{ "cells": [ { "cell_type": "markdown", "id": "2bc817b1", "metadata": {}, "source": [ "\n", "# Basic Data Analysis & Visualization\n", "\n", "This notebook demonstrates how to load a sample dataset, perform quick exploratory analysis, group and pivot the data, and create visualizations.\n", "\n", "We’re using a CSV file from: https://sample-files.com/downloads/data/csv/basic-data.csv\n", " " ] }, { "cell_type": "markdown", "id": "a92fdfd1", "metadata": {}, "source": [ "\n", "## Install Dependencies\n", "\n", "If you haven't installed the required libraries, run:\n", "\n", "```bash\n", "pip install pandas matplotlib seaborn\n", "```\n", " " ] }, { "cell_type": "code", "execution_count": 1, "id": "b5d7308a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting pandas\n", " Downloading pandas-2.3.3-cp314-cp314-macosx_11_0_arm64.whl.metadata (91 kB)\n", "Collecting matplotlib\n", " Downloading 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" ], "text/plain": [ " ID Name Age Country Email\n", "count 100.000000 100 100.000000 100 100\n", "unique NaN 100 NaN 10 100\n", "top NaN Name_1 NaN Country_1 email_1@example.com\n", "freq NaN 1 NaN 10 1\n", "mean 50.500000 NaN 44.530000 NaN NaN\n", "std 29.011492 NaN 15.190012 NaN NaN\n", "min 1.000000 NaN 18.000000 NaN NaN\n", "25% 25.750000 NaN 32.000000 NaN NaN\n", "50% 50.500000 NaN 43.500000 NaN NaN\n", "75% 75.250000 NaN 59.250000 NaN NaN\n", "max 100.000000 NaN 69.000000 NaN NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check shape and summary stats\n", "print(df.shape)\n", "df.describe(include=\"all\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "e775e42f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ID Name Age Country Email\n", "0 1 Name_1 62 Country_1 email_1@example.com\n", "1 2 Name_2 48 Country_2 email_2@example.com\n", "2 3 Name_3 61 Country_3 email_3@example.com\n", "3 4 Name_4 32 Country_4 email_4@example.com\n", "4 5 Name_5 69 Country_5 email_5@example.com\n", ".. ... ... ... ... ...\n", "95 96 Name_96 60 Country_6 email_96@example.com\n", "96 97 Name_97 26 Country_7 email_97@example.com\n", "97 98 Name_98 52 Country_8 email_98@example.com\n", "98 99 Name_99 24 Country_9 email_99@example.com\n", "99 100 Name_100 55 Country_0 email_100@example.com\n", "\n", "[100 rows x 5 columns]\n" ] } ], "source": [ "print(df)" ] }, { "cell_type": "markdown", "id": "dc10be72", "metadata": {}, "source": [ "## Grouping and Pivoting Data" ] }, { "cell_type": "code", "execution_count": 6, "id": "f87ea8af", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Country RecordCount\n", "0 Country_1 10\n", "1 Country_2 10\n", "2 Country_3 10\n", "3 Country_4 10\n", "4 Country_5 10" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Example: Group by 'Country' and count number of records\n", "country_counts = df[\"Country\"].value_counts().reset_index()\n", "country_counts.columns = [\"Country\", \"RecordCount\"]\n", "country_counts.head()" ] }, { "cell_type": "markdown", "id": "f156d6de", "metadata": {}, "source": [ "## Visualizing Record Counts by Country" ] }, { "cell_type": "code", "execution_count": 7, "id": "d644fc06", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/z_/x3_6fwb174s0c7wcsd813s6r0000gn/T/ipykernel_10080/1795714619.py:3: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(data=country_counts, x=\"Country\", y=\"RecordCount\", palette=\"viridis\")\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot bar chart\n", "plt.figure(figsize=(10, 6))\n", "sns.barplot(data=country_counts, x=\"Country\", y=\"RecordCount\", palette=\"viridis\")\n", "plt.title(\"Record Count by Country\")\n", "plt.xticks(rotation=45)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "887b2525", "metadata": {}, "source": [ "## Pivot Table Example" ] }, { "cell_type": "code", "execution_count": 8, "id": "1e26e06b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Age
Country
Country_043.7
Country_143.8
Country_251.0
Country_338.2
Country_444.3
Country_548.3
Country_647.3
Country_741.1
Country_840.5
Country_947.1
\n", "
" ], "text/plain": [ " Age\n", "Country \n", "Country_0 43.7\n", "Country_1 43.8\n", "Country_2 51.0\n", "Country_3 38.2\n", "Country_4 44.3\n", "Country_5 48.3\n", "Country_6 47.3\n", "Country_7 41.1\n", "Country_8 40.5\n", "Country_9 47.1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# If 'Age' exists, average age by country (example only if dataset has relevant column)\n", "if \"Age\" in df.columns:\n", " age_pivot = df.pivot_table(index=\"Country\", values=\"Age\", aggfunc=\"mean\")\n", " display(age_pivot)\n", "else:\n", " print(\"No 'Age' column in dataset to pivot on.\")" ] }, { "cell_type": "markdown", "id": "d9793c4e", "metadata": {}, "source": [ "\n", "## Summary\n", "\n", "In this notebook, we:\n", "- Loaded a sample CSV dataset\n", "- Explored its structure\n", "- Grouped and counted records by country\n", "- Visualized results with a bar chart\n", "- Created a pivot table (if applicable)\n", "\n", "Try modifying this notebook for your own datasets or audit use cases!\n", " " ] } ], "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": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 }