Computer scienceData scienceInstrumentsPandasData preprocessing with pandas

Handling missing values

Tidy it up!

Report a typo

There is some new information about the flats discussed in the topic.

    baby  silence_index
0      0            NaN
1      1            0.7
2      0            0.0
3      0            NaN
4      0            NaN
5      1            0.2
6      0            0.2
7      0            0.2
8      0            0.3
9      1            0.5
10     0            0.4
11     0            NaN
12     0            0.6
13     0            0.1
14     1            0.3
15     0            NaN

baby is 1, when there is a baby on the same floor, otherwise 0
silence_index is a number from 0 to 1, which indicates the silence level in the flat

silence_index has some NaNs and you are going to replace them with the group average. There are two groups: flats with baby being 0 and the ones with baby being 1.

Arrange the methods below in the correct order so that the final command replaces NaNs with the group average in the silence_index column. The dataset is stored in data.

Put the items in the correct order

data.groupby("baby")

x.fillna(x.mean())).round(1)

.apply(lambda x:

data["silence_index"] =

["silence_index"]

___

Create a free account to access the full topic