Converting array of pandas Dataframes into 3D NumPy array

I have a numpy array of pandas Dataframes which I need to convert into a 3D numpy array of the form (samples, rows, columns) in order to feed into a Keras model for training. I have 46 samples in my dataset and each sample is 1101 rows by 64 columns.

Here is the code for my 1D numpy array of Dataframes:

static_dfs = []
#read in static csvs as pandas df
#static files is my np array of csv files
for x in range(0, static_files.size):
  df = pd.read_csv(static_files[x], sep='\t', skiprows=skip_rows, header=(0))
  #append df to list  
  static_dfs.append(df)

#convert list to np array
static_dfs = np.asarray(static_dfs)

Indeed the shape of the array is (46,) [the number of samples]. If I look at one of the Dataframes in the array (static_dfs[0] for instance) the shape is (1101, 64).

I then try to convert this to 3D numpy array:

static_nps = []

for x in range(0, static_dfs.size):
  static_nps.append(static_dfs[x].to_numpy())

#convert to numpy array
static_nps = np.asarray(static_nps)

However it gives me this error:

could not broadcast input array from shape (1101,64) into shape (1101)

for the line of code:

    #convert to numpy array
    static_nps = np.asarray(static_nps)

Worst part is I had it working before, but a collaborator of mine went through my code and edited it after we found a bug in one of our data files. Now I can't seem to get it back to working like before and am stuck :(

The desired shape of my 3D array would look like (46, 1101, 64). If anyone could solve this you would be a huge help! Thanks