{
"cells": [
{
"cell_type": "code",
"execution_count": 65,
"id": "f69daedd-4a85-44ff-b67f-1115e72e6ec8",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: openpyxl in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (3.1.2)\n",
"Collecting matplotlib\n",
" Using cached matplotlib-3.7.1-cp311-cp311-macosx_11_0_arm64.whl (7.3 MB)\n",
"Requirement already satisfied: et-xmlfile in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from openpyxl) (1.1.0)\n",
"Collecting contourpy>=1.0.1 (from matplotlib)\n",
" Using cached contourpy-1.1.0-cp311-cp311-macosx_11_0_arm64.whl (229 kB)\n",
"Collecting cycler>=0.10 (from matplotlib)\n",
" Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
"Collecting fonttools>=4.22.0 (from matplotlib)\n",
" Using cached fonttools-4.40.0-cp311-cp311-macosx_10_9_universal2.whl (2.5 MB)\n",
"Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
" Using cached kiwisolver-1.4.4-cp311-cp311-macosx_11_0_arm64.whl (63 kB)\n",
"Requirement already satisfied: numpy>=1.20 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from matplotlib) (1.25.0)\n",
"Requirement already satisfied: packaging>=20.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from matplotlib) (23.1)\n",
"Collecting pillow>=6.2.0 (from matplotlib)\n",
" Using cached Pillow-9.5.0-cp311-cp311-macosx_11_0_arm64.whl (3.1 MB)\n",
"Collecting pyparsing>=2.3.1 (from matplotlib)\n",
" Using cached pyparsing-3.1.0-py3-none-any.whl (102 kB)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
"Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
"Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
"Successfully installed contourpy-1.1.0 cycler-0.11.0 fonttools-4.40.0 kiwisolver-1.4.4 matplotlib-3.7.1 pillow-9.5.0 pyparsing-3.1.0\n"
]
}
],
"source": [
"!pip install openpyxl matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "671b6c5b-259a-45b1-ba20-cd2ae3cabd39",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "15b8d8fa-4b1d-4594-9749-9ebbf4f30b59",
"metadata": {},
"outputs": [
{
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"text/plain": [
" Date Product Price Quantity\n",
"0 2050-01-01 Microphone 147.16 7\n",
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"3 2050-01-04 Camera 103.48 1\n",
"4 2050-01-05 Microphone 162.92 1"
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},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(1)\n",
"n = 100\n",
"dates = pd.date_range(start='2050-01-01', periods=n, freq='D')\n",
"products = np.random.choice(['Tablet', 'Camera', 'Phone', 'Microphone', 'Keyboard'], n)\n",
"prices = np.round(np.random.uniform(50.0, 200.0, n), 2)\n",
"quantities = np.random.randint(1, 15, n)\n",
"\n",
"data = {\n",
" 'Date': dates,\n",
" 'Product': products,\n",
" 'Price': prices,\n",
" 'Quantity': quantities\n",
"}\n",
"\n",
"df = pd.DataFrame(data)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "5f70c513-89d2-4eba-ac78-550e66a6684f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date datetime64[ns]\n",
"Product object\n",
"Price float64\n",
"Quantity int64\n",
"dtype: object"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "3084f172-d306-4f54-a3ba-d57c748c9282",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"df.to_csv('random_sales.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "929169f1-41fa-4fd8-b4b2-7a29f3356a3f",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
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"outputs": [
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" Date Product Price Quantity\n",
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"3 2050-01-04 Camera 103.48 1\n",
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"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df = pd.read_csv('random_sales.csv', parse_dates=['Date'], date_format='%Y-%m-%d')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "c240f7f5-1901-4b79-967b-dbae0ff8acd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date datetime64[ns]\n",
"Product object\n",
"Price float64\n",
"Quantity int64\n",
"dtype: object"
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},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "e1e6d39d-49b6-4f4f-aa8f-2441a2afd5c2",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"df.to_excel('sales_data.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "42050a3c-0580-4818-91d1-ad33bf5f9480",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"df.to_json('sales_data.json')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "12c1e325-ee23-4b1c-a718-1026ee884569",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(100, 4)"
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},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
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"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "e6a8e1d7-37f5-4f2f-8762-83c218de7110",
"metadata": {},
"outputs": [
{
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\n",
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\n",
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" 100 | \n",
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" 100.00 | \n",
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\n",
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" 2050-02-19 12:00:00 | \n",
" 117.32 | \n",
" 7.79 | \n",
"
\n",
" \n",
" min | \n",
" 2050-01-01 00:00:00 | \n",
" 51.35 | \n",
" 1.00 | \n",
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" \n",
" 25% | \n",
" 2050-01-25 18:00:00 | \n",
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" 2050-02-19 12:00:00 | \n",
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" 2050-03-16 06:00:00 | \n",
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"text/plain": [
" Date Price Quantity\n",
"count 100 100.00 100.00\n",
"mean 2050-02-19 12:00:00 117.32 7.79\n",
"min 2050-01-01 00:00:00 51.35 1.00\n",
"25% 2050-01-25 18:00:00 81.13 5.00\n",
"50% 2050-02-19 12:00:00 114.10 8.00\n",
"75% 2050-03-16 06:00:00 161.21 11.25\n",
"max 2050-04-10 00:00:00 197.26 14.00\n",
"std NaN 42.47 4.03"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "0a1f815b-981c-49ce-ad83-81c286801e2a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[Timestamp('2050-01-01 00:00:00'), 'Microphone', 147.16, 7],\n",
" [Timestamp('2050-01-02 00:00:00'), 'Keyboard', 103.09, 10],\n",
" [Timestamp('2050-01-03 00:00:00'), 'Tablet', 164.48, 7],\n",
" [Timestamp('2050-01-04 00:00:00'), 'Camera', 103.48, 1],\n",
" [Timestamp('2050-01-05 00:00:00'), 'Microphone', 162.92, 1]],\n",
" dtype=object)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.values[:5]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "cba4b4d0-4a0d-4067-8307-fd806ec6c7e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Product\n",
"Camera 186\n",
"Keyboard 173\n",
"Microphone 106\n",
"Phone 124\n",
"Tablet 190\n",
"Name: Quantity, dtype: int64"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product_sales = df.groupby('Product')['Quantity'].sum()\n",
"product_sales"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "7c674f12-467c-4350-8063-254b26d31e4c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Product\n",
"Camera 121.54\n",
"Keyboard 115.61\n",
"Microphone 129.43\n",
"Phone 123.47\n",
"Tablet 100.17\n",
"Name: Price, dtype: float64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"average_prices = df.groupby('Product')['Price'].mean().round(2)\n",
"average_prices"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "5f27df6b-2795-4f38-8884-ecc731d45b27",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(product_sales.index, product_sales.values, color=['red', 'green', 'blue'])\n",
"plt.title('Total Sales per Product')\n",
"plt.xlabel('Product')\n",
"plt.ylabel('Total Sales')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "2c3db61f-dd6c-4fcb-8644-751e6654a69d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(average_prices.index, average_prices.values)\n",
"plt.title('Average Price per Product')\n",
"plt.xlabel('Product')\n",
"plt.ylabel('Average Price')\n",
"plt.show()"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}