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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f3d18d68",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1c0a22eb",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('laptop_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d748587d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>Company</th>\n",
" <th>TypeName</th>\n",
" <th>Inches</th>\n",
" <th>ScreenResolution</th>\n",
" <th>Cpu</th>\n",
" <th>Ram</th>\n",
" <th>Memory</th>\n",
" <th>Gpu</th>\n",
" <th>OpSys</th>\n",
" <th>Weight</th>\n",
" <th>Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Apple</td>\n",
" <td>Ultrabook</td>\n",
" <td>13.3</td>\n",
" <td>IPS Panel Retina Display 2560x1600</td>\n",
" <td>Intel Core i5 2.3GHz</td>\n",
" <td>8GB</td>\n",
" <td>128GB SSD</td>\n",
" <td>Intel Iris Plus Graphics 640</td>\n",
" <td>macOS</td>\n",
" <td>1.37kg</td>\n",
" <td>71378.6832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Apple</td>\n",
" <td>Ultrabook</td>\n",
" <td>13.3</td>\n",
" <td>1440x900</td>\n",
" <td>Intel Core i5 1.8GHz</td>\n",
" <td>8GB</td>\n",
" <td>128GB Flash Storage</td>\n",
" <td>Intel HD Graphics 6000</td>\n",
" <td>macOS</td>\n",
" <td>1.34kg</td>\n",
" <td>47895.5232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>HP</td>\n",
" <td>Notebook</td>\n",
" <td>15.6</td>\n",
" <td>Full HD 1920x1080</td>\n",
" <td>Intel Core i5 7200U 2.5GHz</td>\n",
" <td>8GB</td>\n",
" <td>256GB SSD</td>\n",
" <td>Intel HD Graphics 620</td>\n",
" <td>No OS</td>\n",
" <td>1.86kg</td>\n",
" <td>30636.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Apple</td>\n",
" <td>Ultrabook</td>\n",
" <td>15.4</td>\n",
" <td>IPS Panel Retina Display 2880x1800</td>\n",
" <td>Intel Core i7 2.7GHz</td>\n",
" <td>16GB</td>\n",
" <td>512GB SSD</td>\n",
" <td>AMD Radeon Pro 455</td>\n",
" <td>macOS</td>\n",
" <td>1.83kg</td>\n",
" <td>135195.3360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Apple</td>\n",
" <td>Ultrabook</td>\n",
" <td>13.3</td>\n",
" <td>IPS Panel Retina Display 2560x1600</td>\n",
" <td>Intel Core i5 3.1GHz</td>\n",
" <td>8GB</td>\n",
" <td>256GB SSD</td>\n",
" <td>Intel Iris Plus Graphics 650</td>\n",
" <td>macOS</td>\n",
" <td>1.37kg</td>\n",
" <td>96095.8080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1298</th>\n",
" <td>1299</td>\n",
" <td>Lenovo</td>\n",
" <td>2 in 1 Convertible</td>\n",
" <td>14.0</td>\n",
" <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
" <td>Intel Core i7 6500U 2.5GHz</td>\n",
" <td>4GB</td>\n",
" <td>128GB SSD</td>\n",
" <td>Intel HD Graphics 520</td>\n",
" <td>Windows 10</td>\n",
" <td>1.8kg</td>\n",
" <td>33992.6400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>1300</td>\n",
" <td>Lenovo</td>\n",
" <td>2 in 1 Convertible</td>\n",
" <td>13.3</td>\n",
" <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
" <td>Intel Core i7 6500U 2.5GHz</td>\n",
" <td>16GB</td>\n",
" <td>512GB SSD</td>\n",
" <td>Intel HD Graphics 520</td>\n",
" <td>Windows 10</td>\n",
" <td>1.3kg</td>\n",
" <td>79866.7200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1300</th>\n",
" <td>1301</td>\n",
" <td>Lenovo</td>\n",
" <td>Notebook</td>\n",
" <td>14.0</td>\n",
" <td>1366x768</td>\n",
" <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
" <td>2GB</td>\n",
" <td>64GB Flash Storage</td>\n",
" <td>Intel HD Graphics</td>\n",
" <td>Windows 10</td>\n",
" <td>1.5kg</td>\n",
" <td>12201.1200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1301</th>\n",
" <td>1302</td>\n",
" <td>HP</td>\n",
" <td>Notebook</td>\n",
" <td>15.6</td>\n",
" <td>1366x768</td>\n",
" <td>Intel Core i7 6500U 2.5GHz</td>\n",
" <td>6GB</td>\n",
" <td>1TB HDD</td>\n",
" <td>AMD Radeon R5 M330</td>\n",
" <td>Windows 10</td>\n",
" <td>2.19kg</td>\n",
" <td>40705.9200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1302</th>\n",
" <td>1303</td>\n",
" <td>Asus</td>\n",
" <td>Notebook</td>\n",
" <td>15.6</td>\n",
" <td>1366x768</td>\n",
" <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
" <td>4GB</td>\n",
" <td>500GB HDD</td>\n",
" <td>Intel HD Graphics</td>\n",
" <td>Windows 10</td>\n",
" <td>2.2kg</td>\n",
" <td>19660.3200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1303 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Id Company TypeName Inches \\\n",
"0 1 Apple Ultrabook 13.3 \n",
"1 2 Apple Ultrabook 13.3 \n",
"2 3 HP Notebook 15.6 \n",
"3 4 Apple Ultrabook 15.4 \n",
"4 5 Apple Ultrabook 13.3 \n",
"... ... ... ... ... \n",
"1298 1299 Lenovo 2 in 1 Convertible 14.0 \n",
