{"id":35492669,"url":"https://github.com/vivek-v-rao/cpp-dataframe","last_synced_at":"2026-01-04T17:01:26.586Z","repository":{"id":330995780,"uuid":"1124779884","full_name":"vivek-v-rao/cpp-dataframe","owner":"vivek-v-rao","description":"C++17 dataframe featuring CSV/binary I/O, date/datetime indices, arithmetic transforms, statistics (including rolling windows and multiple correlation types), and random data generators","archived":false,"fork":false,"pushed_at":"2025-12-29T16:48:15.000Z","size":632,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-01T20:14:17.901Z","etag":null,"topics":["cpp","cpp17","dataframe","statistics","time-series","time-series-analysis"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vivek-v-rao.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-12-29T15:49:17.000Z","updated_at":"2025-12-30T18:55:25.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/vivek-v-rao/cpp-dataframe","commit_stats":null,"previous_names":["vivek-v-rao/cpp-dataframe"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/vivek-v-rao/cpp-dataframe","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vivek-v-rao%2Fcpp-dataframe","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vivek-v-rao%2Fcpp-dataframe/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vivek-v-rao%2Fcpp-dataframe/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vivek-v-rao%2Fcpp-dataframe/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vivek-v-rao","download_url":"https://codeload.github.com/vivek-v-rao/cpp-dataframe/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vivek-v-rao%2Fcpp-dataframe/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28206344,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2026-01-04T02:00:06.065Z","response_time":58,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cpp","cpp17","dataframe","statistics","time-series","time-series-analysis"],"created_at":"2026-01-03T16:11:56.229Z","updated_at":"2026-01-04T17:01:26.579Z","avatar_url":"https://github.com/vivek-v-rao.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# C++ DataFrame Library\r\n\r\nA header-only (with small cpp helpers) C++17 implementation of a DataFrame that mimics some features familiar from Python's pandas—CSV/binary I/O, typed indices (including dates/datetimes), arithmetic transformations, statistics, rolling windows, correlations, sampling, and more. This repository also contains multiple sample programs demonstrating common workflows (basic loading, arithmetic, statistics, indexing, I/O, custom construction, intraday data).\r\n\r\n## Motivation\r\n\r\nC++ lacks a convenient, flexible in-memory tabular structure. Most teams end up reinventing custom row/column utilities or fall back to Python/R for data munging even when their core systems are C++. This project aims to provide a pragmatic DataFrame with the following goals:\r\n\r\n- **Native C++17**: no external dependencies apart from the standard library.\r\n- **Typed indices**: indexes can be integers, strings, `Date`, `DateTime`, etc., with custom parsing/formatting.\r\n- **Rich operations**: arithmetic, transforms, rolling statistics, correlations, resampling, selection, sorting.\r\n- **Simple I/O**: read/write CSVs, binary snapshots, contiguous buffers for hand-off to other libraries.\r\n- **Testable examples**: multiple `x_*` sample apps show end-to-end usage.\r\n\r\n## Key Features\r\n\r\n- **Construction \u0026 I/O**\r\n - `from_csv`, `from_vectors`, `random_normal`, `random_uniform`, `from_binary`, `from_binary_file`.\r\n - `to_csv`, `to_csv_file`, `to_binary`, `to_binary_file`, `to_row_major`, `to_column_major`.