Pandas append() will be deprecated and I can't convert a specific df.append() to df.concat()-CodePudding

I have a current iteration to fill new rows to a dataframe based on new series created:

for i in range (x):
    nextMonth = df.index[-1]   DateOffset(months=1)
    newRow = pd.Series({'col_1':None,'col_2':1}, name=nextMonth)
    df = df.append(newRow)

This works fine. New rows are created, on correct df columns (col_1 and col_2) and I have a correct nextMonth named index on the df (2022-02-01 on date).

              col_1  col_2
date
1994-07-01  0.0684   7.177511
1994-08-01  0.0186   6.718000
1994-09-01  0.0153   6.595327
1994-10-01  0.0262   6.495939
1994-11-01  0.0281   6.330091
...            ...        ...
2021-10-01  0.0125   1.035140
2021-11-01  0.0095   1.022360
2021-12-01  0.0073   1.012739
2022-01-01  0.0054   1.005400
2022-02-01     NaN   1.000000 -----> series added

Note that I'm using the series named indexes to match with the df columns, and I´m also using the series name to use it on the final df as a named index (nextMonth).

Since df.append() will be deprecated, I´m struggling to perform the same instructions using df.concat().

CodePudding user response：

By slightly reworking your loop, you could make it a dict comprehension to build a dictionary; construct a DataFrame with it; then use pd.concat to concatenate it to df. For example, if x=3:

x = 3
df = (pd.concat((df, pd.DataFrame.from_dict(
                                {df.index[-1]   DateOffset(months=i 1): {'col_1':np.nan, 'col_2':1} 
                                 for i in range(x)}, orient='index')))
      .rename_axis(index=df.index.name))

Output:

             col_1     col_2
date                        
1994-07-01  0.0684  7.177511
1994-08-01  0.0186  6.718000
1994-09-01  0.0153  6.595327
1994-10-01  0.0262  6.495939
1994-11-01  0.0281  6.330091
2021-10-01  0.0125  1.035140
2021-11-01  0.0095  1.022360
2021-12-01  0.0073  1.012739
2022-01-01  0.0054  1.005400
2022-02-01     NaN  1.000000
2022-03-01     NaN  1.000000
2022-04-01     NaN  1.000000