diff --git a/docs/_toc.yml b/docs/_toc.yml index 76e9a19b..2db37224 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -9,6 +9,8 @@ parts: - file: getting_started/index.md sections: - file: getting_started/online_sandbox/sandbox_intro.md + sections: + - file: getting_started/online_sandbox/sandbox_workbook_blank - file: getting_started/install.md - file: getting_started/tutorials/index.md sections: diff --git a/docs/getting_started/online_sandbox/sandbox_intro.md b/docs/getting_started/online_sandbox/sandbox_intro.md index 79897630..666b9104 100644 --- a/docs/getting_started/online_sandbox/sandbox_intro.md +++ b/docs/getting_started/online_sandbox/sandbox_intro.md @@ -12,6 +12,6 @@ You can open the sandbox tutorial in one of two ways. You may be forced to use o - Open in [Google Colab](https://githubtocolab.com/casact/chainladder-python/blob/master/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb) (faster, but a free Google Account is required) -- Open in [Binder](https://mybinder.org/v2/gh/casact/chainladder-python/blob/master/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb) (slower, no sign up required) +- Open in [Binder](https://mybinder.org/v2/gh/casact/chainladder-python/master?urlpath=tree/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb) (slower, no sign up required) Just in case you want a copy of the sandbox tutorial notebook, you can download it [here](https://github.com/casact/chainladder-python/blob/master/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb). diff --git a/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb b/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb index f5121162..bb3b46a9 100644 --- a/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb +++ b/docs/getting_started/online_sandbox/sandbox_workbook_blank.ipynb @@ -2,11 +2,16 @@ "cells": [ { "cell_type": "markdown", - "id": "ff5ff833-2b83-46c7-994f-12a19ef2a158", + "id": "0f0e5e3b-ac37-4865-8c51-ded927ea9b46", "metadata": {}, "source": [ - "# Got Stuck?\n", - "Click [here](https://nbviewer.org/github/casact/chainladder-python/blob/master/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb) for the filled in workbook. Have questions? Join the [discussion](https://github.com/casact/chainladder-python/discussions) on GitHub." + "# Online Sandbox Tutorial\n", + "\n", + "Welcome! If you've come here to explore the capabilities of the `chainladder-python` package, you've landed in the perfect spot. This online sandbox tutorial is designed to provide you with a glimpse of the package's functionalities. \n", + "\n", + "We recommend setting aside about **one hour** to complete it.\n", + "\n", + "Got Stuck? Click [here](https://nbviewer.org/github/casact/chainladder-python/blob/master/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb) for the filled in workbook. Have questions? Join the [discussion](https://github.com/casact/chainladder-python/discussions) on GitHub." ] }, { diff --git a/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb b/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb index a732a8fa..737d5f2b 100644 --- a/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb +++ b/docs/getting_started/online_sandbox/sandbox_workbook_filled.ipynb @@ -1,5 +1,13 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "90c5da33-d41d-41a2-9599-3d9e5989fe39", + "metadata": {}, + "source": [ + "# Online Sandbox Tutorial" + ] + }, { "cell_type": "markdown", "id": "d8f38e79-5010-4190-b38c-cbc1d85bde47", diff --git a/docs/library/presentation_assets/star-history-2022103.csv b/docs/library/presentation_assets/star-history-20221003.csv similarity index 100% rename from docs/library/presentation_assets/star-history-2022103.csv rename to docs/library/presentation_assets/star-history-20221003.csv diff --git a/docs/library/presentation_assets/star-history-20231006.csv b/docs/library/presentation_assets/star-history-20231006.csv new file mode 100644 index 00000000..36558bbc --- /dev/null +++ b/docs/library/presentation_assets/star-history-20231006.csv @@ -0,0 +1,53 @@ +casact/chainladder-python,Mon Oct 09 2017 00:15:42 GMT-0700 (Pacific Daylight Time),1 +casact/chainladder-python,Sat Jan 05 2019 01:11:49 GMT-0800 (Pacific Standard Time),11 +casact/chainladder-python,Tue Nov 19 2019 18:31:07 GMT-0800 (Pacific Standard Time),21 +casact/chainladder-python,Mon Apr 06 2020 11:24:07 GMT-0700 (Pacific Daylight Time),31 +casact/chainladder-python,Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Daylight Time),41 +casact/chainladder-python,Wed Oct 21 2020 07:03:52 GMT-0700 (Pacific Daylight Time),51 +casact/chainladder-python,Wed Mar 31 2021 20:47:40 GMT-0700 (Pacific Daylight Time),61 +casact/chainladder-python,Tue Jun 01 2021 05:11:26 GMT-0700 (Pacific Daylight Time),71 +casact/chainladder-python,Mon Nov 01 2021 12:03:23 GMT-0700 (Pacific Daylight Time),81 +casact/chainladder-python,Wed Jan 12 2022 20:15:08 GMT-0800 (Pacific Standard Time),91 +casact/chainladder-python,Wed Mar 30 2022 11:26:12 GMT-0700 (Pacific Daylight Time),101 +casact/chainladder-python,Fri Jul 15 2022 22:47:19 GMT-0700 (Pacific Daylight Time),111 +casact/chainladder-python,Sat