Item assignment with .loc and .iloc

The pandas library is optimized for reading, exploring, and evaluating data. Operations that try to mutate or change data are far less efficient.

However, when you must mutate your data, you can use .loc and .iloc to do it.

How to do it

Let’s start with a very small pd.Series:

ser = pd.Series(range(3), index=list("abc"))

pd.Series.loc is useful when you want to assign a value by matching against the label of an index. For example, if we wanted to store the value 42 where our row index contained a value of "b", we would write:

ser.loc["b"] = 42
ser

a     0
b    42
c     2
dtype: int64

pd.Series.iloc is used when you want to assign a value positionally. To assign the value -42 to the second element in our pd.Series, we would write:

ser.iloc[2] = -42
ser

a     0
b    42
c   -42
dtype: int64

There’s more…

The cost of mutating data through pandas can...