Numpy Stack :: all input arrays must have the same shape - arrays

Question

I was Trying to to Calculate initial embedding of all data frame which is first step to implement my GNN which is Heterogeneous in nature.I have used twitter msg data set in the task I loaded it in following way
load_path = '/Users/hemangjiwnani/Desktop/Projects/Paper1/KPGNN/datasets/Twitter/'
save_path = '/Users/hemangjiwnani/Desktop/Projects/Paper1/KPGNN/datasets/Twitter/'
# load dataset`enter code here`
p_part1 = load_path + '68841_tweets_multiclasses_filtered_0722_part1.npy'
p_part2 = load_path + '68841_tweets_multiclasses_filtered_0722_part2.npy'
#"./datasets/Twitter/68841_tweets_multiclasses_filtered_0722_part1.npy"
df_np_part1 = np.load(p_part1, allow_pickle=True)
df_np_part2 = np.load(p_part2, allow_pickle=True)
Then I have created a data frame of the same with the help of following code
df_np = np.concatenate((df_np_part1, df_np_part2), axis = 0) #Axis = 0 means horizontal
print("Loaded data.")
df = pd.DataFrame(data=df_np, columns=["event_id", "tweet_id", "text", "user_id", "created_at", "user_loc",\
"place_type", "place_full_name", "place_country_code", "hashtags", "user_mentions", "image_urls", "entities",
"words", "filtered_words", "sampled_words"])
print("Data converted to dataframe.")
print(df.shape)
print(df.head(5))
Which was having following output
Loaded data.
Data converted to dataframe.
(68841, 16)
event_id ... sampled_words
0 0 ... []
1 0 ... []
2 0 ... []
3 0 ... []
4 0 ... []
[5 rows x 16 columns]
This Function Below is raising a error while returning
def documents_to_features(df):
nlp = spacy.load("en_core_web_sm"
#nlp = en_core_web_lg.load()
features = df.filtered_words.apply(lambda x: nlp(' '.join(x)).vector).values
-->return np.stack(features, axis=0)
ERROR
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-2e8bc5e83009> in <module>()
----> 1 d_features = documents_to_features(df)
2 print("Document features generated.")
3 t_features = df_to_t_features(df)
4 print("Time features generated.")
5 combined_features = np.concatenate((d_features, t_features), axis=1)
1 frames
<ipython-input-9-b772a7744232> in documents_to_features(df)
3 #nlp = en_core_web_lg.load()
4 features = df.filtered_words.apply(lambda x: nlp(' '.join(x)).vector).values
----> 5 return np.stack(features, axis=0)
<__array_function__ internals> in stack(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/numpy/core/shape_base.py in stack(arrays, axis, out)
425 shapes = {arr.shape for arr in arrays}
426 if len(shapes) != 1:
--> 427 raise ValueError('all input arrays must have the same shape')
428
429 result_ndim = arrays[0].ndim + 1
ValueError: all input arrays must have the same shape