\r\n- **Index support**\r\n - Template `DataFrame\u003cIndexT\u003e` with built-in `Date` and `DateTime` helpers and parsing/formatting.\r\n - Selection: `select_rows`, `slice_rows_range`, `head/tail`, `sort_rows_by_column`, `sort_columns_by_row`.\r\n- **Column operations**\r\n - Arithmetic (`add`, `subtract`, `multiply`, `divide`), log/exp, power, normalization, standardization, scaling by scalars or other frames.\r\n - `add_column` for derived series.\r\n- **Statistics \u0026 Analytics**\r\n - Column stats, summary with missing-data info, percentiles, rolling mean/std/rms, EMA, correlations (Pearson, Spearman, Kendall), covariance, percentiles.\r\n - Resampling, NaN removal, random resampling, random-data generators (normal with optional correlation, uniform).\r\n- **Printing utilities**\r\n - `print_frame`, column summaries, percentiles, autocorrelations.\r\n - Sample programs (`x_basic`, `x_arithmetic`, `x_stats`, `x_indexing`, `x_io`, `x_construct`, `x_intraday`) cover different use cases.\r\n\r\n## Sample Programs\r\n\r\nAll executables build via `make`. `make run` executes:\r\n\r\n| Program | Description |\r\n|----------------|-------------|\r\n| `df_demo` | Comprehensive tour: CSV load, returns, stats, rolling metrics, correlations, binary I/O, random data, percentiles. |\r\n| `x_basic` | Load prices and print shapes/head/tail. |\r\n| `x_arithmetic` | Scalar and element-wise arithmetic/log/exp transforms. |\r\n| `x_stats` | Returns, summary stats, correlations, rolling stats. |\r\n| `x_indexing` | Row slicing, selection, sorting. |\r\n| `x_io` | CSV/binary round trip, contiguous buffer export. |\r\n| `x_construct` | Build frames from vectors and add columns. |\r\n| `x_intraday` | Intraday datetime indices, sorting, rolling mean. |\r\n\r\n## Limitations / Future Work\r\n\r\n- **Group-by / joins**: Not yet implemented; all operations treat columns independently. Users needing relational joins or aggregations must implement them manually or extend the library.\r\n- **Type coverage**: Columns are `double` only. Adding string or integer data columns would require significant rework.\r\n- **Performance**: Current storage is `std::vector\u003cstd::vector\u003cdouble\u003e\u003e`; heavy numeric workloads might prefer contiguous storage and SIMD-friendly operations.\r\n- **Error handling**: Many functions throw `std::runtime_error` for invalid input; there is no soft error mode.\r\n- **Thread safety**: No synchronization primitives; users must guard access if using from multiple threads.\r\n- **Binary format**: Custom, undocumented beyond code comments; subject to change.\r\n- **Dependencies**: Standard library only means no GPU/BLAS acceleration; integration with third-party libraries could be added.\r\n\r\n## Building\r\n\r\n```bash\r\n# GCC (default)\r\nmake # build df_demo + samples\r\nmake run # run df_demo + samples sequentially\r\n\r\n# Clang\r\nmake -f Makefile.clang\r\nmake -f Makefile.clang run\r\n\r\n# MSVC (cl.exe in PATH)\r\nmake -f Makefile.msvc\r\nmake -f Makefile.msvc run\r\n\r\n./df_demo # run the main demo manually\r\n```\r\n\r\nEnsure the CSV inputs (e.g., `prices_2000_on.csv`, `SPY_intraday.csv`) are in the working directory.\r\n\r\n## Contributing\r\n\r\nIssues and pull requests are welcome.\r\n\r\n## License\r\n\r\nMIT License.\r\n\r\n## Sample Output\r\n\r\nHere is the output of the sample programs compiled with g++.