Question: I want to make a pandas dataframe that describes the state of a system at different times  I have the initial state which describes the first row Each row correspond to a time I have reveserved the first two columns for "household" / statistics The following columns are state parameters At each iteration/row a number of parameters change - this could be just one or many  I have created a somewhat simplified version that simulates my change data : df_change Question 1 Can you think of a more efficient way of generating the matrix than what i do in this code? i have a state that i update in a loop and insert Question 2 This is what i discovered while trying to write the sample code for this discussion. I see 20 fold performanne boost in loop iteration performance if i do the assignments to the "household" columns after the loop. Why is this? I am using python = 3.12.4 and pandas 2.2.2. df["product"] ="some_product"  #%% import numpy as np import pandas as pd from tqdm import tqdm num_cols =600 n_changes = 40000 # simulate changes extra_colums = ["n_changes","product"] columns = [chr(i+65) for i in range(num_cols)] state = { icol : np.random.random() for icol in columns} change_index = np.random.randint(0,4,n_changes).cumsum() change_col = [columns[np.random.randint(0,num_cols)] for i in range(n_changes)] change_val= np.random.normal(size=n_changes) # create change matrix df_change=pd.DataFrame(index= change_index ) df_change['col'] = change_col df_change['val'] = change_val index = np.unique(change_index) # %% # Slow approach gives 5 iterations/s df = pd.DataFrame(index= index, columns=extra_colums + columns) df["product"] ="some_product" for i in tqdm(index): state.update(zip(df_change.loc[[i],"col"] , df_change.loc[[i],"val"])) df.loc[i,columns] = pd.Series(state) # %% # Fast approach gives 1000 iterations/sec df2 = pd.DataFrame(index= index, columns=extra_colums + columns) for i in tqdm(index): state.update(zip(df_change.loc[[i],"col"] , df_change.loc[[i],"val"])) df2.loc[i,columns] = pd.Series(state) df2["product"] ="some_product"  Edit I marked the answer by ouroboros1 as theaccepted solution - it works really well and answered Question 1. I am still curios about Question 2 : the difference in pandas performance using the two methods where i iterate through the rows. I found that I can also get a performance similar to the original "df2" method depending on how i assign the value before the loop. The interesting point here is that pre assignment changes the performance in loop that follows. # Fast approach gives 1000 iterations/sec df3 = pd.DataFrame(index=index, columns=extra_colums + columns) #df3.loc[index,"product"] = "some_product" # Fast #df3["product"] = "some_product" # Slow df3.product = "some_product" # Fast for i in tqdm(index): state.update(zip(df_change.loc[[i], "col"], df_change.loc[[i], "val"])) df3.loc[i, columns] = np.array(list(state.values())) 

Answer: Here's one approach that should be much faster: Data sample num_cols = 4 n_changes = 6 np.random.seed(0) # reproducibility # setup ...  df_change col val 1 C 0.144044 4 A 1.454274 5 A 0.761038 7 A 0.121675 7 C 0.443863 10 B 0.333674  state {'A': 0.5488135039273248, 'B': 0.7151893663724195, 'C': 0.6027633760716439, 'D': 0.5448831829968969}  Code out = (df_change.reset_index() .pivot_table(index='index', columns='col', values='val', aggfunc='last') .rename_axis(index=None, columns=None) .assign(product='some_product') .reindex(columns=extra_colums + columns) .fillna(pd.DataFrame(state, index=[index[0]])) .ffill() )  Output  n_changes product A B C D 1 NaN some_product 0.548814 0.715189 0.144044 0.544883 4 NaN some_product 1.454274 0.715189 0.144044 0.544883 5 NaN some_product 0.761038 0.715189 0.144044 0.544883 7 NaN some_product 0.121675 0.715189 0.443863 0.544883 10 NaN some_product 0.121675 0.333674 0.443863 0.544883 # note: # A updated in 4, 5, 7 # B updated in 10 # C updated in 1, 7  Explanation / Intermediates  Use df.reset_index to access 'index' inside df.pivot_table. For the aggfunc use 'last'. I.e., we only need to propagate the last value in case of duplicate 'col' values per index value. Cosmetic: use df.rename_axis to reset index and columns names to None.  # df_chagne.reset_index().pivot_table(...).rename_axis(...) A B C 1 NaN NaN 0.144044 4 1.454274 NaN NaN 5 0.761038 NaN NaN 7 0.121675 NaN 0.443863 10 NaN 0.333674 NaN   Use df.assign to add column 'product' with a scalar ('some_product'). Use df.reindex to get the columns in the desired order (with extra_columns up front). Not yet existing column 'n_changes' will be added with NaN values. Now, apply df.fillna and use a pd.DataFrame with state for only the first index value (index[0]), to fill the first row (alternatively, use df.combine_first).  # after .fillna(...) n_changes product A B C D 1 NaN some_product 0.548814 0.715189 0.144044 0.544883 4 NaN some_product 1.454274 NaN NaN NaN 5 NaN some_product 0.761038 NaN NaN NaN 7 NaN some_product 0.121675 NaN 0.443863 NaN 10 NaN some_product NaN 0.333674 NaN NaN   Finally, we want to forward fill: df.ffill.   Performance comparison: num_cols = 100 n_changes = 100 np.random.seed(0) # reproducibility # out: 7.01 ms ± 435 μs per loop (mean ± std. dev. of 7 runs, 100 loops each) # df2 (running this *afterwards*, as you are updating `state` 93.7 ms ± 3.79 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)  Equality check: df2.astype(out.dtypes).equals(out) # True