human
What is a the Python code you requested given the following:
I am working on the file "18_01_26_optimizing_python/Speeding up python code.ipynb".
The first task is:
Sample output:
~~~~
Timer unit: 1e-06 s
Total time: 8.40759 s
File: <ipython-input-34-c7b04c4d293b>
Function: process_checkpoints at line 1
Line #      Hits         Time  Per Hit   % Time  Line Contents
 1                                           def process_checkpoints(num_checkpoints):
 2         1        800.0    800.0      0.0      df = pd.DataFrame()
 3       301        863.0      2.9      0.0      for k in range(num_checkpoints):
 4                                                   
 5                                                   # Create some fake data
 6       300      27055.0     90.2      0.3          array_tmp = np.random.random((1000,7))
 7       300        851.0      2.8      0.0          array_tmp = array_tmp[1:-1] # drop cells with bad data
 8                                                   
 9                                                   
10                                                   # transform the array into a dataframe
11       300       2051.0      6.8      0.0          index     = pd.MultiIndex.from_product([[k], np.arange(array_tmp.shape[0])],
12       300     496092.0   1653.6      5.9                                                 names=["k","i"])
13       300     102367.0    341.2      1.2          df_tmp    = pd.DataFrame(array_tmp, columns=cols_in, index = index)
14                                           
15       300      47172.0    157.2      0.6          df_tmp["Mass"]        = calculate_mass(df_tmp.Density.values,
16       300     207859.0    692.9      2.5                                                 df_tmp.dV.values)
17       300     297848.0    992.8      3.5          df_tmp["M_int"]       = df_tmp.Mass.cumsum()
18       300      37834.0    126.1      0.4          df_tmp["Temperature"] = calculate_temperature(df_tmp.Pressure.values,
19       300     196617.0    655.4      2.3                                                        df_tmp.Density.values, .67)
20       300      37470.0    124.9      0.4          df_tmp["Energy"]      =...
Can you write Python code for it?
assistant
#Here is the Python code you requested.

dataframe_columns = ["Radius",
                     "dR",
                     "dV",
                     "Density",
                     "Pressure",
                     "Velocity",
                     "Z",
                     "Mass",
                     "M_int",
                     "Temperature",
                     "Energy",
                     "C_ad",
                     "Crossing_time",
                    ]

num_checkpoints=10
num_zones=20
index     = pd.MultiIndex.from_product([np.arange(num_checkpoints), 
                                        np.arange(num_zones)],
                                       names=["k","i"])
df = pd.DataFrame(index=index, 
                  columns=dataframe_columns,
                  dtype=float)

df

def process_checkpoints_new(num_checkpoints):
    num_zones = 1000
    
    dataframe_columns = ["Radius",
                         "dR",
                         "dV",
                         "Density",
                         "Pressure",
                         "Velocity",
                         "Z",
                         "Mass",
                         "M_int",
                         "Temperature",
                         "Energy",
                         "C_ad",
                         "Crossing_time",
                        ]

    index     = pd.MultiIndex.from_product([np.arange(num_checkpoints), 
                                            np.arange(num_zones-2)],
                                           names=["k","i"])
    df = pd.DataFrame(index=index, 
                      columns=dataframe_columns,
                      dtype=float)
    
    for k in range(num_checkpoints):
        
        # Create some fake data
        array_tmp = np.random.random((num_zones,7))
        array_tmp = array_tmp[1:-1] # drop cells with bad data
        
        # adding the data into the dataframe is much more complicated now
        df_tmp = df.loc[k]
        df_tmp[cols_in] = array_tmp

        df_tmp["Mass"]        =...