Jul 30 2022 07:44:13 GMT-0700 (Pacific Daylight Time),121 +casact/chainladder-python,Fri Feb 03 2023 10:21:16 GMT-0800 (Pacific Standard Time),131 +casact/chainladder-python,Sat Jun 10 2023 12:57:22 GMT-0700 (Pacific Daylight Time),141 +casact/chainladder-python,Wed Aug 16 2023 11:06:52 GMT-0700 (Pacific Daylight Time),151 +casact/chainladder-python,Fri Oct 06 2023 09:27:35 GMT-0700 (Pacific Daylight Time),154 +lifelib-dev/lifelib,Thu Mar 01 2018 21:21:43 GMT-0800 (Pacific Standard Time),1 +lifelib-dev/lifelib,Wed Dec 12 2018 19:39:52 GMT-0800 (Pacific Standard Time),9 +lifelib-dev/lifelib,Thu Apr 18 2019 10:33:53 GMT-0700 (Pacific Daylight Time),17 +lifelib-dev/lifelib,Sat Sep 28 2019 07:18:21 GMT-0700 (Pacific Daylight Time),25 +lifelib-dev/lifelib,Mon Feb 24 2020 17:13:30 GMT-0800 (Pacific Standard Time),33 +lifelib-dev/lifelib,Sat Jun 27 2020 04:55:22 GMT-0700 (Pacific Daylight Time),41 +lifelib-dev/lifelib,Sun Jan 24 2021 13:39:13 GMT-0800 (Pacific Standard Time),49 +lifelib-dev/lifelib,Tue May 04 2021 21:44:42 GMT-0700 (Pacific Daylight Time),57 +lifelib-dev/lifelib,Sun Oct 17 2021 14:37:44 GMT-0700 (Pacific Daylight Time),65 +lifelib-dev/lifelib,Sat Mar 12 2022 20:32:35 GMT-0800 (Pacific Standard Time),73 +lifelib-dev/lifelib,Fri Jun 10 2022 02:58:11 GMT-0700 (Pacific Daylight Time),81 +lifelib-dev/lifelib,Tue Aug 02 2022 01:07:04 GMT-0700 (Pacific Daylight Time),89 +lifelib-dev/lifelib,Sat Nov 26 2022 13:01:39 GMT-0800 (Pacific Standard Time),97 +lifelib-dev/lifelib,Fri May 12 2023 08:10:40 GMT-0700 (Pacific Daylight Time),105 +lifelib-dev/lifelib,Sat Aug 05 2023 19:31:42 GMT-0700 (Pacific Daylight Time),113 +lifelib-dev/lifelib,Tue Oct 03 2023 06:39:18 GMT-0700 (Pacific Daylight Time),121 +lifelib-dev/lifelib,Fri Oct 06 2023 10:08:13 GMT-0700 (Pacific Daylight Time),121 +mages/ChainLadder,Wed Jan 07 2015 10:15:00 GMT-0800 (Pacific Standard Time),1 +mages/ChainLadder,Mon Jun 15 2015 21:16:32 GMT-0700 (Pacific Daylight Time),5 +mages/ChainLadder,Tue Jul 19 2016 10:12:40 GMT-0700 (Pacific Daylight Time),9 +mages/ChainLadder,Sun Dec 18 2016 10:20:13 GMT-0800 (Pacific Standard Time),13 +mages/ChainLadder,Sun Nov 12 2017 04:30:22 GMT-0800 (Pacific Standard Time),17 +mages/ChainLadder,Sat Mar 03 2018 03:47:25 GMT-0800 (Pacific Standard Time),21 +mages/ChainLadder,Thu Jul 19 2018 20:01:34 GMT-0700 (Pacific Daylight Time),25 +mages/ChainLadder,Tue Nov 20 2018 20:34:09 GMT-0800 (Pacific Standard Time),29 +mages/ChainLadder,Wed May 29 2019 01:35:04 GMT-0700 (Pacific Daylight Time),33 +mages/ChainLadder,Fri Aug 23 2019 12:05:46 GMT-0700 (Pacific Daylight Time),37 +mages/ChainLadder,Tue Mar 31 2020 05:58:14 GMT-0700 (Pacific Daylight Time),41 +mages/ChainLadder,Mon May 25 2020 12:51:22 GMT-0700 (Pacific Daylight Time),45 +mages/ChainLadder,Wed Dec 09 2020 22:11:55 GMT-0800 (Pacific Standard Time),49 +mages/ChainLadder,Tue Jul 20 2021 23:41:51 GMT-0700 (Pacific Daylight Time),53 +mages/ChainLadder,Sat Mar 12 2022 12:03:42 GMT-0800 (Pacific Standard Time),57 +mages/ChainLadder,Fri Oct 14 2022 10:49:30 GMT-0700 (Pacific Daylight Time),61 +mages/ChainLadder,Wed Feb 22 2023 17:49:29 GMT-0800 (Pacific Standard Time),65 +mages/ChainLadder,Thu May 04 2023 19:39:30 GMT-0700 (Pacific Daylight Time),69 +mages/ChainLadder,Fri Oct 06 2023 10:08:17 GMT-0700 (Pacific Daylight Time),72 diff --git a/docs/library/presentation_assets/star_history.ipynb b/docs/library/presentation_assets/star_history.ipynb index 97407ad2..c7531189 100644 --- a/docs/library/presentation_assets/star_history.ipynb +++ b/docs/library/presentation_assets/star_history.ipynb @@ -6,14 +6,22 @@ "id": "8f6681ca-c6b4-43aa-a736-5824a29ebca5", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 1;\n", - " var nbb_unformatted_code = \"import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nplt.style.use('ggplot')\\n%config InlineBackend.figure_format = 'retina'\\n\\n%load_ext nb_black\";\n", - " var nbb_formatted_code = \"import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\nplt.style.use(\\\"ggplot\\\")\\n%config InlineBackend.figure_format = 'retina'\\n\\n%load_ext nb_black\";\n", + " var nbb_unformatted_code = \"import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport matplotlib.dates as mdates\\n\\nplt.style.use('ggplot')\\n%config InlineBackend.figure_format = 'retina'\\n\\n%load_ext nb_black\";\n", + " var nbb_formatted_code = \"import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport matplotlib.dates as mdates\\n\\nplt.style.use(\\\"ggplot\\\")\\n%config InlineBackend.figure_format = 'retina'\\n\\n%load_ext nb_black\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", @@ -38,6 +46,8 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", "plt.style.use('ggplot')\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", @@ -80,32 +90,32 @@ " \n", " 0\n", " casact/chainladder-python\n", - " Thu Sep 27 2018 20:31:36 GMT-0700 (Pacific Day...\n", - " 9\n", + " Sat Jan 05 2019 01:11:49 GMT-0800 (Pacific Sta...\n", + " 11\n", " \n", " \n", " 1\n", " casact/chainladder-python\n", - " Tue Jun 11 2019 01:50:51 GMT-0700 (Pacific Day...\n", - " 17\n", + " Tue Nov 19 2019 18:31:07 GMT-0800 (Pacific Sta...