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Drop certain rows based on quantity of rows with specific values

I am newer data science and am working on a project to analyze sports statistics. I have a dataset of hockey statistics for a group of players over multiple seasons. Players have anywhere between 1 row to 12 rows representing their season statistics over however many seasons they've played.
Example:
Player Season Pos GP G A P +/- PIM P/GP ... PPG PPP SHG SHP OTG GWG S S% TOI/GP FOW%
0 Nathan MacKinnon 2022 1 65 32 56 88 22 42 1.35 ... 7 27 0 0 1 5 299 10.7 21.07 45.4
1 Nathan MacKinnon 2021 1 48 20 45 65 22 37 1.35 ... 8 25 0 0 0 2 206 9.7 20.37 48.5
2 Nathan MacKinnon 2020 1 69 35 58 93 13 12 1.35 ... 12 31 0 0 2 4 318 11.0 21.22 43.1
3 Nathan MacKinnon 2019 1 82 41 58 99 20 34 1.21 ... 12 37 0 0 1 6 365 11.2 22.08 43.7
4 Nathan MacKinnon 2018 1 74 39 58 97 11 55 1.31 ... 12 32 0 1 3 12 284 13.7 19.90 41.9
5 Nathan MacKinnon 2017 1 82 16 37 53 -14 16 0.65 ... 2 14 2 2 2 4 251 6.4 19.95 50.6
6 Nathan MacKinnon 2016 1 72 21 31 52 -4 20 0.72 ... 7 16 0 1 0 6 245 8.6 18.87 48.4
7 Nathan MacKinnon 2015 1 64 14 24 38 -7 34 0.59 ... 3 7 0 0 0 2 192 7.3 17.05 47.0
8 Nathan MacKinnon 2014 1 82 24 39 63 20 26 0.77 ... 8 17 0 0 0 5 241 10.0 17.35 42.9
9 J.T. Compher 2022 2 70 18 15 33 6 25 0.47 ... 4 6 1 1 0 0 102 17.7 16.32 51.4
10 J.T. Compher 2021 2 48 10 8 18 10 19 0.38 ... 1 2 0 0 0 2 47 21.3 14.22 45.9
11 J.T. Compher 2020 2 67 11 20 31 9 18 0.46 ... 1 5 0 3 1 3 106 10.4 16.75 47.7
12 J.T. Compher 2019 2 66 16 16 32 -8 31 0.48 ... 4 9 3 3 0 3 118 13.6 17.48 49.2
13 J.T. Compher 2018 2 69 13 10 23 -29 20 0.33 ... 4 7 2 2 2 3 131 9.9 16.00 45.1
14 J.T. Compher 2017 2 21 3 2 5 0 4 0.24 ... 1 1 0 0 0 1 30 10.0 14.93 47.6
15 Darren Helm 2022 1 68 7 8 15 -5 14 0.22 ... 0 0 1 2 0 1 93 7.5 10.55 44.2
16 Darren Helm 2021 1 47 3 5 8 -3 10 0.17 ... 0 0 0 0 0 0 83 3.6 14.68 66.7
17 Darren Helm 2020 1 68 9 7 16 -6 37 0.24 ... 0 0 1 2 0 0 102 8.8 13.73 53.6
18 Darren Helm 2019 1 61 7 10 17 -11 20 0.28 ... 0 0 1 4 0 0 107 6.5 14.57 44.4
19 Darren Helm 2018 1 75 13 18 31 3 39 0.41 ... 0 0 2 4 0 0 141 9.2 15.57 44.1
[sample of my dataset][1]
[1]: https://i.stack.imgur.com/7CsUd.png
If any player has played more than 6 seasons, I want to drop the row corresponding to Season 2021. This is because COVID drastically shortened the season and it is causing issues as I work with averages.
As you can see from the screenshot, Nathan MacKinnon has played 9 seasons. Across those 9 seasons, except for 2021, he plays in no fewer than 64 games. Due to the shortened season of 2021, he only got 48 games.
Removing Season 2021 results in an Average Games Played of 73.75.
Keeping Season 2021 in the data, the Average Games Played becomes 70.89.
While not drastic, it compounds into the other metrics as well.
I have been trying this for a little while now, but as I mentioned, I am new to this world and am struggling to figure out how to accomplish this.
I don't want to just completely drop ALL rows for 2021 across all players, though, as some players only have 1-5 years' worth of data and for those players, I need to use as much data as I can and remove 1 row from a player with only 2 seasons would also negatively skew averages.
I would really appreciate some assistance from anyone more experienced than me!

