extract column value based on another column pandas dataframe
PythonPandasDataframePython Problem Overview
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.
Python Solutions
Solution 1 - Python
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'
Solution 2 - Python
You can try query, which is less typing:
df.query('B==3')['A']
Solution 3 - Python
df[df['B']==3]['A'], assuming df is your pandas.DataFrame.
Solution 4 - Python
Use df[df['B']==3]['A'].values[0] if you just want item itself without the brackets
Solution 5 - Python
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]
Solution 6 - Python
male_avgtip=(tips_data.loc[tips_data['sex'] == 'Male', 'tip']).mean()
I have also worked on this clausing and extraction operations for my assignment.