\n", + " 21\n", " \n", " \n", " 2\n", " casact/chainladder-python\n", - " Sat Jan 25 2020 15:33:28 GMT-0800 (Pacific Sta...\n", - " 25\n", + " Mon Apr 06 2020 11:24:07 GMT-0700 (Pacific Day...\n", + " 31\n", " \n", " \n", " 3\n", " casact/chainladder-python\n", - " Fri Apr 24 2020 06:26:04 GMT-0700 (Pacific Day...\n", - " 33\n", + " Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day...\n", + " 41\n", " \n", " \n", " 4\n", " casact/chainladder-python\n", - " Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day...\n", - " 41\n", + " Wed Oct 21 2020 07:03:52 GMT-0700 (Pacific Day...\n", + " 51\n", " \n", " \n", "\n", @@ -120,11 +130,11 @@ "4 casact/chainladder-python \n", "\n", " Record_Date Stars \n", - "0 Thu Sep 27 2018 20:31:36 GMT-0700 (Pacific Day... 9 \n", - "1 Tue Jun 11 2019 01:50:51 GMT-0700 (Pacific Day... 17 \n", - "2 Sat Jan 25 2020 15:33:28 GMT-0800 (Pacific Sta... 25 \n", - "3 Fri Apr 24 2020 06:26:04 GMT-0700 (Pacific Day... 33 \n", - "4 Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day... 41 " + "0 Sat Jan 05 2019 01:11:49 GMT-0800 (Pacific Sta... 11 \n", + "1 Tue Nov 19 2019 18:31:07 GMT-0800 (Pacific Sta... 21 \n", + "2 Mon Apr 06 2020 11:24:07 GMT-0700 (Pacific Day... 31 \n", + "3 Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day... 41 \n", + "4 Wed Oct 21 2020 07:03:52 GMT-0700 (Pacific Day... 51 " ] }, "execution_count": 2, @@ -137,8 +147,8 @@ "\n", " setTimeout(function() {\n", " var nbb_cell_id = 2;\n", - " var nbb_unformatted_code = \"# data source: https://star-history.com/#casact/chainladder-python&lifelib-dev/lifelib&mages/ChainLadder&fumitoh/modelx&Date\\ndata = pd.read_csv(\\\"star-history-20230403.csv\\\")\\ndata.columns =[\\\"Package\\\", \\\"Record_Date\\\",\\\"Stars\\\"]\\ndata.head()\";\n", - " var nbb_formatted_code = \"# data source: https://star-history.com/#casact/chainladder-python&lifelib-dev/lifelib&mages/ChainLadder&fumitoh/modelx&Date\\ndata = pd.read_csv(\\\"star-history-20230403.csv\\\")\\ndata.columns = [\\\"Package\\\", \\\"Record_Date\\\", \\\"Stars\\\"]\\ndata.head()\";\n", + " var nbb_unformatted_code = \"# data source: https://star-history.com/#casact/chainladder-python&lifelib-dev/lifelib&mages/ChainLadder&fumitoh/modelx&Date\\ndata = pd.read_csv(\\\"star-history-20231006.csv\\\")\\ndata.columns =[\\\"Package\\\", \\\"Record_Date\\\",\\\"Stars\\\"]\\ndata.head()\";\n", + " var nbb_formatted_code = \"# data source: https://star-history.com/#casact/chainladder-python&lifelib-dev/lifelib&mages/ChainLadder&fumitoh/modelx&Date\\ndata = pd.read_csv(\\\"star-history-20231006.csv\\\")\\ndata.columns = [\\\"Package\\\", \\\"Record_Date\\\", \\\"Stars\\\"]\\ndata.head()\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", @@ -161,7 +171,7 @@ ], "source": [ "# data source: https://star-history.com/#casact/chainladder-python&lifelib-dev/lifelib&mages/ChainLadder&fumitoh/modelx&Date\n", - "data = pd.read_csv(\"star-history-20230403.csv\")\n", + "data = pd.read_csv(\"star-history-20231006.csv\")\n", "data.columns =[\"Package\", \"Record_Date\",\"Stars\"]\n", "data.head()" ] @@ -203,233 +213,233 @@ " \n", " 0\n", " casact/chainladder-python\n", - " Thu Sep 27 2018 20:31:36 GMT-0700 (Pacific Day...\n", - " 9\n", - " 2018-09-27\n", + " Sat Jan 05 2019 01:11:49 GMT-0800 (Pacific Sta...\n", + " 11\n", + " 2019-01-05\n", " \n", " \n", " 1\n", " casact/chainladder-python\n", - " Tue Jun 11 2019 01:50:51 GMT-0700 (Pacific Day...\n", - " 17\n", - " 2019-06-11\n", + " Tue Nov 19 2019 18:31:07 GMT-0800 (Pacific Sta...\n", + " 21\n", + " 2019-11-19\n", " \n", " \n", " 2\n", " casact/chainladder-python\n", - " Sat Jan 25 2020 15:33:28 GMT-0800 (Pacific Sta...\n", - " 25\n", - " 2020-01-25\n", + " Mon Apr 06 2020 11:24:07 GMT-0700 (Pacific Day...\n", + " 31\n", + " 2020-04-06\n", " \n", " \n", " 3\n", " casact/chainladder-python\n", - " Fri Apr 24 2020 06:26:04 GMT-0700 (Pacific Day...\n", - " 33\n", - " 2020-04-24\n", + " Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day...\n", + " 41\n", + " 2020-07-13\n", " \n", " \n", " 4\n", " casact/chainladder-python\n", - " Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day...\n", - " 41\n", - " 2020-07-13\n", + " Wed Oct 21 2020 07:03:52 GMT-0700 (Pacific Day...\n", + " 51\n", + " 2020-10-21\n", " \n", " \n", " 5\n", " casact/chainladder-python\n", - " Thu Sep 17 2020 04:04:50 GMT-0700 (Pacific Day...\n", - " 49\n", - " 2020-09-17\n", + " Wed Mar 31 2021 20:47:40 GMT-0700 (Pacific Day...\n", + " 61\n", + " 2021-03-31\n", " \n", " \n", " 6\n", " casact/chainladder-python\n", - " Sat Jan 30 2021 11:29:55 GMT-0800 (Pacific Sta...\n", - " 57\n", - " 2021-01-30\n", + " Tue Jun 01 2021 05:11:26 GMT-0700 (Pacific Day...\n", + " 71\n", + " 2021-06-01\n", " \n", " \n", " 7\n", " casact/chainladder-python\n", - " Tue Apr 13 2021 04:39:58 GMT-0700 (Pacific Day...\n", - " 65\n", - " 2021-04-13\n", + " Mon Nov 01 2021 12:03:23 GMT-0700 (Pacific Day...\n", + " 81\n", + " 2021-11-01\n", " \n", " \n", " 8\n", " casact/chainladder-python\n", - " Thu Jun 17 2021 06:19:00 GMT-0700 (Pacific Day...