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Web scraping - hp Laptops

alt text

Web Scraping means extracting information from websites by parsing the HTML of the webpage.

website used for scraping : https://store.hp.com/in-en/default/laptops-tablets.html

Libraries used:

  • requests
    Requests gets the web page for you

  • BeautifulSoup
    you need to parse the HTML from the page to retrieve the data. That is done by BeautifulSoup.

  • re
    regular expressions module used to make manipulations on data we got.

  • pandas
    To create the dataframe from the data we got by scraping and creating

Importing the above modules

import requests
from bs4 import BeautifulSoup
import re
import pandas as pd

Let's get started

Each Laptop product is rendered in the webpage as follow

alt text

prod_list=[]
def getData(products):
    '''
    Takes a List of products and scrapes the required specifications of each product and
    append them to the prod_list defined above.
    '''
    
    for product in products:
        
        #name 
        name=product.find('a',{"class":"product-item-link"}).text.strip()

        #rating of product is in div tag with attribute data-by-average-overall-rating
        rating=product.find('div',attrs={'data-bv-average-overall-rating' : True})['data-bv-average-overall-rating']

        #processor family
        processor=product.find('li',{'class':'processorfamily'})
        if processor is not None:
            processor=processor.text.strip()
        else:
            processor=None
            

        # processor name, generation, type
        if processor is not None:
            try:
                if processor.find('Intel')!=-1:
                    proc_company='Intel'
                    if processor.find('Core')!=-1:  # processor type and generation for Intel core processors
                        generation=re.findall(r'\d',processor)[0] 
                        proc_type=re.findall(r'i\d',processor)[0]
                    else:
                        temp=processor.split()
                        proc_type=' '.join(temp[1:-1]) # processor type for Intel pentium and other series processors

                elif processor.find('AMD')!=-1: # processor type for AMD processors
                    proc_company='AMD' 
                    temp=processor.split()
                    proc_type=temp[1]+' '+temp[2]
                    generation=None
                else:                     # processor type for other processors
                    proc_company=None
                    proc_type=None
                    generation=None
            except:
                print('something went wrong')
        else:
            proc_company=None
            proc_type=None
            generation=None
            

        # Os installed
        os_installed=product.find('li',{'class':'osinstalled'})
        if os_installed is not None:
            os_installed=os_installed.text
        else:
            os_installed=None
            

        # RAM
        ram=product.find('li',{'class':'memstdes_01'})
        if ram is not None:
            ram=ram.text
        else:
            ram=None

        #hard disk
        hd=product.find('li',{'class':'hd_01des'})
        if hd is not None:
            hd=hd.text
        else:
            hd=None

        
        # graphic card information is stored in <li> element with two different classes so multiple classes are used below
        if product.find('li',{'class':['graphicseg_01card_01','graphicseg_02card_01']}) is not None:
            graphic_card=product.find('li',{'class':['graphicseg_01card_01','graphicseg_02card_01']}).text
        else:
            graphic_card=None
            

        #display-type
        display_type=product.find('li',{'class':['display-displaydes']})
        if display_type is None:
            display_type=None
        else:
            display_type=display_type.text


        #price
        
        # actual price, discount and final price all stored in span tag with class price hence we get a list
        prices=product.find_all('span',{'class':'price'})    
        if len(prices)==3:
            aprice=prices[0].text[1:]
            fprice=prices[1].text[1:]
            dprice=prices[2].text[1:]
        else:
            aprice=fprice=prices[0].text[1:] 
            dprice='0'

        #included items
        inc_items=product.find_all('ul',{'class':'included'})

        items_list=[]

        if inc_items is not None:
            for i in inc_items:
                items_list=[item.text for item in i.find_all('li')]
                
        inc_items=','.join(items_list) # converting the list of included items into string seperated by commas

        print('Name:',name)
        print('rating:',rating)
        print("processor:",processor)
        print("processor_company:",proc_company)
        print("processor_type:",proc_type)
        print("generation:",generation)
        print("os_installed:",os_installed)
        print("ram:",ram)
        print("hard_disk:",hd)
        print("graphic_card:",graphic_card)
        print("display:",display_type)
        print("Actual_price:",aprice)
        print("final_price:",fprice)
        print("Discount:",dprice)
        print("included_items:",inc_items)
        
