13: Associative Arrays in Python (3 of 3)

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But wait, there’s more! We can also use methods like .min(), .max(), .idxmin(), and .idxmax() to get the “extremes” of a Series – i.e. the lowest and highest values in a Series, or their keys (indexes). Note that .idxmin() does not give you the lowest key in the Series! Instead, it gives you the key of the lowest value. Study this code snippet and its output to test your understanding of this:

Code \(\PageIndex{1}\) (Python):

understanding = pd.Series([15,4,13,3,7], index=[4,10,2,12,9])

print(understanding)

print("The min is {}.".format(understanding.min()))

print("The max is {}.".format(understanding.max()))

print("The idxmin is {}.".format(understanding.idxmin()))

print("The idxmax is {}.".format(understanding.idxmax()))

| 4 15

| 10 4

| 2 13

| 12 3

| 9 7

| dtype: int64

| The min is 3.

| The max is 15.

| The idxmin is 12.

| The idxmax is 4.

The idxmin and idxmax are 12 and 4, respectively, since the smallest value in the series (the 3) has a key of 12, and the largest value (the 15) has a key of 4.

If we did actually want the lowest (or highest) key, we could use the .index syntax to achieve that:

Code \(\PageIndex{2}\) (Python):

print("The lowest key: {}.".format(understanding.index.min()))

print("The highest key: {}.".format(understanding.index.max()))

| The lowest key: 2.

| The highest key: 12.

And remember that “lowest”/“highest” for string data means alphabetical order.

13.1: Queries