pandas GroupBy and cumulative mean of previous rows in group

Question

I have a dataframe which looks like this:

pd.DataFrame({\'category\': [1,1,1,2,2,2,3,3,3,4],
              \'order_sta         


        

Answer 1

"create a new column which contains the mean of the previous times of the same category" sounds like a good use case for GroupBy.expanding (and a shift):

df['mean'] = (
    df.groupby('category')['time'].apply(lambda x: x.shift().expanding().mean()))
df
   category  order_start  time  mean
0         1            1     1   NaN
1         1            2     4   1.0
2         1            3     3   2.5
3         2            1     6   NaN
4         2            2     8   6.0
5         2            3    17   7.0
6         3            1    14   NaN
7         3            2    12  14.0
8         3            3    13  13.0
9         4            1    16   NaN

---

Another way to calculate this is without the apply (chaining two groupby calls):

df['mean'] = (
    df.groupby('category')['time']
      .shift()
      .groupby(df['category'])
      .expanding()
      .mean()
      .to_numpy())  # replace to_numpy() with `.values` for pd.__version__ < 0.24
df
   category  order_start  time  mean
0         1            1     1   NaN
1         1            2     4   1.0
2         1            3     3   2.5
3         2            1     6   NaN
4         2            2     8   6.0
5         2            3    17   7.0
6         3            1    14   NaN
7         3            2    12  14.0
8         3            3    13  13.0
9         4            1    16   NaN

In terms of performance, it really depends on the number and size of your groups.

Answer 2

Inspired by my answer here, one can define a function first:

def mean_previous(df, Category, Order, Var):
    # Order the dataframe first 
    df.sort_values([Category, Order], inplace=True)

    # Calculate the ordinary grouped cumulative sum 
    # and then substract with the grouped cumulative sum of the last order
    csp = df.groupby(Category)[Var].cumsum() - df.groupby([Category, Order])[Var].cumsum()

    # Calculate the ordinary grouped cumulative count 
    # and then substract with the grouped cumulative count of the last order
    ccp = df.groupby(Category)[Var].cumcount() - df.groupby([Category, Order]).cumcount()

    return csp / ccp

And the desired column is

df['mean'] = mean_previous(df, 'category', 'order_start', 'time')

Performance-wise, I believe it's very fast.