"Pivot" a Pandas DataFrame into a 3D numpy array

It is doable using df.pivot_table. I added one more row to your sample to have both Measurement Type. On missing values, it will be represented by np.nan

sample `df`

       Date Site Measurement_Type Value
0  1/1/2020    A      Temperature  32.3
1  1/1/2020    A         Humidity   60%
2  1/2/2020    B         Humidity   70%

Try the followings

iix = pd.MultiIndex.from_product([np.unique(df.Date), np.unique(df.Measurement_Type)])
df_pivot = (df.pivot_table('Value', 'Site', ['Date', 'Measurement_Type'], aggfunc='first')
              .reindex(iix, axis=1))
arr = np.array(df_pivot.groupby(level=0, axis=1).agg(lambda x: [*x.values])
                       .to_numpy().tolist())

print(arr)

Out[1447]:
array([[['60%', '32.3'],
        [nan, nan]],

       [[nan, nan],
        ['70%', nan]]], dtype=object)

---

Method 2: using pivot_table on different columns and numpy reshape

iix_n = pd.MultiIndex.from_product([np.unique(df.Site), np.unique(df.Date)])
arr = (df.pivot_table('Value', ['Site', 'Date'], 'Measurement_Type', aggfunc='first')
         .reindex(iix_n).to_numpy()
         .reshape(df.Site.nunique(),df.Date.nunique(),-1))

Out[1501]:
array([[['60%', '32.3'],
        [nan, nan]],

       [[nan, nan],
        ['70%', nan]]], dtype=object)