Pandas can not construct index

I have an Excel file from which I need to extract data at given intervals of time and angles. Easy taskwith SQL but I need to do it with pandas now. I can't figure ou if I can attach the actual file, but here are the names of the columns from the CSV file :

time;ms;interface;packet;GEN_Src;GEN_Dest;GEN_Class;GEN_Type;GEN_Length;GEN_cycle_cnt;lrt_data_type;corr_src;corr_acq_cnt;corr_fragment;corr_fragment_cnt;corr_acq_time;;;;;;;;;;;;;;;;;;;

There are actually dozens of columns without a name at the end, in which lie the data I need to extract. Slicing should work for this.

However, I need to separate them according to intervals of time (column 'time' ) and angles (column 'corr_src').

When I run :

df = pd.read_csv(r'path\EM_Cell_A-Sample1.csv', index_col='time')

I get :

pandas.errors.ParserError: Could not construct index. Requested to use 1 number of columns, but 1088 left to parse.

Previous attempts, driven by solutions found here :
How to check if a column exists in Pandas indicate that the 'time' column does not exist.

Update

It seems the real problem is reading a CSV file that uses ; as the field separator. Half the world uses , as the decimal separator which means another character, typically ;, is used as the field separator

df=pd.read_csv(path,index_col='time',sep=';',decimal=',')

The question is unclear. There's no reason to create a copy of the original dataframe and reset_index() won't create a new index. The warning itself may not be relevant in Pandas 2.0.0

It seems that the real question is how to read a text file with comma separated values, using the time column as index. This can be done by specifying the index column in read_csv

df=pd.read_csv(path,index_col='time')

CSV isn't an Excel format, it's a text format containing Values Separated by Commas. Excel files (xlsx) are ZIP packages containing XML files. You can't read a text file with read_excel or an Excel file with read_csv.

Indexes are series, not rows. When no index is specified, the row number is used instead.

To check whether a value exists in a Python container use in. That includes Series or indexes and df.columns :

if 'time' in df.columns :
...

To change the index to a different one use set_index :

df.set_index(['interface','time']

reset_index does the opposite - it reverts the dataframe to the original index.