Column names after transposing a dataframe - pandas

Question

I have a small dataframe - six rows (not counting the header) and 53 columns (a store name, and the rest weekly sales for the past year). Each row contains a particular store and each column the store's name and sales for each week. I need to transpose the data so that the weeks appear as rows, the stores appear as columns, and their sales appear as the rows.
To generate the input data:
df_store = pd.read_excel(SourcePath+SourceFile, sheet_name='StoreSales', header=0, usecols=['StoreName'])
# Number rows of all irrelevant stores.
row_numbers = [x+1 for x in df_stores[(df_store['StoreName'] != 'Store1') & (df_store['StoreName'] != 'Store2')
& (df_store['StoreName'] !='Store3')].index]
# Read in entire Excel file, skipping the rows of irrelevant stores.
df_store = pd.read_excel(SourcePath+SourceFile, sheet_name='StoreSales', header=0, usecols = "A:BE",
skiprows = row_numbers, converters = {'StoreName' : str})
# Transpose dataframe
df_store_t = df_store.transpose()
My output puts index numbers above each store name ( 0 to 5), and then each column starts out as StoreName (above the week), then each store name. Yet, I cannot manipulate them by their names.
Is there a way to clear those index numbers so that I can work directly with the resulting column names (e.g., rename "StoreName" to "WeekEnding" and make reference to each store columns ("Store1", "Store2", etc.?)

Answer 1

IIUC, you need to set_index first, then transpose, T:
See this example:
df = pd.DataFrame({'Store':[*'ABCDE'],
'Week 1':np.random.randint(50,200, 5),
'Week 2':np.random.randint(50,200, 5),
'Week 3':np.random.randint(50,200, 5)})
Input Dataframe:
Store Week 1 Week 2 Week 3
0 A 99 163 148
1 B 119 86 92
2 C 145 98 162
3 D 144 143 199
4 E 50 181 177
Now, set_index and transpose:
df_out = df.set_index('Store').T
df_out
Output:
Store A B C D E
Week 1 99 119 145 144 50
Week 2 163 86 98 143 181
Week 3 148 92 162 199 177