        
        # creating a dictionary with keys as specification names and values as their respective information
        prod_data={
            'Name':name,
            'rating':rating,
            'processor':processor,
            'processor_company':proc_company,
            'processor_type':proc_type,
            'generation':generation,
            'os_installed':os_installed,
            'ram':ram,
            'hard_disk':hd,
            'graphic_card':graphic_card,
            'display':display_type,
            'actual_price':aprice,
            'final_price':fprice,
            'discout':dprice,
            'included_items':inc_items
        }
        
        #Appending the each product to list
        prod_list.append(prod_data)
        
        # end of the product
        print('-'*50)
        
    # end of all products
    print('*'*50)

All the products in website is rendered across 6 pages so we set a loop and get the all products of each page at time and call the getData function defined above

for page_count in range(1,7):
    url='https://store.hp.com/in-en/default/laptops-tablets.html?p='+str(page_count)+'&product_list_limit=30'
    data=requests.get(url) 
    data_soup=BeautifulSoup(data.text,'lxml')
    products=data_soup.find_all("div", {"class": "product-item-details"})
    getData(products)
Name: HP ENVY x360 - 13-ag0035au
rating: 3.6667
processor: AMD Ryzen™ 5 processor
processor_company: AMD
processor_type: Ryzen™ 5
generation: None
os_installed: Windows 10 Home Single Language 64
ram: 8 GB DDR4-2400 SDRAM (onboard)
hard_disk: 256 GB SSD
graphic_card: AMD Radeon™ Vega 8 Graphics
display: 13.3" FHD multitouch-enabled edge-to-edge glass (1920 x 1080)
Actual_price: 83,496
final_price: 72,990
Discount: 10,506
included_items: 
--------------------------------------------------
Name: HP Gaming Pavilion - 15-cx0140tx
rating: 5.0000
processor: 8th Generation Intel® Core™ i5 processor
processor_company: Intel
processor_type: i5
generation: 8
os_installed: Windows 10 Home Single Language 64
ram: 8 GB DDR4-2666 SDRAM (1 x 8 GB)
hard_disk: 1 TB 7200 rpm SATA
graphic_card: NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated)
display: None
Actual_price: 86,476
final_price: 72,990
Discount: 13,486
included_items: HP Odyssey backpack (Worth ₹3,499),Microsoft Office Home and Student
--------------------------------------------------
Name: HP Notebook - 15-da0435tx
rating: 4.0000
processor: 7th Generation Intel® Core™ i3 processor
processor_company: Intel
processor_type: i3
generation: 7
os_installed: Windows 10 Home Single Language 64
ram: 8 GB DDR4-2133 SDRAM (1 x 8 GB)
hard_disk: 1 TB 5400 rpm SATA
graphic_card: NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated)
display: None
Actual_price: 50,292
final_price: 44,580
Discount: 5,712
included_items: 
--------------------------------------------------
|
|
|
--------------------------------------------------
Name: HP ProBook 445 G2 Notebook PC (ENERGY STAR)
rating: 0.0000
processor: AMD A10 APU
processor_company: AMD
processor_type: A10 APU
generation: None
os_installed: Windows 10 Pro 64
ram: 8 GB DDR3L-1600 SDRAM (1 x 8 GB)
hard_disk: 500 GB 5400 rpm SATA
graphic_card: AMD Radeon™ R6
display: 14" diagonal HD anti-glare LED-backlit (1366 x 768)
Actual_price: 79,000
final_price: 78,000
Discount: 1,000
included_items: HP Overnighter Backpack (Worth ₹2,499)
--------------------------------------------------
**************************************************

creating a dataframe with the List of products

df=pd.DataFrame(prod_list)
df.head()
<style> .dataframe thead tr:only-child th { text-align: right; } 
.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
</style>
Name actual_price discout display final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating
0 HP ENVY x360 - 13-ag0035au 83,496 10,506 13.3" FHD multitouch-enabled edge-to-edge glas... 72,990 None AMD Radeon™ Vega 8 Graphics 256 GB SSD Windows 10 Home Single Language 64 AMD Ryzen™ 5 processor AMD Ryzen™ 5 8 GB DDR4-2400 SDRAM (onboard) 3.6667
1 HP Gaming Pavilion - 15-cx0140tx 86,476 13,486 None 72,990 8 NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Odyssey backpack (Worth ₹3,499),Microsoft O... Windows 10 Home Single Language 64 8th Generation Intel® Core™ i5 processor Intel i5 8 GB DDR4-2666 SDRAM (1 x 8 GB) 5.0000
2 HP Notebook - 15-da0435tx 50,292 5,712 None 44,580 7 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA Windows 10 Home Single Language 64 7th Generation Intel® Core™ i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 4.0000
3 HP Notebook - 15g-dr0006tx 66,137 7,146 None 58,991 8 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ₹1,123),1 Year O... Windows 10 Home Single Language 64 8th Generation Intel® Core™ i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 4.0625
4 HP Notebook 15-da1030tu 50,720 3,730 None 46,990 8 None 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ₹1,123),Microsof... Windows 10 Home 64 8th Generation Intel® Core™ i5 processor Intel i5 4 GB DDR4-2400 SDRAM 2.0000