\n", - " 73\n", - " 2021-06-17\n", + " Wed Jan 12 2022 20:15:08 GMT-0800 (Pacific Sta...\n", + " 91\n", + " 2022-01-12\n", " \n", " \n", " 9\n", " casact/chainladder-python\n", - " Mon Nov 01 2021 12:03:23 GMT-0700 (Pacific Day...\n", - " 81\n", - " 2021-11-01\n", + " Wed Mar 30 2022 11:26:12 GMT-0700 (Pacific Day...\n", + " 101\n", + " 2022-03-30\n", " \n", " \n", " 10\n", " casact/chainladder-python\n", - " Sat Dec 25 2021 18:39:47 GMT-0800 (Pacific Sta...\n", - " 89\n", - " 2021-12-25\n", + " Fri Jul 15 2022 22:47:19 GMT-0700 (Pacific Day...\n", + " 111\n", + " 2022-07-15\n", " \n", " \n", " 11\n", " casact/chainladder-python\n", - " Thu Mar 10 2022 09:50:37 GMT-0800 (Pacific Sta...\n", - " 97\n", - " 2022-03-10\n", + " Sat Jul 30 2022 07:44:13 GMT-0700 (Pacific Day...\n", + " 121\n", + " 2022-07-30\n", " \n", " \n", " 12\n", " casact/chainladder-python\n", - " Sun Jun 05 2022 22:43:51 GMT-0700 (Pacific Day...\n", - " 105\n", - " 2022-06-05\n", + " Fri Feb 03 2023 10:21:16 GMT-0800 (Pacific Sta...\n", + " 131\n", + " 2023-02-03\n", " \n", " \n", " 13\n", " casact/chainladder-python\n", - " Wed Jul 27 2022 06:14:14 GMT-0700 (Pacific Day...\n", - " 113\n", - " 2022-07-27\n", + " Sat Jun 10 2023 12:57:22 GMT-0700 (Pacific Day...\n", + " 141\n", + " 2023-06-10\n", " \n", " \n", " 14\n", " casact/chainladder-python\n", - " Sat Jul 30 2022 07:44:13 GMT-0700 (Pacific Day...\n", - " 121\n", - " 2022-07-30\n", + " Wed Aug 16 2023 11:06:52 GMT-0700 (Pacific Day...\n", + " 151\n", + " 2023-08-16\n", " \n", " \n", " 15\n", " casact/chainladder-python\n", - " Fri Jan 13 2023 00:33:24 GMT-0800 (Pacific Sta...\n", - " 129\n", - " 2023-01-13\n", + " Fri Oct 06 2023 09:27:35 GMT-0700 (Pacific Day...\n", + " 154\n", + " 2023-10-06\n", " \n", " \n", " 16\n", - " casact/chainladder-python\n", - " Mon Apr 03 2023 09:40:56 GMT-0700 (Pacific Day...\n", - " 133\n", - " 2023-04-03\n", - " \n", - " \n", - " 17\n", " lifelib-dev/lifelib\n", " Thu Mar 01 2018 21:21:43 GMT-0800 (Pacific Sta...\n", " 1\n", " 2018-03-01\n", " \n", " \n", + " 17\n", + " lifelib-dev/lifelib\n", + " Wed Dec 12 2018 19:39:52 GMT-0800 (Pacific Sta...\n", + " 9\n", + " 2018-12-12\n", + " \n", + " \n", " 18\n", " lifelib-dev/lifelib\n", - " Tue Aug 28 2018 22:54:53 GMT-0700 (Pacific Day...\n", - " 8\n", - " 2018-08-28\n", + " Thu Apr 18 2019 10:33:53 GMT-0700 (Pacific Day...\n", + " 17\n", + " 2019-04-18\n", " \n", " \n", " 19\n", " lifelib-dev/lifelib\n", - " Mon Mar 04 2019 09:44:14 GMT-0800 (Pacific Sta...\n", - " 15\n", - " 2019-03-04\n", + " Sat Sep 28 2019 07:18:21 GMT-0700 (Pacific Day...\n", + " 25\n", + " 2019-09-28\n", " \n", " \n", " 20\n", " lifelib-dev/lifelib\n", - " Thu Aug 15 2019 00:03:58 GMT-0700 (Pacific Day...\n", - " 22\n", - " 2019-08-15\n", + " Mon Feb 24 2020 17:13:30 GMT-0800 (Pacific Sta...\n", + " 33\n", + " 2020-02-24\n", " \n", " \n", " 21\n", " lifelib-dev/lifelib\n", - " Fri Nov 29 2019 06:55:13 GMT-0800 (Pacific Sta...\n", - " 29\n", - " 2019-11-29\n", + " Sat Jun 27 2020 04:55:22 GMT-0700 (Pacific Day...\n", + " 41\n", + " 2020-06-27\n", " \n", " \n", " 22\n", " lifelib-dev/lifelib\n", - " Fri Apr 24 2020 06:25:48 GMT-0700 (Pacific Day...\n", - " 36\n", - " 2020-04-24\n", + " Sun Jan 24 2021 13:39:13 GMT-0800 (Pacific Sta...\n", + " 49\n", + " 2021-01-24\n", " \n", " \n", " 23\n", " lifelib-dev/lifelib\n", - " Wed Jul 22 2020 06:18:20 GMT-0700 (Pacific Day...\n", - " 43\n", - " 2020-07-22\n", + " Tue May 04 2021 21:44:42 GMT-0700 (Pacific Day...\n", + " 57\n", + " 2021-05-04\n", " \n", " \n", " 24\n", " lifelib-dev/lifelib\n", - " Wed Jan 27 2021 01:29:12 GMT-0800 (Pacific Sta...\n", - " 50\n", - " 2021-01-27\n", + " Sun Oct 17 2021 14:37:44 GMT-0700 (Pacific Day...\n", + " 65\n", + " 2021-10-17\n", " \n", " \n", " 25\n", " lifelib-dev/lifelib\n", - " Tue May 04 2021 21:44:42 GMT-0700 (Pacific Day...\n", - " 57\n", - " 2021-05-04\n", + " Sat Mar 12 2022 20:32:35 GMT-0800 (Pacific Sta...\n", + " 73\n", + " 2022-03-12\n", " \n", " \n", " 26\n", " lifelib-dev/lifelib\n", - " Sat Oct 02 2021 19:23:53 GMT-0700 (Pacific Day...\n", - " 64\n", - " 2021-10-02\n", + " Fri Jun 10 2022 02:58:11 GMT-0700 (Pacific Day...\n", + " 81\n", + " 2022-06-10\n", " \n", " \n", " 27\n", " lifelib-dev/lifelib\n", - " Mon Jan 10 2022 19:48:55 GMT-0800 (Pacific Sta...\n", - " 71\n", - " 2022-01-10\n", + " Tue Aug 02 2022 01:07:04 GMT-0700 (Pacific Day...\n", + " 89\n", + " 2022-08-02\n", " \n", " \n", " 28\n", " lifelib-dev/lifelib\n", - " Fri May 06 2022 02:51:27 GMT-0700 (Pacific Day...\n", - " 78\n", - " 2022-05-06\n", + " Sat Nov 26 2022 13:01:39 GMT-0800 (Pacific Sta...\n", + " 97\n", + " 2022-11-26\n", " \n", " \n", " 29\n", " lifelib-dev/lifelib\n", - " Sat Jul 23 2022 19:29:04 GMT-0700 (Pacific Day...\n", - " 85\n", - " 2022-07-23\n", + " Fri May 12 2023 08:10:40 GMT-0700 (Pacific Day...\n", + " 105\n", + " 2023-05-12\n", " \n", " \n", " 30\n", " lifelib-dev/lifelib\n", - " Thu Sep 15 2022 13:33:50 GMT-0700 (Pacific Day...\n", - " 92\n", - " 2022-09-15\n", + " Sat Aug 05 2023 19:31:42 GMT-0700 (Pacific Day...\n", + " 113\n", + " 2023-08-05\n", " \n", " \n", " 31\n", " lifelib-dev/lifelib\n", - " Tue Nov 29 2022 01:33:51 GMT-0800 (Pacific Sta...