How to skip over np.nan while iterating through a dataframe for sentiment analysis - python-3.x

Question

I have a data frame with 201279 entries, the last column is labeled "text" with customer reviews. The problem is that most of them are missing values, and come up as NaN.
I read some interesting information from this question:
Python numpy.nan and logical functions: wrong results
and I tried applying it to my problem:
df1.columns
Index(['id', 'sku', 'title', 'reviewCount', 'commentCount', 'averageRating',
'date', 'time', 'ProductName', 'CountOfBigTransactions', 'ClassID',
'Weight', 'Width', 'Depth', 'Height', 'LifeCycleName', 'FinishName',
'Color', 'Season', 'SizeOrUtility', 'Material', 'CountryOfOrigin',
'Quartile', 'display-name', 'online-flag', 'long-description', 'text'],
dtype='object')
I tried experimentingby doing this:
df['firstName'][202360]== np.nan
which returns False but indeed that index contains an np.nan.
So I looked for an answer, read through the question I linked, and saw that
np.bool(df1['text'][201279])==True
is a true statement. I thought, okay, I can run with this.
So, here's my code so far:
from textblob import TextBlob
import string
def remove_num_punct(aText):
p = string.punctuation
d = string.digits
j = p + d
table = str.maketrans(j, len(j)* ' ')
return aText.translate(table)
#Process text
aList = []
for text in df1['text']:
if np.bool(df1['text'])==True:
aList.append(np.nan)
else:
b = remove_num_punct(text)
pol = TextBlob(b).sentiment.polarity
aList.append(pol)
Then I would just convert aList with the sentiment to pd.DataFrame and join it to df1, then impute the missing values with K-nearest neighbors.
My problem is that the little routine I made throws a value error
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
So I'm not really sure what else to try. Thanks in advance!
EDIT: I have tried this:
i = 0
aList = []
for txt in df1['text'].isnull():
i += 1
if txt == True:
aList.append(np.nan)
which correctly populates the list with NaN.
But this gives me a different error:
i = 0
aList = []
for txt in df1['text'].isnull():
if txt == True:
aList.append(np.nan)
else:
b = remove_num_punct(df1['text'][i])
pol = TextBlob(b).sentiment.polarity
aList.append(pol)
i+=1
AttributeError: 'float' object has no attribute 'translate'
Which doesn't make sense, since if it is not NaN, then it contains text, right?

Answer 1

import pandas as pd
import numpy as np
df = pd.DataFrame({'age': [5, 6, np.NaN],
'born': [pd.NaT, pd.Timestamp('1939-05-27'), pd.Timestamp('1940-04-25')],
'name': ['Alfred', 'Batman', ''],
'toy': [None, 'Batmobile', 'Joker']})
df1 = df['toy']
for i in range(len(df1)):
if not df1[i]:
df2 = df1.drop(i)
df2
you can try in this way to deal the text which is null

Answer 2

I fixed it, I had to move the i += 1 back from the else indentation to the for indentation:
i = 0
aList = []
for txt in df1['text'].isnull():
if txt == True:
aList.append(np.nan)
else:
b = remove_num_punct(df1['text'][i])
pol = TextBlob(b).sentiment.polarity
aList.append(pol)
i+=1