Pandas - Get all rows that have same selected column values as specific row

I have this code below and features is a list of column names and filtered is a Pandas dataframe. What I'm trying to do is given a specific row (index=0) in this case I'm trying to find all the rows with the same features values as row at index = 0. The below throws an error, but essentially the idea was to check each row and if it had the same features and row with index 0 then it would return those rows. The error it throws is pandas.core.indexing.IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).

print(filtered[features].drop_duplicates().iloc[0][features])
print(filtered[filtered[features].eq(filtered[features].drop_duplicates().iloc[0][features]).all()])

Also tried:

for idx, row in filtered.iterrows():
    sub_filt = filtered[filtered[sub_features].eq(row[sub_features]).all(axis=1)]
    print(len(sub_filt))

and it prints all 0s, but it should be printing at least a 1.

You are close, for create mask use DataFrame.all with axis=1 :

d = {'c1': ['a','b','c','a','b','c'],
     'c2': [1,2,3,1,2,1],
     'c3':range(2,8)}
filtered = pd.DataFrame(data=d)
    
features = ['c1','c2']

print(filtered[features].eq(filtered[features].iloc[0]))
      c1     c2
0   True   True
1  False  False
2  False  False
3   True   True
4  False  False
5  False   True

print(filtered[features].eq(filtered[features].iloc[0]).all(axis=1))
0     True
1    False
2    False
3     True
4    False
5    False
dtype: bool

print(filtered[filtered[features].eq(filtered[features].iloc[0]).all(axis=1)])
  c1  c2  c3
0  a   1   2
3  a   1   5

This should answer your question.
https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html

rows_based_on_condition = df[df["column"] == value]

When you're trying to apply it based on multiple columns, you can replace the input df (or a copy of the df) multiple times using a for-loop:

columns = ["column_1", "column_2"]
val_dict = dict()

for col in columns:
    val_dict.update({col:df.iloc[0][col]})

for key in val_dict.keys():
    value = val_dict[key]
    df = df[df[key] == value]

Also try to write your code in such a way, that each line only contains one step. Makes it easier to read.
This old project of mine, contains a good example at:
# reshaping wdi_df from wide to long form (<- copy into ctrl-F searchbar)
https://github.com/MaximilianHauser/CC_DS21_Life_Expectancy_and_GDP/blob/main/life_expectancy_gdp.ipynb
Also might be interesting if you're doing an online data science course.
Happy Coding!