This can be accomplished by using groupby and apply. For example:
edited_players = (players
.groupby("Player")
.apply(lambda subset: subset if len(subset) <= 6 else subset.query("Season != 2021"))
)
Round brackets for formatting purposes.
The combination of groupby and apply basically feeds a grouped subset of your dataframe to a function. So, first all the rows of Nathan MacKinnon will be used, then rows for J.T. Compher, then Darren Helm rows, etc.
The function used is an anonymous/lambda function which operates under the following logic: "if the dataframe subset that I receive has 6 or fewer rows, I'll return the subset unedited. Otherwise, I will filter out rows within that subset which have the value 2021 in the Season column".

Not able to view CSV from Python Webscrape

I am new to python and am doing a webscraping tutorial. I am having trouble getting my CSV file in the appropriate folder. Basically, I am not able to view the resulting CSV. Does anyone have a solution regarding this problem?
import pandas as pd
import re
from bs4 import BeautifulSoup
import requests
#Pulling in website source code#
url = 'https://www.espn.com/mlb/history/leaders/_/breakdown/season/year/2022'
page = requests.get(url)
soup = BeautifulSoup(page.text, 'html.parser')
#Pulling in player rows
##Identify Player Rows
players = soup.find_all('tr', attrs= {'class':re.compile('row-player-10-')})
for players in players:
##Pulling stats for each players
stats = [stat.get_text() for stat in players.findall('td')]
##Create a data frame for the single player stats
temp.df = pd.DataFrame(stats).transpose()
temp.df = columns
##Join single players stats with the overall dataset
final_dataframe = pd.concat([final_df,temp_df], ignore_index=True)
print(final_dataframe)
final_dataframe.to_csv(r'C\Users\19794\OneDrive\Desktop\Coding Projects', index = False, sep =',', encoding='utf-8')

I've checked your code.
I've found one issue.
This one.
for players in players:
##Pulling stats for each players
stats = [stat.get_text() for stat in players.findall('td')]
##Create a data frame for the single player stats
temp.df = pd.DataFrame(stats).transpose()
temp.df = columns
##Join single players stats with the overall dataset
final_dataframe = pd.concat([final_df,temp_df], ignore_index=True)
print(final_dataframe)
final_dataframe.to_csv(r'C\Users\19794\OneDrive\Desktop\Coding Projects', index = False, sep =',', encoding='utf-8')
You have to use this. (players to player, filename with csv)
for player in players:
##Pulling stats for each players
stats = [stat.get_text() for stat in player.findall('td')]
##Create a data frame for the single player stats
temp.df = pd.DataFrame(stats).transpose()
temp.df = columns
##Join single players stats with the overall dataset
final_dataframe = pd.concat([final_df,temp_df], ignore_index=True)
print(final_dataframe)
final_dataframe.to_csv(r'C\Users\19794\OneDrive\Desktop\Coding Projects\result.csv', index = False, sep =',', encoding='utf-8')