saving the dataframe into Hp_laptops.csv

df.to_csv("Hp_laptops.csv")
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # visualization
import re
%matplotlib inline

To get a brief overview, this notebook is dedicated to 5 sections as follows

  1. Context of dataset
  2. Data cleaning
  3. EDA
  4. Results
  5. Conclusion

Loading file Hp_laptos.csv into dataframe

df=pd.read_csv('Hp_laptops.csv',encoding='unicode-escape')

Let's have glance at a bit of data

df.head()
<style> .dataframe thead tr:only-child th { text-align: right; } 
.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
</style>
Unnamed: 0 Name actual_price discout display final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating
0 0 HP ENVY x360 - 13-ag0035au 83,496 10,506 13.3" FHD multitouch-enabled edge-to-edge glas... 72,990 NaN AMD Radeon� Vega 8 Graphics 256 GB SSD NaN Windows 10 Home Single Language 64 AMD Ryzen� 5 processor AMD Ryzen� 5 8 GB DDR4-2400 SDRAM (onboard) 3.6667
1 1 HP Gaming Pavilion - 15-cx0140tx 86,476 13,486 NaN 72,990 8.0 NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Odyssey backpack (Worth ?3,499),Microsoft O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2666 SDRAM (1 x 8 GB) 5.0000
2 2 HP Notebook - 15-da0435tx 50,292 5,712 NaN 44,580 7.0 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA NaN Windows 10 Home Single Language 64 7th Generation Intel® Core� i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 4.0000
3 3 HP Notebook - 15g-dr0006tx 66,137 7,146 NaN 58,991 8.0 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),1 Year O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 4.0625
4 4 HP Notebook 15-da1030tu 50,720 3,730 NaN 46,990 8.0 NaN 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),Microsof... Windows 10 Home 64 8th Generation Intel® Core� i5 processor Intel i5 4 GB DDR4-2400 SDRAM 2.0000

Information of each column in the dataframe

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 163 entries, 0 to 162
Data columns (total 16 columns):
Unnamed: 0           163 non-null int64
Name                 163 non-null object
actual_price         163 non-null object
discout              163 non-null object
display              29 non-null object
final_price          163 non-null object
generation           131 non-null float64
graphic_card         161 non-null object
hard_disk            161 non-null object
included_items       131 non-null object
os_installed         163 non-null object
processor            149 non-null object
processor_company    146 non-null object
processor_type       146 non-null object
ram                  161 non-null object
rating               158 non-null float64
dtypes: float64(2), int64(1), object(13)
memory usage: 20.5+ KB

Shape of the dataframe

df.shape
(163, 16)

In the dataframe first column is no use at all

Dropping the first column

df.drop(['Unnamed: 0'],axis=1,inplace=True)

Data cleaning

  • check if any NA's are present
df.isnull().sum()
Name                   0
actual_price           0
discout                0
display              134
final_price            0
generation            32
graphic_card           2
hard_disk              2
included_items        32
os_installed           0
processor             14
processor_company     17
processor_type        17
ram                    2
rating                 5
dtype: int64

