Complete NAN value based on a Column


As a Dataquest member working in a personal project for improve my data skills knowledge,

I have a dataset with the following information:

<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 78702 entries, 0 to 78701
Data columns (total 15 columns):

Column Non-Null Count Dtype

0 type 3818 non-null object
1 title 78702 non-null object
2 date_added 3193 non-null object
3 release_year 3193 non-null float64
4 duration 3191 non-null object
5 listed_in 3668 non-null object
6 year_added 3193 non-null float64
7 Profile Name 78702 non-null object
8 Start Time 78702 non-null object
9 Duration 78702 non-null object
10 Device Type 78698 non-null object
11 Country 78702 non-null object
12 Period 78702 non-null object
13 title.1 78702 non-null object
14 _merge 78702 non-null object
dtypes: float64(2), object(13)
memory usage: 9.0+ MB

Missing value
type 74884
title 0
date_added 75509
release_year 75509
duration 75511
listed_in 75034
year_added 75509
Profile Name 0
Start Time 0
Duration 0
Device Type 4
Country 0
Period 0
title.1 0
_merge 0

I want to fill the missing value from type based on the title.
Tilte: Cars2
Type: Movie

but I have cases where a Title have more than 35 concurrent values, but the Type part is NAN.

Any help please.

Hi @infinetw:

Apologies for the late reply, but hopefully this examples would help.

In this case, if there is only one movie title that is a NaN value, you might wanna try using the above method to resolve the issue.

Let me know if you need more guidance.