The drop function in pandas is not recognizing my index

Question

So, I am posting a copy of my code below. its pretty simple because Im still learning. I have read a bunch of things online, but I cant figure out where my error is coming from. If you have any suggestions, I would love to hear them.

"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn

data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],
        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}

labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']

df = pd.DataFrame(data, index = labels)
print(df)
df = df.append({'animal':'monkey', 'age':5, 'visits':1,'priority':'yes'}, ignore_index = True)
labels.append('k')
df.set_index(pd.Series(labels))
print(df)
df = df.drop('k')
print(df)

"""

I get the error when I try to drop row k. Any help is greatly appreciated

Answer 1

You have a problem with setting the idex not dropping it. A correct line when you set the index with 'k' included is:

...
df = df.set_index(pd.Series(labels))
...

note that set_index does not change the df it is applied to, it returns a new df. Although you can force the change by setting the argument inplace = True in set_index.

Also probably a slightly better style is to use pd.Index not Series:

df = df.set_index(pd.Index(labels))

Answer 2

You need to either add inplace=True to set_index or reassign the return value of set_index back to df:

This:

df.set_index(pd.Series(labels), inplace=True)

Or this:

df = df.set_index(pd.Series(labels))

Output:

>>> df
   animal  age  visits priority
a     cat  2.5       1      yes
b     cat  3.0       3      yes
c   snake  0.5       2       no
d     dog  NaN       3      yes
e     dog  5.0       2       no
f     cat  2.0       3       no
g   snake  4.5       1       no
h     cat  NaN       1      yes
i     dog  7.0       2       no
j     dog  3.0       1       no
k  monkey  5.0       1      yes

Now df.drop('k') will work:

>>> df = df.drop('k')
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
c  snake  0.5       2       no
d    dog  NaN       3      yes
e    dog  5.0       2       no
f    cat  2.0       3       no
g  snake  4.5       1       no
h    cat  NaN       1      yes
i    dog  7.0       2       no
j    dog  3.0       1       no