DataError: No numeric types to aggregate pandas pivot

I have a pandas dataframe like this:

    User-Id Training-Id TrainingTaken
0   4327024 25  10
1   6662572 3   10
2   3757520 26  10

and I need to convert it to a Matrix like they do here: https://github.com/tr1ten/Anime-Recommender-System/blob/main/HybridRecommenderSystem.ipynb Cell 13.

So I did the following:

from lightfm import LightFM
from lightfm.evaluation import precision_at_k
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas_profiling
from scipy.sparse import csr_matrix
from lightfm.evaluation import auc_score
from lightfm.data import Dataset

user_training_interaction = pd.pivot_table(trainingtaken, index='User-Id', columns='Training-Id', values='TrainingTaken')
user_training_interaction.fillna(0,inplace=True)

user_training_csr = csr_matrix(user_training_interaction.values)

But I get this error:

---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
<ipython-input-96-5a2c7ba28976> in <module>
     10 from lightfm.data import Dataset
     11 
---> 12 user_training_interaction = pd.pivot_table(trainingtaken, index='User-Id', columns='Training-Id', values='TrainingTaken')
     13 user_training_interaction.fillna(0,inplace=True)
     14 user_training_csr = csr_matrix(user_training_interaction.values)

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/reshape/pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
    110 
    111     grouped = data.groupby(keys, observed=observed)
--> 112     agged = grouped.agg(aggfunc)
    113     if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
    114         agged = agged.dropna(how="all")

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
    949         func = maybe_mangle_lambdas(func)
    950 
--> 951         result, how = self._aggregate(func, *args, **kwargs)
    952         if how is None:
    953             return result

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
    305 
    306         if isinstance(arg, str):
--> 307             return self._try_aggregate_string_function(arg, *args, **kwargs), None
    308 
    309         if isinstance(arg, dict):

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/base.py in _try_aggregate_string_function(self, arg, *args, **kwargs)
    261         if f is not None:
    262             if callable(f):
--> 263                 return f(*args, **kwargs)
    264 
    265             # people may try to aggregate on a non-callable attribute

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in mean(self, numeric_only)
   1396             "mean",
   1397             alt=lambda x, axis: Series(x).mean(numeric_only=numeric_only),
-> 1398             numeric_only=numeric_only,
   1399         )
   1400 

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/generic.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
   1020     ) -> DataFrame:
   1021         agg_blocks, agg_items = self._cython_agg_blocks(
-> 1022             how, alt=alt, numeric_only=numeric_only, min_count=min_count
   1023         )
   1024         return self._wrap_agged_blocks(agg_blocks, items=agg_items)

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/generic.py in _cython_agg_blocks(self, how, alt, numeric_only, min_count)
   1128 
   1129         if not (agg_blocks or split_frames):
-> 1130             raise DataError("No numeric types to aggregate")
   1131 
   1132         if split_items:

DataError: No numeric types to aggregate

What am I missing?

Answer

The Pandas Documentation states:

While pivot() provides general purpose pivoting with various data types (strings, numerics, etc.), pandas also provides pivot_table() for pivoting with aggregation of numeric data

Make sure the column is numeric. Without seeing how you create trainingtaken I can’t provide more specific guidance. However the following may help:

1. Make sure you handle “empty” values in that column. The Pandas guide is a very good place to start. Pandas points out that “a column of integers with even one missing values is cast to floating-point dtype”.
2. If working with a dataframe, the column can be cast to a specific type via your_df.your_col.astype(int) or for your example, pd.trainingtaken.astype(int)