extract column value based on another column pandas dataframe

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I am kind of getting stuck on extracting value of one variable conditioning on another variable. For example, the following dataframe:

A  B
p1 1
p1 2
p3 3
p2 4

How can I get the value of A when B=3? Every time when I extracted the value of A, I got an object, not a string.

You could use loc to get series which satisfying your condition and then iloc to get first element:

In [2]: df
    A  B
0  p1  1
1  p1  2
2  p3  3
3  p2  4

In [3]: df.loc[df['B'] == 3, 'A']
2    p3
Name: A, dtype: object

In [4]: df.loc[df['B'] == 3, 'A'].iloc[0]
Out[4]: 'p3'

You can try query, which is less typing:


df[df['B']==3]['A'], assuming df is your pandas.DataFrame.

Use df[df['B']==3]['A'].values[0] if you just want item itself without the brackets

Edited: What I described below under Previous is chained indexing and may not work in some situations. The best practice is to use loc, but the concept is the same:

df.loc[row, col]

row and col can be specified directly (e.g., ‘A’ or [‘A’, ‘B’]) or with a mask (e.g. df[‘B’] == 3). Using the example below:

df.loc[df['B'] == 3, 'A']

Previous: It’s easier for me to think in these terms, but borrowing from other answers. The value you want is located in a dataframe:


where column and row point to the values you want returned. For your example, column is ‘A’ and for row you use a mask:

df['B'] == 3

To get the first matched value from the series there are several options:

df['A'][df['B'] == 3].values[0]
df['A'][df['B'] == 3].iloc[0]
df['A'][df['B'] == 3].to_numpy()[0]

male_avgtip=(tips_data.loc[tips_data['sex'] == 'Male', 'tip']).mean()

I have also worked on this clausing and extraction operations for my assignment.

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