\n", - " 99\n", - " 2022-11-29\n", + " Tue Oct 03 2023 06:39:18 GMT-0700 (Pacific Day...\n", + " 121\n", + " 2023-10-03\n", " \n", " \n", " 32\n", " lifelib-dev/lifelib\n", - " Mon Apr 03 2023 09:40:57 GMT-0700 (Pacific Day...\n", - " 106\n", - " 2023-04-03\n", + " Fri Oct 06 2023 10:08:13 GMT-0700 (Pacific Day...\n", + " 121\n", + " 2023-10-06\n", " \n", " \n", " 33\n", @@ -553,9 +563,16 @@ " \n", " 50\n", " mages/ChainLadder\n", - " Mon Apr 03 2023 09:40:58 GMT-0700 (Pacific Day...\n", - " 66\n", - " 2023-04-03\n", + " Thu May 04 2023 19:39:30 GMT-0700 (Pacific Day...\n", + " 69\n", + " 2023-05-04\n", + " \n", + " \n", + " 51\n", + " mages/ChainLadder\n", + " Fri Oct 06 2023 10:08:17 GMT-0700 (Pacific Day...\n", + " 72\n", + " 2023-10-06\n", " \n", " \n", "\n", @@ -579,7 +596,7 @@ "13 casact/chainladder-python \n", "14 casact/chainladder-python \n", "15 casact/chainladder-python \n", - "16 casact/chainladder-python \n", + "16 lifelib-dev/lifelib \n", "17 lifelib-dev/lifelib \n", "18 lifelib-dev/lifelib \n", "19 lifelib-dev/lifelib \n", @@ -614,41 +631,42 @@ "48 mages/ChainLadder \n", "49 mages/ChainLadder \n", "50 mages/ChainLadder \n", + "51 mages/ChainLadder \n", "\n", " Record_Date Stars Record_Date_P \n", - "0 Thu Sep 27 2018 20:31:36 GMT-0700 (Pacific Day... 9 2018-09-27 \n", - "1 Tue Jun 11 2019 01:50:51 GMT-0700 (Pacific Day... 17 2019-06-11 \n", - "2 Sat Jan 25 2020 15:33:28 GMT-0800 (Pacific Sta... 25 2020-01-25 \n", - "3 Fri Apr 24 2020 06:26:04 GMT-0700 (Pacific Day... 33 2020-04-24 \n", - "4 Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day... 41 2020-07-13 \n", - "5 Thu Sep 17 2020 04:04:50 GMT-0700 (Pacific Day... 49 2020-09-17 \n", - "6 Sat Jan 30 2021 11:29:55 GMT-0800 (Pacific Sta... 57 2021-01-30 \n", - "7 Tue Apr 13 2021 04:39:58 GMT-0700 (Pacific Day... 65 2021-04-13 \n", - "8 Thu Jun 17 2021 06:19:00 GMT-0700 (Pacific Day... 73 2021-06-17 \n", - "9 Mon Nov 01 2021 12:03:23 GMT-0700 (Pacific Day... 81 2021-11-01 \n", - "10 Sat Dec 25 2021 18:39:47 GMT-0800 (Pacific Sta... 89 2021-12-25 \n", - "11 Thu Mar 10 2022 09:50:37 GMT-0800 (Pacific Sta... 97 2022-03-10 \n", - "12 Sun Jun 05 2022 22:43:51 GMT-0700 (Pacific Day... 105 2022-06-05 \n", - "13 Wed Jul 27 2022 06:14:14 GMT-0700 (Pacific Day... 113 2022-07-27 \n", - "14 Sat Jul 30 2022 07:44:13 GMT-0700 (Pacific Day... 121 2022-07-30 \n", - "15 Fri Jan 13 2023 00:33:24 GMT-0800 (Pacific Sta... 129 2023-01-13 \n", - "16 Mon Apr 03 2023 09:40:56 GMT-0700 (Pacific Day... 133 2023-04-03 \n", - "17 Thu Mar 01 2018 21:21:43 GMT-0800 (Pacific Sta... 1 2018-03-01 \n", - "18 Tue Aug 28 2018 22:54:53 GMT-0700 (Pacific Day... 8 2018-08-28 \n", - "19 Mon Mar 04 2019 09:44:14 GMT-0800 (Pacific Sta... 15 2019-03-04 \n", - "20 Thu Aug 15 2019 00:03:58 GMT-0700 (Pacific Day... 22 2019-08-15 \n", - "21 Fri Nov 29 2019 06:55:13 GMT-0800 (Pacific Sta... 29 2019-11-29 \n", - "22 Fri Apr 24 2020 06:25:48 GMT-0700 (Pacific Day... 36 2020-04-24 \n", - "23 Wed Jul 22 2020 06:18:20 GMT-0700 (Pacific Day... 43 2020-07-22 \n", - "24 Wed Jan 27 2021 01:29:12 GMT-0800 (Pacific Sta... 50 2021-01-27 \n", - "25 Tue May 04 2021 21:44:42 GMT-0700 (Pacific Day... 57 2021-05-04 \n", - "26 Sat Oct 02 2021 19:23:53 GMT-0700 (Pacific Day... 64 2021-10-02 \n", - "27 Mon Jan 10 2022 19:48:55 GMT-0800 (Pacific Sta... 71 2022-01-10 \n", - "28 Fri May 06 2022 02:51:27 GMT-0700 (Pacific Day... 78 2022-05-06 \n", - "29 Sat Jul 23 2022 19:29:04 GMT-0700 (Pacific Day... 85 2022-07-23 \n", - "30 Thu Sep 15 2022 13:33:50 GMT-0700 (Pacific Day... 92 2022-09-15 \n", - "31 Tue Nov 29 2022 01:33:51 GMT-0800 (Pacific Sta... 99 2022-11-29 \n", - "32 Mon Apr 03 2023 09:40:57 GMT-0700 (Pacific Day... 106 2023-04-03 \n", + "0 Sat Jan 05 2019 01:11:49 GMT-0800 (Pacific Sta... 11 2019-01-05 \n", + "1 Tue Nov 19 2019 18:31:07 GMT-0800 (Pacific Sta... 21 2019-11-19 \n", + "2 Mon Apr 06 2020 11:24:07 GMT-0700 (Pacific Day... 31 2020-04-06 \n", + "3 Mon Jul 13 2020 22:36:41 GMT-0700 (Pacific Day... 41 2020-07-13 \n", + "4 Wed Oct 21 2020 07:03:52 GMT-0700 (Pacific Day... 51 2020-10-21 \n", + "5 Wed Mar 31 2021 20:47:40 GMT-0700 (Pacific Day... 61 2021-03-31 \n", + "6 Tue Jun 01 2021 05:11:26 GMT-0700 (Pacific Day... 71 2021-06-01 \n", + "7 Mon Nov 01 2021 12:03:23 GMT-0700 (Pacific Day... 81 2021-11-01 \n", + "8 Wed Jan 12 2022 20:15:08 GMT-0800 (Pacific Sta... 91 2022-01-12 \n", + "9 Wed Mar 30 2022 11:26:12 GMT-0700 (Pacific Day... 101 2022-03-30 \n", + "10 Fri Jul 15 2022 22:47:19 GMT-0700 (Pacific Day... 111 2022-07-15 \n", + "11 Sat Jul 30 2022 07:44:13 GMT-0700 (Pacific Day... 121 2022-07-30 \n", + "12 Fri Feb 03 2023 10:21:16 GMT-0800 (Pacific Sta... 131 2023-02-03 \n", + "13 Sat Jun 10 2023 12:57:22 GMT-0700 (Pacific Day... 141 2023-06-10 \n", + "14 Wed Aug 16 2023 11:06:52 GMT-0700 (Pacific Day... 151 2023-08-16 \n", + "15 Fri Oct 06 2023 09:27:35 GMT-0700 (Pacific Day... 154 2023-10-06 \n", + "16 Thu Mar 01 2018 21:21:43 GMT-0800 (Pacific Sta... 1 2018-03-01 \n", + "17 Wed Dec 12 2018 19:39:52 GMT-0800 (Pacific Sta... 9 2018-12-12 \n", + "18 Thu Apr 18 2019 10:33:53 GMT-0700 (Pacific Day... 17 2019-04-18 \n", + "19 Sat Sep 28 2019 07:18:21 GMT-0700 (Pacific Day... 25 2019-09-28 \n", + "20 Mon Feb 24 2020 17:13:30 GMT-0800 (Pacific Sta... 33 2020-02-24 \n", + "21 Sat Jun 27 2020 04:55:22 GMT-0700 (Pacific Day... 41 2020-06-27 \n", + "22 Sun Jan 24 2021 13:39:13 GMT-0800 (Pacific Sta... 49 2021-01-24 \n", + "23 Tue May 04 2021 21:44:42 GMT-0700 (Pacific Day... 57 2021-05-04 \n", + "24 Sun Oct 17 2021 14:37:44 GMT-0700 (Pacific Day... 65 2021-10-17 \n", + "25 Sat Mar 12 2022 20:32:35 GMT-0800 (Pacific Sta... 73 2022-03-12 \n", + "26 Fri Jun 10 2022 02:58:11 GMT-0700 (Pacific Day... 81 2022-06-10 \n", + "27 Tue Aug 02 2022 01:07:04 GMT-0700 (Pacific Day... 89 2022-08-02 \n", + "28 Sat Nov 26 2022 13:01:39 GMT-0800 (Pacific Sta... 97 2022-11-26 \n", + "29 Fri May 12 2023 08:10:40 GMT-0700 (Pacific Day... 105 2023-05-12 \n", + "30 Sat Aug 05 2023 19:31:42 GMT-0700 (Pacific Day... 113 2023-08-05 \n", + "31 Tue Oct 03 2023 06:39:18 GMT-0700 (Pacific Day... 121 2023-10-03 \n", + "32 Fri Oct 06 2023 10:08:13 GMT-0700 (Pacific Day... 121 2023-10-06 \n", "33 Wed Jan 07 2015 10:15:00 GMT-0800 (Pacific Sta... 1 2015-01-07 \n", "34 Mon Jun 15 2015 21:16:32 GMT-0700 (Pacific Day... 5 2015-06-15 \n", "35 Tue Jul 19 2016 10:12:40 GMT-0700 (Pacific Day... 9 2016-07-19 \n", @@ -666,7 +684,8 @@ "47 Sat Mar 12 2022 12:03:42 GMT-0800 (Pacific Sta... 57 2022-03-12 \n", "48 Fri Oct 14 2022 10:49:30 GMT-0700 (Pacific Day... 61 2022-10-14 \n", "49 Wed Feb 22 2023 17:49:29 GMT-0800 (Pacific Sta... 65 2023-02-22 \n", - "50 Mon Apr 03 2023 09:40:58 GMT-0700 (Pacific Day... 66 2023-04-03 " + "50 Thu May 04 2023 19:39:30 GMT-0700 (Pacific Day... 69 2023-05-04 \n", + "51 Fri Oct 06 2023 10:08:17 GMT-0700 (Pacific Day... 72 2023-10-06 " ] }, "execution_count": 3, @@ -805,81 +824,30 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "04178763-623e-420a-9824-5313b395ba9f", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 12;\n", - " var nbb_unformatted_code = \"star_axis_max = 150\";\n", - " var nbb_formatted_code = \"star_axis_max = 150\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "star_axis_max = 150" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b7004efa-2eeb-48e9-bd18-154c57da0205", - "metadata": {}, + "execution_count": 6, + "id": "a2445847-9999-4240-93df-9bee557b295e", + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "(0.0, 150.0)" + "Timestamp('2023-10-06 00:00:00')" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 758, - "width": 1627 - } - }, - "output_type": "display_data" - }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", - " var nbb_cell_id = 13;\n", - " var nbb_unformatted_code = \"plt.figure(figsize=(13, 6), dpi=150)\\n\\nplt.plot(\\n data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"][\\\"Record_Date_P\\\"], \\n data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"][\\\"Stars\\\"],\\n label = \\\"mages/ChainLadder\\\"\\n)\\n# plt.plot(\\n# data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Record_Date_P\\\"], \\n# data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Stars\\\"],\\n# label = \\\"lifelib-dev/lifelib\\\"\\n# )\\n# plt.plot(\\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Record_Date_P\\\"], \\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Stars\\\"],\\n# label = \\\"casact/chainladder-python\\\"\\n# )\\nplt.ylabel('Github Stars')\\nplt.legend(loc = \\\"upper left\\\")\\n\\nx_ticks = pd.Series(\\n pd.date_range(\\\"2015/01/01\\\", periods=5, freq=\\\"2YS\\\")\\n)\\nplt.xticks(x_ticks,x_ticks.dt.year)\\nplt.ylim(0, star_axis_max)\";\n", - " var nbb_formatted_code = \"plt.figure(figsize=(13, 6), dpi=150)\\n\\nplt.plot(\\n data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"][\\\"Record_Date_P\\\"],\\n data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"][\\\"Stars\\\"],\\n label=\\\"mages/ChainLadder\\\",\\n)\\n# plt.plot(\\n# data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Record_Date_P\\\"],\\n# data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Stars\\\"],\\n# label = \\\"lifelib-dev/lifelib\\\"\\n# )\\n# plt.plot(\\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Record_Date_P\\\"],\\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Stars\\\"],\\n# label = \\\"casact/chainladder-python\\\"\\n# )\\nplt.ylabel(\\\"Github Stars\\\")\\nplt.legend(loc=\\\"upper left\\\")\\n\\nx_ticks = pd.Series(pd.date_range(\\\"2015/01/01\\\", periods=5, freq=\\\"2YS\\\"))\\nplt.xticks(x_ticks, x_ticks.dt.year)\\nplt.ylim(0, star_axis_max)\";\n", + " var nbb_cell_id = 6;\n", + " var nbb_unformatted_code = \"data[\\\"Record_Date_P\\\"].max()\";\n", + " var nbb_formatted_code = \"data[\\\"Record_Date_P\\\"].max()\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", @@ -901,72 +869,23 @@ } ], "source": [ - "plt.figure(figsize=(13, 6), dpi=150)\n", - "\n", - "plt.plot(\n", - " data[data[\"Package\"]==\"mages/ChainLadder\"][\"Record_Date_P\"], \n", - " data[data[\"Package\"]==\"mages/ChainLadder\"][\"Stars\"],\n", - " label = \"mages/ChainLadder\"\n", - ")\n", - "# plt.plot(\n", - "# data[data[\"Package\"]==\"lifelib-dev/lifelib\"][\"Record_Date_P\"], \n", - "# data[data[\"Package\"]==\"lifelib-dev/lifelib\"][\"Stars\"],\n", - "# label = \"lifelib-dev/lifelib\"\n", - "# )\n", - "# plt.plot(\n", - "# data[data[\"Package\"]==\"casact/chainladder-python\"][\"Record_Date_P\"], \n", - "# data[data[\"Package\"]==\"casact/chainladder-python\"][\"Stars\"],\n", - "# label = \"casact/chainladder-python\"\n", - "# )\n", - "plt.ylabel('Github Stars')\n", - "plt.legend(loc = \"upper left\")\n", - "\n", - "x_ticks = pd.Series(\n", - " pd.date_range(\"2015/01/01\", periods=5, freq=\"2YS\")\n", - ")\n", - "plt.xticks(x_ticks,x_ticks.dt.year)\n", - "plt.ylim(0, star_axis_max)" + "data[\"Record_Date_P\"].max()" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "610a16c0-eeff-4b62-9ddf-90544c10e408", + "execution_count": 7, + "id": "04178763-623e-420a-9824-5313b395ba9f", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 150.0)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 758, - "width": 1627 - } - }, - "output_type": "display_data" - }, { "data": { "application/javascript": [ "\n", " setTimeout(function() {\n", - " var nbb_cell_id = 14;\n", - " var nbb_unformatted_code = \"plt.figure(figsize=(13, 6), dpi=150)\\n\\nplt.plot(\\n data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"][\\\"Record_Date_P\\\"], \\n data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"][\\\"Stars\\\"],\\n label = \\\"mages/ChainLadder\\\"\\n)\\nplt.plot(\\n data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Record_Date_P\\\"], \\n data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Stars\\\"],\\n label = \\\"lifelib-dev/lifelib\\\"\\n)\\n# plt.plot(\\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Record_Date_P\\\"], \\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Stars\\\"],\\n# label = \\\"casact/chainladder-python\\\"\\n# )\\nplt.ylabel('Github Stars')\\nplt.legend(loc = \\\"upper left\\\")\\n\\nx_ticks = pd.Series(\\n pd.date_range(\\\"2015/01/01\\\", periods=5, freq=\\\"2YS\\\")\\n)\\nplt.xticks(x_ticks,x_ticks.dt.year)\\nplt.ylim(0, star_axis_max)\";\n", - " var nbb_formatted_code = \"plt.figure(figsize=(13, 6), dpi=150)\\n\\nplt.plot(\\n data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"][\\\"Record_Date_P\\\"],\\n data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"][\\\"Stars\\\"],\\n label=\\\"mages/ChainLadder\\\",\\n)\\nplt.plot(\\n data[data[\\\"Package\\\"] == \\\"lifelib-dev/lifelib\\\"][\\\"Record_Date_P\\\"],\\n data[data[\\\"Package\\\"] == \\\"lifelib-dev/lifelib\\\"][\\\"Stars\\\"],\\n label=\\\"lifelib-dev/lifelib\\\",\\n)\\n# plt.plot(\\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Record_Date_P\\\"],\\n# data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Stars\\\"],\\n# label = \\\"casact/chainladder-python\\\"\\n# )\\nplt.ylabel(\\\"Github Stars\\\")\\nplt.legend(loc=\\\"upper left\\\")\\n\\nx_ticks = pd.Series(pd.date_range(\\\"2015/01/01\\\", periods=5, freq=\\\"2YS\\\"))\\nplt.xticks(x_ticks, x_ticks.dt.year)\\nplt.ylim(0, star_axis_max)\";\n", + " var nbb_cell_id = 7;\n", + " var nbb_unformatted_code = \"star_axis_max = 175\";\n", + " var nbb_formatted_code = \"star_axis_max = 175\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", @@ -988,60 +907,38 @@ } ], "source": [ - "plt.figure(figsize=(13, 6), dpi=150)\n", - "\n", - "plt.plot(\n", - " data[data[\"Package\"]==\"mages/ChainLadder\"][\"Record_Date_P\"], \n", - " data[data[\"Package\"]==\"mages/ChainLadder\"][\"Stars\"],\n", - " label = \"mages/ChainLadder\"\n", - ")\n", - "plt.plot(\n", - " data[data[\"Package\"]==\"lifelib-dev/lifelib\"][\"Record_Date_P\"], \n", - " data[data[\"Package\"]==\"lifelib-dev/lifelib\"][\"Stars\"],\n", - " label = \"lifelib-dev/lifelib\"\n", - ")\n", - "# plt.plot(\n", - "# data[data[\"Package\"]==\"casact/chainladder-python\"][\"Record_Date_P\"], \n", - "# data[data[\"Package\"]==\"casact/chainladder-python\"][\"Stars\"],\n", - "# label = \"casact/chainladder-python\"\n", - "# )\n", - "plt.ylabel('Github Stars')\n", - "plt.legend(loc = \"upper left\")\n", - "\n", - "x_ticks = pd.Series(\n", - " pd.date_range(\"2015/01/01\", periods=5, freq=\"2YS\")\n", - ")\n", - "plt.xticks(x_ticks,x_ticks.dt.year)\n", - "plt.ylim(0, star_axis_max)" + "star_axis_max = 175" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "cf1b0a71-9b38-4ef6-873d-dcebe4c96203", - "metadata": {}, + "execution_count": 10, + "id": "3406ae02-e233-4fb3-a173-52d10221447f", + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "(0.0, 150.0)" + "Text(0, 0.5, 'GitHub Stars ★')" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 758, - "width": 1627 + "height": 917, + "width": 1967 } }, "output_type": "display_data" @@ -1051,9 +948,9 @@ "application/javascript": [ "\n", " setTimeout(function() {\n", - " var nbb_cell_id = 15;\n", - " var nbb_unformatted_code = \"plt.figure(figsize=(13, 6), dpi=150)\\n\\nplt.plot(\\n data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"][\\\"Record_Date_P\\\"], \\n data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"][\\\"Stars\\\"],\\n label = \\\"mages/ChainLadder\\\"\\n)\\nplt.plot(\\n data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Record_Date_P\\\"], \\n