The column display has 134 NaN values which is more than 40% of total data. So we can drop it

df.drop(['display'],axis=1,inplace=True)
df.head()
<style> .dataframe thead tr:only-child th { text-align: right; } 
.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
</style>
Name actual_price discout final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating
0 HP ENVY x360 - 13-ag0035au 83,496 10,506 72,990 NaN AMD Radeon� Vega 8 Graphics 256 GB SSD NaN Windows 10 Home Single Language 64 AMD Ryzen� 5 processor AMD Ryzen� 5 8 GB DDR4-2400 SDRAM (onboard) 3.6667
1 HP Gaming Pavilion - 15-cx0140tx 86,476 13,486 72,990 8.0 NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Odyssey backpack (Worth ?3,499),Microsoft O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2666 SDRAM (1 x 8 GB) 5.0000
2 HP Notebook - 15-da0435tx 50,292 5,712 44,580 7.0 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA NaN Windows 10 Home Single Language 64 7th Generation Intel® Core� i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 4.0000
3 HP Notebook - 15g-dr0006tx 66,137 7,146 58,991 8.0 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),1 Year O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 4.0625
4 HP Notebook 15-da1030tu 50,720 3,730 46,990 8.0 NaN 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),Microsof... Windows 10 Home 64 8th Generation Intel® Core� i5 processor Intel i5 4 GB DDR4-2400 SDRAM 2.0000 
df.actual_price.dtype
dtype('O')

In the dataframe actual_price,final_price and discount columns should be of numerical datatype but they are of object datatype in the dataframe so function strtoint is definded below which removes the comma in the values and convert them into int datatype

def strtoint(column):
    return column.apply(lambda x:int(x.replace(',','')))
df['actual_price']=strtoint(df['actual_price'])
df['discout']=strtoint(df['discout'])
df['final_price']=strtoint(df['final_price'])
df.head()
<style> .dataframe thead tr:only-child th { text-align: right; } 
.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
</style>
Name actual_price discout final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating
0 HP ENVY x360 - 13-ag0035au 83496 10506 72990 NaN AMD Radeon� Vega 8 Graphics 256 GB SSD NaN Windows 10 Home Single Language 64 AMD Ryzen� 5 processor AMD Ryzen� 5 8 GB DDR4-2400 SDRAM (onboard) 3.6667
1 HP Gaming Pavilion - 15-cx0140tx 86476 13486 72990 8.0 NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Odyssey backpack (Worth ?3,499),Microsoft O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2666 SDRAM (1 x 8 GB) 5.0000
2 HP Notebook - 15-da0435tx 50292 5712 44580 7.0 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA NaN Windows 10 Home Single Language 64 7th Generation Intel® Core� i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 4.0000
3 HP Notebook - 15g-dr0006tx 66137 7146 58991 8.0 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),1 Year O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 4.0625
4 HP Notebook 15-da1030tu 50720 3730 46990 8.0 NaN 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),Microsof... Windows 10 Home 64 8th Generation Intel® Core� i5 processor Intel i5 4 GB DDR4-2400 SDRAM 2.0000

The generation column has 32 missing values Let's see the unique values and their frequency details

df.generation.value_counts()
8.0    99
7.0    29
6.0     3
Name: generation, dtype: int64

generations are for the Intel core processor series. AMD, Intel pentium series doesn't have generation so replace those with version 1

df.generation.fillna('1.0',inplace=True)
df.generation.isnull().sum()
0

The column graphic_card has two null values.Let's fill them with mode of that column

df.graphic_card.value_counts()[:5]
Intel® UHD Graphics 620                                    54
Intel® HD Graphics 620                                     19
AMD Radeon� Pro WX 3100 Graphics (2 GB GDDR5 dedicated)    12
NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated)                8
NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated)                 7
Name: graphic_card, dtype: int64

df.graphic_card.fillna(df.graphic_card.mode()[0],inplace=True)
df.graphic_card.isnull().sum()
0

df.hard_disk.value_counts()[:5]
1 TB 5400 rpm SATA        51
1 TB 7200 rpm SATA        16
512 GB PCIe® NVMe� SSD    13
512 GB SSD                13
256 GB SSD                11
Name: hard_disk, dtype: int64

df.hard_disk.fillna(df.hard_disk.mode()[0],inplace=True)
df.hard_disk.isnull().sum()
0

df.included_items.fillna('Not provided',inplace=True)
df.included_items.isnull().sum()
0