Few issues.
As stated in the previous solution, your for loop you need to change to for player in players: You cant use the same variable as the variable you are looping through
You shouldn't use . in your variables as you have temp.df. That indicates the use of a method. Use underscore instead _
You never define final_df, then try to call it in your pd.concat()
You never define columns and then try to use that (and it would then overwrite your temp_df as well). What you are wanting to do is change instead is temp_df.columns = columns. But note you need to define columns.
Your find_all() for the players is incorrect in that you're searching for a class that contains row-player-10-. There is no class with that. It is row player-10. Very subtle difference, but it's the difference of returning None elements, and 50 elements.
stats = [stat.get_text() for stat in player.findall('td')] - again needs to be referencing player from the for loop as mentioned in 1). And in fact, there's a few syntax things in there that we need to change to actually pull out the text. So that should be [stat.text for stat in player.find_all('td')]
You use pd.concat the temp_df to a final_df within your loop. You can do that (provided you create an initial final_dataframe or final_df (you use 2 different variable names...not sure which you really wanted), but that will lead to repeating the headers/column names in it and require an extra step. What I would rather do, is store each temp_df into a list. Then after it loops through all the players, THEN concat the list of dataframes into a final one.
So here is the full code:
import pandas as pd
import re
from bs4 import BeautifulSoup
import requests
#Pulling in website source code#
url = 'https://www.espn.com/mlb/history/leaders/_/breakdown/season/year/2022'
page = requests.get(url)
soup = BeautifulSoup(page.text, 'html.parser')
#Pulling in player rows
##Identify Player Rows
players = soup.find_all('tr', attrs= {'class':re.compile('.*row player-10-.*')})
columns = soup.find('tr', {'class':'colhead'})
columns = [x.text for x in columns.find_all('td')]
#Initialize a list of dataframes
final_df_list = []
# Loop through the players
for player in players:
##Pulling stats for each players
stats = [stat.text for stat in player.find_all('td')]
##Create a data frame for the single player stats
temp_df = pd.DataFrame(stats).transpose()
temp_df.columns = columns
#Put temp_df in a list of dataframes
final_df_list.append(temp_df)
##Join your list of single players stats
final_dataframe = pd.concat(final_df_list, ignore_index=True)
print(final_dataframe)
final_dataframe.to_csv(r'C\Users\19794\OneDrive\Desktop\Coding Projects', index = False, sep =',', encoding='utf-8')
Output:
print(final_dataframe)
PLAYER YRS G AB R H ... HR RBI BB SO SB CS BA
0 1 J.D. Martinez 11 54 211 38 74 ... 8 28 24 55 0 0 .351
1 2 Paul Goldschmidt 11 62 236 47 82 ... 16 56 35 50 3 0 .347
2 3 Xander Bogaerts 9 62 232 39 77 ... 6 31 23 50 3 0 .332
3 4 Rafael Devers 5 63 258 53 85 ... 16 40 18 49 1 0 .329
4 5 Manny Machado 10 63 244 46 80 ... 11 43 29 46 7 1 .328
