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df.at warning message would like to optimize the code

I am using the following code to add a nuts value into new columns in dfEU dataframe. I came up with this code from the following past
pandas .at versus .loc
Is there a way to solve this warning?
dfEU was created using the following query:
dfEU = df.query('continent == "EU" & country_id == "Belgium"')
for row in dfEU.itertuples():
lati=float(getattr(row, 'locationlatitude'))
longi=float(getattr(row, 'locationlongitude'))
nuts = nf.find(lat=lati, lon=longi)
if nuts:
dfEU.at[row.Index, 'nuts1'] = nuts[0].get('NUTS_NAME')
dfEU.at[row.Index, 'nuts1id'] = nuts[0].get('FID')
dfEU.at[row.Index, 'nuts2'] = nuts[1].get('NUTS_NAME')
dfEU.at[row.Index, 'nuts2id'] = nuts[1].get('FID')
dfEU.at[row.Index, 'nuts3'] = nuts[2].get('NUTS_NAME')
dfEU.at[row.Index, 'nuts3id'] = nuts[2].get('FID')
else:
dfEU.at[row.Index, 'nuts1'] = 'Nan'
When launching the code I receive the following warning:
C:\Users\win\anaconda3\lib\site-packages\pandas\core\indexing.py:1596: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[key] = _infer_fill_value(value)
C:\Users\win\anaconda3\lib\site-packages\pandas\core\indexing.py:1765: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
isetter(loc, value)

Using loc still rises SettingWithCopyWarning Warning while changing column

I want to filter URLs form text column of my df by filtering all http https like below:
data.loc[:,'text_'] = data['text_'].str.replace(r'\s*https?://\S+(\s+|$)', ' ').str.strip()
I used the loc as advised in other answers but I still keep getting the warning.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
"""Entry point for launching an IPython kernel.
time: 9.81 s (started: 2022-03-19 06:35:42 +00:00)
/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self._setitem_single_column(ilocs[0], value, pi)
How to do this operation correctly ie. without the warning?
UPDATE:
I've generated data from kaggle dataset:
kaggle datasets download clmentbisaillon/fake-and-real-news-dataset
and then:
true_df.drop_duplicates(keep='first')
fake_df.drop_duplicates(keep='first')
true_df['is_fake'] = 0
fake_df['is_fake'] = 1
news_df = pd.concat([true_df, fake_df])
news_df = news_df.sample(frac=1).reset_index(drop=True)
drop_list = ['subject', 'date']
column_filter = news_df.filter(drop_list)
news_df.drop(column_filter, axis=1)
news_df['text_'] = news_df['title'] + news_df['text']
data = news_df[['text_', 'is_fake']]
Next for the following line:
data.loc[:,'text_'] = data['text_'].str.replace(r'\s*https?://\S+(\s+|$)', ' ').str.strip()
I get that error from the start of the post.
UPDATE 2:
As mentioned by #Riley Adding the
data = data.copy()
Fix the SettingWithCopyWarning however the:
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
"""Entry point for launching an IPython kernel.
Still remains. To fix it change regex=True fo replace:
data.loc[:,'text_'] = data['text_'].str.replace(r'\s*https?://\S+(\s+|$)', ' ', regex=True).str.strip()

Python DataFrame Issue with Warning

I am having trouble finding a solution for SettingWithCopyWarning in Jupyter Notebook. I would appreciate any insight and/or solutions. Thank you in advance.
Code:
matches2['players'] = list(zip(matches2['player_1_name'], matches2['player_2_name']))
g = matches2.groupby('players')
df_list = []
for group, df in g:
df = df[['winner']]
n = df.shape[0]
player_1_h2h = np.zeros(n)
player_2_h2h = np.zeros(n)
p1 = group[0]
p2 = group[1]
for i in range(1,n):
if df.iloc[i-1,0] == p1:
player_1_h2h[i] = player_1_h2h[i-1] + 1
player_2_h2h[i] = player_2_h2h[i-1]
else:
player_1_h2h[i] = player_1_h2h[i-1]
player_2_h2h[i] = player_2_h2h[i-1] + 1
df['player_1_h2h'] = player_1_h2h
df['player_2_h2h'] = player_2_h2h
df_list.append(df)
Error:
<ipython-input-214-d8e04df2295c>:32: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df['player_1_h2h'] = player_1_h2h
<ipython-input-214-d8e04df2295c>:33: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df['player_2_h2h'] = player_2_h2h

I would recomend disabling the warning.
import pandas as pd
pd.options.mode.chained_assignment = None
For more information on this behavior see this question and search for the Garrett's answer

You can ignore this warning, as it's a false positive in this case, but if you want to avoid it entirely, you can change
df['player_1_h2h'] = player_1_h2h
df['player_2_h2h'] = player_2_h2h
to
df = df.assign(
player_1_h2h=player_1_h2h,
player_2_h2h=player_2_h2h
)

How to assign Numpy array values to other variable

This is my code:
y_predForThisMatchType = model.predict(X_test, num_iteration=model.best_iteration)
print(type(y_predForThisMatchType))
y_predForThisMatchType = y_predForThisMatchType.reshape(-1)
print(type(y_predForThisMatchType))
count = 0
for i in range (len(y_pred)):
if y_pred.loc[i] == abType:
y_pred.loc[i] = y_predForThisMatchType[count]
count = count + 1
Output:
class 'numpy.ndarray'
class 'numpy.ndarray'
/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self._setitem_with_indexer(indexer, value)
Python just print the above output, and that's all. The program is technically running, but below code do not get executed, no real error is shown.
Error Line:
y_pred.loc[i] = y_predForThisMatchType[count]
y_pred variable is a pandas dataframe.

Have you checked your outputs completely?
In my experience, your code is working.
The display is just a warning and can be disabled with:
pandas.options.mode.chained_assignment = None # default='warn'

'A value is trying to be set on a copy of a slice from a DataFrame' error while using 'iloc'

Jupiter nootbook is returning this warning:
*C:\anaconda\lib\site-packages\pandas\core\indexing.py:337: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[key] = _infer_fill_value(value)
C:\anaconda\lib\site-packages\pandas\core\indexing.py:517: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[item] = s*
After runing the following code:
def group_df(df,num):
ln = len(df)
rang = np.arange(ln)
splt = np.array_split(rang,num)
lst = []
finel_lst = []
for i,x in enumerate(splt):
lst.append([i for x in range(len(x))])
for k in lst:
for j in k:
finel_lst.append(j)
df['group'] = finel_lst
return df
def KNN(dafra,folds,K,fi,target):
df = group_df(dafra,folds)
avarge_e = []
for i in range(folds):
train = df.loc[df['group'] != i]
test = df.loc[df['group'] == i]
test.loc[:,'pred_price'] = np.nan
test.loc[:,'rmse'] = np.nan
print(test.columns)
KNN(data,5,5,'GrLivArea','SalePrice')
In the error message, it is recommended to use .loc indexing- which i did, but it did not help. Please help me- what is the problem ? I have went through the related questions and read the documentation, but i still don't get it.

I think you need copy:
train = df.loc[df['group'] != i].copy()
test = df.loc[df['group'] == i].copy()
If you modify values in test later you will find that the modifications do not propagate back to the original data (df), and that Pandas does warning.