Data/Table Scraping from Website using Python

Question

I'm trying to scrape a data from a table on a website. However, I am continuously running into "ValueError: cannot set a row with mismatched columns".

The set-up is:

url = 'https://kr.youtubers.me/united-states/all/top-500-youtube-channels-in-united-states/en'
page = requests.get(url)
soup = BeautifulSoup(page.text,'lxml')
table1 = soup.find('div', id = 'content')

headers = []
for i in table1.find_all('th'):
    title = i.text
    headers.append(title)

my_data = pd.DataFrame(columns = headers)
my_data = my_data.iloc[:,:-4]

Here, I was able to make an empty dataframe with headers same as the table (I did iloc because there were some repeating columns at the end).

Now, I wanted to fill in the empty dataframe through:

for j in table1.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [i.text for i in row_data]
    length = len(my_data)
    my_data.loc[length] = row

However, as mentioned, I get "ValueError: cannot set a row with mismatched columns" in this line: length = len(my_data). I would really appreciate any help to solve this problem and to fill in the empty dataframe.

Thanks in advance.

Driftr95 · Accepted Answer · 2023-01-14 19:08:16Z

Rather than trying to fill an empty DataFrame, it would be simpler to utilize .read_html, which returns a list of DataFrames after parsing every table tag within the HTML.

Even though this page has only two tables (_{"Top Youtube channels" and "Top Youtube channels - detail stats"}), 3 DataFrames are returned because the second table is split into two table tags between rows 12 and 13 for some reason; but they can all be combined into DataFrame.

dfList = pd.read_html(url) # OR
# dfList = pd.read_html(page.text) # OR
# dfList = pd.read_html(soup.prettify())

allTime = dfList[0].set_index(['rank', 'Youtuber'])

# (header row in 1st half so 2nd half reads as headerless to pandas)
dfList[2].columns = dfList[1].columns 
perYear = pd.concat(dfList[1:]).set_index(['rank', 'Youtuber'])


columns_ordered = [
    'started', 'category', 'subscribers', 'subscribers/year', 
    'video views', 'Video views/Year', 'video count', 'Video count/Year'
] # re-order columns as preferred
combinedDf = pd.concat([allTime, perYear], axis='columns')[columns_ordered]

_{If the [columns_ordered] part is omitted from the last line, then the expected column order would be 'subscribers', 'video views', 'video count', 'category', 'started', 'subscribers/year', 'Video views/Year', 'Video count/Year'.}

combinedDf should look like

Andrej Kesely · Accepted Answer · 2023-01-14 18:39:41Z

You can try to use pd.read_html to read the table into a dataframe:

import pandas as pd

url = "https://kr.youtubers.me/united-states/all/top-500-youtube-channels-in-united-states/en"

df = pd.read_html(url)[0]
print(df)

Prints:

     rank                                            Youtuber  subscribers  video views  video count              category  started
0       1                                   ✿ Kids Diana Show    106000000  86400421379         1052        People & Blogs     2015
1       2                                          Movieclips     58500000  59672883333        39903      Film & Animation     2006
2       3                                        Ryan's World     34100000  53568277882         2290         Entertainment     2015
3       4                                     Toys and Colors     38300000  44050683425          901         Entertainment     2016
4       5   LooLoo Kids - Nursery Rhymes and Children's Songs     52200000  30758617681          605                 Music     2014
5       6                                            LankyBox     22500000  30147589773         6913                Comedy     2016
6       7                                          D Billions     24200000  27485780190          582                   NaN     2019
7       8                   BabyBus - Kids Songs and Cartoons     31200000  25202247059         1946             Education     2016
8       9                                              FGTeeV     21500000  23255537029         1659                Gaming     2013

...and so on.

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