5 6 Jeff McNeil 4 61 216 30 70 ... 4 32 16 27 2 0 .324
6 7 Ty France 3 63 249 29 79 ... 10 41 18 40 0 0 .317
7 8 Bryce Harper 10 58 225 46 71 ... 15 46 24 48 7 2 .316
8 9 Yordan Alvarez 3 57 205 39 64 ... 17 45 31 38 0 1 .312
9 10 Aaron Judge 6 61 232 53 72 ... 25 49 31 66 4 0 .310
10 11 Jose Ramirez 9 59 222 40 68 ... 16 62 34 19 11 3 .306
11 12 Andrew Benintendi 6 61 226 23 68 ... 2 22 24 37 0 0 .301
12 13 Michael Brantley 13 55 207 23 62 ... 4 21 28 24 1 1 .300
13 14 Trea Turner 7 62 242 32 72 ... 8 47 21 48 13 2 .298
14 15 J.P. Crawford 5 59 216 28 64 ... 5 16 28 37 3 1 .296
15 16 Dansby Swanson 6 64 234 39 69 ... 9 37 23 70 9 2 .295
16 17 Mike Trout 11 57 201 44 59 ... 18 38 30 64 0 0 .294
17 Josh Bell 6 65 235 33 69 ... 8 39 28 37 0 1 .294
18 19 Santiago Espinal 2 63 219 25 64 ... 5 31 18 40 3 2 .292
19 20 Trey Mancini 5 58 217 25 63 ... 6 25 24 47 0 0 .290
20 21 Austin Hays 4 60 228 33 66 ... 9 37 18 41 1 3 .289
21 22 Eric Hosmer 11 59 222 23 64 ... 4 29 22 38 0 0 .288
22 23 Freddie Freeman 12 62 241 40 69 ... 5 34 32 43 6 0 .286
23 24 C.J. Cron 8 64 249 36 71 ... 14 44 16 74 0 0 .285
24 Tommy Edman 3 63 246 52 70 ... 7 26 26 45 15 2 .285
25 26 Starling Marte 10 54 222 40 63 ... 7 34 10 45 8 5 .284
26 27 Ian Happ 5 61 209 30 59 ... 7 31 34 50 5 1 .282
27 28 Pete Alonso 3 64 239 41 67 ... 18 59 26 56 2 1 .280
28 29 Lourdes Gurriel Jr. 4 58 206 21 57 ... 3 25 15 41 2 1 .277
29 30 Nathaniel Lowe 3 58 217 25 60 ... 8 24 15 57 1 1 .276
30 31 Mookie Betts 8 60 245 53 67 ... 17 40 27 47 6 1 .273
31 32 Jose Abreu 8 59 224 34 61 ... 9 30 33 42 0 0 .272
32 Amed Rosario 5 53 217 31 59 ... 1 16 10 31 7 1 .272
33 Ke'Bryan Hayes 2 57 213 26 58 ... 2 22 26 53 7 3 .272
34 35 Nolan Arenado 9 61 229 28 62 ... 11 41 25 31 0 2 .271
35 George Springer 8 58 218 39 59 ... 12 33 20 51 4 1 .271
36 37 Ryan Mountcastle 2 53 211 28 57 ... 12 35 11 57 2 0 .270
37 Vladimir Guerrero Jr. 3 62 233 34 63 ... 16 39 27 45 0 1 .270
38 39 Cesar Hernandez 9 65 271 37 73 ... 0 16 17 55 2 2 .269
39 Ketel Marte 7 61 223 33 60 ... 4 22 22 45 4 0 .269
40 Connor Joe 2 60 238 32 64 ... 5 16 32 52 3 2 .269
41 42 Brandon Nimmo 6 57 209 36 56 ... 4 21 27 44 0 1 .268
42 Thairo Estrada 3 59 205 34 55 ... 4 26 14 31 9 1 .268
43 44 Shohei Ohtani 4 63 243 42 64 ... 13 37 24 67 7 5 .263
44 45 Randy Arozarena 3 61 233 30 61 ... 7 31 14 58 12 5 .262
45 46 Nelson Cruz 17 60 222 29 58 ... 7 36 25 50 2 0 .261
46 Hunter Dozier 5 55 203 25 53 ... 6 21 15 50 1 2 .261
47 48 Kyle Tucker 4 58 204 24 53 ... 12 39 31 41 11 1 .260
48 Bo Bichette 3 63 265 35 69 ... 10 33 17 65 4 3 .260
49 50 Charlie Blackmon 11 57 232 29 60 ... 10 33 17 41 2 1 .259
[50 rows x 16 columns]
Lastly, tables are a great way to learn how to use BeautifulSoup because of the structure. But do want to throw out there that pandas can parse <table> tags for you with less code:
import pandas as pd
url = 'https://www.espn.com/mlb/history/leaders/_/breakdown/season/year/2022'
final_dataframe = pd.read_html(url, header=1)[0]
final_dataframe = final_dataframe[final_dataframe['PLAYER'].ne('PLAYER')]

