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Data-Science-For-Beginners/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb

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1.2 KiB

{
"cells": [
{
"cell_type": "markdown",
"source": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import pandas as pd\r\n",
"import glob\r\n",
"\r\n",
"#Loading the dataset\r\n",
"path = '../../data/emails.csv'\r\n",
"email_df = pd.read_csv(path)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Using Describe on the email dataset\r\n",
"print(email_df.describe())"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {}
}
],
"metadata": {
"orig_nbformat": 4,
"language_info": {
"name": "python",
"version": "3.9.7",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.9.7 64-bit ('venv': venv)"
},
"interpreter": {
"hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
}
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"nbformat": 4,
"nbformat_minor": 2
}