Do you get the same error if you change

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']

to

temp['total'] = project_data.groupby(col1)[col2].agg(total=('total','count')).reset_index()['total']

change

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']

temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']

to

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg(total='count')).reset_index()['total']
temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg(Avg='mean')).reset_index()['Avg']

reason: in new pandas version named aggregation is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output of column-specific aggregations (Deprecate groupby.agg() with a dictionary when renaming).

source: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html

This error also happens if a column specified in the aggregation function dict does not exist in the dataframe:

In [190]: group = pd.DataFrame([[1, 2]], columns=['A', 'B']).groupby('A')
In [195]: group.agg({'B': 'mean'})
Out[195]: 
   B
A   
1  2

In [196]: group.agg({'B': 'mean', 'non-existing-column': 'mean'})
...
SpecificationError: nested renamer is not supported

I have got the similar issue as @akshay jindal, but I check the documentation as suggested by @artikay Khanna, the problem solved, some functions has been adjusted, the old is deprecated. Here is the code warning provided per last time execute.

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: using a dict on a Series for aggregation
is deprecated and will be removed in a future version. Use                 named aggregation instead.

    >>> grouper.agg(name_1=func_1, name_2=func_2)

  """Entry point for launching an IPython kernel.

Therefore, I will suggest try

grouper.agg(name_1=func_1, name_2=func_2)

Hope this will help

I have tried alll the solutions and turned out to be the error with the name. If your column name has some inbuilt keywords such as "in", "is",etc., It is throwing error. In my case, My column name is "Points in Polygon" and I have resolved the issue by renaming the column to "Points"

@Rishi's solution worked for me. The original name of the column in my dataframe was net_value_budgeted_rate, which was essentially dollar value of the sale. I changed it to dollars and it worked.

Instead of using .agg({'total':'count'})), you can pass name with the function as a list of tuple like .agg([('total', 'count')])and use the same for Avg also. Hope it would work.

Not a very elegant solution but this one works. As renaming the column is deprecated with the way you are doing. But there is work around. Create a temporary variable 'approved' , store the col2 in it. Because when you apply agg function , the original column values will change with column name. You can preserve the column name but then values in those column will change. So in order to preserve the original dataframe and to have two new columns with desired names, you can use the following code.

approved = temp[col2]
temp = pd.DataFrame(project_data.groupby(col1)[col2].agg([('Avg','mean'),('total','count')]).reset_index())
temp[col2] = approved

P.S : Seems like an assignment of AAIC, I am working on same :)

Sometimes it's convenient to keep an aggdict of how each column should be transformed under aggregation that will work with different column sets and different group by columns. You can do this with the new syntax fairly easily by unpacking the dict with **. Here's a minimal working example for simple data.

dfx=pd.DataFrame(columns=["A","B","C"],data=np.random.randint(0,5,size=(10,3)))
#dfx
#
#   A  B  C
#0  4  4  1
#1  2  4  4
#2  1  3  3
#3  2  4  3
#4  1  2  1
#5  0  4  2
#6  2  3  4
#7  1  0  2
#8  2  1  4
#9  3  0  3

Maybe when you agg you want the first "A", the last "B", the mean "C" and sometimes your pipeline has a "D" (but not this time) that you also want the mean of.

aggdict = {"A":lambda x: x.iloc[0], "B": lambda x: x.iloc[-1], "C" : "mean" , "D":lambda x: "mean"}

You can build a simple dict like the old days and then unpack it with ** filtering on the relevant keys:

gb_col="C"
gbc = dfx.groupby(gb_col).agg(**{k:(k,v) for k,v in aggdict.items() if k in dfx.columns and k != gb_col})
#       A  B
#C      
#1  4  2
#2  0  0
#3  1  4
#4  2  3

And then you can slice and dice how you want with the same syntax:

mygb = lambda gb_col: dfx.groupby(gb_col).agg(**{k:(k,v) for k,v in aggdict.items() if k in dfx.columns and k != gb_col})
allgb = [mygb(c) for c in dfx.columns]

I found the way: Instead of going like

g2 = df.groupby(["Description","CustomerID"],as_index=False).agg({'Quantity':{"maxQ":np.max,"minQ":np.min,"meanQ":np.mean}})
g2.columns = ["Description","CustomerID","maxQ","minQ",'meanQ']

Do as follows:

g2 = df.groupby(["Description","CustomerID"],as_index=False).agg({'Quantity':{np.max,np.min,np.mean}})
g2.columns = ["Description","CustomerID","maxQ","minQ",'meanQ']

I had the same error and this is how I resolved it!

Info = pd.DataFrame(df.groupby("school_state").agg(Approved=("project_is_approved",lambda x: x.eq(1).sum()),Total=("project_is_approved","count"),Avg=("project_is_approved","mean"))).reset_index().sort_values(by=["Total"],ascending=False).head()

You can break this into individual commands for better readability.