.transform('first') 有什么作用？

Question

Somebody helped me for a code. I understood everything in the code except the very last row .transform('first') I see what it does (I can see it), but I'd like to precisely know what it's doing behind to obtain this result.

This is the part of the code I understand :

df['Date'] = pd.to_datetime(df['Date'])
df['YEP'] = ( df[::-1].loc[df['Type'].eq('Budget')]
                     .groupby(df['Date'].dt.year)
                     .Value
                     .cumsum()
                     .sub(df['Value'])
                     .add(df['YTD'])
)

This is the output of this first part :

    Value    Type       Date    YTD     YEP
0     100  Budget 2019-01-01  101.0   974.0
1      50  Budget 2019-02-01  199.0  1022.0
2      20  Budget 2019-03-01  275.0  1078.0
3     123  Budget 2019-04-01  332.0  1012.0
4      56  Budget 2019-05-01    NaN     NaN
5      76  Budget 2019-06-01    NaN     NaN
6      98  Budget 2019-07-01    NaN     NaN
7     126  Budget 2019-08-01    NaN     NaN
8      90  Budget 2019-09-01    NaN     NaN
9      80  Budget 2019-10-01    NaN     NaN
10     67  Budget 2019-11-01    NaN     NaN
11     87  Budget 2019-12-01    NaN     NaN
12    101  Actual 2019-01-01  101.0     NaN
13     98  Actual 2019-02-01  199.0     NaN
14     76  Actual 2019-03-01  275.0     NaN
15     57  Actual 2019-04-01  332.0     NaN

This is the entire code :

df['Date'] = pd.to_datetime(df['Date'])
df['YEP'] = ( df[::-1].loc[df['Type'].eq('Budget')]
                     .groupby(df['Date'].dt.year)
                     .Value
                     .cumsum()
                     .sub(df['Value'])
                     .add(df['YTD'])
                     .groupby(df['Date'])
                     .transform('first') )

I got this after running the entire code :

    Value    Type       Date    YTD     YEP
0     100  Budget 2019-01-01  101.0   974.0
1      50  Budget 2019-02-01  199.0  1022.0
2      20  Budget 2019-03-01  275.0  1078.0
3     123  Budget 2019-04-01  332.0  1012.0
4      56  Budget 2019-05-01    NaN     NaN
5      76  Budget 2019-06-01    NaN     NaN
6      98  Budget 2019-07-01    NaN     NaN
7     126  Budget 2019-08-01    NaN     NaN
8      90  Budget 2019-09-01    NaN     NaN
9      80  Budget 2019-10-01    NaN     NaN
10     67  Budget 2019-11-01    NaN     NaN
11     87  Budget 2019-12-01    NaN     NaN
12    101  Actual 2019-01-01  101.0   974.0
13     98  Actual 2019-02-01  199.0  1022.0
14     76  Actual 2019-03-01  275.0  1078.0
15     57  Actual 2019-04-01  332.0  1012.0

I know that "transform" is like "apply". But I don't get what it means to apply - or transform -with this parameter first . What does first do here combined with transform ?

Thank you

Answer 1

1. What does it mean 'first'?

   The parameter in the .transform() method may be a NumPy function, a string function name or a user defined function. It means that in the line

   .transform('first')

   it's a string function name . So it represents the function first() .

---

2. Where is the function first() come?

   It's a GroupBy method .first() .

---

3. What does the function first() return?

   It returns the first non- NaN value in a series, or NaN if there is none.

---

4. What does the method .transform() do?

   It applies its parameter-function to every column (ie the series) of dataframe to obtain a new (transformed) column. Then it returns a dataframe consisting of such (transformed) columns.

   In the case of series it returns — of course — a transformed series .

---

5. It means that function-parameter of .transform method must return a series with the same size?

   No, it is only one possibility.
   The other is a scalar — it will be broadcasted (repeated) to make a series with the same size.

   The used function (the GroupBy method first() ) is a good example of such a function.

---

6. So what does the method .transform('first') return?

   It returns a series / dataframe with the same shape as the source group chunk, in which all values in every individual column are replaced with the first non- NaN value in this column, or with NaN if there is none.

---

Conclusion:

The lines

                 .groupby(df['Date'])
                 .transform('first') 

first split your (intermediate) series into groups of individual dates and then — just before recombination — apply the first() function to every series in every group.

It effectively replaces every value in every group with the first non- NaN value in its series if such a value exists.

This means that in the resulting series (your new column) will be all values of (intermediate) series replaced with the first non- NaN value in the same day (if such a value in the same day exists).

Answer 2

After grouping by dates ( groupby(df['Date'].dt.year) ), each value is changed to the value of the row where this date first appears. This changes the last value of the 'Actual' rows to the original values from 'Budget' rows.