\r\n\r\n```\r\n--- running df_demo ---\r\nloaded prices dataframe with 6536 rows and 10 columns\r\n\r\nprice data\r\n Date VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2000-01-03 nan 91.617100 nan nan nan nan nan nan nan nan\r\n 2000-01-04 nan 88.034200 nan nan nan nan nan nan nan nan\r\n 2000-01-05 nan 88.191800 nan nan nan nan nan nan nan nan\r\n 2000-01-06 nan 86.774400 nan nan nan nan nan nan nan nan\r\n 2000-01-07 nan 91.813900 nan nan nan nan nan nan nan nan\r\n...\r\n 2025-12-19 335.269000 680.590000 95.460000 53.720000 96.270000 87.550000 96.240000 82.760000 110.130000 80.360000\r\n 2025-12-22 337.600000 684.830000 95.700000 54.010000 96.310000 87.360000 96.140000 82.720000 110.110000 80.430000\r\n 2025-12-23 338.720000 687.960000 96.290000 54.310000 96.260000 87.500000 96.100000 82.680000 110.220000 80.490000\r\n 2025-12-24 339.880000 690.380000 96.410000 54.420000 96.550000 88.030000 96.350000 82.730000 110.650000 80.640000\r\n 2025-12-26 339.670000 690.310000 96.570000 54.800000 96.540000 87.740000 96.440000 82.790000 110.640000 80.600000\r\n\r\nreturn scaling factor: 100\r\n\r\ncomputed simple returns (proportional changes)\r\n\r\nreturns\r\n Date VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2000-01-04 nan -3.910733 nan nan nan nan nan nan nan nan\r\n 2000-01-05 nan 0.179021 nan nan nan nan nan nan nan nan\r\n 2000-01-06 nan -1.607179 nan nan nan nan nan nan nan nan\r\n 2000-01-07 nan 5.807588 nan nan nan nan nan nan nan nan\r\n 2000-01-10 nan 0.343085 nan nan nan nan nan nan nan nan\r\n...\r\n 2025-12-19 0.891223 0.906347 0.674963 0.977444 -0.048797 -0.373245 -0.224971 -0.018121 -0.257214 -0.048508\r\n 2025-12-22 0.695263 0.622989 0.251414 0.539836 0.041550 -0.217019 -0.103907 -0.048333 -0.018160 0.087108\r\n 2025-12-23 0.331754 0.457048 0.616510 0.555453 -0.051916 0.160256 -0.041606 -0.048356 0.099900 0.074599\r\n 2025-12-24 0.342466 0.351765 0.124624 0.202541 0.301267 0.605714 0.260146 0.060474 0.390129 0.186359\r\n 2025-12-26 -0.061787 -0.010139 0.165958 0.698273 -0.010357 -0.329433 0.093409 0.072525 -9.037506e-03 -0.049603\r\n\r\nreturn statistics\r\n statistic VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n n 6169 6535 6119 5713 4533 5891 5891 5891 5891 4709\r\n median 0.0815 0.0693 0.0734 0.0958 0.0356 0.0517 0.0238 0.0000 0.0411 0.0319\r\n mean 0.0437 0.0384 0.0327 0.0499 0.0206 0.0189 0.0154 7.9134e-03 0.0198 0.0217\r\n sd 1.2084 1.2219 1.3128 1.7051 0.6834 0.9060 0.4307 0.0960 0.5286 0.6936\r\n skew -0.0994 0.0412 -0.0294 0.5021 -1.6047 0.0545 0.0818 0.2979 0.0122 0.7469\r\n ex_kurtosis 11.1870 12.0317 13.1951 18.4334 35.4483 3.4569 2.5309 7.1314 57.7707 42.2739\r\n min -11.3808 -10.9424 -11.1630 -16.1662 -10.1104 -6.6683 -2.5073 -0.6567 -9.1110 -8.0974\r\n max 12.8299 14.5197 15.8874 22.7701 6.7298 7.5195 3.4263 0.9975 9.7678 12.2690\r\n\r\nreturn summary with missing data\r\n column first_idx last_idx n median mean sd skew ex_kurt min max\r\n VTI 2001-06-18 2025-12-26 6169 0.0815 0.0437 1.2084 -0.0994 11.1870 -11.3808 12.8299\r\n SPY 2000-01-04 2025-12-26 6535 0.0693 0.0384 1.2219 0.0412 12.0317 -10.9424 14.5197\r\n EFA 2001-08-28 2025-12-26 6119 0.0734 0.0327 1.3128 -0.0294 13.1951 -11.1630 15.8874\r\n EEM 2003-04-15 2025-12-26 5713 0.0958 0.0499 1.7051 0.5021 18.4334 -16.1662 22.7701\r\n EMB 2007-12-20 2025-12-26 4533 0.0356 0.0206 0.6834 -1.6047 35.4483 -10.1104 6.7298\r\n TLT 2002-07-31 2025-12-26 5891 0.0517 0.0189 0.9060 0.0545 3.4569 -6.6683 7.5195\r\n IEF 2002-07-31 2025-12-26 5891 0.0238 0.0154 0.4307 0.0818 2.5309 -2.5073 3.4263\r\n SHY 2002-07-31 2025-12-26 5891 0.0000 0.0079 0.0960 0.2979 7.1314 -0.6567 0.9975\r\n LQD 2002-07-31 2025-12-26 5891 0.0411 0.0198 0.5286 0.0122 57.7707 -9.1110 9.7678\r\n HYG 2007-04-12 2025-12-26 4709 0.0319 0.0217 0.6936 0.7469 42.2739 -8.0974 12.2690\r\n\r\nreturn percentiles\r\n percentile VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 0 -11.3808 -10.9424 -11.1630 -16.1662 -10.1104 -6.6683 -2.5073 -0.6567 -9.1110 -8.0974\r\n 1 -3.3743 -3.4067 -3.6176 -4.6095 -1.7526 -2.2932 -1.1116 -0.2538 -1.2139 -1.9958\r\n 5 -1.8150 -1.9025 -1.9351 -2.3772 -0.8258 -1.4526 -0.6793 -0.1316 -0.7004 -0.8363\r\n 25 -0.4428 -0.4596 -0.5514 -0.7309 -0.2157 -0.5124 -0.2433 -0.0358 -0.2153 -0.1913\r\n 50 0.0815 0.0693 0.0734 0.0958 0.0356 0.0517 0.0238 0.0000 0.0411 0.0319\r\n 75 0.5980 0.5992 0.6649 0.8551 0.2755 0.5461 0.2684 0.0484 0.2667 0.2503\r\n 95 1.7093 1.7258 1.7947 2.2937 0.8211 1.4115 0.6765 0.1606 0.6702 0.8589\r\n 99 3.2556 3.3479 3.4150 4.4955 1.8954 2.3580 1.1482 0.2846 1.2054 1.8140\r\n 100 12.8299 14.5197 15.8874 22.7701 6.7298 7.5195 3.4263 0.9975 9.7678 12.2690\r\n\r\nrow completeness for returns\r\nrows with complete data: 4533\r\nfirst complete index: 2007-12-20\r\nlast complete index: 2025-12-26\r\n\r\nreturn autocorrelations\r\n lag VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 1 -0.085 -0.088 -0.099 -0.111 0.092 -0.027 -0.027 -0.070 -0.001 0.001\r\n 2 0.001 -0.020 0.004 -0.036 0.058 -0.050 -0.042 -0.026 -0.052 0.013\r\n 3 0.009 -0.004 0.006 0.030 -0.002 -0.028 -0.004 -0.018 0.005 -0.064\r\n 4 -0.041 -0.026 -0.015 -0.042 0.021 0.005 0.015 0.012 -0.036 0.005\r\n 5 -0.016 -0.013 -0.015 -0.010 -0.017 -0.004 -0.012 0.011 0.018 0.007\r\n\r\nbootstrapped return autocorrelations\r\n lag VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 1 0.005 0.001 0.002 -0.009 0.012 0.007 0.028 0.029 0.006 -0.008\r\n 2 -0.021 -0.014 -0.021 -0.025 -0.004 0.006 0.006 -0.008 0.007 -0.005\r\n 3 -0.015 -0.023 -0.019 -0.013 0.005 0.007 0.016 0.019 0.003 0.017\r\n 4 -0.002 0.013 0.022 0.022 0.009 0.007 -0.007 0.006 0.009 0.000\r\n 5 0.007 -0.002 -0.010 -0.013 0.019 -0.015 -0.009 -0.003 0.012 0.017\r\n\r\nreturn correlation matrix\r\n column VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n VTI 1.000 0.994 0.885 0.826 0.406 -0.314 -0.297 -0.224 0.204 0.689\r\n SPY 0.994 1.000 0.887 0.826 0.393 -0.316 -0.300 -0.227 0.211 0.691\r\n EFA 0.885 0.887 1.000 0.878 0.398 -0.300 -0.271 -0.187 0.234 0.671\r\n EEM 0.826 0.826 0.878 1.000 0.367 -0.281 -0.274 -0.212 0.193 0.621\r\n EMB 0.406 0.393 0.398 0.367 1.000 0.164 0.198 0.175 0.452 0.487\r\n TLT -0.314 -0.316 -0.300 -0.281 0.164 1.000 0.912 0.567 0.538 -0.140\r\n IEF -0.297 -0.300 -0.271 -0.274 0.198 0.912 1.000 0.758 0.556 -0.099\r\n SHY -0.224 -0.227 -0.187 -0.212 0.175 0.567 0.758 1.000 0.363 -0.058\r\n LQD 0.204 0.211 0.234 0.193 0.452 0.538 0.556 0.363 1.000 0.456\r\n HYG 0.689 0.691 0.671 0.621 0.487 -0.140 -0.099 -0.058 0.456 1.000\r\n\r\nreturn Spearman correlation\r\n column VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n VTI 1.000 0.991 0.826 0.747 0.386 -0.270 -0.274 -0.192 0.072 0.679\r\n SPY 0.991 1.000 0.825 0.744 0.386 -0.272 -0.275 -0.194 0.071 0.676\r\n EFA 0.826 0.825 1.000 0.809 0.433 -0.233 -0.217 -0.116 0.111 0.647\r\n EEM 0.747 0.744 0.809 1.000 0.421 -0.232 -0.228 -0.144 0.080 0.591\r\n EMB 0.386 0.386 0.433 0.421 1.000 0.229 0.253 0.237 0.471 0.534\r\n TLT -0.270 -0.272 -0.233 -0.232 0.229 1.000 0.920 0.597 0.733 -0.076\r\n IEF -0.274 -0.275 -0.217 -0.228 0.253 0.920 1.000 0.762 0.744 -0.053\r\n SHY -0.192 -0.194 -0.116 -0.144 0.237 0.597 0.762 1.000 0.562 0.024\r\n LQD 0.072 0.071 0.111 0.080 0.471 0.733 0.744 0.562 1.000 0.297\r\n HYG 0.679 0.676 0.647 0.591 0.534 -0.076 -0.053 0.024 0.297 1.000\r\n\r\nreturn Kendall tau\r\n column VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n VTI 1.000 0.909 0.627 0.563 0.272 -0.180 -0.188 -0.140 0.021 0.497\r\n SPY 0.909 