pandasObject.index() Vs reindexing using series

Question

The functionality of reindexing in python pandas can also be done python Series as below.

import pandas as pd
order = ['a','c','b']
series_data = pd.Series([1,2,3],index=order)
series_data

In that case why do we explicitly go for reindex?

Answer 1

Let's take an example using index available in Series

s = pd.Series([1,2,3], index=['k','f','t'])
s
# k    1
# f    2
# t    3
# dtype: int64

We can state that above series got assigned index with a datatype of int64.

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Now let's proceed with reindex:

order = ['k','c','b']
s.reindex(order)
# k    1.0
# c    NaN
# b    NaN
# dtype: float64

As you can observe we passed two new index c and b which were not there in original series, so those values are assigned equal to NaN. Since NaN has dtype of float64, hence a final series results into only three indexes k, c and b with dtype as float64.

I hope this clears how index inside Series is different from reindex outside.

Answer 2

You can refer below link to understand about reindexing. https://www.tutorialspoint.com/python_pandas/python_pandas_reindexing.htm