[python] Python Pandas Replacing Header with Top Row

I currently have a dataframe that looks like this:

           Unnamed: 1    Unnamed: 2   Unnamed: 3  Unnamed: 4
0   Sample Number  Group Number  Sample Name  Group Name
1             1.0           1.0          s_1         g_1
2             2.0           1.0          s_2         g_1
3             3.0           1.0          s_3         g_1
4             4.0           2.0          s_4         g_2

I'm looking for a way to delete the header row and make the first row the new header row, so the new dataframe would look like this:

    Sample Number  Group Number  Sample Name  Group Name
0             1.0           1.0          s_1         g_1
1             2.0           1.0          s_2         g_1
2             3.0           1.0          s_3         g_1
3             4.0           2.0          s_4         g_2

I've tried stuff along the lines of if 'Unnamed' in df.columns: then make the dataframe without the header df.to_csv(newformat,header=False,index=False) but I don't seem to be getting anywhere.

The best practice and Best OneLiner:

df.to_csv(newformat,header=1)

Notice the header value:

Header refer to the Row number(s) to use as the column names. Make no mistake, the row number is not the df but from the excel file(0 is the first row, 1 is the second and so on).

This way, you will get the column name you want and won't have to write additional codes or create new df.

Good thing is, it drops the replaced row.

Another one-liner using Python swapping:

df, df.columns = df[1:] , df.iloc[0]

This won't reset the index

Although, the opposite won't work as expected df.columns, df = df.iloc[0], df[1:]

@ostrokach answer is best. Most likely you would want to keep that throughout any references to the dataframe, thus would benefit from inplace = True.
df.rename(columns=df.iloc[0], inplace = True) df.drop([0], inplace = True)

--another way to do this

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

    Sample Number  Group Number  Sample Name  Group Name
0             1.0           1.0          s_1         g_1
1             2.0           1.0          s_2         g_1
2             3.0           1.0          s_3         g_1
3             4.0           2.0          s_4         g_2

If you like it hit up arrow. Thanks

header = table_df.iloc[0]
table_df.drop([0], axis =0, inplace=True)
table_df.reset_index(drop=True)
table_df.columns = header
table_df

Here's a simple trick that defines column indices "in place". Because set_index sets row indices in place, we can do the same thing for columns by transposing the data frame, setting the index, and transposing it back:

df = df.T.set_index(0).T

Note you may have to change the 0 in set_index(0) if your rows have a different index already.

If you want a one-liner, you can do:

df.rename(columns=df.iloc[0]).drop(df.index[0])

The dataframe can be changed by just doing

df.columns = df.iloc[0]
df = df[1:]

Then

df.to_csv(path, index=False) 

Should do the trick.