1.000 0.626 0.557 0.271 -0.179 -0.186 -0.139 0.022 0.492\r\n EFA 0.627 0.626 1.000 0.616 0.306 -0.148 -0.143 -0.085 0.054 0.466\r\n EEM 0.563 0.557 0.616 1.000 0.297 -0.144 -0.146 -0.098 0.034 0.421\r\n EMB 0.272 0.271 0.306 0.297 1.000 0.164 0.181 0.166 0.341 0.389\r\n TLT -0.180 -0.179 -0.148 -0.144 0.164 1.000 0.761 0.443 0.574 -0.051\r\n IEF -0.188 -0.186 -0.143 -0.146 0.181 0.761 1.000 0.591 0.589 -0.035\r\n SHY -0.140 -0.139 -0.085 -0.098 0.166 0.443 0.591 1.000 0.429 0.017\r\n LQD 0.021 0.022 0.054 0.034 0.341 0.574 0.589 0.429 1.000 0.201\r\n HYG 0.497 0.492 0.466 0.421 0.389 -0.051 -0.035 0.017 0.201 1.000\r\n\r\nreturn covariance matrix\r\n column VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n VTI 1.611 1.585 1.550 1.830 0.352 -0.387 -0.167 -0.027 0.147 0.611\r\n SPY 1.585 1.579 1.538 1.812 0.338 -0.386 -0.167 -0.027 0.150 0.606\r\n EFA 1.550 1.538 1.903 2.115 0.375 -0.402 -0.166 -0.024 0.183 0.646\r\n EEM 1.830 1.812 2.115 3.047 0.438 -0.477 -0.212 -0.035 0.191 0.757\r\n EMB 0.352 0.338 0.375 0.438 0.467 0.109 0.060 0.011 0.175 0.232\r\n TLT -0.387 -0.386 -0.402 -0.477 0.109 0.945 0.393 0.052 0.297 -0.095\r\n IEF -0.167 -0.167 -0.166 -0.212 0.060 0.393 0.196 0.032 0.140 -0.031\r\n SHY -0.027 -0.027 -0.024 -0.035 0.011 0.052 0.032 8.902e-03 0.019 -3.850e-03\r\n LQD 0.147 0.150 0.183 0.191 0.175 0.297 0.140 0.019 0.321 0.180\r\n HYG 0.611 0.606 0.646 0.757 0.232 -0.095 -0.031 -3.850e-03 0.180 0.487\r\n\r\nreturns (%) first rows\r\n Date SPY EFA\r\n 2000-01-04 -3.910733 nan\r\n 2000-01-05 0.179021 nan\r\n 2000-01-06 -1.607179 nan\r\n 2000-01-07 5.807588 nan\r\n 2000-01-10 0.343085 nan\r\n\r\nreturns sorted by SPY\r\n Date VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2020-03-16 -11.380825 -10.942361 -10.319491 -12.479065 -4.617264 6.476601 2.641720 0.162222 -1.422204 -5.496592\r\n 2008-10-15 -9.352784 -9.844733 -10.669929 -16.166236 -0.578211 0.956207 0.819137 0.334834 0.000000 -4.363554\r\n 2020-03-12 -9.723928 -9.567668 -10.990059 -10.010762 -4.924777 0.619703 0.050845 -0.023237 -4.771914 -3.995457\r\n 2008-12-01 -8.938975 -8.857721 -8.171475 -9.629850 -2.990074 3.829675 1.353810 0.164502 1.563262 -2.062293\r\n 2008-09-29 -6.588890 -7.836196 -11.163045 -11.679326 -1.800770 2.909952 1.394669 0.575469 -9.111039 -8.097432\r\n\r\nreturns columns sorted by first row\r\n Date SPY VTI EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2000-01-04 -3.910733 nan nan nan nan nan nan nan nan nan\r\n 2000-01-05 0.179021 nan nan nan nan nan nan nan nan nan\r\n 2000-01-06 -1.607179 nan nan nan nan nan nan nan nan nan\r\n\r\ncustom dataframe from vectors\r\n CustomDate Alpha Beta\r\n 2025-01-01 1.000000 2.000000\r\n 2025-01-02 3.000000 4.000000\r\n 2025-01-03 5.000000 6.000000\r\n\r\nstandardized returns (z-scores)\r\n Date SPY EFA\r\n 2000-01-04 -3.232033 nan\r\n 2000-01-05 0.115109 nan\r\n 2000-01-06 -1.346755 nan\r\n 2000-01-07 4.721647 nan\r\n 2000-01-10 0.249382 nan\r\n\r\nnormalized returns (last rows)\r\n Date SPY EFA\r\n 2025-12-19 0.465347 0.437627\r\n 2025-12-22 0.454219 0.421969\r\n 2025-12-23 0.447702 0.435466\r\n 2025-12-24 0.443567 0.417282\r\n 2025-12-26 0.429353 0.418810\r\n\r\nreturns 2003-04-15..2003-04-22\r\n Date SPY EFA\r\n 2003-04-15 0.933140 1.371317\r\n 2003-04-16 -1.704200 -0.660585\r\n 2003-04-17 1.484394 1.444811\r\n 2003-04-21 0.100523 -0.348365\r\n 2003-04-22 1.885125 1.449605\r\n\r\nreturns at endpoints\r\n Date SPY TLT\r\n 2000-01-04 -3.910733 nan\r\n 2025-12-26 -0.010139 -0.329433\r\n\r\nlog price preview\r\n Date SPY TLT\r\n 2000-01-03 4.517618 nan\r\n 2000-01-04 4.477725 nan\r\n 2000-01-05 4.479514 nan\r\n\r\nexp(log