df.ram.fillna(df.ram.mode()[0],inplace=True)
df.included_items.isnull().sum()
0

df.rating.fillna(df.rating.mean(),inplace=True)
df.rating.isnull().sum()
0

df[df.processor_company.isnull()]
<style> .dataframe thead tr:only-child th { text-align: right; } 
.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
</style>
Name actual_price discout final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating
40 HP ZBook 15v G5 Mobile Workstation 225937 22095 203842 1.0 NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y... Windows 10 Pro 64 16 GB DDR4-2666 SDRAM (1 x 16 GB) NaN NaN 8 GB DDR4-2400 SDRAM (1 x 8 GB) 1.194378
41 HP ZBook 15v G5 Mobile Workstation 233611 22861 210750 1.0 NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated) 256 GB SSD HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y... Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2666 SDRAM (1 x 16 GB) 1.194378
42 HP ZBook 15v G5 Mobile Workstation 216245 21125 195120 1.0 NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y... Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2666 SDRAM (1 x 16 GB) 1.194378
43 HP ZBook 15v G5 Mobile Workstation 203281 19828 183453 1.0 NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated) 2 TB 5400 rpm SATA HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y... Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2666 SDRAM (1 x 16 GB) 1.194378
44 HP ZBook 15v G5 Mobile Workstation 181301 17100 164201 1.0 NVIDIA® Quadro® P600 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Original Bag (Worth ?1,499) (#5DD44PA),3 Y... Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2666 SDRAM (1 x 16 GB) 1.194378
106 HP ZBook 15 G5 Mobile Workstation 350406 124853 225553 1.0 NVIDIA® Quadro® P2000 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA + 512 GB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 16 GB (1x16 GB) DDR4 2666 0.000000
107 HP ZBook 15 G5 Mobile Workstation 316190 110771 205419 1.0 NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA + 128 GB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 8 GB DDR4-2666 SDRAM (1 x 8 GB) 0.000000
108 HP ZBook 15 G5 Mobile Workstation 240765 83941 156824 1.0 NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 8 GB DDR4-2666 SDRAM (1 x 8 GB) 0.000000
109 HP ZBook x2 G4 Detachable Workstation 408283 145897 262386 1.0 NVIDIA® Quadro® M620 (2 GB GDDR5 dedicated) 512 GB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2133 SDRAM (2 x 8 GB) 0.000000
110 HP ZBook x2 G4 Detachable Workstation 369052 128360 240692 1.0 NVIDIA® Quadro® M620 (2 GB GDDR5 dedicated) 512 GB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 8 GB DDR4-2133 SDRAM (2 x 4 GB) 0.000000
111 HP ZBook Studio x360 G5 Convertible Workstation 400665 144380 256285 1.0 NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated) 1 TB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2666 SDRAM (1 x 16 GB) 0.000000
113 HP ZBook Studio G5 Mobile Workstation 254780 89781 164999 1.0 NVIDIA® Quadro® P1000 (4 GB GDDR5 dedicated) 512 GB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 8 GB DDR4-2666 SDRAM (1 x 8 GB) 0.000000
122 HP ProBook 640 G4 Notebook PC 95603 2830 92773 1.0 Intel® UHD Graphics 620 1 TB 7200 rpm SATA HP Overnighter Backpack (Worth ?2,499) 8th Generation Intel® Core� i5 processor NaN NaN NaN 8 GB DDR4-2400 SDRAM (1 x 8 GB) 0.000000
123 HP ProBook 640 G4 Notebook PC 106867 3164 103703 1.0 Intel® UHD Graphics 620 1 TB 7200 rpm SATA HP Overnighter Backpack (Worth ?2,499) Intel Core i7-8550U Processor NaN NaN NaN 8 GB DDR4-2400 SDRAM (1 x 8 GB) 0.000000
126 HP ZBook 15u G4 Mobile Workstation 176453 5453 171000 1.0 AMD FirePro� W4190M Graphics (2 GB GDDR5 dedic... 1 TB 5400 rpm SATA Not provided Windows 10 Pro 64 16 GB DDR4-2133 SDRAM (1 x 16 GB) NaN NaN 8 GB DDR4-2400 SDRAM (1 x 8 GB) 0.000000
146 HP 245 G6 24200 0 24200 1.0 Intel® UHD Graphics 620 1 TB 5400 rpm SATA Not provided FreeDOS BU IDS UMA A9-9425 NaN NaN 4GB (1x4GB) 1866 DDR4 0.000000
150 HP ZBook 15u G5 Mobile Workstation 264534 103017 161517 1.0 AMD Radeon� Pro WX 3100 Graphics (2 GB GDDR5 d... 512 GB SSD HP Business Backpack (Worth ?4,200) Windows 10 Pro 64 NaN NaN NaN 16 GB DDR4-2400 SDRAM (1 x 16 GB) 0.000000 
print(df.processor_type.mode()[0])
print(df.processor.mode()[0])
print(df.processor_company.mode()[0])
i5
8th Generation Intel® Core� i5 processor
Intel

