataFrame. For more advanced operations and a complete list of features
// with code samples, see documentation at:
// "https://htmlpreview.github.io/?https://github.com/hosseinmoein/DataFrame/blob/master/docs/HTML/DataFrame.html"
//
int main(int argc, char *argv[]) {
std::vector<unsigned long> idx_col1 = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
std::vector<MyData> mydata_col(10);
std::vector<int> int_col1 = { 1, 2, -3, -4, 5, 6, 7, 8, 9, -10 };
std::vector<double> dbl_col1 = { 0.01, 0.02, 0.03, 0.03, 0.05, 0.06, 0.03, 0.08, 0.09, 0.03 };
ULDataFrame ul_df1;
// One way to load data into the DataFrame is one column at a time.
// A DataFram column could be at most as long as its index column. So, you
// must load the index first before loading any column
//
// Once you load a column or index, the data is moved to DataFrame. The
// original vectors are now empty. There are other ways of loading data
// without the move
//
ul_df1.load_index(std::move(idx_col1));
ul_df1.load_column<double>("dbl_col", std::move(dbl_col1));
ul_df1.load_column<MyData>("my_data_col", std::move(mydata_col));
ul_df1.load_column<int>("integers", std::move(int_col1));
std::vector<unsigned long> idx_col2 = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
std::vector<std::string> str_col1 = { "A", "B", "C", "D", "E", "F", "G", "H", "I", "J" };
std::vector<std::string> str_col2 = { "K", "H", "L", "M", "N", "O", "P", "Q", "R", "S" };
std::vector<double> dbl_col2 = { 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0};
ULDataFrame ul_df2;
// Also, you can load data into a DataFrame all at once
//
ul_df2.load_data(std::move(idx_col2),
std::make_pair("string col", str_col1),
std::make_pair("New York", str_col2),
std::make_pair("numbers", dbl_col2));
StrDataFrame ibm_df;
// Also, you can load data into a DataFrame from a file, suporting a few
// different formats.
// If the file cannot be found, an exception will be thrown. If the DataFrame
// test directory is your current directory when running this, it should work
// fine.
//
ibm_df.read("data/SHORT_IBM.csv", io_format::csv2);
// To access a column, you must know its name (or index) and its type
//
auto &str_col_ref = ul_df2.get_column<std::string>("string col");
const auto &close_const_ref = ibm_df.get_column<double>("IBM_Close");
const auto &index_vec = ibm_df.get_index();
// In case of a "standard" DataFrame (not a view), the columns are returned
// as a reference to a std::vector of type of that column.
//
for (auto citer : str_col_ref)
std::cout << citer << std::endl;
for (std::size_t i = 0; i < str_col_ref.size(); ++i)
std::cout << str_col_ref[i] << std::endl;
// You can write the data to a file or stdout in a few formats
// You must specify all the column types, but only once
// When writing to a file, the file name/path must be create-able.
//
ul_df2.write<std::ostream, std::string, double>(std::cout, io_format::csv2);
ibm_df.write<double, long>("/tmp/test.json", io_format::json);
// You can sort by one or multiple columns
// You must specify all the column types, but only once
//
// Sort first by the index column in ascending order than by "string col"
// column in descending order
//
ul_df2.sort<ULDataFrame::IndexType, std::string, double, std::string>(DF_INDEX_COL_NAME, sort_spec::ascen, "string col", sort_spec::desce);
// You could get another DataFrame by selecting on one or multiple columns
// You must specify all the column types, but only once
//
auto functor = [](const std::string &, const double &val)-> bool { return (val > 150.0); };
auto above_150_df = ibm_df.get_data_by_sel<double, decltype(functor), double, long>("IBM_Close", functor);
// Or, you could choose to get a view. See docs for views
//
auto above_150_view = ibm_df.get_view_by_sel<double, decltype(functor), double, long>("IBM_Close", functor);
// You can get another DataFrame by group-bying on one or multiple columns
// You must specify only the type(s) of column(s), you are group-bying
//
// Group-by column dbl_col, and I am specifying how to summarize the index
// column and each of the other columns
//
auto gb_df = ul_df1.groupby1<double>("dbl_col",
LastVisitor<ULDataFrame::IndexType, ULDataFrame::IndexType>(),
std::make_tuple("integers", "sum_int", SumVisitor<int>()),
std::make_tuple("my_data_col", "last_my_data", LastVisitor<MyData>()));
// You can run statistical, financial, ML, … algorithms on one or multiple
// columns by using visitors. You must specify the column(s) type(s)
//
StdVisitor<double, std::string> stdev;
CorrVisitor<double, std::string> corrl;
ibm_df.visit<double>("IBM_Close", stdev);
std::cout << "Standard deviation of IBM close prices: "
<< stdev.get_result()
<< std::endl;
std::cout << "Correlation between IBM open and close prices: "
<< ibm_df.visit<double, double>("IBM_Open", "IBM_Close", corrl).get_result()
<< std::endl;
return (0);
}
// -----------------------------------------------------------------------------
// Local Variables:
// mode:C++
// tab-width:4
// c-basic-offset:4
// End: