From 834cf4277f6eecbd1ed00c841a9352d601815d54 Mon Sep 17 00:00:00 2001
From: Kiran Patel <7103956+kiran94@users.noreply.github.com>
Date: Sat, 20 May 2023 13:28:48 +0100
Subject: [PATCH] feat(analysis): add built-in plots and view (#17)

* chore(deps): add analysis dependencies

* feat(analysis): add pull request list view

* feat(analysis): implemented state distribution

* feat(analysis): implemented overall timeline distribution

* feat(analysis): implemented author association distribution

* feat(analysis): implemented conventional commit breakdown

* chore(deps): consolidated

* feat(analysis): implemented reaction distribution

* docs(sample): add plot and view samples

* chore(poetry): update lock

* chore(precommit): removed eof fixer

* chore(notebooks): moved view pull requests

* chore(verify): add papermill and jupyter verification scripts

* chore(notebooks): refresh

* chore(ci): verify notebooks

* chore(ci): build project

* chore(pyproject): comment out readme for now

* chore(ci): run poetry shell

* fix(ci): ensure poetry run is used for validate notebooks

* fix(ci): update validate notebooks

* chore(verbose): disable validation

* chore(ci): add amend

* fix(poetry): pin urllib below version 2

https://github.com/python-poetry/poetry/issues/7936
https://github.com/kiran94/prfiesta/actions/runs/5031995017/jobs/9025385196#step:11:26

* chore(ci): restrict verify notebooks to linux

* chore(notebooks): refresh

* chore(notebooks): remove echo

* style(analysis): trailing whitespace
---
 .github/workflows/main.yml                    |   9 +
 .gitignore                                    |   1 +
 .pre-commit-config.yaml                       |   1 -
 docs/analysis.md                              |  61 ++
 makefile                                      |   4 +
 notebooks/plots/plot_author_association.ipynb | 129 +++
 .../plot_conventional_commit_breakdown.ipynb  | 132 +++
 .../plot_overall_contribution_timeline.ipynb  | 132 +++
 notebooks/plots/plot_reactions.ipynb          | 132 +++
 notebooks/plots/plot_state_distribution.ipynb | 132 +++
 notebooks/samples_data/charliermarsh.parquet  | Bin 0 -> 231696 bytes
 notebooks/samples_data/kiran94.parquet        | Bin 0 -> 116657 bytes
 notebooks/scripts/run_all.sh                  |   6 +
 notebooks/views/view_pull_requests.ipynb      | 733 +++++++++++++++++
 poetry.lock                                   | 760 ++++++++++++++++--
 prfiesta/analysis/__init__.py                 |   0
 prfiesta/analysis/plot.py                     | 128 +++
 prfiesta/analysis/view.py                     |  34 +
 pyproject.toml                                |  12 +-
 tests/analysis/__init__.py                    |   0
 tests/analysis/test_plot.py                   | 343 ++++++++
 tests/analysis/test_view.py                   | 102 +++
 22 files changed, 2791 insertions(+), 60 deletions(-)
 create mode 100644 docs/analysis.md
 create mode 100644 notebooks/plots/plot_author_association.ipynb
 create mode 100644 notebooks/plots/plot_conventional_commit_breakdown.ipynb
 create mode 100644 notebooks/plots/plot_overall_contribution_timeline.ipynb
 create mode 100644 notebooks/plots/plot_reactions.ipynb
 create mode 100644 notebooks/plots/plot_state_distribution.ipynb
 create mode 100644 notebooks/samples_data/charliermarsh.parquet
 create mode 100644 notebooks/samples_data/kiran94.parquet
 create mode 100755 notebooks/scripts/run_all.sh
 create mode 100644 notebooks/views/view_pull_requests.ipynb
 create mode 100644 prfiesta/analysis/__init__.py
 create mode 100644 prfiesta/analysis/plot.py
 create mode 100644 prfiesta/analysis/view.py
 create mode 100644 tests/analysis/__init__.py
 create mode 100644 tests/analysis/test_plot.py
 create mode 100644 tests/analysis/test_view.py

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index d1ee2d5..83f62b5 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -56,6 +56,15 @@ jobs:
           lcov-file: coverage.lcov
           github-token: ${{ secrets.GITHUB_TOKEN }}
 
+      - name: Verify Notebooks
+        if: matrix.os == 'ubuntu-latest'
+        env:
+          PYDEVD_DISABLE_FILE_VALIDATION: 1
+        run: |
+          poetry build
+          poetry install
+          make validate_notebooks
+
   terraform-lint:
     runs-on: ubuntu-latest
     timeout-minutes: 10
diff --git a/.gitignore b/.gitignore
index 5763ff7..cc4a264 100644
--- a/.gitignore
+++ b/.gitignore
@@ -222,5 +222,6 @@ requirements-dev.txt
 
 *.csv
 *.parquet
+!notebooks/samples_data/*.parquet
 
 coverage.lcov
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 148b84b..5b4212e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,7 +2,6 @@ repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v2.3.0
     hooks:
-      - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-merge-conflict
       - id: check-yaml
diff --git a/docs/analysis.md b/docs/analysis.md
new file mode 100644
index 0000000..b899e69
--- /dev/null
+++ b/docs/analysis.md
@@ -0,0 +1,61 @@
+# Analysis
+
+prfiesta ships with built in plots to help analyze your pull request data. These serve as a starting point in your analysis.
+
+**It's recommended to run these within the context of a [Jupyter Notebook](https://docs.jupyter.org/en/latest/)**
+
+## Built In Views
+
+| View                                       | Description                                                                                                                  | Sample                                              |
+| ---------------                            | ---------------                                                                                                              | -------------                                       |
+| `prfiesta.analysis.view.view_pull_request` | Produces a table of pull requests which summarizes contribution. Each pull request is linked to enable further investigation | [Link](../notebooks/views/view_pull_requests.ipynb) |
+
+## Built In Plots
+
+  Plot                                                        | Description                                                                                                                                                                                                                                                      | Sample                                                              |
+| ---------------                                             | ---------------                                                                                                                                                                                                                                                  | ------                                                              |
+| `prfiesta.analysis.plot.plot_overall_timeline`              | Produces a plot showing contributions over time catagorized by month and year                                                                                                                                                                                    | [Link](../notebooks/plots/plot_overall_contribution_timeline.ipynb) |
+| `prfiesta.analysis.plot.plot_state_distribution`            | Produces a plot showing the distribution of state (open or closed PR) catagorized by repository                                                                                                                                                                  | [Link](../notebooks/plots/plot_state_distribution.ipynb)            |
+| `prfiesta.analysis.plot.plot_author_associations`           | Produces a plot showing authors association to the repository contribued to                                                                                                                                                                                      | [Link](../notebooks/plots/plot_author_association.ipynb)            |
+| `prfiesta.analysis.plot.plot_conventional_commit_breakdown` | Produces a plot showing [git conventional commit](https://www.conventionalcommits.org/en/v1.0.0/) breakdown for contributions catagorized by repository name. Note that this requires the user to use conventional commit messages in their pull request titles. | [Link](../notebooks/plots/plot_conventional_commit_breakdown.ipynb) |
+| `prfiesta.analysis.plot.plot_reactions`                     | Produces a plot showing distribution of [GitHub Reactions](https://docs.github.com/en/rest/reactions?apiVersion=2022-11-28)                                                                                                                                      | [Link](../notebooks/plots/plot_reactions.ipynb)                     |
+
+
+All prfiesta plots have the same signature:
+
+```python
+def plot_NAME(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes]:
+    ...
+```
+
+*Where `NAME` is the placeholder for the actual plot name.*
+
+- The plotting functions always take a `pd.DataFrame` as the first argument. This Dataframe should originate from the prfiesta collection process.
+- The plotting functions always return something which can be displayed in a Jupyter Notebook
+- The plotting functions always take `**kwargs` which can be used to further customize the output
+    - The exact specifics of this is up to the plotting function however in general the following common *optional* options should exist:
+
+| Option           | Type                             | Description                                                                                                                                                                                    |
+| ---------------- | ---------------                  | ---------------                                                                                                                                                                                |
+| `ax`             | `Optional[matplotlib.axes.Axes]` | The `matplotlib` axis to plot into. This allows users to add the plot into a `plt.subplots`. If omitted, the plot will just plot into the default location                                     |
+| `palette`        | `Optional[str]`                  | A color palette to apply to the plot (e.g a [seaborn color palette](https://seaborn.pydata.org/tutorial/color_palettes.html))                                                                  |
+| `title`          | `Optional[str]`                  | The title of the plot                                                                                                                                                                          |
+| `hue`            | `Optional[str]`                  | Used to distinguise different catagories. This is relevant to [seaborn backed plots](https://seaborn.pydata.org/tutorial/color_palettes.html?highlight=hue#vary-hue-to-distinguish-categories) |
+
+
+## Building a Custom Plot
+
+You can build your own plot by creating a function that follows this signature:
+
+```python
+from typing import Union
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def plot_my_cool_plot(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes, pd.DataFrame]:
+    pass
+```
+
+All prfiesta should accept the the same `kwargs` mentioned in the above table however implementation of these `kwargs` per plot is on a best effort basis.
+
+**If you build a plot which you think will be useful for others then feel free to contribute it to this project 🚀**
diff --git a/makefile b/makefile
index 1ebd774..a6f32c4 100644
--- a/makefile
+++ b/makefile
@@ -28,6 +28,10 @@ precommit_install:
 precommit_run:
 	pre-commit run --all-files
 
+validate_notebooks:
+	poetry run bash ./notebooks/scripts/run_all.sh './notebooks/plots/*.ipynb' 'notebooks/plots'
+	poetry run bash ./notebooks/scripts/run_all.sh './notebooks/views/*.ipynb' 'notebooks/views'
+
 clean:
 	rm ./coverage.xml
 	rm -rf ./htmlcov
diff --git a/notebooks/plots/plot_author_association.ipynb b/notebooks/plots/plot_author_association.ipynb
new file mode 100644
index 0000000..67bf7a1
--- /dev/null
+++ b/notebooks/plots/plot_author_association.ipynb
@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0b5ed03e-df70-4e18-a6b0-d7c221809a01",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001243,
+     "end_time": "2023-05-20T12:06:45.583853",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:45.582610",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Author Associations Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fba06e86-90ec-4f92-ad44-8ad926a0f782",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:45.594675Z",
+     "iopub.status.busy": "2023-05-20T12:06:45.594400Z",
+     "iopub.status.idle": "2023-05-20T12:06:46.273870Z",
+     "shell.execute_reply": "2023-05-20T12:06:46.273497Z"
+    },
+    "papermill": {
+     "duration": 0.683576,
+     "end_time": "2023-05-20T12:06:46.275068",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:45.591492",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from prfiesta.analysis.plot import plot_author_associations\n",
+    "\n",
+    "df = pd.read_parquet('../samples_data/kiran94.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "db275ddb-43ca-4a6d-8ae5-233f1541511b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:46.284601Z",
+     "iopub.status.busy": "2023-05-20T12:06:46.284285Z",
+     "iopub.status.idle": "2023-05-20T12:06:46.439906Z",
+     "shell.execute_reply": "2023-05-20T12:06:46.439357Z"
+    },
+    "papermill": {
+     "duration": 0.164895,
+     "end_time": "2023-05-20T12:06:46.440993",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:46.276098",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Author Associations'}, ylabel='count'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_author_associations(df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 2.366139,
+   "end_time": "2023-05-20T12:06:46.858732",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "./notebooks/plots/plot_author_association.ipynb",
+   "output_path": "./notebooks/plots/plot_author_association.ipynb",
+   "parameters": {},
+   "start_time": "2023-05-20T12:06:44.492593",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/plots/plot_conventional_commit_breakdown.ipynb b/notebooks/plots/plot_conventional_commit_breakdown.ipynb
new file mode 100644
index 0000000..6b23ca1
--- /dev/null
+++ b/notebooks/plots/plot_conventional_commit_breakdown.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "521cca80-f25e-44a9-b87d-8c0900ba6449",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001411,
+     "end_time": "2023-05-20T12:06:48.758994",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:48.757583",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Conventional Commit Breakdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "12aba7b4-833a-4c7d-b2b9-0200499955dd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:48.770928Z",
+     "iopub.status.busy": "2023-05-20T12:06:48.769863Z",
+     "iopub.status.idle": "2023-05-20T12:06:49.494612Z",
+     "shell.execute_reply": "2023-05-20T12:06:49.494188Z"
+    },
+    "papermill": {
+     "duration": 0.729599,
+     "end_time": "2023-05-20T12:06:49.495780",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:48.766181",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from prfiesta.analysis.plot import plot_conventional_commit_breakdown\n",
+    "\n",
+    "df = pd.read_parquet('../samples_data/kiran94.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ed7a6558-cd4b-4438-9b25-593550200579",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:49.505009Z",
+     "iopub.status.busy": "2023-05-20T12:06:49.504668Z",
+     "iopub.status.idle": "2023-05-20T12:06:49.870899Z",
+     "shell.execute_reply": "2023-05-20T12:06:49.869988Z"
+    },
+    "papermill": {
+     "duration": 0.375413,
+     "end_time": "2023-05-20T12:06:49.872056",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:49.496643",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Conventional Commit Breakdown'}, xlabel='count', ylabel='repository_name'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, axes = plt.subplots(ncols=1, nrows=1, figsize=(12, 6))\n",
+    "\n",
+    "plot_conventional_commit_breakdown(df, ax=axes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 2.784694,
+   "end_time": "2023-05-20T12:06:50.291382",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "./notebooks/plots/plot_conventional_commit_breakdown.ipynb",
+   "output_path": "./notebooks/plots/plot_conventional_commit_breakdown.ipynb",
+   "parameters": {},
+   "start_time": "2023-05-20T12:06:47.506688",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/plots/plot_overall_contribution_timeline.ipynb b/notebooks/plots/plot_overall_contribution_timeline.ipynb
new file mode 100644
index 0000000..2d6238b
--- /dev/null
+++ b/notebooks/plots/plot_overall_contribution_timeline.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6e9f32b0-3244-4106-aca2-978e109b150a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001219,
+     "end_time": "2023-05-20T12:06:51.996352",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:51.995133",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Overall Contribution Timeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "188e7d19-ee71-4334-ad44-d541333a6f22",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:52.008977Z",
+     "iopub.status.busy": "2023-05-20T12:06:52.008362Z",
+     "iopub.status.idle": "2023-05-20T12:06:52.701195Z",
+     "shell.execute_reply": "2023-05-20T12:06:52.700775Z"
+    },
+    "papermill": {
+     "duration": 0.6987,
+     "end_time": "2023-05-20T12:06:52.703170",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:52.004470",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from prfiesta.analysis.plot import plot_overall_timeline\n",
+    "\n",
+    "df = pd.read_parquet('../samples_data/kiran94.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f28206d1-2eae-4619-abb3-00df25af53ff",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:52.712585Z",
+     "iopub.status.busy": "2023-05-20T12:06:52.712381Z",
+     "iopub.status.idle": "2023-05-20T12:06:53.090038Z",
+     "shell.execute_reply": "2023-05-20T12:06:53.089543Z"
+    },
+    "papermill": {
+     "duration": 0.386396,
+     "end_time": "2023-05-20T12:06:53.091008",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:52.704612",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Overall Contributions'}, xlabel='year', ylabel='id'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, axes = plt.subplots(ncols=1, nrows=1, figsize=(12, 6))\n",
+    "\n",
+    "plot_overall_timeline(df, ax=axes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 2.564333,
+   "end_time": "2023-05-20T12:06:53.508406",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "./notebooks/plots/plot_overall_contribution_timeline.ipynb",
+   "output_path": "./notebooks/plots/plot_overall_contribution_timeline.ipynb",
+   "parameters": {},
+   "start_time": "2023-05-20T12:06:50.944073",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/plots/plot_reactions.ipynb b/notebooks/plots/plot_reactions.ipynb
new file mode 100644
index 0000000..4d17e0e
--- /dev/null
+++ b/notebooks/plots/plot_reactions.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f12cbc57-9f0f-4b47-beb4-b13d53835f2e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001304,
+     "end_time": "2023-05-20T12:06:54.906428",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:54.905124",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Reaction Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d488591b-05a6-443f-bca3-26143479e547",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:54.915844Z",
+     "iopub.status.busy": "2023-05-20T12:06:54.915567Z",
+     "iopub.status.idle": "2023-05-20T12:06:55.494799Z",
+     "shell.execute_reply": "2023-05-20T12:06:55.494316Z"
+    },
+    "papermill": {
+     "duration": 0.582251,
+     "end_time": "2023-05-20T12:06:55.495925",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:54.913674",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from prfiesta.analysis.plot import plot_reactions\n",
+    "\n",
+    "df = pd.read_parquet('../samples_data/charliermarsh.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c6ac45a1-3904-4527-bdef-d54e5cb1a9ca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:55.505212Z",
+     "iopub.status.busy": "2023-05-20T12:06:55.505015Z",
+     "iopub.status.idle": "2023-05-20T12:06:55.765792Z",
+     "shell.execute_reply": "2023-05-20T12:06:55.765270Z"
+    },
+    "papermill": {
+     "duration": 0.270008,
+     "end_time": "2023-05-20T12:06:55.766836",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:55.496828",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Reactions'}>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, axes = plt.subplots(ncols=1, nrows=1, figsize=(12, 6))\n",
+    "\n",
+    "plot_reactions(df, ax=axes, threshold=10)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 1.954643,
+   "end_time": "2023-05-20T12:06:55.983441",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "./notebooks/plots/plot_reactions.ipynb",
+   "output_path": "./notebooks/plots/plot_reactions.ipynb",
+   "parameters": {},
+   "start_time": "2023-05-20T12:06:54.028798",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/plots/plot_state_distribution.ipynb b/notebooks/plots/plot_state_distribution.ipynb
new file mode 100644
index 0000000..f56f367
--- /dev/null
+++ b/notebooks/plots/plot_state_distribution.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62d0395b-43a4-407e-80ef-083aba2336a5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001576,
+     "end_time": "2023-05-20T12:06:57.467065",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:57.465489",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# State Distribution "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a8aeb1cc-751f-48c8-9094-ae2e0dc78d60",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:57.478277Z",
+     "iopub.status.busy": "2023-05-20T12:06:57.478023Z",
+     "iopub.status.idle": "2023-05-20T12:06:58.049893Z",
+     "shell.execute_reply": "2023-05-20T12:06:58.049525Z"
+    },
+    "papermill": {
+     "duration": 0.576294,
+     "end_time": "2023-05-20T12:06:58.050968",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:57.474674",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from prfiesta.analysis.plot import plot_state_distribution\n",
+    "\n",
+    "df = pd.read_parquet('../samples_data/kiran94.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f00ccc33-0f69-4b99-82bb-8b8c73a9c190",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:06:58.062130Z",
+     "iopub.status.busy": "2023-05-20T12:06:58.061636Z",
+     "iopub.status.idle": "2023-05-20T12:06:58.392673Z",
+     "shell.execute_reply": "2023-05-20T12:06:58.392214Z"
+    },
+    "papermill": {
+     "duration": 0.341873,
+     "end_time": "2023-05-20T12:06:58.393713",
+     "exception": false,
+     "start_time": "2023-05-20T12:06:58.051840",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'State Distribution'}, xlabel='state', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, axes = plt.subplots(nrows=1, ncols=1, figsize=(12, 6))\n",
+    "\n",
+    "plot_state_distribution(df, ax=axes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 2.121935,
+   "end_time": "2023-05-20T12:06:58.611249",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "./notebooks/plots/plot_state_distribution.ipynb",
+   "output_path": "./notebooks/plots/plot_state_distribution.ipynb",
+   "parameters": {},
+   "start_time": "2023-05-20T12:06:56.489314",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/samples_data/charliermarsh.parquet b/notebooks/samples_data/charliermarsh.parquet
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SoBu~C0qV+q6jkadGyZ?!Z5k;6

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diff --git a/notebooks/scripts/run_all.sh b/notebooks/scripts/run_all.sh
new file mode 100755
index 0000000..7286517
--- /dev/null
+++ b/notebooks/scripts/run_all.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+LIST_EXPRESSION=$1
+CWD=$2
+
+ls $LIST_EXPRESSION | xargs -I _ papermill --cwd $CWD _ _
diff --git a/notebooks/views/view_pull_requests.ipynb b/notebooks/views/view_pull_requests.ipynb
new file mode 100644
index 0000000..3f2e03f
--- /dev/null
+++ b/notebooks/views/view_pull_requests.ipynb
@@ -0,0 +1,733 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cc82a0e1-6dd7-46f3-95da-e218f2dbabbe",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001382,
+     "end_time": "2023-05-20T12:07:00.472962",
+     "exception": false,
+     "start_time": "2023-05-20T12:07:00.471580",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Pull Request View"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "84198985-b27b-4247-bdab-59b35e1ae916",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:07:00.482264Z",
+     "iopub.status.busy": "2023-05-20T12:07:00.481926Z",
+     "iopub.status.idle": "2023-05-20T12:07:00.847769Z",
+     "shell.execute_reply": "2023-05-20T12:07:00.847362Z"
+    },
+    "papermill": {
+     "duration": 0.369415,
+     "end_time": "2023-05-20T12:07:00.849592",
+     "exception": false,
+     "start_time": "2023-05-20T12:07:00.480177",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from prfiesta.analysis.view import view_pull_requests\n",
+    "\n",
+    "df = pd.read_parquet('../samples_data/kiran94.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "98cdd299-1e18-4c23-8714-ac8fbcc6b6fb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-05-20T12:07:00.859247Z",
+     "iopub.status.busy": "2023-05-20T12:07:00.859060Z",
+     "iopub.status.idle": "2023-05-20T12:07:00.880396Z",
+     "shell.execute_reply": "2023-05-20T12:07:00.879697Z"
+    },
+    "papermill": {
+     "duration": 0.031377,
+     "end_time": "2023-05-20T12:07:00.882476",
+     "exception": false,
+     "start_time": "2023-05-20T12:07:00.851099",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>number</th>\n",
+       "      <th>title</th>\n",
+       "      <th>repository_name</th>\n",
+       "      <th>updated_at</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>758</td>\n",
+       "      <td><a href=\"https://github.com/rockerBOO/awesome-neovim/pull/758\">Add `kiran94/edit-markdown-table.nvim`</a></td>\n",
+       "      <td>rockerBOO/awesome-neovim</td>\n",
+       "      <td>2 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/edit-markdown-table.nvim/pull/3\">chore(readme): corrected typo</a></td>\n",
+       "      <td>kiran94/edit-markdown-table.nvim</td>\n",
+       "      <td>2 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/edit-markdown-table.nvim/pull/2\">fix(ci): updated permissions</a></td>\n",
+       "      <td>kiran94/edit-markdown-table.nvim</td>\n",
+       "      <td>2 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/edit-markdown-table.nvim/pull/1\">feat(core): implement edit markdown table</a></td>\n",
+       "      <td>kiran94/edit-markdown-table.nvim</td>\n",
+       "      <td>2 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/7\">fix(s3): ensure s3api calls always output json</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>3 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/8\">chore(ci): update matrix with neovim 0.9</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>3 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>29914</td>\n",
+       "      <td><a href=\"https://github.com/hashicorp/terraform-provider-aws/pull/29914\">docs(api-gateway-usage-plan-key): correct example</a></td>\n",
+       "      <td>hashicorp/terraform-provider-aws</td>\n",
+       "      <td>4 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>587</td>\n",
+       "      <td><a href=\"https://github.com/rockerBOO/awesome-neovim/pull/587\">Add `kiran94/s3edit.nvim`</a></td>\n",
+       "      <td>rockerBOO/awesome-neovim</td>\n",
+       "      <td>29 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/5\">docs(vim): generate vimdoc from markdown</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/11\">refactor(plugin): update healthcheck for lua</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/9\">fix(output): ensure that current buffer is respected</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/10\">chore(ci): add testing</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/4\">feat(plugin): add healthcheck</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/3\">chore(ci): add semantic release</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/2\">docs(readme): add preview</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>30 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/s3edit.nvim/pull/1\">chore(ci): add unit test checks</a></td>\n",
+       "      <td>kiran94/s3edit.nvim</td>\n",
+       "      <td>31 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/vvanglro/gredlock/pull/2\">refactor(redlock): apply interface </a></td>\n",
+       "      <td>vvanglro/gredlock</td>\n",
+       "      <td>49 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/vvanglro/gredlock/pull/1\">chore(dev-workflow): add testing docker compose</a></td>\n",
+       "      <td>vvanglro/gredlock</td>\n",
+       "      <td>49 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/vvanglro/gredlock/pull/3\">refactor(commands): update to constants</a></td>\n",
+       "      <td>vvanglro/gredlock</td>\n",
+       "      <td>49 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/graphql-voyager-go/pull/4\">docs(readme): add godocs badge and license</a></td>\n",
+       "      <td>kiran94/graphql-voyager-go</td>\n",
+       "      <td>50 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/graphql-voyager-go/pull/3\">docs(voyager): add example screenshot</a></td>\n",
+       "      <td>kiran94/graphql-voyager-go</td>\n",
+       "      <td>50 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/graphql-voyager-go/pull/2\">chore(action): add integration test</a></td>\n",
+       "      <td>kiran94/graphql-voyager-go</td>\n",
+       "      <td>50 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/graphql-voyager-go/pull/1\">chore(ci): add github action</a></td>\n",
+       "      <td>kiran94/graphql-voyager-go</td>\n",
+       "      <td>51 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td><a href=\"https://github.com/matkapi/postpy2/pull/15\">chore(ci): add testing action</a></td>\n",
+       "      <td>matkapi/postpy2</td>\n",
+       "      <td>51 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td><a href=\"https://github.com/matkapi/postpy2/pull/14\">docs(readme): fix code snippets</a></td>\n",
+       "      <td>matkapi/postpy2</td>\n",
+       "      <td>51 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td><a href=\"https://github.com/matkapi/postpy2/pull/12\">feat(core): add request override support</a></td>\n",
+       "      <td>matkapi/postpy2</td>\n",
+       "      <td>51 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td><a href=\"https://github.com/matkapi/postpy2/pull/13\">feat(core): add graphql support</a></td>\n",
+       "      <td>matkapi/postpy2</td>\n",
+       "      <td>51 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>28</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/28\">docs(diagram): swap architecture diagram</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>62 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>27</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/27\">fix(lambda): ensure execute and process attachment names are unique</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>67 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/terraform-provider-example/pull/3\">refactor(release): add release process + badges</a></td>\n",
+       "      <td>kiran94/terraform-provider-example</td>\n",
+       "      <td>67 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/terraform-provider-example/pull/2\">refactor(sample): update concept and attach server</a></td>\n",
+       "      <td>kiran94/terraform-provider-example</td>\n",
+       "      <td>67 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/8\">docs(readme): updated badges</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/7\">refactor(ci): add release process</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>26</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/26\">chore(github): add main branch protection</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/25\">chore(readme): update build badge</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/24\">refactor(ci): release process</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>23</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/23\">style(golint): correct linting errors and add to ci</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>22</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/22\">chore(ci): add code coverage</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>21</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/21\">style(staticcheck): integrate</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/20\">refactor(logging): add structured logging</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/19\">refactor(cmd): update lambda directory</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/18\">docs(readme): add go badges</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/17\">tests(lambdas): implement unit tests</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>68 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/16\">refactor(schema): add descriptions and use raw url</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>69 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/15\">feat(config): add json schema validation</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>69 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/14\">chore(s3): updated transaction object lifecycle</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>70 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/13\">chore(terraform): update providers</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>70 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/12\">fix(lambda): paramatize and increase timeout</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>71 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/11\">docs(license): add and adjust readme</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>71 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/6\">chore(license): add mit license</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>72 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/5\">chore(ci): add stylua gh workflow</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>72 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/4\">docs(readme): updated demo gif</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>72 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/3\">docs(help): add help document</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>72 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/2\">feat(healthcheck): implement</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>72 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/maim.nvim/pull/1\">feat(plugin): implemented screenshot and markdown commands</a></td>\n",
+       "      <td>kiran94/maim.nvim</td>\n",
+       "      <td>73 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/10\">feat(analytics): submit glue job from process transactions</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>73 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/9\">docs(readme): updated build badge and hudi docs</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>73 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/8\">feat(analytics): add glue job for hudi table</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>73 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/7\">docs(diagram): add architecture diagram</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>73 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/6\">feat(process-transactions): implement lambda</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>75 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/5\">feat(execute-orders): add success notifications</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>76 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/4\">feat(alerting): execute order failures</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>76 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/3\">refactor(pending-transaction): moved s3 prefix to terraform</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>77 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/2\">feat(schedule): add multi-schedule support</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>77 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dca-manager/pull/1\">feat(orders): implement execute orders lambda</a></td>\n",
+       "      <td>kiran94/dca-manager</td>\n",
+       "      <td>77 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/terraform-provider-example/pull/1\">chore(action): added build, lint and test steps</a></td>\n",
+       "      <td>kiran94/terraform-provider-example</td>\n",
+       "      <td>88 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/25\">docs: added logging docs</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>106 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/24\">docs: added mk documentation</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>106 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>23</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/23\">feat: implement type generation</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>107 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>22</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/22\">docs: updated samples</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>108 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>21</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/21\">feat: implement schema generation</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>108 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/19\">style: correct markdown linting issues</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>109 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/18\">build: added coveralls integration</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>109 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/17\">build: added coverage report</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>109 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/16\">feat: centralise overall rdf generation logic</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>109 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/15\">feat: added custom predicate and object options</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/14\">refactor: update cli to take logging arguments and add description</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/13\">feat: added custom date format option</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/12\">feat: added custom csv separator option for csv_edges</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/11\">feat: added custom read_csv file configuration</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/10\">refactor: added tests for horizontal transform</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/9\">feat: added chunking support</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/8\">docs: added netflix jupyter example</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/7\">docs: added jupyter sample</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/6\">docs: updated readme with examples and usage</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/3\">docs: added build badge</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/5\">build: added deploy github workflow</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/4\">build: added setup.py for package</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/2\">build: added github action</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dgraphpandas/pull/1\">feat: implemented vertical and horizontal transform</a></td>\n",
+       "      <td>kiran94/dgraphpandas</td>\n",
+       "      <td>110 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/9\">feat: added environment variables for config and config directory</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/8\">feat: added cross option in config</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/7\">feat: added filtering option</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/6\">feat: added disable option in configuration</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/5\">bug: updated matching function, added allowed match status within update/sync</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/4\">docs: added readme and additional metadata</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/3\">build: added github workflow and updated package id</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/2\">style: made flake8 compliant</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/dotfiles-sync/pull/1\">feat: implement list, sync and update functions</a></td>\n",
+       "      <td>kiran94/dotfiles-sync</td>\n",
+       "      <td>125 weeks ago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td><a href=\"https://github.com/kiran94/closest-city/pull/3\">feat: implemented api</a></td>\n",
+       "      <td>kiran94/closest-city</td>\n",
+       "      <td>126 weeks ago</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "view_pull_requests(df, relative_dates=True, head=100)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 1.677437,
+   "end_time": "2023-05-20T12:07:01.203503",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "./notebooks/views/view_pull_requests.ipynb",
+   "output_path": "./notebooks/views/view_pull_requests.ipynb",
+   "parameters": {},
+   "start_time": "2023-05-20T12:06:59.526066",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 3d99e28..58df659 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -28,6 +28,21 @@ files = [
 dev = ["aiounittest (==1.4.1)", "attribution (==1.6.2)", "black (==23.3.0)", "coverage[toml] (==7.2.3)", "flake8 (==5.0.4)", "flake8-bugbear (==23.3.12)", "flit (==3.7.1)", "mypy (==1.2.0)", "ufmt (==2.1.0)", "usort (==1.0.6)"]
 docs = ["sphinx (==6.1.3)", "sphinx-mdinclude (==0.5.3)"]
 
