What is the difference between pandas agg and apply function?

(Note: These comparisons are relevant for DataframeGroupby objects)

Some plausible advantages of using .agg() compared to .apply(), for DataFrame GroupBy objects would be:

1. .agg() gives the flexibility of applying multiple functions at once, or pass a list of function to each column.

2. Also, applying different functions at once to different columns of dataframe.

That means you have pretty much control over each column with each operation.

Here is the link for more details: http://pandas.pydata.org/pandas-docs/version/0.13.1/groupby.html

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However, the apply function could be limited to apply one function to each column of the dataframe at a time. So, you might have to call the apply function repeatedly to call upon different operations to the same column.

Here are some example comparisons for .apply() vs .agg() for DataframeGroupBy objects :

Given the following dataframe:

In [261]: df = pd.DataFrame({"name":["Foo", "Baar", "Foo", "Baar"], "score_1":[5,10,15,10], "score_2" :[10,15,10,25], "score_3" : [10,20,30,40]})

In [262]: df
Out[262]: 
   name  score_1  score_2  score_3
0   Foo        5       10       10
1  Baar       10       15       20
2   Foo       15       10       30
3  Baar       10       25       40

Lets first see the operations using .apply():

In [263]: df.groupby(["name", "score_1"])["score_2"].apply(lambda x : x.sum())
Out[263]: 
name  score_1
Baar  10         40
Foo   5          10
      15         10
Name: score_2, dtype: int64

In [264]: df.groupby(["name", "score_1"])["score_2"].apply(lambda x : x.min())
Out[264]: 
name  score_1
Baar  10         15
Foo   5          10
      15         10
Name: score_2, dtype: int64

In [265]: df.groupby(["name", "score_1"])["score_2"].apply(lambda x : x.mean())
Out[265]: 
name  score_1
Baar  10         20.0
Foo   5          10.0
      15         10.0
Name: score_2, dtype: float64

Now, look at the same operations using .agg( ) effortlessly:

In [276]: df.groupby(["name", "score_1"]).agg({"score_3" :[np.sum, np.min, np.mean, np.max], "score_2":lambda x : x.mean()})
Out[276]: 
              score_2 score_3               
             <lambda>     sum amin mean amax
name score_1                                
Baar 10            20      60   20   30   40
Foo  5             10      10   10   10   10
     15            10      30   30   30   30

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So, .agg() could be really handy at handling the DataFrameGroupBy objects, as compared to .apply(). But, if you are handling only pure dataframe objects and not DataFrameGroupBy objects, then apply() can be very useful, as apply() can apply a function along any axis of the dataframe.

(For Eg: axis = 0 implies column-wise operation with .apply(), which is a default mode, and axis = 1 would imply for row-wise operation while dealing with pure dataframe objects).

When using apply to a groupby I have encountered that .apply will return the grouped columns. There is a note in the documentation (pandas.pydata.org/pandas-docs/stable/groupby.html):

“…Thus the grouped columns(s) may be included in the output as well as set the indices.”

.aggregate will not return the grouped columns.

The main difference between apply and aggregate is:

apply()- 
    cannot be applied to multiple groups together 
    For apply() - We have to get_group()
    ERROR : -iris.groupby('Species').apply({'Sepal.Length':['min','max'],'Sepal.Width':['mean','min']})# It will throw error
    Work Fine:-iris.groupby('Species').get_group('Setosa').apply({'Sepal.Length':['min','max'],'Sepal.Width':['mean','min']})# It will throw error
        #because functions are applied to one data frame

agg()- 
    can be applied to multiple groups together
    For apply() - We do not have to get_group() 
    iris.groupby('Species').agg({'Sepal.Length':['min','max'],'Sepal.Width':['mean','min']})
    iris.groupby('Species').get_group('versicolor').agg({'Sepal.Length':['min','max'],'Sepal.Width':['mean','min']})        

Refer here . Let me requote the same statement here

Some operations on the grouped data might not fit into either the aggregate or transform categories. Or, you may simply want GroupBy to infer how to combine the results. For these, use the apply function, which can be substituted for both aggregate and transform in many standard use cases. However, apply can handle some exceptional use cases, for example:

More details with example are presented in the pandas documentation (link provided above)

Kindly refer this great write up from @ted Petrou and @Eric O Lebigot. We can reapply the logic they have used to investigate difference between Apply and transform to Apply and Agg

Then to understand how axis works refer this link

These three link should help is getting better clarity on how they are different.

Besides everything other mentioned, another difference I think no one highlighted yet is that apply can be used to apply a function to a group of columns together. Agg only applies a function to one column separately. An example is:

Let’s use the same example as of other example:

d = pd.DataFrame({"name":["Foo", "Baar", "Foo", "Baar"], "score_1":[5,10,15,10], "score_2" :[10,15,10,25], "score_3" : [10,20,30,40]})

Here, the apply is using a function that sums up the values of all the columns of a group together.

d.groupby(["name", "score_1"]).apply(lambda x: x.values.sum())