How to join/merge and sum columns with the same name

How can I merge and sum the columns with the same name?
So the output should be 1 Column named Canada as a result of the sum of the 4 columns named Canada.
Country/Region Brazil Canada Canada Canada Canada
Week 1 0 3 0 0 0
Week 2 0 17 0 0 0
Week 3 0 21 0 0 0
Week 4 0 21 0 0 0
Week 5 0 23 0 0 0
Week 6 0 80 0 5 0
Week 7 0 194 0 20 0
Week 8 12 702 3 199 20
Week 9 182 2679 16 2395 260
Week 10 737 8711 80 17928 892
Week 11 1674 25497 153 48195 1597
Week 12 2923 46392 175 85563 2003
Week 13 4516 76095 182 122431 2180
Week 14 6002 105386 183 163539 2431
Week 15 6751 127713 189 210409 2995
Week 16 7081 147716 189 258188 3845

From its current state, this should give the outcome you're looking for:
df = df.set_index('Country/Region') # optional
df.groupby(df.columns, axis=1).sum() # Stolen from Scott Boston as it's a superior method.
Output:
index Brazil Canada
Country/Region
Week 1 0 3
Week 2 0 17
Week 3 0 21
Week 4 0 21
Week 5 0 23
Week 6 0 85
Week 7 0 214
Week 8 12 924
Week 9 182 5350
Week 10 737 27611
Week 11 1674 75442
Week 12 2923 134133
Week 13 4516 200888
Week 14 6002 271539
Week 15 6751 341306
Week 16 7081 409938
I found your dataset interesting, here's how I would clean it up from step 1:
df = pd.read_csv('file.csv')
df = df.set_index(['Province/State', 'Country/Region', 'Lat', 'Long']).stack().reset_index()
df.columns = ['Province/State', 'Country/Region', 'Lat', 'Long', 'date', 'value']
df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date')
df = df.pivot_table(index=df.index, columns='Country/Region', values='value', aggfunc=np.sum)
print(df)
Output:
Country/Region Afghanistan Albania Algeria Andorra Angola ... West Bank and Gaza Western Sahara Yemen Zambia Zimbabwe
date ...
2020-01-22 0 0 0 0 0 ... 0 0 0 0 0
2020-01-23 0 0 0 0 0 ... 0 0 0 0 0
2020-01-24 0 0 0 0 0 ... 0 0 0 0 0
2020-01-25 0 0 0 0 0 ... 0 0 0 0 0
2020-01-26 0 0 0 0 0 ... 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ...
2020-07-30 36542 5197 29831 922 1109 ... 11548 10 1726 5555 3092
2020-07-31 36675 5276 30394 925 1148 ... 11837 10 1728 5963 3169
2020-08-01 36710 5396 30950 925 1164 ... 12160 10 1730 6228 3659
2020-08-02 36710 5519 31465 925 1199 ... 12297 10 1734 6347 3921
2020-08-03 36747 5620 31972 937 1280 ... 12541 10 1734 6580 4075
If you now want to do weekly aggregations, it's as simple as:
print(df.resample('w').sum())
Output:
Country/Region Afghanistan Albania Algeria Andorra Angola ... West Bank and Gaza Western Sahara Yemen Zambia Zimbabwe
date ...
2020-01-26 0 0 0 0 0 ... 0 0 0 0 0
2020-02-02 0 0 0 0 0 ... 0 0 0 0 0
2020-02-09 0 0 0 0 0 ... 0 0 0 0 0
2020-02-16 0 0 0 0 0 ... 0 0 0 0 0
2020-02-23 0 0 0 0 0 ... 0 0 0 0 0
2020-03-01 7 0 6 0 0 ... 0 0 0 0 0
2020-03-08 10 0 85 7 0 ... 43 0 0 0 0
2020-03-15 57 160 195 7 0 ... 209 0 0 0 0
2020-03-22 175 464 705 409 5 ... 309 0 0 11 7
2020-03-29 632 1142 2537 1618 29 ... 559 0 0 113 31
2020-04-05 1783 2000 6875 2970 62 ... 1178 4 0 262 59
2020-04-12 3401 2864 11629 4057 128 ... 1847 30 3 279 84
2020-04-19 5838 3603 16062 4764 143 ... 2081 42 7 356 154
2020-04-26 8918 4606 21211 5087 174 ... 2353 42 7 541 200
2020-05-03 15149 5391 27943 5214 208 ... 2432 42 41 738 244
2020-05-10 25286 5871 36315 5265 274 ... 2607 42 203 1260 241
2020-05-17 39634 6321 45122 5317 327 ... 2632 42 632 3894 274
2020-05-24 61342 6798 54185 5332 402 ... 2869 45 1321 5991 354
2020-05-31 91885 7517 62849 5344 536 ... 3073 63 1932 7125 894
2020-06-07 126442 8378 68842 5868 609 ... 3221 63 3060 7623 1694
2020-06-14 159822 9689 74147 5967 827 ... 3396 63 4236 8836 2335
2020-06-21 191378 12463 79737 5981 1142 ... 4466 63 6322 9905 3089
2020-06-28 210487 15349 87615 5985 1522 ... 10242 70 7360 10512 3813
2020-07-05 224560 18707 102918 5985 2186 ... 21897 70 8450 11322 4426
2020-07-12 237087 22399 124588 5985 2940 ... 36949 70 9489 13002 6200
2020-07-19 245264 26845 149611 6098 4279 ... 52323 70 10855 16350 9058
2020-07-26 250970 31255 178605 6237 5919 ... 68154 70 11571 26749 14933
2020-08-02 255739 36370 208457 6429 7648 ... 80685 70 12023 38896 22241
2020-08-09 36747 5620 31972 937 1280 ... 12541 10 1734 6580 4075

Try:
np.random.seed(0)
df = pd.DataFrame(np.random.randint(0,100,(20,5)), columns=[*'ZAABC'])
df.groupby(df.columns, axis=1, sort=False).sum()
Output:
Z A B C
0 44 111 67 67
1 9 104 36 87
2 70 176 12 58
3 65 126 46 88
4 81 62 77 72
5 9 100 69 79
6 47 146 99 88
7 49 48 19 14
8 39 97 9 57
9 32 105 23 35
10 75 83 34 0
11 0 89 5 38
12 17 83 42 58
13 31 66 41 57
14 35 57 82 91
15 0 113 53 12
16 42 159 68 6
17 68 50 76 52
18 78 35 99 58
19 23 92 85 48

