Should this Dataset be Transposed for best Neural-Net Performance?

I’ve been working with this Kaggle dataset and intend to use it to make some time-series predictions from it. I’ve preprocessed it to the point where now I need to start making some decisions about how best to feed it into the neural-net. Coming from a background in Python’s Numpy, I was taught that computations on rows are much more efficient that going wide and doing computations column-wise which is how the kernel would need to read this data if I am understanding its structure correctly. (It’s almost 2 years worth of daily data.) The documentation on the subject is not exactly clear.

I just wanted to check if that assumption is correct? And if I should be trying to find a way to transpose this dataset what’s the best way to do that? Or if I’m mistaken and using the data how it came out of the pre-processing step will be fine? A sample of the data follows:

<|"title" -> "1984-(roman)", "2015-07-01" -> 421, "2015-07-02" -> 438,
  "2015-07-03" -> 351, "2015-07-04" -> 259, "2015-07-05" -> 329, 
 "2015-07-06" -> 383, "2015-07-07" -> 361, "2015-07-08" -> 333, 
 "2015-07-09" -> 327|>, <|"title" -> "24-Heures-du-Mans", 
 "2015-07-01" -> 203, "2015-07-02" -> 188, "2015-07-03" -> 208, 
 "2015-07-04" -> 169, "2015-07-05" -> 170, "2015-07-06" -> 172, 
 "2015-07-07" -> 147, "2015-07-08" -> 194, 
 "2015-07-09" -> 143|>, <|"title" -> "24-Heures-du-Mans-2016", 
 "2015-07-01" -> 19, "2015-07-02" -> 14, "2015-07-03" -> 20, 
 "2015-07-04" -> 8, "2015-07-05" -> 10, "2015-07-06" -> 26, 
 "2015-07-07" -> 24, "2015-07-08" -> 17, 
 "2015-07-09" -> 9|>, <|"title" -> "2-Broke-Girls", 
 "2015-07-01" -> 250, "2015-07-02" -> 200, "2015-07-03" -> 179, 
 "2015-07-04" -> 183, "2015-07-05" -> 204, "2015-07-06" -> 204, 
 "2015-07-07" -> 212, "2015-07-08" -> 212, "2015-07-09" -> 185|>