"1299 1300 Lenovo 2 in 1 Convertible 13.3 \n",
"1300 1301 Lenovo Notebook 14.0 \n",
"1301 1302 HP Notebook 15.6 \n",
"1302 1303 Asus Notebook 15.6 \n",
"\n",
" ScreenResolution \\\n",
"0 IPS Panel Retina Display 2560x1600 \n",
"1 1440x900 \n",
"2 Full HD 1920x1080 \n",
"3 IPS Panel Retina Display 2880x1800 \n",
"4 IPS Panel Retina Display 2560x1600 \n",
"... ... \n",
"1298 IPS Panel Full HD / Touchscreen 1920x1080 \n",
"1299 IPS Panel Quad HD+ / Touchscreen 3200x1800 \n",
"1300 1366x768 \n",
"1301 1366x768 \n",
"1302 1366x768 \n",
"\n",
" Cpu Ram Memory \\\n",
"0 Intel Core i5 2.3GHz 8GB 128GB SSD \n",
"1 Intel Core i5 1.8GHz 8GB 128GB Flash Storage \n",
"2 Intel Core i5 7200U 2.5GHz 8GB 256GB SSD \n",
"3 Intel Core i7 2.7GHz 16GB 512GB SSD \n",
"4 Intel Core i5 3.1GHz 8GB 256GB SSD \n",
"... ... ... ... \n",
"1298 Intel Core i7 6500U 2.5GHz 4GB 128GB SSD \n",
"1299 Intel Core i7 6500U 2.5GHz 16GB 512GB SSD \n",
"1300 Intel Celeron Dual Core N3050 1.6GHz 2GB 64GB Flash Storage \n",
"1301 Intel Core i7 6500U 2.5GHz 6GB 1TB HDD \n",
"1302 Intel Celeron Dual Core N3050 1.6GHz 4GB 500GB HDD \n",
"\n",
" Gpu OpSys Weight Price \n",
"0 Intel Iris Plus Graphics 640 macOS 1.37kg 71378.6832 \n",
"1 Intel HD Graphics 6000 macOS 1.34kg 47895.5232 \n",
"2 Intel HD Graphics 620 No OS 1.86kg 30636.0000 \n",
"3 AMD Radeon Pro 455 macOS 1.83kg 135195.3360 \n",
"4 Intel Iris Plus Graphics 650 macOS 1.37kg 96095.8080 \n",
"... ... ... ... ... \n",
"1298 Intel HD Graphics 520 Windows 10 1.8kg 33992.6400 \n",
"1299 Intel HD Graphics 520 Windows 10 1.3kg 79866.7200 \n",
"1300 Intel HD Graphics Windows 10 1.5kg 12201.1200 \n",
"1301 AMD Radeon R5 M330 Windows 10 2.19kg 40705.9200 \n",
"1302 Intel HD Graphics Windows 10 2.2kg 19660.3200 \n",
"\n",
"[1303 rows x 12 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df\n",
"# df.drop(columns='Unnamed: 0',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a5948ef3",
"metadata": {},
"outputs": [],
"source": [
"df['Ram']=df['Ram'].str.replace('GB','').astype(int)\n",
"df['Weight']=df['Weight'].str.replace('kg','').astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c14ff78d",
"metadata": {},
"outputs": [],
"source": [
"# sns.distplot(df['Price'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "da316e02",
"metadata": {},
"outputs": [],
"source": [
"# df['Company'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e6ed26f6",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(x=df['Company'],y=df['Price'])\n",
"# plt.xticks(rotation='vertical')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5ddacce5",
"metadata": {},
"outputs": [],
"source": [
"# df['TypeName'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f7d32037",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(df['TypeName'],df['Price'])\n",
"# plt.xticks(rotation='vertical')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e498c16d",
"metadata": {},
"outputs": [],
"source": [
"# sns.distplot(df['Inches'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2088f54c",
"metadata": {},
"outputs": [],
"source": [
"# sns.scatterplot(x=df['Inches'],y=df['Price'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3b21f8b8",
"metadata": {},
"outputs": [],
"source": [
"# df['ScreenResolution'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "55ebde11",
"metadata": {},
"outputs": [],
"source": [
"df['TouchScreen']=df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0a68cc6f",
"metadata": {},
"outputs": [],
"source": [
"# df['TouchScreen'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "114f8855",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='TouchScreen', ylabel='Price'>"
]
},
"execution_count": 15,
"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": [
"sns.barplot(x=df['TouchScreen'],y=df['Price'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3a5727a5",
"metadata": {},
"outputs": [],
"source": [
"df['IPS']=df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1a672454",
"metadata": {},
"outputs": [],
"source": [
"# df['IPS'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d42f5d46",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(x=df['IPS'],y=df['Price'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "0bb82bac",
"metadata": {},
"outputs": [],
"source": [
"new = df['ScreenResolution'].str.split('x',expand=True,n=1)\n",
"df['X_res']=new[0]\n",
"df['Y_res']=new[1].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "51053fcf",
"metadata": {},
"outputs": [],
"source": [