+[[package]]
+name = "ansiwrap"
+version = "0.8.4"
+description = "textwrap, but savvy to ANSI colors and styles"
+category = "dev"
+optional = false
+python-versions = "*"
+files = [
+    {file = "ansiwrap-0.8.4-py2.py3-none-any.whl", hash = "sha256:7b053567c88e1ad9eed030d3ac41b722125e4c1271c8a99ade797faff1f49fb1"},
+    {file = "ansiwrap-0.8.4.zip", hash = "sha256:ca0c740734cde59bf919f8ff2c386f74f9a369818cdc60efe94893d01ea8d9b7"},
+]
+
+[package.dependencies]
+textwrap3 = ">=0.9.2"
+
 [[package]]
 name = "anyio"
 version = "3.6.2"
@@ -217,6 +232,56 @@ soupsieve = ">1.2"
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
+[[package]]
+name = "black"
+version = "23.3.0"
+description = "The uncompromising code formatter."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "black-23.3.0-cp310-cp310-macosx_10_16_arm64.whl", hash = "sha256:0945e13506be58bf7db93ee5853243eb368ace1c08a24c65ce108986eac65915"},
+    {file = "black-23.3.0-cp310-cp310-macosx_10_16_universal2.whl", hash = "sha256:67de8d0c209eb5b330cce2469503de11bca4085880d62f1628bd9972cc3366b9"},
+    {file = "black-23.3.0-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:7c3eb7cea23904399866c55826b31c1f55bbcd3890ce22ff70466b907b6775c2"},
+    {file = "black-23.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:32daa9783106c28815d05b724238e30718f34155653d4d6e125dc7daec8e260c"},
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+pathspec = ">=0.9.0"
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+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
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+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
 [[package]]
 name = "bleach"
 version = "6.0.0"
@@ -238,14 +303,14 @@ css = ["tinycss2 (>=1.1.0,<1.2)"]
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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 test = ["pytest"]
 typing = ["mypy (>=0.990)"]
 
+[[package]]
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+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "chromedriver", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"]
+test = ["Pillow", "matplotlib", "pytest"]
+test-no-images = ["pytest"]
+
 [[package]]
 name = "coverage"
 version = "7.2.5"
@@ -578,6 +718,18 @@ test = ["iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-co
 test-randomorder = ["pytest-randomly"]
 tox = ["tox"]
 
+[[package]]
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+version = "0.11.0"
+description = "Composable style cycles"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "debugpy"
 version = "1.6.7"
@@ -660,6 +812,18 @@ files = [
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 ]
 
+[[package]]
+name = "entrypoints"
+version = "0.4"
+description = "Discover and load entry points from installed packages."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.1.1"
@@ -721,6 +885,32 @@ files = [
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 testing = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
 
+[[package]]
+name = "fonttools"
+version = "4.39.4"
+description = "Tools to manipulate font files"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fonttools-4.39.4-py3-none-any.whl", hash = "sha256:106caf6167c4597556b31a8d9175a3fdc0356fdcd70ab19973c3b0d4c893c461"},
+    {file = "fonttools-4.39.4.zip", hash = "sha256:dba8d7cdb8e2bac1b3da28c5ed5960de09e59a2fe7e63bb73f5a59e57b0430d2"},
+]
+
+[package.extras]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["munkres", "scipy"]
+lxml = ["lxml (>=4.0,<5)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=15.0.0)"]
+woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+
 [[package]]
 name = "fqdn"
 version = "1.5.1"
@@ -784,7 +974,7 @@ testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packag
 name = "importlib-resources"
 version = "5.12.0"
 description = "Read resources from Python packages"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -813,14 +1003,14 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.22.0"
+version = "6.23.1"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "ipykernel-6.23.1-py3-none-any.whl", hash = "sha256:77aeffab056c21d16f1edccdc9e5ccbf7d96eb401bd6703610a21be8b068aadc"},
+    {file = "ipykernel-6.23.1.tar.gz", hash = "sha256:1aba0ae8453e15e9bc6b24e497ef6840114afcdb832ae597f32137fa19d42a6f"},
 ]
 
 [package.dependencies]
@@ -897,6 +1087,28 @@ files = [
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 ]
 
+[[package]]
+name = "ipywidgets"
+version = "8.0.6"
+description = "Jupyter interactive widgets"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "ipywidgets-8.0.6.tar.gz", hash = "sha256:de7d779f2045d60de9f6c25f653fdae2dba57898e6a1284494b3ba20b6893bb8"},
+]
+
+[package.dependencies]
+ipykernel = ">=4.5.1"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.7,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.7,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
 [[package]]
 name = "isoduration"
 version = "20.11.0"
@@ -952,14 +1164,14 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "json5"
-version = "0.9.11"
+version = "0.9.14"
 description = "A Python implementation of the JSON5 data format."
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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+    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
+    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
 ]
 
 [package.extras]
@@ -1007,6 +1219,27 @@ webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-n
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
 format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
 
+[[package]]
+name = "jupyter-black"
+version = "0.3.4"
+description = "A simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically using Black. Fork of dnanhkhoa/nb_black."
+category = "dev"
+optional = false
+python-versions = "*"
+files = [
+    {file = "jupyter-black-0.3.4.tar.gz", hash = "sha256:2a38f33d4c321eb768f426103635ac9b80b40c9e42aa06072a729e3caddca4c3"},
+    {file = "jupyter_black-0.3.4-py3-none-any.whl", hash = "sha256:8690ec1d16d173a74eecc44419755ca7209c494b213e0d7b04c40a878239e247"},
+]
+
+[package.dependencies]
+black = ">=21"
+ipython = ">=7"
+tokenize-rt = ">=4"
+
+[package.extras]
+dev = ["build (==0.9.0)", "twine (==4.0.1)", "wheel (==0.37.0)"]
+test = ["flake8 (==3.9.2)", "flake8-docstrings (==1.6.0)", "flake8-import-order (==0.18.1)", "isort (==5.11.5)", "jupyterlab (==3.6.3)", "mypy (==1.2.0)", "pep8-naming (==0.12.1)", "playwright (==1.14.1)", "pytest (==7.3.1)", "tox (==4.5.1)"]
+
 [[package]]
 name = "jupyter-client"
 version = "8.2.0"
@@ -1261,6 +1494,96 @@ docs = ["autodoc-traits", "docutils (<0.20)", "jinja2 (<3.2.0)", "mistune (<3)",
 openapi = ["openapi-core (>=0.16.1,<0.17.0)", "ruamel-yaml"]
 test = ["hatch", "ipykernel", "jupyterlab-server[openapi]", "openapi-spec-validator (>=0.5.1,<0.6.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.7"
+description = "Jupyter interactive widgets for JupyterLab"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
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+version = "1.4.4"
+description = "A fast implementation of the Cassowary constraint solver"
+category = "main"
+optional = false
+python-versions = ">=3.7"
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+fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
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+packaging = ">=20.0"
+pillow = ">=6.2.0"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.6"
@@ -1385,6 +1771,32 @@ files = [
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 ]
 
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+files = [
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+
+[[package]]
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+version = "0.2.0"
+description = "Convert data to their natural (human-readable) format"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+six = "*"
+
 [[package]]
 name = "nbclassic"
 version = "1.0.0"
@@ -1446,14 +1858,14 @@ test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "p
 
 [[package]]
 name = "nbconvert"
-version = "7.3.1"
+version = "7.4.0"
 description = "Converting Jupyter Notebooks"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1627,7 +2039,7 @@ files = [
 name = "packaging"
 version = "23.1"
 description = "Core utilities for Python packages"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1715,6 +2127,40 @@ files = [
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 ]
 
+[[package]]
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+version = "2.4.0"
+description = "Parametrize and run Jupyter and nteract Notebooks"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
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+nbformat = ">=5.1.2"
+pyyaml = "*"
+requests = "*"
+tenacity = "*"
+tqdm = ">=4.32.2"
+
+[package.extras]
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+black = ["black (>=19.3b0)"]
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+gcs = ["gcsfs (>=0.2.0)"]
+github = ["PyGithub (>=1.55)"]
+hdfs = ["pyarrow (>=2.0)"]
+s3 = ["boto3"]
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+
 [[package]]
 name = "parso"
 version = "0.8.3"
@@ -1731,6 +2177,18 @@ files = [
 qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 testing = ["docopt", "pytest (<6.0.0)"]
 
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+version = "0.11.1"
+description = "Utility library for gitignore style pattern matching of file paths."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
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+]
+
 [[package]]
 name = "pexpect"
 version = "4.8.0"
@@ -1758,6 +2216,86 @@ files = [
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+version = "9.5.0"
+description = "Python Imaging Library (Fork)"
+category = "main"
+optional = false
+python-versions = ">=3.7"
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+    {file = "PyJWT-2.7.0-py3-none-any.whl", hash = "sha256:ba2b425b15ad5ef12f200dc67dd56af4e26de2331f965c5439994dad075876e1"},
+    {file = "PyJWT-2.7.0.tar.gz", hash = "sha256:bd6ca4a3c4285c1a2d4349e5a035fdf8fb94e04ccd0fcbe6ba289dae9cc3e074"},
 ]
 
 [package.dependencies]
@@ -2066,6 +2604,21 @@ cffi = ">=1.4.1"
 docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
 tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
 
+[[package]]
+name = "pyparsing"
+version = "3.0.9"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+category = "main"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"},
+    {file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pyrsistent"
 version = "0.19.3"
@@ -2475,6 +3028,28 @@ files = [
     {file = "ruff-0.0.263.tar.gz", hash = "sha256:1008f211ad8aa1d998517ac5bf3d68fbc68ec516d1da89b6081f25ff2f30b687"},
 ]
 
+[[package]]
+name = "seaborn"
+version = "0.12.2"
+description = "Statistical data visualization"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "seaborn-0.12.2-py3-none-any.whl", hash = "sha256:ebf15355a4dba46037dfd65b7350f014ceb1f13c05e814eda2c9f5fd731afc08"},
+    {file = "seaborn-0.12.2.tar.gz", hash = "sha256:374645f36509d0dcab895cba5b47daf0586f77bfe3b36c97c607db7da5be0139"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.1,<3.6.1 || >3.6.1"
+numpy = ">=1.17,<1.24.0 || >1.24.0"
+pandas = ">=0.25"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
+
 [[package]]
 name = "send2trash"
 version = "1.8.2"
@@ -2494,19 +3069,19 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "67.7.2"
+version = "67.8.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "setuptools-67.7.2-py3-none-any.whl", hash = "sha256:23aaf86b85ca52ceb801d32703f12d77517b2556af839621c641fca11287952b"},
-    {file = "setuptools-67.7.2.tar.gz", hash = "sha256:f104fa03692a2602fa0fec6c6a9e63b6c8a968de13e17c026957dd1f53d80990"},
+    {file = "setuptools-67.8.0-py3-none-any.whl", hash = "sha256:5df61bf30bb10c6f756eb19e7c9f3b473051f48db77fddbe06ff2ca307df9a6f"},
+    {file = "setuptools-67.8.0.tar.gz", hash = "sha256:62642358adc77ffa87233bc4d2354c4b2682d214048f500964dbe760ccedf102"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
@@ -2565,6 +3140,21 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "tenacity"
+version = "8.2.2"
+description = "Retry code until it succeeds"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "tenacity-8.2.2-py3-none-any.whl", hash = "sha256:2f277afb21b851637e8f52e6a613ff08734c347dc19ade928e519d7d2d8569b0"},
+    {file = "tenacity-8.2.2.tar.gz", hash = "sha256:43af037822bd0029025877f3b2d97cc4d7bb0c2991000a3d59d71517c5c969e0"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx", "tornado (>=4.5)"]
+
 [[package]]
 name = "terminado"
 version = "0.17.1"
@@ -2586,6 +3176,18 @@ tornado = ">=6.1.0"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 
+[[package]]
+name = "textwrap3"
+version = "0.9.2"
+description = "textwrap from Python 3.6 backport (plus a few tweaks)"
+category = "dev"
+optional = false
+python-versions = "*"
+files = [
+    {file = "textwrap3-0.9.2-py2.py3-none-any.whl", hash = "sha256:bf5f4c40faf2a9ff00a9e0791fed5da7415481054cef45bb4a3cfb1f69044ae0"},
+    {file = "textwrap3-0.9.2.zip", hash = "sha256:5008eeebdb236f6303dcd68f18b856d355f6197511d952ba74bc75e40e0c3414"},
+]
+
 [[package]]
 name = "tinycss2"
 version = "1.2.1"
@@ -2605,6 +3207,18 @@ webencodings = ">=0.4"
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["flake8", "isort", "pytest"]
 
+[[package]]
+name = "tokenize-rt"
+version = "5.0.0"
+description = "A wrapper around the stdlib `tokenize` which roundtrips."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tokenize_rt-5.0.0-py2.py3-none-any.whl", hash = "sha256:c67772c662c6b3dc65edf66808577968fb10badfc2042e3027196bed4daf9e5a"},
+    {file = "tokenize_rt-5.0.0.tar.gz", hash = "sha256:3160bc0c3e8491312d0485171dea861fc160a240f5f5766b72a1165408d10740"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -2619,25 +3233,46 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.3.1"
+version = "6.3.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 category = "dev"
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "tornado-6.3.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:db181eb3df8738613ff0a26f49e1b394aade05034b01200a63e9662f347d4415"},
-    {file = "tornado-6.3.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b4e7b956f9b5e6f9feb643ea04f07e7c6b49301e03e0023eedb01fa8cf52f579"},
-    {file = "tornado-6.3.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9661aa8bc0e9d83d757cd95b6f6d1ece8ca9fd1ccdd34db2de381e25bf818233"},
-    {file = "tornado-6.3.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:81c17e0cc396908a5e25dc8e9c5e4936e6dfd544c9290be48bd054c79bcad51e"},
-    {file = "tornado-6.3.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a27a1cfa9997923f80bdd962b3aab048ac486ad8cfb2f237964f8ab7f7eb824b"},
-    {file = "tornado-6.3.1-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:d7117f3c7ba5d05813b17a1f04efc8e108a1b811ccfddd9134cc68553c414864"},
-    {file = "tornado-6.3.1-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:ffdce65a281fd708da5a9def3bfb8f364766847fa7ed806821a69094c9629e8a"},
-    {file = "tornado-6.3.1-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:90f569a35a8ec19bde53aa596952071f445da678ec8596af763b9b9ce07605e6"},
-    {file = "tornado-6.3.1-cp38-abi3-win32.whl", hash = "sha256:3455133b9ff262fd0a75630af0a8ee13564f25fb4fd3d9ce239b8a7d3d027bf8"},
-    {file = "tornado-6.3.1-cp38-abi3-win_amd64.whl", hash = "sha256:1285f0691143f7ab97150831455d4db17a267b59649f7bd9700282cba3d5e771"},
-    {file = "tornado-6.3.1.tar.gz", hash = "sha256:5e2f49ad371595957c50e42dd7e5c14d64a6843a3cf27352b69c706d1b5918af"},
+    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:c367ab6c0393d71171123ca5515c61ff62fe09024fa6bf299cd1339dc9456829"},
+    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b46a6ab20f5c7c1cb949c72c1994a4585d2eaa0be4853f50a03b5031e964fc7c"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c2de14066c4a38b4ecbbcd55c5cc4b5340eb04f1c5e81da7451ef555859c833f"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b17b1cf5f8354efa3d37c6e28fdfd9c1c1e5122f2cb56dac121ac61baa47cbe"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:29e71c847a35f6e10ca3b5c2990a52ce38b233019d8e858b755ea6ce4dcdd19d"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:834ae7540ad3a83199a8da8f9f2d383e3c3d5130a328889e4cc991acc81e87a0"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:6a0848f1aea0d196a7c4f6772197cbe2abc4266f836b0aac76947872cd29b411"},
+    {file = "tornado-6.3.2-cp38-abi3-win32.whl", hash = "sha256:7efcbcc30b7c654eb6a8c9c9da787a851c18f8ccd4a5a3a95b05c7accfa068d2"},
+    {file = "tornado-6.3.2-cp38-abi3-win_amd64.whl", hash = "sha256:0c325e66c8123c606eea33084976c832aa4e766b7dff8aedd7587ea44a604cdf"},
+    {file = "tornado-6.3.2.tar.gz", hash = "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.65.0"
+description = "Fast, Extensible Progress Meter"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.65.0-py3-none-any.whl", hash = "sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671"},
+    {file = "tqdm-4.65.0.tar.gz", hash = "sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "traitlets"
 version = "5.9.0"
@@ -2695,21 +3330,20 @@ dev = ["flake8 (<4.0.0)", "flake8-annotations", "flake8-bugbear", "flake8-commas
 
