feat: case studies

Python/Python_Count_characters_from_text.ipynb

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+{
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+   "cell_type": "markdown",
+   "id": "88c104cc-bf08-4242-821b-b3a40908152a",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "<img width=\"8%\" alt=\"Naas.png\" src=\"https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/.github/assets/logos/Naas.png\" style=\"border-radius: 15%\">"
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+  {
+   "cell_type": "markdown",
+   "id": "compressed-wilson",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "# Python - Count characters from text\n",
+    "\n",
+    "<a href=\"https://bit.ly/3JyWIk6\">Give Feedback</a> | <a href=\"https://github.com/jupyter-naas/awesome-notebooks/issues/new?assignees=&labels=bug&template=bug_report.md&title=Bubble+-+Send+data:+Error+short+description\">Bug report</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "religious-programmer",
+   "metadata": {},
+   "source": [
+    "**Tags:** #letters #words #count #text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fe9f56e-561c-4f52-aef8-b861c9462107",
+   "metadata": {},
+   "source": [
+    "**Author:** [Varsha Kumar](https://www.linkedin.com/in/varsha-kumar-590466305/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0de144be-594d-463e-9849-696fb2f6d1a8",
+   "metadata": {},
+   "source": [
+    "**Last update:** 2024-05-11 (Created: 2024-05-11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31ea7cdb-e10d-43fc-b026-f69249a59736",
+   "metadata": {},
+   "source": [
+    "**Description:** This notebook counts the appearance of a chosen word/letter or phrase in the text."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4b89388-5cd5-409a-8169-c53cc8dfab96",
+   "metadata": {},
+   "source": [
+    "**References:**\n",
+    "- [Naas Documentation](https://site.naas.ai/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "distinguished-truth",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "## Input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "numeric-mediterranean",
+   "metadata": {},
+   "source": [
+    "### Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "potential-surfing",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# No imports being used for this code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aggressive-trustee",
+   "metadata": {},
+   "source": [
+    "### Setup variables\n",
+    "\n",
+    "- `text`: Inputed sentence\n",
+    "- `characters`: Chosen letter/word/phrase to count for"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "continuous-melbourne",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "text = \"This is an example text. This text contains the phrase 'example text' multiple times.\"\n",
+    "characters = \"e\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "registered-showcase",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tested-astrology",
+   "metadata": {},
+   "source": [
+    "### Count the appearances of the chosen text in a sentence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "crude-louisville",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def count_phrase_occurrences(text, chosenText):\n",
+    "    count = 0\n",
+    "    phrases = text.split(chosenText)\n",
+    "    count = len(phrases) - 1\n",
+    "    return count\n",
+    "\n",
+    "occurrences = count_phrase_occurrences(text, characters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lonely-pacific",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-07-02T23:32:10.789097Z",
+     "iopub.status.busy": "2021-07-02T23:32:10.788829Z",
+     "iopub.status.idle": "2021-07-02T23:32:10.796900Z",
+     "shell.execute_reply": "2021-07-02T23:32:10.796358Z",
+     "shell.execute_reply.started": "2021-07-02T23:32:10.789033Z"
+    }
+   },
+   "source": [
+    "## Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "890f7c86-b7bb-4f5d-9a1b-e492dd9580fd",
+   "metadata": {},
+   "source": [
+    "### Display result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c4e3b7b-6440-4844-8054-265f1aec65eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"'{characters}' appears {occurrences} times in the text.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8c7aeb8-d588-4ad2-9726-19fb88d9fdc8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
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+   "name": "python3"
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+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "environment_variables": {},
+   "parameters": {},
+   "version": "2.3.3"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}