Question: I want to store a numpy array in pandas cell. This does not work: import numpy as np import pandas as pd bnd1 = np.random.rand(74,8) bnd2 = np.random.rand(74,8) df = pd.DataFrame(columns = ["val", "unit"]) df.loc["bnd"] = [bnd1, "N/A"] df.loc["bnd"] = [bnd2, "N/A"]  But this does: import numpy as np import pandas as pd bnd1 = np.random.rand(74,8) bnd2 = np.random.rand(74,8) df = pd.DataFrame(columns = ["val"]) df.loc["bnd"] = [bnd1] df.loc["bnd"] = [bnd2]  Can someone explain why, and what's the solution? Edit: The first returns:  ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.  The complete traceback is below: > --------------------------------------------------------------------------- AttributeError Traceback (most recent call > last) File > ~/anaconda3/envs/py38mats/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3185, > in ndim(a) 3184 try: > -> 3185 return a.ndim 3186 except AttributeError: > > AttributeError: 'list' object has no attribute 'ndim' > > During handling of the above exception, another exception occurred: > > ValueError Traceback (most recent call > last) Cell In[10], line 8 > 6 df = pd.DataFrame(columns = ["val", "unit"]) > 7 df.loc["bnd"] = [bnd1, "N/A"] > ----> 8 df.loc["bnd"] = [bnd2, "N/A"] > > File > ~/anaconda3/envs/py38mats/lib/python3.8/site-packages/pandas/core/indexing.py:849, > in _LocationIndexer.__setitem__(self, key, value) > 846 self._has_valid_setitem_indexer(key) > 848 iloc = self if self.name == "iloc" else self.obj.iloc > --> 849 iloc._setitem_with_indexer(indexer, value, self.name) > > File > ~/anaconda3/envs/py38mats/lib/python3.8/site-packages/pandas/core/indexing.py:1835, > in _iLocIndexer._setitem_with_indexer(self, indexer, value, name) > 1832 # align and set the values 1833 if take_split_path: 1834 > # We have to operate column-wise > -> 1835 self._setitem_with_indexer_split_path(indexer, value, name) 1836 else: 1837 self._setitem_single_block(indexer, > value, name) > > File > ~/anaconda3/envs/py38mats/lib/python3.8/site-packages/pandas/core/indexing.py:1872, > in _iLocIndexer._setitem_with_indexer_split_path(self, indexer, value, > name) 1869 if isinstance(value, ABCDataFrame): 1870 > self._setitem_with_indexer_frame_value(indexer, value, name) > -> 1872 elif np.ndim(value) == 2: 1873 # TODO: avoid np.ndim call in case it isn't an ndarray, since 1874 # that will > construct an ndarray, which will be wasteful 1875 > self._setitem_with_indexer_2d_value(indexer, value) 1877 elif > len(ilocs) == 1 and lplane_indexer == len(value) and not > is_scalar(pi): 1878 # We are setting multiple rows in a single > column. > > File <__array_function__ internals>:200, in ndim(*args, **kwargs) > > File > ~/anaconda3/envs/py38mats/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3187, > in ndim(a) 3185 return a.ndim 3186 except AttributeError: > -> 3187 return asarray(a).ndim > > ValueError: setting an array element with a sequence. The requested > array has an inhomogeneous shape after 1 dimensions. The detected > shape was (2,) + inhomogeneous part.  I'm using pandas 2.0.3 and numpy 1.24.4

Answer: The issue is that when you try to insert a numpy array into a pandas DataFrame, pandas can't process the data correctly. To fix this, you can use either a pd.Series or a dictionary for better alignment: first way: Using pd.Series: df.loc["bnd"] = pd.Series([bnd2, "N/A"], index=["val", "unit"])  OR second way: Using dictionary: df.loc["bnd"] = {"val": bnd2, "unit": "N/A"}  good luck mate