You can try a transpose and groupby, e.g. something similar to the below.
df_T = df.tranpose()
df_T.groupby(df_T.index).sum()['Canada']

Here's a way to do it:
df.columns = [(col + str(i)) if col.startswith('Canada') else col for i, col in enumerate(df.columns)]
df = df.assign(Canada=df.filter(like='Canada').sum(axis=1)).drop(columns=[x for x in df.columns if x.startswith('Canada') and x != 'Canada'])
First we rename the columns starting with Canada by appending their integer position, which ensures they are no longer duplicates.
Then we use sum() to add across columns like Canada, put the result in a new column named Canada, and drop the columns that were originally named Canada.
Full test code is:
import pandas as pd
df = pd.DataFrame(
columns=[x.strip() for x in 'Brazil Canada Canada Canada Canada'.split()],
index=['Week ' + str(i) for i in range(1, 17)],
data=[[i] * 5 for i in range(1, 17)])
df.columns.names=['Country/Region']
print(df)
df.columns = [(col + str(i)) if col.startswith('Canada') else col for i, col in enumerate(df.columns)]
df = df.assign(Canada=df.filter(like='Canada').sum(axis=1)).drop(columns=[x for x in df.columns if x.startswith('Canada') and x != 'Canada'])
print(df)
Output:
Country/Region Brazil Canada Canada Canada Canada
Week 1 1 1 1 1 1
Week 2 2 2 2 2 2
Week 3 3 3 3 3 3
Week 4 4 4 4 4 4
Week 5 5 5 5 5 5
Week 6 6 6 6 6 6
Week 7 7 7 7 7 7
Week 8 8 8 8 8 8
Week 9 9 9 9 9 9
Week 10 10 10 10 10 10
Week 11 11 11 11 11 11
Week 12 12 12 12 12 12
Week 13 13 13 13 13 13
Week 14 14 14 14 14 14
Week 15 15 15 15 15 15
Week 16 16 16 16 16 16
Brazil Canada
Week 1 1 4
Week 2 2 8
Week 3 3 12
Week 4 4 16
Week 5 5 20
Week 6 6 24
Week 7 7 28
Week 8 8 32
Week 9 9 36
Week 10 10 40
Week 11 11 44
Week 12 12 48
Week 13 13 52
Week 14 14 56
Week 15 15 60
Week 16 16 64

In Selenium see if the 'a' anchor tag contains the text I want, and then extract multiple td's of text in the same row

For my python code, I have been trying to scrape data from NCAAF Stats. I have been having issues extracting the td's text after I evaluate if the anchor tag 'a', contains the text I want. I want to be able to find the teams amount of tds, points, and ppg. I have been able to successfully find the school by text in selenium, but after that I am unable to extract the info I want. Here is what I have coded so far.
from selenium import webdriver
driver = webdriver.Chrome('C:\\Users\\Carl\\Downloads\\chromedriver.exe')
driver.get('https://www.ncaa.com/stats/football/fbs/current/team/27')
# I plan to make a while or for loop later, that is why I used f strings
team = "Coastal Carolina"
first = driver.find_element_by_xpath(f'//a[text()="{team}"]')
# This was the way another similiarly asked question was answered but did not work
#tds = driver.find_element_by_xpath(f'//td//a[text()="{apples}"]/../td[4]').text
# This grabs data from the very first row of data... not the one I want
tds = first.find_element_by_xpath('//following-sibling::td[4]').text
total_points = first.find_element_by_xpath('//following-sibling::td[10]').text
ppg = first.find_element_by_xpath('//following-sibling::td[11]').text
print(tds, total_points, ppg)
driver.quit()
I have tried to look around for a similarly asked question and was able to find this snippet
tds = driver.find_element_by_xpath(f'//td//a[text()="{apples}"]/../td[4]').text
it unfortunately did not help me out much. The html structure looks like this. I appreciate any help, and thank you!