data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"][\\\"Stars\\\"],\\n label = \\\"lifelib-dev/lifelib\\\"\\n)\\nplt.plot(\\n data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Record_Date_P\\\"], \\n data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"][\\\"Stars\\\"],\\n label = \\\"casact/chainladder-python\\\"\\n)\\nplt.ylabel('Github Stars')\\nplt.legend(loc = \\\"upper left\\\")\\n\\nx_ticks = pd.Series(\\n pd.date_range(\\\"2015/01/01\\\", periods=5, freq=\\\"2YS\\\")\\n)\\nplt.xticks(x_ticks,x_ticks.dt.year)\\nplt.ylim(0, star_axis_max)\";\n", - " var nbb_formatted_code = \"plt.figure(figsize=(13, 6), dpi=150)\\n\\nplt.plot(\\n data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"][\\\"Record_Date_P\\\"],\\n data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"][\\\"Stars\\\"],\\n label=\\\"mages/ChainLadder\\\",\\n)\\nplt.plot(\\n data[data[\\\"Package\\\"] == \\\"lifelib-dev/lifelib\\\"][\\\"Record_Date_P\\\"],\\n data[data[\\\"Package\\\"] == \\\"lifelib-dev/lifelib\\\"][\\\"Stars\\\"],\\n label=\\\"lifelib-dev/lifelib\\\",\\n)\\nplt.plot(\\n data[data[\\\"Package\\\"] == \\\"casact/chainladder-python\\\"][\\\"Record_Date_P\\\"],\\n data[data[\\\"Package\\\"] == \\\"casact/chainladder-python\\\"][\\\"Stars\\\"],\\n label=\\\"casact/chainladder-python\\\",\\n)\\nplt.ylabel(\\\"Github Stars\\\")\\nplt.legend(loc=\\\"upper left\\\")\\n\\nx_ticks = pd.Series(pd.date_range(\\\"2015/01/01\\\", periods=5, freq=\\\"2YS\\\"))\\nplt.xticks(x_ticks, x_ticks.dt.year)\\nplt.ylim(0, star_axis_max)\";\n", + " var nbb_cell_id = 10;\n", + " var nbb_unformatted_code = \"plt.rcParams.update({'font.size': 14})\\n\\nfig, ax = plt.subplots(figsize=(13, 6), dpi=150, layout='constrained')\\nax.plot('Record_Date_P', 'Stars', data=data[data[\\\"Package\\\"]==\\\"casact/chainladder-python\\\"],label = \\\"casact/chainladder-python\\\")\\nax.plot('Record_Date_P', 'Stars', data=data[data[\\\"Package\\\"]==\\\"lifelib-dev/lifelib\\\"], label=\\\"lifelib-dev/lifelib\\\")\\nax.plot('Record_Date_P', 'Stars', data=data[data[\\\"Package\\\"]==\\\"mages/ChainLadder\\\"], label = \\\"mages/ChainLadder\\\")\\n\\nax.legend(loc = \\\"upper left\\\")\\n\\nax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))\\n# ax.set_xlabel(\\\"Time\\\")\\nax.set_ylabel(\\\"GitHub Stars \\u2605\\\")\";\n", + " var nbb_formatted_code = \"plt.rcParams.update({\\\"font.size\\\": 14})\\n\\nfig, ax = plt.subplots(figsize=(13, 6), dpi=150, layout=\\\"constrained\\\")\\nax.plot(\\n \\\"Record_Date_P\\\",\\n \\\"Stars\\\",\\n data=data[data[\\\"Package\\\"] == \\\"casact/chainladder-python\\\"],\\n label=\\\"casact/chainladder-python\\\",\\n)\\nax.plot(\\n \\\"Record_Date_P\\\",\\n \\\"Stars\\\",\\n data=data[data[\\\"Package\\\"] == \\\"lifelib-dev/lifelib\\\"],\\n label=\\\"lifelib-dev/lifelib\\\",\\n)\\nax.plot(\\n \\\"Record_Date_P\\\",\\n \\\"Stars\\\",\\n data=data[data[\\\"Package\\\"] == \\\"mages/ChainLadder\\\"],\\n label=\\\"mages/ChainLadder\\\",\\n)\\n\\nax.legend(loc=\\\"upper left\\\")\\n\\nax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))\\n# ax.set_xlabel(\\\"Time\\\")\\nax.set_ylabel(\\\"GitHub Stars \\u2605\\\")\";\n", " var nbb_cells = Jupyter.notebook.get_cells();\n", " for (var i = 0; i < nbb_cells.length; ++i) {\n", " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", @@ -1075,31 +972,18 @@ } ], "source": [ - "plt.figure(figsize=(13, 6), dpi=150)\n", + "plt.rcParams.update({'font.size': 14})\n", + "\n", + "fig, ax = plt.subplots(figsize=(13, 6), dpi=150, layout='constrained')\n", + "ax.plot('Record_Date_P', 'Stars', data=data[data[\"Package\"]==\"casact/chainladder-python\"],label = \"casact/chainladder-python\")\n", + "ax.plot('Record_Date_P', 'Stars', data=data[data[\"Package\"]==\"lifelib-dev/lifelib\"], label=\"lifelib-dev/lifelib\")\n", + "ax.plot('Record_Date_P', 'Stars', data=data[data[\"Package\"]==\"mages/ChainLadder\"], label = \"mages/ChainLadder\")\n", "\n", - "plt.plot(\n", - " data[data[\"Package\"]==\"mages/ChainLadder\"][\"Record_Date_P\"], \n", - " data[data[\"Package\"]==\"mages/ChainLadder\"][\"Stars\"],\n", - " label = \"mages/ChainLadder\"\n", - ")\n", - "plt.plot(\n", - " data[data[\"Package\"]==\"lifelib-dev/lifelib\"][\"Record_Date_P\"], \n", - " data[data[\"Package\"]==\"lifelib-dev/lifelib\"][\"Stars\"],\n", - " label = \"lifelib-dev/lifelib\"\n", - ")\n", - "plt.plot(\n", - " data[data[\"Package\"]==\"casact/chainladder-python\"][\"Record_Date_P\"], \n", - " data[data[\"Package\"]==\"casact/chainladder-python\"][\"Stars\"],\n", - " label = \"casact/chainladder-python\"\n", - ")\n", - "plt.ylabel('Github Stars')\n", - "plt.legend(loc = \"upper left\")\n", + "ax.legend(loc = \"upper left\")\n", "\n", - "x_ticks = pd.Series(\n", - " pd.date_range(\"2015/01/01\", periods=5, freq=\"2YS\")\n", - ")\n", - "plt.xticks(x_ticks,x_ticks.dt.year)\n", - "plt.ylim(0, star_axis_max)" + "ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))\n", + "# ax.set_xlabel(\"Time\")\n", + "ax.set_ylabel(\"GitHub Stars ★\")" ] } ], @@ -1119,7 +1003,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.17" }, "vscode": { "interpreter": {