Pandas - How to identify `nan` values in a Series-CodePudding

I am currently playing with Kaggle Titanic dataset (train.csv)

I can load the data fine.
I understood that some data in Embarked column has nan value. But when I tried to filter it using the following code, I am getting an empty array

    import pandas as pd
    df = df.read_csv(<file_loc>, header=0)
    df[df.Embarked == 'nan']

I tried to import numpy.nan to replace the string nan above. But it doesn't work.

What am I trying to find - is all the cells which are not 'S', 'C', 'Q'.

Also realised later that.... the nan is a Float type using type(df.Embarked.unique()[-1]). Could someone help me understand how to identify those nan cells?

CodePudding user response：

NaN is used to represent missing values.

To find them, use .isna()

Detect missing values.
To replace them, use .fillna(value)

Fill NA/NaN values

Some examples on a series called col:

>>> col
0    1.0
1    NaN
2    2.0
dtype: float64
>>> col[col.isna()]
1   NaN
dtype: float64
>>> col.index[col.isna()]
Int64Index([1], dtype='int64')
>>> col.fillna(-1)
0    1.0
1   -1.0
2    2.0
dtype: float64