No need to use Selenium, the page isn't dynamic. Just use pandas to parse the table for you:
import pandas as pd
url = 'https://www.ncaa.com/stats/football/fbs/current/team/27'
dfs = pd.read_html(url)[0]
Output:
print(df)
Rank Team G TDs PAT 2PT Def Pts FG Saf Pts PPG
0 1 Ohio St. 6 39 39 0 0 6 0 291.0 48.5
1 2 Pittsburgh 6 40 36 0 0 4 1 290.0 48.3
2 3 Coastal Carolina 7 43 42 0 0 6 1 320.0 45.7
3 4 Alabama 7 41 40 1 0 9 0 315.0 45.0
4 5 Ole Miss 6 35 30 1 0 6 1 262.0 43.7
5 6 Cincinnati 6 36 34 1 0 3 0 261.0 43.5
6 7 Oklahoma 7 35 34 1 1 17 0 299.0 42.7
7 - SMU 7 40 36 1 0 7 0 299.0 42.7
8 9 Texas 7 38 37 0 0 8 1 291.0 41.6
9 10 Western Ky. 6 31 27 1 0 10 0 245.0 40.8
10 11 Tennessee 7 36 36 0 0 7 1 275.0 39.3
11 12 Wake Forest 6 28 24 2 0 12 0 232.0 38.7
12 13 UTSA 7 33 33 0 0 13 0 270.0 38.6
13 14 Michigan 6 28 25 1 0 12 0 231.0 38.5
14 15 Georgia 7 34 33 0 0 10 1 269.0 38.4
15 16 Baylor 7 35 35 0 0 7 1 268.0 38.3
16 17 Houston 6 30 28 0 0 5 0 223.0 37.2
17 - TCU 6 29 28 0 0 7 0 223.0 37.2
18 19 Marshall 7 34 33 0 0 7 0 258.0 36.9
19 - North Carolina 7 34 32 2 0 6 0 258.0 36.9
20 21 Nevada 6 26 24 1 0 12 0 218.0 36.3
21 22 Virginia 7 31 29 2 0 10 2 253.0 36.1
22 23 Fresno St. 7 32 27 1 0 10 0 251.0 35.9
23 - Memphis 7 33 26 3 0 7 0 251.0 35.9
24 25 Texas Tech 7 32 31 0 0 9 0 250.0 35.7
25 26 Auburn 7 29 28 1 0 12 1 242.0 34.6
26 27 Florida 7 33 29 1 0 4 0 241.0 34.4
27 - Missouri 7 31 31 0 0 8 0 241.0 34.4
28 29 Liberty 7 33 29 1 0 3 1 240.0 34.3
29 - Michigan St. 7 30 30 0 0 10 0 240.0 34.3
30 31 UCF 6 28 26 0 0 3 1 205.0 34.2
31 32 Oregon St. 6 27 27 0 0 5 0 204.0 34.0
32 33 Oregon 6 26 26 0 0 7 0 203.0 33.8
33 34 Iowa St. 6 23 22 0 0 14 0 202.0 33.7
34 35 UCLA 7 30 28 0 0 9 0 235.0 33.6
35 36 San Diego St. 6 25 24 1 0 7 0 197.0 32.8
36 37 LSU 7 29 29 0 0 8 0 227.0 32.4
37 38 Louisville 6 24 23 0 0 9 0 194.0 32.3
38 - Miami (FL) 6 24 22 1 0 8 1 194.0 32.3
39 - NC State 6 25 24 0 0 6 1 194.0 32.3
40 41 Southern California 6 22 19 3 0 12 0 193.0 32.2
41 42 Tulane 7 31 23 4 0 2 0 223.0 31.9
42 43 Arizona St. 7 30 25 2 0 4 0 221.0 31.6
43 44 Utah 6 25 22 1 0 5 0 189.0 31.5
44 45 Air Force 7 29 27 1 0 5 1 220.0 31.4
45 46 App State 7 27 24 0 0 11 0 219.0 31.3
46 47 Arkansas 7 27 25 0 0 10 0 217.0 31.0
47 - Army West Point 6 25 22 0 0 4 1 186.0 31.0
48 - Notre Dame 6 23 20 2 0 8 0 186.0 31.0
49 - Western Mich. 7 28 25 0 0 8 0 217.0 31.0

Delete rows in pandas which match your header

I'm kind of new with pandas and now i have a question.
I read a table from a html site and set my header according to the table on the website.
df = pd.read_html('http://www.espn.com/nhl/statistics/player/_/stat/points/sort/points/year/2015/seasontype/2', header = 1)
Now I have the my dataframe with a matching header BUT I have some rows that are the same as the header, like the example below.
RK PLAYER TEAM GP G A PTS +/- PIM PTS/G SOG
1 Jamie Benn, LW DAL 82 35 52 87 1 64 1.06 253
2 John Tavares, C NYI 82 38 48 86 5 46 1.05 278
...
10 Vladimir Tarasenko, RW STL 77 37 36 73 27 31 0.95 264
RK PLAYER TEAM GP G A PTS +/- PIM PTS/G SOG
14 Steven Stamkos, C TB 82 43 29 72 2 49 0.88 268
I know that it's possible to delete duplicate rows with panda but is it possible to delete rows that are duplicates of the header or a specific row?
Hope you can help me out !