 [[package]]
 name = "urllib3"
-version = "2.0.2"
+version = "1.26.15"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 files = [
-    {file = "urllib3-2.0.2-py3-none-any.whl", hash = "sha256:d055c2f9d38dc53c808f6fdc8eab7360b6fdbbde02340ed25cfbcd817c62469e"},
-    {file = "urllib3-2.0.2.tar.gz", hash = "sha256:61717a1095d7e155cdb737ac7bb2f4324a858a1e2e6466f6d03ff630ca68d3cc"},
+    {file = "urllib3-1.26.15-py2.py3-none-any.whl", hash = "sha256:aa751d169e23c7479ce47a0cb0da579e3ede798f994f5816a74e4f4500dcea42"},
+    {file = "urllib3-1.26.15.tar.gz", hash = "sha256:8a388717b9476f934a21484e8c8e61875ab60644d29b9b39e11e4b9dc1c6b305"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
-socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
+secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
+socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "virtualenv"
@@ -2789,6 +3423,18 @@ docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.7"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "widgetsnbextension-4.0.7-py3-none-any.whl", hash = "sha256:be3228a73bbab189a16be2d4a3cd89ecbd4e31948bfdc64edac17dcdee3cd99c"},
+    {file = "widgetsnbextension-4.0.7.tar.gz", hash = "sha256:ea67c17a7cd4ae358f8f46c3b304c40698bc0423732e3f273321ee141232c8be"},
+]
+
 [[package]]
 name = "wrapt"
 version = "1.15.0"
@@ -2974,7 +3620,7 @@ test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
 name = "zipp"
 version = "3.15.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2989,4 +3635,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "08891faac6129dc214d6a4da112e23ceb8d25072698c8c92c692f37a7518ea80"
+content-hash = "828b2efc027aea9ac7f462d0df010df784aae867c490ae20fcd0a2031499697e"
diff --git a/prfiesta/analysis/__init__.py b/prfiesta/analysis/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/prfiesta/analysis/plot.py b/prfiesta/analysis/plot.py
new file mode 100644
index 0000000..dad4734
--- /dev/null
+++ b/prfiesta/analysis/plot.py
@@ -0,0 +1,128 @@
+import calendar
+import logging
+from typing import Optional, Union
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from matplotlib.axes import Axes
+
+logger = logging.getLogger(__name__)
+
+_months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
+
+def plot_state_distribution(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes, pd.DataFrame]:
+
+    ax: Optional[Axes] = kwargs.get('ax')
+    palette: Optional[str] = kwargs.get('palette')
+    title: Optional[str] = kwargs.get('title', 'State Distribution')
+    hue: Optional[str] = kwargs.get('hue', 'repository_name')
+
+    if ax:
+        ax.set_title(title)
+
+    return sns.histplot(data, x='state', hue=hue, ax=ax, palette=palette)
+
+def plot_overall_timeline(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes, pd.DataFrame]:
+
+    ax: Optional[Axes] = kwargs.get('ax')
+    palette: Optional[str] = kwargs.get('palette')
+    title: Optional[str] = kwargs.get('title', 'Overall Contributions')
+    hue: Optional[str] = kwargs.get('hue', 'month')
+
+    temp = data.copy()
+
+    temp['month'] = temp['created_at'].dt.month_name()
+    temp['year'] = temp['created_at'].dt.year.astype(str)
+
+    temp = temp.groupby(['month', 'year'])['id'].count()
+    temp = temp.reset_index()
+
+    # X Axis Ordering (Year)
+    x_order = temp['year'].unique().tolist()
+    x_order = [int(x) for x in x_order]
+    x_order.sort()
+    x_order = [str(x) for x in x_order]
+
+    # Hue Ordering (Months)
+    sorted_months = sorted(_months, key=lambda x: list(calendar.month_name).index(x))
+
+    p = sns.barplot(temp, x='year', y='id', hue=hue, ax=ax, order=x_order, hue_order=sorted_months, palette=palette)
+
+    if ax:
+        ax.set_title(title)
+        ax.legend(loc='upper left')
+
+    return p
+
+def plot_author_associations(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes, pd.DataFrame]:
+
+    ax: Optional[Axes] = kwargs.get('ax')
+    palette: Optional[str] = kwargs.get('palette')
+    title: Optional[str] = kwargs.get('title', 'Author Associations')
+
+    temp = data.groupby('author_association')['id'].count()
+    temp.name = 'count'
+
+    return temp.plot.pie(ax=ax, title=title, legend=True, colormap=palette)
+
+def plot_conventional_commit_breakdown(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes, pd.DataFrame]:
+
+    ax: Optional[Axes] = kwargs.get('ax')
+    palette: Optional[str] = kwargs.get('palette')
+    title: Optional[str] = kwargs.get('title', 'Conventional Commit Breakdown')
+    hue: Optional[str] = kwargs.get('hue', 'type')
+
+    conventional_commit_frame = data['title'] \
+        .str \
+        .extract(r'^(?P<type>feat|fix|docs|style|refactor|test|chore|build|ci|perf)(\((?P<scope>[A-Za-z-]+)\))?: (?P<subject>[^\n]+)$').copy()
+
+    conventional_commit_frame = conventional_commit_frame.drop(columns=[1, 'subject'])
+    conventional_commit_frame = pd.concat([conventional_commit_frame, data['repository_name']], axis=1)
+    conventional_commit_frame = conventional_commit_frame.dropna(subset=['type'])
+
+    if conventional_commit_frame.empty:
+        logger.warning('passed data did not seem to have any conventional commits')
+        return None
+
+    type_count = conventional_commit_frame.groupby(['type', 'repository_name']).count().reset_index()
+    type_count = type_count[type_count['scope'] != 0]
+    type_count = type_count.sort_values(by='scope', ascending=False)
+    type_count = type_count.rename(columns={'scope': 'count'})
+
+    p = sns.barplot(type_count, y='repository_name', x='count', hue=hue, ax=ax, palette=palette)
+
+    if ax:
+        ax.set_title(title)
+        ax.legend(loc='upper right')
+
+    return p
+
+def plot_reactions(data: pd.DataFrame, **kwargs) -> Union[plt.Figure, plt.Axes, pd.DataFrame]:
+
+    ax: Optional[Axes] = kwargs.get('ax')
+    palette: Optional[str] = kwargs.get('palette')
+    title: Optional[str] = kwargs.get('title', 'Reactions')
+    hue: Optional[str] = kwargs.get('hue')
+    threshold: Optional[int] = kwargs.get('threshold')
+
+    reaction_columns = [x for x in data.columns.tolist() if x.startswith('reactions.') and x not in ['reactions.url']]
+
+    reaction_df = data[reaction_columns]
+    reaction_df = reaction_df[reaction_df['reactions.total_count'] > 0]
+    reaction_df = reaction_df.drop(columns=['reactions.total_count'])
+
+    if reaction_df.empty:
+        logger.warning('passed data did not seem to have any reactions 🙁')
+        return None
+
+    if threshold:
+        reaction_df = reaction_df[reaction_df < threshold]
+
+    p = sns.scatterplot(reaction_df, ax=ax, palette=palette, hue=hue)
+
+    if ax:
+        ax.set_title(title)
+        ax.legend(loc='upper right')
+
+    return p
diff --git a/prfiesta/analysis/view.py b/prfiesta/analysis/view.py
new file mode 100644
index 0000000..1d52af0
--- /dev/null
+++ b/prfiesta/analysis/view.py
@@ -0,0 +1,34 @@
+from datetime import datetime, timezone
+
+import pandas as pd
+from IPython.display import HTML, DisplayObject
+from natural.date import duration
+
+
+def _enrich_pr_link(data: pd.DataFrame) -> pd.DataFrame:
+    def make_link(row: pd.Series) -> str:
+        return f'<a href="{row["html_url"]}">{row["title"]}</a>'
+
+    data['title'] = data.apply(make_link, axis=1)
+    return data.drop(columns='html_url')
+
+
+def view_pull_requests(data: pd.DataFrame, **kwargs) -> DisplayObject:
+
+    as_frame: bool = kwargs.get('as_frame', False)
+    relative_dates: bool = kwargs.get('relative_dates', True)
+    head: int = kwargs.get('head')
+
+    temp = data[['number', 'title', 'repository_name', 'updated_at', 'html_url']].copy()
+    temp = _enrich_pr_link(temp)
+
+    if relative_dates:
+        temp['updated_at'] = temp['updated_at'].apply(duration, now=datetime.now(timezone.utc))
+
+    if head:
+        temp = temp.head(head)
+
+    if as_frame:
+        return temp
+
+    return HTML(temp.to_html(escape=False, index=False))
diff --git a/pyproject.toml b/pyproject.toml
index 7949457..b0294b8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.0.1"
 description = "Analyze Individual Contributor Pull Requests"
 authors = ["kiran94"]
 license = "MIT"
-readme = "README.md"
+# readme = "README.md"
 homepage = "https://github.com/kiran94/prfiesta"
 repository = "https://github.com/kiran94/prfiesta"
 documentation = "https://github.com/kiran94/prfiesta/README.md"
@@ -17,15 +17,23 @@ click = "^8.1.3"
 pandas = "^2.0.1"
 pyarrow = "^11.0.0"
 rich = "^13.3.5"
+seaborn = "^0.12.2"
+matplotlib = "^3.7.1"
+natural = "^0.2.0"
+urllib3 = "<2"
 
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.0.263"
-jupyterlab = "^3.6.3"
 polars = "^0.17.10"
 pytest = "^7.3.1"
 pytest-cov = "^4.0.0"
 pytest-github-actions-annotate-failures = "^0.2.0"
+jupyterlab = "^3.6.3"
+jupyterlab-widgets = "^3.0.7"
+jupyter-black = "^0.3.4"
+ipywidgets = "^8.0.6"
 pre-commit = "^3.3.2"
+papermill = "^2.4.0"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/tests/analysis/__init__.py b/tests/analysis/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/analysis/test_plot.py b/tests/analysis/test_plot.py
new file mode 100644
index 0000000..68af62d
--- /dev/null
+++ b/tests/analysis/test_plot.py
@@ -0,0 +1,343 @@
+from datetime import datetime
+from typing import Dict
+from unittest.mock import ANY, Mock, call, patch
+
+import pandas as pd
+import pytest
+
+from prfiesta.analysis.plot import (
+    _months,
+    plot_author_associations,
+    plot_conventional_commit_breakdown,
+    plot_overall_timeline,
+    plot_reactions,
+    plot_state_distribution,
+)
+
+_mock_state_distribution_axis = Mock()
+_mock_overall_timeline_axis = Mock()
+_mock_author_association_axis = Mock()
+_mock_conventional_commit_axis = Mock()
+_mock_reaction_commit_axis = Mock()
+
+
+@pytest.mark.parametrize(('options', 'expected_options'), [
+    pytest.param(
+        {},
+        { 'x': 'state', 'hue': 'repository_name', 'ax': None, 'palette': None },
+        id='default'),
+    pytest.param(
+        {'ax': _mock_state_distribution_axis},
+        { 'x': 'state', 'hue': 'repository_name', 'ax': _mock_state_distribution_axis, 'palette': None },
+        id='with_custom_axis'),
+    pytest.param(
+        {'hue': 'user.login'},
+        { 'x': 'state', 'hue': 'user.login', 'ax': None, 'palette': None },
+        id='with_custom_hue'),
+    pytest.param(
+        {'palette': 'tab10'},
+        { 'x': 'state', 'hue': 'repository_name', 'ax': None, 'palette': 'tab10' },
+        id='with_custom_hue'),
+    pytest.param(
+        {'ax': _mock_state_distribution_axis, 'title': 'my_title'},
+        { 'x': 'state', 'hue': 'repository_name', 'ax': _mock_state_distribution_axis, 'palette': None },
+        id='with_custom_title'),
+])
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_state_distribution(mock_seaborn: Mock, options: Dict, expected_options: Dict) -> None:
+
+    data = pd.DataFrame(data={
+        'state': ['opened', 'closed'],
+        'repository_name': ['repo', 'repo2'],
+        'user.login': ['user', 'user2'],
+    })
+
+    mock_histplot = Mock()
+    mock_seaborn.histplot.return_value = mock_histplot
+
+    result = plot_state_distribution(data, **options)
+
+    if ('title' in options) and (options['title']):
+        assert _mock_state_distribution_axis.set_title.call_args_list == [call(options['title'])]
+
+    assert mock_histplot == result
+    assert mock_seaborn.histplot.call_args_list == [call(data, **expected_options)]
+
+    _mock_state_distribution_axis.reset_mock()
+
+
+@pytest.mark.parametrize(('options', 'expected_options'), [
+   pytest.param(
+        {},
+        {'x':'year', 'y':'id', 'hue':'month', 'ax': None, 'order':['2021', '2022'], 'hue_order':_months, 'palette':None},
+        id='default',
+    ),
+   pytest.param(
+        {'ax': _mock_overall_timeline_axis },
+        {'x':'year', 'y':'id', 'hue':'month', 'ax': _mock_overall_timeline_axis, 'order':['2021', '2022'], 'hue_order':_months, 'palette':None},
+        id='with_custom_axis',
+    ),
+   pytest.param(
+        {'palette': 'tab10'},
+        {'x':'year', 'y':'id', 'hue':'month', 'ax': None, 'order':['2021', '2022'], 'hue_order':_months, 'palette':'tab10'},
+        id='with_custom_pallete',
+    ),
+   pytest.param(
+        { 'ax': _mock_overall_timeline_axis, 'title': 'my_title'},
+        {'x':'year', 'y':'id', 'hue':'month', 'ax': _mock_overall_timeline_axis, 'order':['2021', '2022'], 'hue_order':_months, 'palette':None},
+        id='with_custom_title',
+    ),
+   pytest.param(
+        {'hue': 'custom_field'},
+        {'x':'year', 'y':'id', 'hue':'custom_field', 'ax': None, 'order':['2021', '2022'], 'hue_order':_months, 'palette':None},
+        id='with_custom_hue',
+    ),
+])
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_overall_timeline(mock_seaborn: Mock, options: Dict, expected_options: Dict) -> None:
+
+    mock_barplot = Mock()
+    mock_seaborn.barplot.return_value = mock_barplot
+
+    data = pd.DataFrame(data={
+        'created_at': [datetime(2021, 1, 1), datetime(2021, 2, 1), datetime(2022, 4, 14)],
+        'id': ['id1', 'id2', 'id3'],
+    })
+
+    result = plot_overall_timeline(data, **options)
+
+    assert mock_barplot == result
+    assert mock_seaborn.barplot.call_args_list == [call(ANY, **expected_options)]
+
+    if ('title' in options) and (options['title']):
+        assert _mock_overall_timeline_axis.set_title.call_args_list == [call(options['title'])]
+
+    _mock_overall_timeline_axis.reset_mock()
+
+
+@pytest.mark.parametrize(('options', 'expected_options'), [
+   pytest.param(
+        {},
+        {'ax': None, 'title': 'Author Associations', 'legend': True, 'colormap': None},
+        id='default',
+    ),
+   pytest.param(
+        {'ax': _mock_author_association_axis},
+        {'ax': _mock_author_association_axis, 'title': 'Author Associations', 'legend': True, 'colormap': None},
+        id='with_custom_axis',
+    ),
+   pytest.param(
+        {'title': 'my_custom_title'},
+        {'ax': None, 'title': 'my_custom_title', 'legend': True, 'colormap': None},
+        id='with_custom_title',
+    ),
+   pytest.param(
+        {'palette': 'my_colors'},
+        {'ax': None, 'title': 'Author Associations', 'legend': True, 'colormap': 'my_colors'},
+        id='with_custom_pallete',
+    ),
+])
+def test_plot_author_associations(options: Dict, expected_options: Dict) -> None:
+
+    agg_mock = Mock()
+
+    data = Mock()
+    data.groupby = Mock(return_value = {'id': Mock()})
+    data.groupby.return_value['id'] = agg_mock
+
+    plot_author_associations(data, **options)
+
+    assert data.groupby.called
+    assert agg_mock.count.called
+    assert agg_mock.count.return_value.plot.pie.call_args_list == [call(**expected_options)]
+
+
+@pytest.mark.parametrize(('data', 'options', 'expected_options'),[
+   pytest.param(
+        pd.DataFrame({
+            'title': ['feat(hello): my change', 'fix: my other change'],
+            'repository_name': ['repo1', 'repo2'],
+        }),
+        {},
+        {'y': 'repository_name', 'x': 'count', 'hue': 'type', 'ax': None, 'palette': None},
+        id='default',
+    ),
+   pytest.param(
+        pd.DataFrame({
+            'title': ['refactor(hello): my change', 'chore: my other change'],
+            'repository_name': ['repo1', 'repo2'],
+        }),
+        {'ax': _mock_conventional_commit_axis},
+        {'y': 'repository_name', 'x': 'count', 'hue': 'type', 'ax': _mock_conventional_commit_axis, 'palette': None},
+        id='with_custom_axis',
+    ),
+   pytest.param(
+        pd.DataFrame({
+            'title': ['docs(hello): my change', 'style: my other change'],
+            'repository_name': ['repo1', 'repo2'],
+        }),
+        {'palette': 'my_colors'},
+        {'y': 'repository_name', 'x': 'count', 'hue': 'type', 'ax': None, 'palette': 'my_colors'},
+        id='with_custom_pallete',
+    ),
+   pytest.param(
+        pd.DataFrame({
+            'title': ['feat(hello): my change', 'fix: my other change'],
+            'repository_name': ['repo1', 'repo2'],
+        }),
+        {'ax': _mock_conventional_commit_axis, 'title': 'my_title'},
+        {'y': 'repository_name', 'x': 'count', 'hue': 'type', 'ax': _mock_conventional_commit_axis, 'palette': None},
+        id='with_custom_title',
+    ),
+   pytest.param(
+        pd.DataFrame({
+            'title': ['feat(hello): my change', 'fix: my other change'],
+            'repository_name': ['repo1', 'repo2'],
+        }),
+        {'hue': 'my_field'},
+        {'y': 'repository_name', 'x': 'count', 'hue': 'my_field', 'ax': None, 'palette': None},
+        id='with_custom_hue',
+    ),
+])
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_conventional_commit_breakdown(mock_seaborn: Mock, data: pd.DataFrame, options: Dict, expected_options: Dict) -> None:
+
+    mock_barplot = Mock()
+    mock_seaborn.barplot.return_value = mock_barplot
+
+    result = plot_conventional_commit_breakdown(data, **options)
+
+    assert mock_barplot == result
+    assert mock_seaborn.barplot.call_args_list == [call(ANY, **expected_options)]
+
+    if ('title' in options) and (options['title']):
+        assert _mock_conventional_commit_axis.set_title.call_args_list == [call(options['title'])]
+
+    _mock_conventional_commit_axis.reset_mock()
+
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_conventional_commit_breakdown_no_valid_commits(mock_seaborn: Mock) -> None:
+
+    mock_seaborn.barplot.return_value = Mock()
+
+    data = pd.DataFrame({
+            'title': ['my change', 'fixed a thing'],
+            'repository_name': ['repo1', 'repo2'],
+    })
+
+    options = {}
+
+    assert not plot_conventional_commit_breakdown(data, **options)
+    assert not mock_seaborn.called
+    assert not mock_seaborn.barplot.called
+
+
+@pytest.mark.parametrize(('data', 'options', 'expected_options'), [
+    pytest.param(
+        pd.DataFrame(data={
+            'reactions.total_count': [1, 0, 1],
+            'reactions.+1': [1, 0, 1],
+            'reactions.-1': [0, 0, 1],
+            'reactions.laugh': [1, 1, 1],
+            'reactions.hooray': [1, 0, 0],
+            'reactions.confused': [0, 0, 1],
+        }),
+        {},
+        {'ax': None, 'palette': None, 'hue': None },
+        id='default',
+    ),
+    pytest.param(
+        pd.DataFrame(data={
+            'reactions.total_count': [1, 0, 1],
+            'reactions.+1': [1, 0, 1],
+            'reactions.-1': [0, 0, 1],
+            'reactions.laugh': [1, 1, 1],
+            'reactions.hooray': [1, 0, 0],
+            'reactions.confused': [0, 0, 1],
+        }),
+        {'ax': _mock_reaction_commit_axis},
+        {'ax': _mock_reaction_commit_axis, 'palette': None, 'hue': None },
+        id='with_custom_axis',
+    ),
+    pytest.param(
+        pd.DataFrame(data={
+            'reactions.total_count': [1, 0, 1],
+            'reactions.+1': [1, 0, 1],
+            'reactions.-1': [0, 0, 1],
+            'reactions.laugh': [1, 1, 1],
+            'reactions.hooray': [1, 0, 0],
+            'reactions.confused': [0, 0, 1],
+        }),
+        {'palette': 'tab10'},
+        {'ax': None, 'palette': 'tab10', 'hue': None },
+        id='with_custom_pallete',
+    ),
+    pytest.param(
+        pd.DataFrame(data={
+            'reactions.total_count': [1, 0, 1],
+            'reactions.+1': [1, 0, 1],
+            'reactions.-1': [0, 0, 1],
+            'reactions.laugh': [1, 1, 1],
+            'reactions.hooray': [1, 0, 0],
+            'reactions.confused': [0, 0, 1],
+        }),
+        {'hue': 'custom'},
+        {'ax': None, 'palette': None, 'hue': 'custom' },
+        id='with_custom_hue',
+    ),
+
+])
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_reactions(mock_seaborn: Mock, data: pd.DataFrame, options: Dict, expected_options: Dict) -> None:
+
+    mock_scatter = Mock()
+    mock_seaborn.scatterplot.return_value = mock_scatter
+
+    result = plot_reactions(data, **options)
+
+    expected_df = data[data['reactions.total_count'] > 0].copy()
+    expected_df = expected_df.drop(columns='reactions.total_count')
+
+    assert mock_scatter == result
+    assert mock_seaborn.scatterplot.call_args_list == [call(ANY, **expected_options)]
+
+    pd.testing.assert_frame_equal(expected_df, mock_seaborn.scatterplot.call_args_list[0][0][0])
+
+
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_reaction_no_reactions(mock_seaborn: Mock) -> None:
+
+    data = pd.DataFrame(data={
+        'reactions.total_count': [0, 0, 0],
+        'reactions.+1': [0, 0, 0],
+        'reactions.-1': [0, 0, 0],
+        'reactions.laugh': [0, 0, 0],
+        'reactions.hooray': [0, 0, 0],
+        'reactions.confused': [0, 0, 0],
+    })
+
+    assert not plot_reactions(data)
+    assert not mock_seaborn.scatterplot.called
+
+
+@patch('prfiesta.analysis.plot.sns')
+def test_plot_reaction_threshold(mock_seaborn: Mock) -> None:
+
+    threshold = 15
+
+    data = pd.DataFrame(data={
+        'reactions.total_count': [1, 1, 1],
+        'reactions.+1': [threshold*3, 2, 2],
+        'reactions.-1': [1, 1, 1],
+        'reactions.laugh': [0, 0, threshold*2],
+        'reactions.hooray': [0, 0, 0],
+        'reactions.confused': [0, 0, 0],
+    })
+
+    plot_reactions(data, threshold=threshold)
+
+    expected_df = data.copy()
+    expected_df = expected_df[expected_df < threshold]
+    expected_df = expected_df.drop(columns=['reactions.total_count'])
+
+    pd.testing.assert_frame_equal(expected_df, mock_seaborn.scatterplot.call_args_list[0][0][0])
diff --git a/tests/analysis/test_view.py b/tests/analysis/test_view.py
new file mode 100644
index 0000000..51dccc2
--- /dev/null
+++ b/tests/analysis/test_view.py
@@ -0,0 +1,102 @@
+from datetime import datetime, timezone
+from typing import Dict
+from unittest.mock import Mock, patch
+
+import pandas as pd
+import pytest
+
+from prfiesta.analysis.view import view_pull_requests
+
+_now = datetime.now(timezone.utc)
+
+
+@pytest.mark.parametrize(('data', 'options', 'expected'), [
+    pytest.param(
+        pd.DataFrame(data =
+        {
+            'number': [1, 2],
+            'title': ['title', 'title2'],
+            'repository_name': ['repository', 'repository'],
+            'updated_at': [datetime.now(timezone.utc), datetime.now(timezone.utc)],
+            'html_url': ['url1', 'url2'],
+         }),
+        {'as_frame': True},
+        pd.DataFrame(data={
+            'number': [1, 2],
+            'title': ['<a href="url1">title</a>', '<a href="url2">title2</a>'],
+            'repository_name': ['repository']*2,
+            'updated_at': ['just now']*2,
+        }),
+        id='as_frame_with_defaults',
+    ),
+    pytest.param(
+        pd.DataFrame(data =
+        {
+            'number': [1, 2],
+            'title': ['title', 'title2'],
+            'repository_name': ['repository', 'repository'],
+            'updated_at': [_now, _now],
+            'html_url': ['url1', 'url2'],
+         }),
+        {'as_frame': True, 'relative_dates': False},
+        pd.DataFrame(data={
+            'number': [1, 2],
+            'title': ['<a href="url1">title</a>', '<a href="url2">title2</a>'],
+            'repository_name': ['repository']*2,
+            'updated_at': [_now, _now],
+        }),
+        id='as_frame_with_no_relative_dates',
+    ),
+    pytest.param(
+        pd.DataFrame(data =
+        {
+            'number': [1, 2],
+            'title': ['title', 'title2'],
+            'repository_name': ['repository', 'repository'],
+            'updated_at': [datetime.now(timezone.utc), datetime.now(timezone.utc)],
+            'html_url': ['url1', 'url2'],
+         }),
+        {'as_frame': True, 'head': 1},
+        pd.DataFrame(data={
+            'number': [1],
+            'title': ['<a href="url1">title</a>'],
+            'repository_name': ['repository'],
+            'updated_at': ['just now'],
+        }),
+        id='as_frame_with_head',
+    ),
+    pytest.param(
+        pd.DataFrame(data =
+        {
+            'number': [1, 2],
+            'title': ['title', 'title2'],
+            'repository_name': ['repository', 'repository'],
+            'updated_at': [datetime.now(timezone.utc), datetime.now(timezone.utc)],
+            'html_url': ['url1', 'url2'],
+         }),
+        {},
+        pd.DataFrame(data={
+            'number': [1, 2],
+            'title': ['<a href="url1">title</a>', '<a href="url2">title2</a>'],
+            'repository_name': ['repository']*2,
+            'updated_at': ['just now']*2,
+        }),
+        id='html_default',
+    ),
+])
+@patch('prfiesta.analysis.view.HTML')
+def test_view_pull_request(
+        mock_html: Mock,
+        data: pd.DataFrame,
+        options: Dict,
+        expected: pd.DataFrame) -> None:
+
+    mock_html.return_value = 'DUMMY'
+
+    result = view_pull_requests(data, **options)
+
+    if 'as_frame' in options and options['as_frame']:
+        pd.testing.assert_frame_equal(expected, result)
+    else:
+        assert mock_html.called
+        assert result == mock_html.return_value