Remove top row from a dataframe

Question

I have a dataframe that looks like this:

         level_0              level_1 Repo Averages for 27 Jul 2018
0  Business Date           Instrument                           Ccy
1     27/07/2018  GC_AUSTRIA_SUB_10YR                           EUR
2     27/07/2018    R_RAGB_1.15_10/18                           EUR
3     27/07/2018    R_RAGB_4.35_03/19                           EUR
4     27/07/2018    R_RAGB_1.95_06/19                           EUR

I am trying to get rid of the top row and only keep

   Business Date           Instrument         Ccy
0     27/07/2018  GC_AUSTRIA_SUB_10YR         EUR
1     27/07/2018    R_RAGB_1.15_10/18         EUR
2     27/07/2018    R_RAGB_4.35_03/19         EUR
3     27/07/2018    R_RAGB_1.95_06/19         EUR

I tried df.columns.droplevel(0) but not successful any help is more than welcome

Answer 1

You can try so:

df.columns = df.iloc[0]
df = df.reindex(df.index.drop(0)).reset_index(drop=True)
df.columns.name = None

Output:

  Business Date           Instrument  Ccy
0    27/07/2018  GC_AUSTRIA_SUB_10YR  EUR
1    27/07/2018    R_RAGB_1.15_10/18  EUR
2    27/07/2018    R_RAGB_4.35_03/19  EUR
3    27/07/2018    R_RAGB_1.95_06/19  EUR

Answer 2

You can try using slicing.

df = df[1:]

This will remove the first row of your dataframe.

Answer 3

You can take advantage of the parameter header (Read here more about the header parameter in pandas).

Let's say that you have the following dataset

df = pd.read_csv("Prices.csv")
print(df)

That outputs

              0       1     2         3         4
0      DATA      SESSAO  HORA  PRECO_PT  PRECO_ES
1      1/1/2020  0       1     41,88     41,88   
2      1/1/2020  0       2     38,60     38,60   
3      1/1/2020  0       3     36,55     36,55 

By simply passing the header = 0 like this

df = pd.read_csv("Prices.csv", header=0)
print(df)

You will get what you want

           DATA  SESSAO  HORA PRECO_PT PRECO_ES
0      1/1/2009  0       1     55,01    55,01  
1      1/1/2009  0       2     56,13    56,13  
2      1/1/2009  0       3     50,59    50,59  
3      1/1/2009  0       4     45,83    45,83  
4      1/1/2009  0       5     42,07    41,90