Handling Missing Values in Pandas Dataframe | GeeksforGeeks

In this video, we're going to discuss how to handle missing values in Pandas. In Pandas DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed. And as we can't provide null values to our Machine Learning model, we need to handle them properly. Now, let's get started.

00:00 Let's Start
01:44 Checking for Missing Values using isnull()
03:31 Filling Null Values Using fillna()
05:35 Filling Null Values Using fillna(method = 'pad')
07:05 Filling Null Values Using fillna(method = 'bfill')
11:41 Filling Null Values with the Mean, Max or Min of a Column
13:38 Dropping Null Values Using dropna()
16:12 Filling Null Values Using replace()
18:47 Filling Null Values Using interpolate()
21:50 Closing Notes

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This video is contributed by Akshit Madan.

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[mariamartin]

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[CuriousLearner_]

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[hmuhamadzulfitri]

Addition:
1- In case, you want to **impute** the null values of numerical column, you can check the distribution of the values first; If it's not skewed, you may use mean. In case it is skewed, you may use median.
2- Generally, before we **delete** null values, we can check the proportion of the null values. If it's less than 5 % of the total dataset, we can opt to delete the null values.

However, always take the context of our data before making any decision with the null values. 😬

[Ayanshedipelly]

We have to take care of data types also before filling with any value