deprecated

[maly]

Good day,
I am trying to solve the next lab https://github.com/hse-aml/competitive-d...ek_4.ipynb

This is the code, but unfortunately, the following function is deprecated:
gb = sales.groupby(['shop_id', 'date_block_num'],as_index=False).agg({'item_cnt_day':{'target_shop':'sum'}})
I don't know what to do to solve, I have tried different configurations but when I solve these lines issues, others appear because of it. Can you please help me?

import numpy as np
import pandas as pd 
import sklearn
import scipy.sparse 
import lightgbm 

for p in [np, pd, scipy, sklearn, lightgbm
    print (p.__name__, p.__version__)]

mport pandas as pd
import numpy as np
import gc
import matplotlib.pyplot as plt
%matplotlib inline 

pd.set_option('display.max_rows', 600)
pd.set_option('display.max_columns', 50)

import lightgbm as lgb
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from tqdm import tqdm_notebook

from itertools import product


def downcast_dtypes(df):
    '''
        Changes column types in the dataframe: 
                
                `float64` type to `float32`
                `int64`   type to `int32`
    '''
    
    # Select columns to downcast
    float_cols = [c for c in df if df[c].dtype == "float64"]
    int_cols =   [c for c in df if df[c].dtype == "int64"]
    
    # Downcast
    df[float_cols] = df[float_cols].astype(np.float32)
    df[int_cols]   = df[int_cols].astype(np.int32)
    
    return df

sales = pd.read_csv('../readonly/final_project_data/sales_train.csv.gz')
shops = pd.read_csv('../readonly/final_project_data/shops.csv')
items = pd.read_csv('../readonly/final_project_data/items.csv')
item_cats = pd.read_csv('../readonly/final_project_data/item_categories.csv')

sales = sales[sales['shop_id'].isin([26, 27, 28])]

# Create "grid" with columns
index_cols = ['shop_id', 'item_id', 'date_block_num']

# For every month we create a grid from all shops/items combinations from that month
grid = [] 
for block_num in sales['date_block_num'].unique():
    cur_shops = sales.loc[sales['date_block_num'] == block_num, 'shop_id'].unique()
    cur_items = sales.loc[sales['date_block_num'] == block_num, 'item_id'].unique()
    grid.append(np.array(list(product(*[cur_shops, cur_items, [block_num]])),dtype='int32'))

# Turn the grid into a dataframe
grid = pd.DataFrame(np.vstack(grid), columns = index_cols,dtype=np.int32)

# Groupby data to get shop-item-month aggregates
gb = sales.groupby(index_cols,as_index=False).agg({'item_cnt_day':{'target':'sum'}})
# Fix column names
gb.columns = [col[0] if col[-1]=='' else col[-1] for col in gb.columns.values] 
# Join it to the grid
all_data = pd.merge(grid, gb, how='left', on=index_cols).fillna(0)

# Same as above but with shop-month aggregates
gb = sales.groupby(['shop_id', 'date_block_num'],as_index=False).agg({'item_cnt_day':{'target_shop':'sum'}})
gb.columns = [col[0] if col[-1]=='' else col[-1] for col in gb.columns.values]
all_data = pd.merge(all_data, gb, how='left', on=['shop_id', 'date_block_num']).fillna(0)

# Same as above but with item-month aggregates
gb = sales.groupby(['item_id', 'date_block_num'],as_index=False).agg({'item_cnt_day':{'target_item':'sum'}})
gb.columns = [col[0] if col[-1] == '' else col[-1] for col in gb.columns.values]
all_data = pd.merge(all_data, gb, how='left', on=['item_id', 'date_block_num']).fillna(0)

# Downcast dtypes from 64 to 32 bit to save memory
all_data = downcast_dtypes(all_data)
del grid, gb 
gc.collect();

The mistake is:
---------------------------------------------------------------------------
SpecificationError Traceback (most recent call last)
<ipython-input-6-1ebfd0479d3e> in <module>
13
14 # Groupby data to get shop-item-month aggregates
---> 15 gb = sales.groupby(index_cols,as_index=False).agg({'item_cnt_day':{'target':'sum'}})
16 # Fix column names
17 gb.columns = [col[0] if col[-1]=='' else col[-1] for col in gb.columns.values]

~\anaconda3\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
926 func = _maybe_mangle_lambdas(func)
927
--> 928 result, how = self._aggregate(func, *args, **kwargs)
929 if how is None:
930 return result

~\anaconda3\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
340 # {'ra' : { 'A' : 'mean' }}
341 if isinstance(v, dict):
--> 342 raise SpecificationError("nested renamer is not supported")
343 elif isinstance(obj, ABCSeries):
344 raise SpecificationError("nested renamer is not supported")

SpecificationError: nested renamer is not supported