"df['X_res']=df['X_res'].apply(lambda x:x.split()[-1]).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c4c685e5",
"metadata": {},
"outputs": [],
"source": [
"# df.corr()['Price']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f296a680",
"metadata": {},
"outputs": [],
"source": [
"df['ppi']=((df['X_res']**2 + df['Y_res']**2)**0.5/df['Inches']).astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ac06e960",
"metadata": {},
"outputs": [],
"source": [
"df.drop(columns=['ScreenResolution','Inches','X_res','Y_res'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e238f52f",
"metadata": {},
"outputs": [],
"source": [
"# df['Cpu'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "eb369924",
"metadata": {},
"outputs": [],
"source": [
"df['Cpu_name']=df['Cpu'].apply(lambda x : ' '.join(x.split()[:3]))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "a01e463b",
"metadata": {},
"outputs": [],
"source": [
"# df['Cpu_name'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2e133cd8",
"metadata": {},
"outputs": [],
"source": [
"def fetch_processor(text):\n",
" if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3' or text == 'Intel Celeron Dual' : \n",
" return text\n",
" else :\n",
" if text.split()[0] == 'Intel':\n",
" return 'Other Intel Processor'\n",
" else :\n",
" return 'AMD Processor'"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "8c800f19",
"metadata": {},
"outputs": [],
"source": [
"df['Cpu_name']=df['Cpu_name'].apply(fetch_processor)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "3dc915bd",
"metadata": {},
"outputs": [],
"source": [
"# df['Cpu_name'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "3d5234ab",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(df['Cpu_name'],df['Price'])\n",
"# plt.xticks(rotation='vertical')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "85fcceac",
"metadata": {},
"outputs": [],
"source": [
"df['Speed'] = df['Cpu'].apply(lambda x:x.split()[-1].split('GHz')[0]).astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "e900de26",
"metadata": {},
"outputs": [],
"source": [
"df['Cpu']=df['Cpu_name']"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "9584264a",
"metadata": {},
"outputs": [],
"source": [
"df.drop(columns='Cpu_name',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "9d4a1779",
"metadata": {},
"outputs": [],
"source": [
"# df['Ram'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "113d86b0",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(df['Ram'],df['Price'])\n",
"# plt.xticks(rotation='vertical')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "e16676cd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Memory\n",
"256GB SSD 412\n",
"1TB HDD 223\n",
"500GB HDD 132\n",
"512GB SSD 118\n",
"128GB SSD + 1TB HDD 94\n",
"128GB SSD 76\n",
"256GB SSD + 1TB HDD 73\n",
"32GB Flash Storage 38\n",
"2TB HDD 16\n",
"64GB Flash Storage 15\n",
"512GB SSD + 1TB HDD 14\n",
"1TB SSD 14\n",
"256GB SSD + 2TB HDD 10\n",
"1.0TB Hybrid 9\n",
"256GB Flash Storage 8\n",
"16GB Flash Storage 7\n",
"32GB SSD 6\n",
"180GB SSD 5\n",
"128GB Flash Storage 4\n",
"512GB SSD + 2TB HDD 3\n",
"16GB SSD 3\n",
"512GB Flash Storage 2\n",
"1TB SSD + 1TB HDD 2\n",
"256GB SSD + 500GB HDD 2\n",
"128GB SSD + 2TB HDD 2\n",
"256GB SSD + 256GB SSD 2\n",
"512GB SSD + 256GB SSD 1\n",
"512GB SSD + 512GB SSD 1\n",
"64GB Flash Storage + 1TB HDD 1\n",
"1TB HDD + 1TB HDD 1\n",
"32GB HDD 1\n",
"64GB SSD 1\n",
"128GB HDD 1\n",
"240GB SSD 1\n",
"8GB SSD 1\n",
"508GB Hybrid 1\n",
"1.0TB HDD 1\n",
"512GB SSD + 1.0TB Hybrid 1\n",
"256GB SSD + 1.0TB Hybrid 1\n",
"Name: count, dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Memory'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "dd26544f",
"metadata": {},
"outputs": [],
"source": [
"df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
"df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
"df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
"new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
"\n",
"df[\"first\"]= new[0]\n",
"df[\"first\"]=df[\"first\"].str.strip()\n",
"\n",
"\n",
"df[\"second\"]= new[1]\n",
"\n",
"df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
"df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
"df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
"df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
"\n",
"df['first'] = df['first'].str.replace(r'\\D', '')\n",
"\n",
"df[\"second\"].fillna(\"0\", inplace = True)\n",
"\n",
"df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
"df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
