Reputation: 555
I would like to ask an question that is an extension on this thread:
Select rows from a DataFrame based on values in a column in pandas.
The code from this thread is listed below:
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
'B': 'one one two three two two one three'.split(),
'C': np.arange(8), 'D': np.arange(8) * 2})
print(df)
# A B C D
# 0 foo one 0 0
# 1 bar one 1 2
# 2 foo two 2 4
# 3 bar three 3 6
# 4 foo two 4 8
# 5 bar two 5 10
# 6 foo one 6 12
# 7 foo three 7 14
print(df.loc[df['D'] == 14])
This will yield the following result:
A B C D
7 foo three 7 14
Based on the code above, how can I return a single 'value' not a row. That is, how can I return the value '7'
or value 'foo'
as opposed to the entire row?
Upvotes: 13
Views: 48019
Reputation: 15953
@JonahWilliams was close, here's a working one:
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
'B': 'one one two three two two one three'.split(),
'C': np.arange(8), 'D': np.arange(8) * 2})
print(df.loc[df['D'] == 14]['A'].index.values)
>>>[7]
print(df.loc[df['D'] == 14]['A'].values)
>>>['foo']
Upvotes: 19