df.at[38, 'processor']=df.processor.mode()[0]
df.at[120,'processor']=df.processor.mode()[0]
df.at[147,'processor']=df.processor.mode()[0]
df.processor.fillna(df.processor.mode()[0],inplace=True)
df.processor_company.fillna(df.processor_company.mode()[0],inplace=True)
df.processor_type.fillna(df.processor_type.mode()[0],inplace=True)
df.isnull().sum()
Name                 0
actual_price         0
discout              0
final_price          0
generation           0
graphic_card         0
hard_disk            0
included_items       0
os_installed         0
processor            0
processor_company    0
processor_type       0
ram                  0
rating               0
dtype: int64

creating a new column with ram size

import re
def getSize(value):
    b=re.findall(r'\b\d+',str(value))
    if len(b)>0:
        return b[0]
    return None
df['ram_size']=df['ram'].apply(getSize)
df['ram_size'].value_counts()
8     86
4     44
16    32
32     1
Name: ram_size, dtype: int64

df.ram_size.isnull().sum()
0

df.ram_size=df.ram_size.astype('int')

creating a new column with hard disk size

def getSize(value):
    p=re.compile(r'\b\d+\s\w+')
    val=p.findall(value)
    size=None
    if len(val)>0:
        size=val[0]
        size_val=re.findall(r'\d+',size)[0]
        if 'TB' in size:
            size_val=int(size_val)*1024
        return size_val
    return size
df['hd_size(GB)']=df.hard_disk.apply(getSize)
df['hd_size(GB)'].isnull().sum()
1

df['hd_size(GB)'].fillna(df['hd_size(GB)'].mode()[0],inplace=True)
df['hd_size(GB)']=df['hd_size(GB)'].astype('int')
df.head()
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Name actual_price discout final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating ram_size hd_size(GB)
0 HP ENVY x360 - 13-ag0035au 83496 10506 72990 1.0 AMD Radeon� Vega 8 Graphics 256 GB SSD Not provided Windows 10 Home Single Language 64 AMD Ryzen� 5 processor AMD Ryzen� 5 8 GB DDR4-2400 SDRAM (onboard) 3.6667 8 256
1 HP Gaming Pavilion - 15-cx0140tx 86476 13486 72990 8 NVIDIA® GeForce® GTX 1050 (4 GB GDDR5 dedicated) 1 TB 7200 rpm SATA HP Odyssey backpack (Worth ?3,499),Microsoft O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2666 SDRAM (1 x 8 GB) 5.0000 8 1024
2 HP Notebook - 15-da0435tx 50292 5712 44580 7 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA Not provided Windows 10 Home Single Language 64 7th Generation Intel® Core� i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 4.0000 8 1024
3 HP Notebook - 15g-dr0006tx 66137 7146 58991 8 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),1 Year O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 4.0625 8 1024
4 HP Notebook 15-da1030tu 50720 3730 46990 8 Intel® UHD Graphics 620 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),Microsof... Windows 10 Home 64 8th Generation Intel® Core� i5 processor Intel i5 4 GB DDR4-2400 SDRAM 2.0000 4 1024 
df.isnull().sum()
Name                 0
actual_price         0
discout              0
final_price          0
generation           0
graphic_card         0
hard_disk            0
included_items       0
os_installed         0
processor            0
processor_company    0
processor_type       0
ram                  0
rating               0
ram_size             0
hd_size(GB)          0
dtype: int64

creating a new .csv file

df.to_csv('Hp_laptops_new.csv')

