#df
index  a   b   c
1      2   3   4
2      3   4   5
Please help me how to extract columns "a" and "c" with all the rows but without the index column.
df[["a","c"]] # But index no. is also coming, so how to remove the index no.?
#df
index  a   b   c
1      2   3   4
2      3   4   5
df[["a","c"]] # But index no. is also coming, so how to remove the index no.?
 
    
    DataFrames and Series will always have an index, you can use:
df[["a","c"]].values
output:
array([[2, 4],
       [3, 5]], dtype=int64)
 
    
    The simple answer to achieve what the OP was specifically asking for is to add the index parameter as follows. e.g.
df[["b","c"]].to_csv("C:\\Desktop\\File.csv",index=False)
 
    
     
    
    This might work for your case:
# import pandas package as pd
import pandas as pd
  
# Define a dictionary containing students data
data = {'a': [2, 3],
        'b': [3, 4],
        'c': [4, 5]}
  
# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns=['a', 'b','c'])
  
print("Given Dataframe :\n", df)
  
print("\nIterating over rows using iloc function :\n")
  
# iterate through each row and select
# 0th and 2nd index column respectively.
for i in range(len(df)):
    print(df.iloc[i, 0], df.iloc[i, 2])
Output:
Given Dataframe :
    a  b  c
0  2  3  4
1  3  4  5
Iterating over rows using iloc function :
2 4
3 5
Tested here:
https://onecompiler.com/python/3zht3ravp
Solution source -
Method 3: Using iloc[] function of the DataFrame. :
https://www.geeksforgeeks.org/different-ways-to-iterate-over-rows-in-pandas-dataframe/
