将 Pandas Dataframe 中的行按索引替换为另一个 Dataframe 中具有相应索引的值

Question

I am trying to write a function that, given a number of user specified time step inputs, will overwrite the value at the given index with a value from another dataframe. For example:

df1

index   date  skew
92  2019-09-02  0
93  2019-09-03  0
94  2019-09-04  0
95  2019-09-05  0
96  2019-09-06  0
97  2019-09-09  0

df2

index  change
13      0.63
14      0.61
15      0.98
16      0.11
17      0.43

The result I am after:

result_df

index   date         skew
92  2019-09-02  0
93  2019-09-03  0.63
94  2019-09-04  0.61
95  2019-09-05  0.98
96  2019-09-06  0.11
97  2019-09-09  0.43

Using a for loop and df1.at[-i, 'skew'] = df2.loc[-i, 'change']

I am getting the following result:

index   date   skew
92  2019-09-02  0
93  2019-09-03  0
94  2019-09-04  0
95  2019-09-05  0
96  2019-09-06  0
97  2019-09-09  0
-5  NaT 0.63
-4  NaT 0.61
-3  NaT 0.98
-2  NaT 0.11
-1  NaT 0.43

my current function:

    num_timesteps = 5

    def append_changes (df1, df2, num_timesteps):
      # Reverse loop to start from index df1.iloc[-num_timsteps:]
      for i in range(num_timesteps, 0, -1):
        df1.at[-i:, 'filler'] = df2.loc[-i:, 'change']
    return df1

I expect the row values under the skew column from index -5 (as per num_timesteps) to the end of the dataframe to be replaced with those values from the 'change' column in df2 at the same index.

Answer 1

I think no loop is necessary, only use DataFrame.iloc with positions of columns by Index.get_loc for select ans set new values - for avoid match index values assign numpy array created by .values :

num_timesteps = 5
def append_changes (df1, df2, num_timesteps):
    arr = df2.iloc[-num_timesteps:, df2.columns.get_loc('change')].values
    df1.iloc[-num_timesteps:, df1.columns.get_loc('skew')] = arr
    return df1
print (append_changes(df1, df2, num_timesteps))
             date  skew
index                  
92     2019-09-02  0.00
93     2019-09-03  0.63
94     2019-09-04  0.61
95     2019-09-05  0.98
96     2019-09-06  0.11
97     2019-09-09  0.43