price) preview\r\n Date SPY TLT\r\n 2000-01-03 91.617100 nan\r\n 2000-01-04 88.034200 nan\r\n 2000-01-05 88.191800 nan\r\n\r\nfirst two price columns\r\n Date VTI SPY\r\n 2000-01-03 nan 91.617100\r\n 2000-01-04 nan 88.034200\r\n 2000-01-05 nan 88.191800\r\n\r\nlast two price columns\r\n Date LQD HYG\r\n 2000-01-03 nan nan\r\n 2000-01-04 nan nan\r\n 2000-01-05 nan nan\r\n\r\nSPY returns sample: first=-3.91073, last=-0.0101393, count=6535\r\n\r\nSPY returns with squared column\r\n Date SPY SPY_sq\r\n 2000-01-04 -3.910733 15.293831\r\n 2000-01-05 0.179021 0.032049\r\n 2000-01-06 -1.607179 2.583024\r\n\r\nrow-major buffer sample: [nan, -3.91073, nan, ...]\r\n\r\nreturns reloaded from binary\r\n Date SPY EFA\r\n 2000-01-04 -3.910733 nan\r\n 2000-01-05 0.179021 nan\r\n 2000-01-06 -1.607179 nan\r\n\r\nsample datetime-indexed returns\r\n timestamp SPY_return\r\n2000-01-04 00:00:00 -3.910730\r\n2000-01-05 01:00:00 0.179021\r\n2000-01-06 02:00:00 -1.607180\r\n2000-01-07 03:00:00 5.807590\r\n2000-01-10 04:00:00 0.343085\r\nfirst row values: SPY=nan, EFA=-3.91073\r\n\r\n5-day rolling mean\r\n Date SPY EFA\r\n 2000-01-10 0.162357 nan\r\n 2000-01-11 0.705186 nan\r\n 2000-01-12 0.470429 nan\r\n\r\n5-day rolling std\r\n Date SPY EFA\r\n 2000-01-10 3.592454 nan\r\n 2000-01-11 2.975157 nan\r\n 2000-01-12 3.071794 nan\r\n\r\n5-day rolling rms\r\n Date SPY EFA\r\n 2000-01-10 3.217288 nan\r\n 2000-01-11 2.752914 nan\r\n 2000-01-12 2.787479 nan\r\n\r\nEMA(alpha=0.1) first rows\r\n Date SPY EFA\r\n 2000-01-04 -3.910733 nan\r\n 2000-01-05 -3.501757 nan\r\n 2000-01-06 -3.312300 nan\r\nrows before NaN removal: 3, after: 0, columns after dropping NaNs: 0\r\n\r\nrandom normal target correlation: 0.7\r\n\r\nrandom normal stats\r\n statistic Alpha Beta Gamma\r\n n 1000 1000 1000\r\n median -0.028973 0.034076 -0.029050\r\n mean -0.019795 0.043795 9.845398e-04\r\n sd 1.013260 1.007175 1.034801\r\n skew 0.055334 0.068212 0.148109\r\n ex_kurtosis 9.546673e-03 0.166948 0.061524\r\n min -3.364096 -2.849686 -3.249447\r\n max 3.187033 3.316192 4.018276\r\n\r\nrandom normal correlations\r\n column Alpha Beta Gamma\r\n Alpha 1.000 0.837 0.837\r\n Beta 0.837 1.000 0.683\r\n Gamma 0.837 0.683 1.000\r\n\r\nrandom normal covariances\r\n column Alpha Beta Gamma\r\n Alpha 1.027 0.854 0.877\r\n Beta 0.854 1.014 0.712\r\n Gamma 0.877 0.712 1.071\r\n\r\nrandom uniform sample\r\n index U1 U2 U3\r\n 0 0.7225 0.8319 0.7341\r\n 1 0.7010 0.1933 0.9773\r\n 2 0.5777 0.5138 0.8515\r\n 3 0.3811 0.2739 0.6406\r\n 4 0.4936 0.3368 0.6163\r\n\r\nreturns shape: (6535, 10)\r\n\r\nSPY intraday sample (first 5 rows)\r\n Datetime Open High Low Close Volume\r\n2025-10-28 13:20:00 687.059998 687.299988 687.049988 687.265015 0.000000\r\n2025-10-28 13:25:00 687.270020 687.270020 686.969971 687.010010 3.912220e+05\r\n2025-10-28 13:30:00 687.020020 687.140015 686.875000 687.075012 3.632480e+05\r\n2025-10-28 13:35:00 687.059998 687.280029 687.059998 687.125000 3.217930e+05\r\n2025-10-28 13:40:00 687.130005 687.325500 687.114990 687.299988 4.112810e+05\r\n\r\n--- running x_basic ---\r\nprices shape: (6536, 10)\r\ncolumns: VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n\r\nfirst rows\r\n\r\nprices head\r\n Date VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2000-01-03 nan 91.617100 nan nan nan nan nan nan nan nan\r\n 2000-01-04 nan 88.034200 nan nan nan nan nan nan nan nan\r\n 2000-01-05 nan 88.191800 nan nan nan nan nan nan nan nan\r\n\r\nprices tail\r\n Date VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2025-12-23 338.720000 687.960000 96.290000 54.310000 96.260000 87.500000 96.100000 82.680000 110.220000 80.490000\r\n 