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 could use loc to get series which satisfying your condition and then iloc to get first element:
In [2]: df Out[2]: A B 0 p1 1 1 p1 2 2 p3 3 3 p2 4 In [3]: df.loc[df['B'] == 3, 'A'] Out[3]: 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.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.
