You can create an empty DataFrame with either column names or an Index:

In [4]: import pandas as pd
In [5]: df = pd.DataFrame(columns=['A','B','C','D','E','F','G'])
In [6]: df
Out[6]:
Empty DataFrame
Columns: [A, B, C, D, E, F, G]
Index: []

Or

In [7]: df = pd.DataFrame(index=range(1,10))
In [8]: df
Out[8]:
Empty DataFrame
Columns: []
Index: [1, 2, 3, 4, 5, 6, 7, 8, 9]

Edit: Even after your amendment with the .to_html, I can't reproduce. This:

df = pd.DataFrame(columns=['A','B','C','D','E','F','G'])
df.to_html('test.html')

Produces:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
      <th>D</th>
      <th>E</th>
      <th>F</th>
      <th>G</th>
    </tr>
  </thead>
  <tbody>
  </tbody>
</table>

I think inplace=True has to be removed, because it return None:

df2 = df1.rename(columns = {"" : "Signal"})

df1.rename(columns = {"" : "Signal"}, inplace = True)

Another solution is asign new name by position:

df.columns.values[0] = 'Signal'

Sample:

df1 = pd.DataFrame({'':[1,2,3],
                   'B':[4,5,6],
                   'C':[7,8,9]})

print (df1)
      B  C
0  1  4  7
1  2  5  8
2  3  6  9

df2 = df1.rename(columns = {"" : "Signal"})
print (df2)
   Signal  B  C
0       1  4  7
1       2  5  8
2       3  6  9