2025-12-24 339.880000 690.380000 96.410000 54.420000 96.550000 88.030000 96.350000 82.730000 110.650000 80.640000\r\n 2025-12-26 339.670000 690.310000 96.570000 54.800000 96.540000 87.740000 96.440000 82.790000 110.640000 80.600000\r\n\r\n--- running x_arithmetic ---\r\n\r\noriginal subset\r\n Date SPY EFA\r\n 2000-01-03 91.617100 nan\r\n 2000-01-04 88.034200 nan\r\n 2000-01-05 88.191800 nan\r\n 2000-01-06 86.774400 nan\r\n 2000-01-07 91.813900 nan\r\n\r\n+2\r\n Date SPY EFA\r\n 2000-01-03 93.617100 nan\r\n 2000-01-04 90.034200 nan\r\n 2000-01-05 90.191800 nan\r\n 2000-01-06 88.774400 nan\r\n 2000-01-07 93.813900 nan\r\n\r\n-1\r\n Date SPY EFA\r\n 2000-01-03 90.617100 nan\r\n 2000-01-04 87.034200 nan\r\n 2000-01-05 87.191800 nan\r\n 2000-01-06 85.774400 nan\r\n 2000-01-07 90.813900 nan\r\n\r\n*1.05\r\n Date SPY EFA\r\n 2000-01-03 96.197955 nan\r\n 2000-01-04 92.435910 nan\r\n 2000-01-05 92.601390 nan\r\n 2000-01-06 91.113120 nan\r\n 2000-01-07 96.404595 nan\r\n\r\n/2\r\n Date SPY EFA\r\n 2000-01-03 45.808550 nan\r\n 2000-01-04 44.017100 nan\r\n 2000-01-05 44.095900 nan\r\n 2000-01-06 43.387200 nan\r\n 2000-01-07 45.906950 nan\r\n\r\nlog subset\r\n Date SPY EFA\r\n 2000-01-03 4.517618 nan\r\n 2000-01-04 4.477725 nan\r\n 2000-01-05 4.479514 nan\r\n 2000-01-06 4.463312 nan\r\n 2000-01-07 4.519764 nan\r\n\r\nexp(log subset)\r\n Date SPY EFA\r\n 2000-01-03 91.617100 nan\r\n 2000-01-04 88.034200 nan\r\n 2000-01-05 88.191800 nan\r\n 2000-01-06 86.774400 nan\r\n 2000-01-07 91.813900 nan\r\n\r\n--- running x_stats ---\r\n\r\nreturn scaling factor: 100\r\n\r\nreturns head\r\n Date SPY EFA\r\n 2000-01-04 -3.910733 nan\r\n 2000-01-05 0.179021 nan\r\n 2000-01-06 -1.607179 nan\r\n 2000-01-07 5.807588 nan\r\n 2000-01-10 0.343085 nan\r\n\r\nsummary stats\r\n statistic VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n n 6169 6535 6119 5713 4533 5891 5891 5891 5891 4709\r\n median 0.0815 0.0693 0.0734 0.0958 0.0356 0.0517 0.0238 0.0000 0.0411 0.0319\r\n mean 0.0437 0.0384 0.0327 0.0499 0.0206 0.0189 0.0154 7.9134e-03 0.0198 0.0217\r\n sd 1.2084 1.2219 1.3128 1.7051 0.6834 0.9060 0.4307 0.0960 0.5286 0.6936\r\n skew -0.0994 0.0412 -0.0294 0.5021 -1.6047 0.0545 0.0818 0.2979 0.0122 0.7469\r\n\r\ncorrelation matrix\r\n column VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n VTI 1.000 0.994 0.885 0.826 0.406 -0.314 -0.297 -0.224 0.204 0.689\r\n SPY 0.994 1.000 0.887 0.826 0.393 -0.316 -0.300 -0.227 0.211 0.691\r\n EFA 0.885 0.887 1.000 0.878 0.398 -0.300 -0.271 -0.187 0.234 0.671\r\n EEM 0.826 0.826 0.878 1.000 0.367 -0.281 -0.274 -0.212 0.193 0.621\r\n EMB 0.406 0.393 0.398 0.367 1.000 0.164 0.198 0.175 0.452 0.487\r\n TLT -0.314 -0.316 -0.300 -0.281 0.164 1.000 0.912 0.567 0.538 -0.140\r\n IEF -0.297 -0.300 -0.271 -0.274 0.198 0.912 1.000 0.758 0.556 -0.099\r\n SHY -0.224 -0.227 -0.187 -0.212 0.175 0.567 0.758 1.000 0.363 -0.058\r\n LQD 0.204 0.211 0.234 0.193 0.452 0.538 0.556 0.363 1.000 0.456\r\n HYG 0.689 0.691 0.671 0.621 0.487 -0.140 -0.099 -0.058 0.456 1.000\r\n\r\ncovariance matrix\r\n column VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n VTI 1.611295 1.585318 1.550055 1.829655 0.351758 -0.386986 -0.167359 -0.026852 0.146677 0.611001\r\n SPY 1.585318 1.579159 1.538016 1.812257 0.337551 -0.385572 -0.166881 -0.026874 0.150494 0.606072\r\n EFA 1.550055 1.538016 1.903266 2.115369 0.375153 -0.401769 -0.165611 -0.024368 0.183189 0.646452\r\n EEM 1.829655 1.812257 2.115369 3.046807 0.438151 -0.477047 -0.211801 -0.034985 0.191193 0.756800\r\n EMB 0.351758 0.337551 0.375153 0.438151 0.467010 0.109193 0.060052 0.011253 0.175042 0.232344\r\n TLT -0.386986 -0.385572 -0.401769 -0.477047 0.109193 0.945435 0.392953 0.051999 0.296596 -0.095113\r\n IEF -0.167359 -0.166881 -0.165611 -0.211801 0.060052 0.392953 0.196425 0.031692 0.139667 -0.030547\r\n SHY -0.026852 -0.026874 -0.024368 -0.034985 0.011253 0.051999 0.031692 8.902280e-03 0.019416 -3.850090e-03\r\n LQD 0.146677 0.150494 0.183189 0.191193 0.175042 0.296596 0.139667 0.019416 0.320950 0.180481\r\n HYG 0.611001 0.606072 0.646452 0.756800 0.232344 -0.095113 -0.030547 -3.850090e-03 0.180481 0.487371\r\n\r\n5-day rolling mean\r\n Date SPY EFA\r\n 2000-01-10 0.162357 nan\r\n 2000-01-11 0.705186 nan\r\n 2000-01-12 0.470429 nan\r\n\r\n--- running x_indexing ---\r\n\r\nreturn scaling factor: 100\r\n\r\nslice 2002-01-02..2002-01-10\r\n Date SPY EFA\r\n 2002-01-02 1.076130 0.553313\r\n 2002-01-03 1.133950 0.900263\r\n 2002-01-04 0.667484 0.660823\r\n 2002-01-07 -0.705654 -1.329560\r\n 2002-01-08 -0.231223 -1.139470\r\n 2002-01-09 -0.815216 -1.052018\r\n 2002-01-10 0.441290 0.238177\r\n\r\nselected rows\r\n Date SPY TLT\r\n 2000-01-04 -3.910733 nan\r\n 2000-01-05 0.179021 nan\r\n 2000-01-06 -1.607179 nan\r\n\r\nsorted by SPY\r\n Date SPY EFA\r\n 2020-03-16 -10.942361 -10.319491\r\n 2008-10-15 -9.844733 -10.669929\r\n 2020-03-12 -9.567668 -10.990059\r\n 2008-12-01 -8.857721 -8.171475\r\n 2008-09-29 -7.836196 -11.163045\r\n\r\ncolumns sorted by first row\r\n Date SPY VTI EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2000-01-04 -3.910733 nan nan nan nan nan nan nan nan nan\r\n 2000-01-05 0.179021 nan nan nan nan nan nan nan nan nan\r\n 2000-01-06 -1.607179 nan nan nan nan nan nan nan nan nan\r\n\r\n--- running x_io ---\r\n\r\nbinary reload\r\n Date VTI SPY EFA EEM EMB TLT IEF SHY LQD HYG\r\n 2000-01-03 nan 91.617100 nan nan nan nan nan nan nan nan\r\n 2000-01-04 nan 88.034200 nan nan nan nan nan nan nan nan\r\n 2000-01-05 nan 88.191800 nan nan nan nan nan nan nan nan\r\nrow-major dump: nan 91.6171 nan nan nan nan nan nan nan nan nan 88.0342 nan nan nan nan nan nan nan nan nan 88.1918 nan nan nan nan nan nan nan nan\r\ncolumn-major dump: nan nan nan 91.6171 88.0342 88.1918 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\r\n\r\n--- running x_construct ---\r\n\r\nfrom_vectors\r\n CustomDate Alpha Beta\r\n 2024-01-01 1.000000 2.000000\r\n 2024-01-02 3.000000 4.000000\r\n 2024-01-03 5.000000 6.000000\r\n\r\nafter add_column\r\n CustomDate Alpha Beta Gamma\r\n 2024-01-01 1.000000 2.000000 10.000000\r\n 2024-01-02 3.000000 4.000000 20.000000\r\n 2024-01-03 5.000000 6.000000 30.000000\r\n\r\n--- running x_intraday ---\r\n\r\nintraday head\r\n Datetime Open High Low Close\r\n2025-10-28 13:20:00 687.059998 687.299988 687.049988 687.265015\r\n2025-10-28 13:25:00 687.270020 687.270020 686.969971 687.010010\r\n2025-10-28 13:30:00 687.020020 687.140015 686.875000 687.075012\r\n2025-10-28 13:35:00 687.059998 687.280029 687.059998 687.125000\r\n2025-10-28 13:40:00 687.130005 687.325500 687.114990 687.299988\r\n\r\nsorted by close\r\n Datetime Close Volume\r\n2025-11-21 10:35:00 651.429993 2.344265e+06\r\n2025-11-21 11:00:00 651.890015 8.309780e+05\r\n2025-11-20 15:45:00 652.000000 3.757045e+06\r\n2025-11-21 11:15:00 652.239990 7.683840e+05\r\n2025-11-20 15:55:00 652.530029 6.592158e+06\r\n\r\n3-period rolling mean\r\n Datetime Close\r\n2025-10-28 13:30:00 687.116679\r\n2025-10-28 13:35:00 687.070007\r\n2025-10-28 13:40:00 687.166667\r\n```\r\n\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvivek-v-rao%2Fcpp-dataframe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvivek-v-rao%2Fcpp-dataframe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvivek-v-rao%2Fcpp-dataframe/lists"}