Exploratory Data Analysis

df.actual_price.plot(kind='hist')
<matplotlib.axes._subplots.AxesSubplot at 0x1476f947cf8>

png

Laptops in the range of 30k-60k with 1 TB hard disk and 8GB ram (Economical range)

df[(df.final_price>30000)&(df.final_price<60000) & (df['hd_size(GB)']==1024) & (df['ram_size']==8)]
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Name actual_price discout final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating ram_size hd_size(GB)
2 HP Notebook - 15-da0435tx 50292 5712 44580 7 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA Not provided Windows 10 Home Single Language 64 7th Generation Intel® Core� i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 4.0000 8 1024
3 HP Notebook - 15g-dr0006tx 66137 7146 58991 8 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),1 Year O... Windows 10 Home Single Language 64 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 4.0625 8 1024
5 HP Pavilion - 15-cw0027au 70025 10035 59990 1.0 AMD Radeon� Vega 8 Graphics 1 TB 5400 rpm SATA + 128 GB SSD HP Trendsetter Bag (Worth ?2,356) Windows 10 Home Single Language 64 AMD Ryzen� 5 processor AMD Ryzen� 5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 3.0000 8 1024
48 HP 348 G4 Notebook PC 55539 2756 52783 7 Intel® HD Graphics 620 1 TB 7200 rpm SATA HP Original Bag (Worth ?1,499) (#5DD44PA) FreeDOS 7th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2133 SDRAM (1 x 8 GB) 5.0000 8 1024
115 HP Notebook - 15-da0074tx 46010 6516 39494 7 NVIDIA® GeForce® MX110 (2 GB DDR3 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123) FreeDOS 2.0 7th Generation Intel® Core� i3 processor Intel i3 8 GB DDR4-2133 SDRAM (1 x 8 GB) 5.0000 8 1024
117 HP Notebook - 15-da0077tx 57786 7065 50721 8 NVIDIA® GeForce® MX110 (2 GB GDDR5 dedicated) 1 TB 5400 rpm SATA HP Original Laptop Bag (Worth ?1,123),1 Year O... FreeDOS 2.0 8th Generation Intel® Core� i5 processor Intel i5 8 GB DDR4-2400 SDRAM (1 x 8 GB) 5.0000 8 1024

which processor is used more

df.processor_company.value_counts().plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x1476fe8ae80>

png

Intel company processor is used more

which processor is mostly used

df.processor.value_counts().plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x1476ffea390>

png

df.groupby(['processor_company']).mean()
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actual_price discout final_price rating ram_size hd_size(GB)
processor_company
AMD 57390.000000 2974.181818 54415.818182 1.712127 5.818182 625.454545
Intel 134072.809211 22852.592105 111220.217105 1.156910 8.842105 796.894737

which os is mostly used

df.os_installed.value_counts()
Windows 10 Pro 64                                                     85
Windows 10 Home Single Language 64                                    43
FreeDOS 2.0                                                           13
Chrome OS� 64                                                          5
Windows 10 Home 64                                                     3
Windows 10 Home Single Language 64 � HP recommends Windows 10 Pro.     3
FreeDOS                                                                2
Windows 10 Pro 64 � HP recommends Windows 10 Pro.                      2
Free DOS                                                               1
FreeDos 2.0                                                            1
Windows 7 Professional 64                                              1
FreeDOS 3.0                                                            1
8th Generation Intel® Core� i5 processor                               1
Windows 10 Pro                                                         1
Intel Core i7-8550U Processor                                          1
Name: os_installed, dtype: int64

df.os_installed.value_counts().plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x147700539b0>

png

Does ram size effects price

df.ram_size.corr(df.actual_price)
0.76195099638699681

Those two columns have a good correlation

df.ram_size.value_counts().plot(kind='pie')
<matplotlib.axes._subplots.AxesSubplot at 0x14771156f98>

png

df.hard_disk.value_counts().plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x14771176668>

