Extract Column Value Based on Another Column Pandas Dataframe
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...
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.
6 Answers
You can try query, which is less typing:
df.query('B==3')['A']
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:
df[*column*][*row*]
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.
Sarah Jenkins is a veteran tech journalist with over 12 years of experience covering artificial intelligence, mobile innovations, and digital ethics. Her insights have appeared in leading technology publications worldwide.