"df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
"df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
"\n",
"\n",
"df['first'] = df['first'].apply(lambda x:x.split(' ')[0] )\n",
"df['second'] = df['second'].str.replace(r' ', '')\n",
"df['second'] = df['second'].apply(lambda x:x.split(' ')[0] )\n",
"\n",
"df[\"first\"] = df[\"first\"].astype(int)\n",
"df[\"second\"] = df[\"second\"].astype(int)\n",
"\n",
"\n",
"\n",
"df.head()\n",
"\n",
"df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
"df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
"df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
"df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
"\n",
"df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
" 'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
" 'Layer2Flash_Storage'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "a8d2e506",
"metadata": {},
"outputs": [],
"source": [
"\n",
"df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "a46c6ac6",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'DataFrame' object has no attribute 'second'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13292\\3910391168.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msecond\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\Users\\Rajat\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 6200\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6201\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6202\u001b[0m ):\n\u001b[0;32m 6203\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6204\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'second'"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 43,
"id": "0d11314d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Gpu\n",
"Intel HD 639\n",
"Nvidia GeForce 368\n",
"AMD Radeon 173\n",
"Intel UHD 68\n",
"Nvidia Quadro 31\n",
"Intel Iris 14\n",
"AMD FirePro 5\n",
"AMD R4 1\n",
"Nvidia GTX 1\n",
"AMD R17M-M1-70 1\n",
"Intel Graphics 1\n",
"ARM Mali 1\n",
"Name: count, dtype: int64"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Gpu'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "22d62b9c",
"metadata": {},
"outputs": [],
"source": [
"df['Gpu'] =df['Gpu'].apply(lambda x: ' '.join(x.split()[:2]))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "10abdf9a",
"metadata": {},
"outputs": [],
"source": [
"df = df[df['Gpu'] != 'ARM Mali']"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "027b27dd",
"metadata": {},
"outputs": [],
"source": [
"def change_gpu(i):\n",
" if i in ['AMD FirePro','AMD R4','Nvidia GTX','AMD R17M-M1-70','Intel Graphics']:\n",
" return 'Other'\n",
" else :\n",
" return i"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "368a3e89",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Rajat\\AppData\\Local\\Temp\\ipykernel_13292\\1586382374.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['Gpu']=df['Gpu'].apply(change_gpu)\n"
]
}
],
"source": [
"df['Gpu']=df['Gpu'].apply(change_gpu)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "053d8b7e",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(df['Gpu'],df['Price'],estimator=np.median)\n",
"# plt.xticks(rotation='vertical')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0319a31e",
"metadata": {},
"outputs": [],
"source": [
"# df.OpSys.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "545cc2ac",
"metadata": {},
"outputs": [],
"source": [
"# sns.barplot(x=df.OpSys,y=df.Price)\n",
"# plt.xticks(rotation='vertical')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "bb8b947d",
"metadata": {},
"outputs": [],
"source": [
"def cat_os(inp):\n",
" if inp=='Windows 10' or inp=='Windows 7' or inp=='Windows 10 S':\n",
" return 'Windows'\n",
" elif inp=='macOS' or inp=='Mac OS X':\n",
" return 'Mac OS'\n",
" elif inp=='Android' or inp=='Chrome OS':\n",
" return 'Other'\n",
" else :\n",
" return inp"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "d4d1a058",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Rajat\\AppData\\Local\\Temp\\ipykernel_13292\\1040093113.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['OpSys'] = df['OpSys'].apply(cat_os)\n"
]
}
],
"source": [
"df['OpSys'] = df['OpSys'].apply(cat_os)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32a334f5",
"metadata": {},
"outputs": [],
"source": [
"# sns.distplot(df['Weight'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae371fc2",
"metadata": {},
"outputs": [],
"source": [
"# sns.scatterplot(x=df.Weight,y=df.Price)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c49049d",
"metadata": {},