png

df.corr()
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actual_price discout final_price rating ram_size hd_size(GB)
actual_price 1.000000 0.856027 0.956428 -0.237846 0.761951 -0.124959
discout 0.856027 1.000000 0.667800 -0.233100 0.498787 -0.133535
final_price 0.956428 0.667800 1.000000 -0.210822 0.815429 -0.104509
rating -0.237846 -0.233100 -0.210822 1.000000 -0.033313 0.152389
ram_size 0.761951 0.498787 0.815429 -0.033313 1.000000 -0.032704
hd_size(GB) -0.124959 -0.133535 -0.104509 0.152389 -0.032704 1.000000 
df.describe()
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actual_price discout final_price rating ram_size hd_size(GB)
count 163.000000 163.000000 163.000000 163.000000 163.000000 163.000000
mean 128897.895706 21511.104294 107386.791411 1.194378 8.638037 785.325153
std 100316.252958 39349.265400 69667.796932 1.831696 4.487049 511.257767
min 20999.000000 0.000000 20999.000000 0.000000 4.000000 16.000000
25% 61889.500000 1886.000000 58016.000000 0.000000 4.000000 512.000000
50% 88742.000000 3286.000000 84390.000000 0.000000 8.000000 1024.000000
75% 173869.500000 11370.000000 146606.000000 2.235350 8.000000 1024.000000
max 667152.000000 224481.000000 442671.000000 5.000000 32.000000 5400.000000 
df.describe(include=[np.object]).transpose()
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count unique top freq
Name 163 93 HP ZBook 15u G5 Mobile Workstation 9
generation 163 4 8 99
graphic_card 163 38 Intel® UHD Graphics 620 56
hard_disk 163 31 1 TB 5400 rpm SATA 53
included_items 163 23 Not provided 32
os_installed 163 15 Windows 10 Pro 64 85
processor 163 30 8th Generation Intel® Core� i5 processor 58
processor_company 163 2 Intel 152
processor_type 163 16 i5 73
ram 163 33 8 GB DDR4-2400 SDRAM (1 x 8 GB) 56 
print(df[df.actual_price>100000]['Name'].unique())
['HP Pavilion 14-ce1003tx' 'HP EliteBook x360 1030 G3 Notebook PC'
 'HP EliteBook 830 G5 Notebook PC' 'HP EliteBook 840r G4 Notebook PC'
 'HP ProBook x360 440 G1 Notebook PC' 'HP ZBook 15v G5 Mobile Workstation'
 'OMEN by HP - 15-dc0084tx' 'HP EliteBook x360 1030 G2'
 'HP EliteBook 1040 G4 Notebook PC'
 'HP EliteBook x360 1030 G2 (ENERGY STAR)' 'HP ProBook 430 G6 Notebook PC'
 'HP ZBook 15u G5 Mobile Workstation' 'HP Pavilion x360 14-dh0045tx'
 'HP ZBook 14u G5 Mobile Workstation' 'HP Spectre x360 - 13-ap0122tu'
 'HP Spectre x360 - 13-ap0121tu' 'HP Spectre x360 - 13-ap0102tu'
 'HP Spectre x360 - 13-ap0101tu' 'HP Spectre x360 - 13-ap0100tu'
 'HP ZBook 17 G5 Mobile Workstation' 'HP EliteBook 735 G5 Notebook PC'
 'HP EliteBook 1050 G1 Notebook PC' 'HP ZBook Studio G5 Mobile Workstation'
 'HP ZBook 15 G5 Mobile Workstation'
 'HP ZBook x2 G4 Detachable Workstation'
 'HP ZBook Studio x360 G5 Convertible Workstation' 'HP ENVY - 13-ah0044tx'
 'HP ProBook 640 G4 Notebook PC' 'HP ZBook 15u G4 Mobile Workstation'
 'HP Elite x2 1012 G1 Tablet (ENERGY STAR)' 'OMEN by HP 15-dc1006tx'
 'HP Spectre Folio - 13-ak0040tu' 'HP Elite x2 1013 G3 Tablet'
 'HP EliteBook x360 1020 G2']

Laptop with highest price

df[df.actual_price==df.actual_price.max()]
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Name actual_price discout final_price generation graphic_card hard_disk included_items os_installed processor processor_company processor_type ram rating ram_size hd_size(GB)
85 HP ZBook 17 G5 Mobile Workstation 667152 224481 442671 7 NVIDIA® Quadro® P4200 (8 GB GDDR5 dedicated) 512 GB PCIe® NVMe� SSD Not provided Windows 10 Pro 64 Intel® Xeon® processor Intel Xeon® 32 GB DDR4-2666 ECC SDRAM (2 X 16 GB) 0.0 32 512 
plt.title("ram size vs actual price")
plt.xlabel("ram size")
plt.ylabel("price")
plt.scatter(df['ram_size'],df['actual_price'])
<matplotlib.collections.PathCollection at 0x14771fb99b0>

png

Conclusion

  • Intel company processors are mostly preferred
  • Windows 10 Pro 64 and 8th Generation Intel i5 processor is used in good number
  • ram size effect the price of laptops.They have good correlation 0.7
  • 9 laptops models are there in economical range
  • 34 laptop models are there with price greater than one lakh
  • 1 TB 5400 RPM SATA is used for many models

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Webscrapping And EDA on Hp Laptops

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