You can use boolean indexing:
df = df[df.PLAYER != 'PLAYER']
If need also remove rows with PP in column PLAYER use isin:
Notice: I add [0] to the end of read_html, because it return list of dataframes an you need select first item of list:
df = pd.read_html('http://www.espn.com/nhl/statistics/player/_/stat/points/sort/points/year/2015/seasontype/2', header = 1)[0]
print (df)
RK PLAYER TEAM GP G A PTS +/- PIM PTS/G \
0 1 Jamie Benn, LW DAL 82 35 52 87 1 64 1.06
1 2 John Tavares, C NYI 82 38 48 86 5 46 1.05
2 3 Sidney Crosby, C PIT 77 28 56 84 5 47 1.09
3 4 Alex Ovechkin, LW WSH 81 53 28 81 10 58 1.00
4 NaN Jakub Voracek, RW PHI 82 22 59 81 1 78 0.99
5 6 Nicklas Backstrom, C WSH 82 18 60 78 5 40 0.95
6 7 Tyler Seguin, C DAL 71 37 40 77 -1 20 1.08
7 8 Jiri Hudler, LW CGY 78 31 45 76 17 14 0.97
8 NaN Daniel Sedin, LW VAN 82 20 56 76 5 18 0.93
9 10 Vladimir Tarasenko, RW STL 77 37 36 73 27 31 0.95
10 NaN PP SH NaN NaN NaN NaN NaN NaN NaN
11 RK PLAYER TEAM GP G A PTS +/- PIM PTS/G
12 NaN Nick Foligno, LW CBJ 79 31 42 73 16 50 0.92
13 NaN Claude Giroux, C PHI 81 25 48 73 -3 36 0.90
14 NaN Henrik Sedin, C VAN 82 18 55 73 11 22 0.89
15 14 Steven Stamkos, C TB 82 43 29 72 2 49 0.88
...
...
mask = df['PLAYER'].isin(['PLAYER', 'PP'])
print (df[~mask])
RK PLAYER TEAM GP G A PTS +/- PIM PTS/G SOG \
0 1 Jamie Benn, LW DAL 82 35 52 87 1 64 1.06 253
1 2 John Tavares, C NYI 82 38 48 86 5 46 1.05 278
2 3 Sidney Crosby, C PIT 77 28 56 84 5 47 1.09 237
3 4 Alex Ovechkin, LW WSH 81 53 28 81 10 58 1.00 395
4 NaN Jakub Voracek, RW PHI 82 22 59 81 1 78 0.99 221
5 6 Nicklas Backstrom, C WSH 82 18 60 78 5 40 0.95 153
6 7 Tyler Seguin, C DAL 71 37 40 77 -1 20 1.08 280
7 8 Jiri Hudler, LW CGY 78 31 45 76 17 14 0.97 158
8 NaN Daniel Sedin, LW VAN 82 20 56 76 5 18 0.93 226
9 10 Vladimir Tarasenko, RW STL 77 37 36 73 27 31 0.95 264
12 NaN Nick Foligno, LW CBJ 79 31 42 73 16 50 0.92 182
13 NaN Claude Giroux, C PHI 81 25 48 73 -3 36 0.90 279
14 NaN Henrik Sedin, C VAN 82 18 55 73 11 22 0.89 101
15 14 Steven Stamkos, C TB 82 43 29 72 2 49 0.88 268
16 NaN Tyler Johnson, C TB 77 29 43 72 33 24 0.94 203
17 16 Ryan Johansen, C CBJ 82 26 45 71 -6 40 0.87 202
18 17 Joe Pavelski, C SJ 82 37 33 70 12 29 0.85 261
19 NaN Evgeni Malkin, C PIT 69 28 42 70 -2 60 1.01 212
20 NaN Ryan Getzlaf, C ANA 77 25 45 70 15 62 0.91 191
21 20 Rick Nash, LW NYR 79 42 27 69 29 36 0.87 304
...
...