Python Pandas: add a column based on another column

Question

I am new to pandas and quite confused, especially compared to lists and using lists.

I have a dataframe with 4 columns. I want to create the 5th column "c" based on the 4th column "m". I can get the value for "c" by applying my function for each row in the "m" column.

If "m" was a list and using list comprehension, that would be

c = [myfunction(x) for x in m] 

How do I apply this “logic” to a data frame?

Answer 1

You can assign a sample from doc :

 import pandas as pd import numpy as np df = pd.DataFrame({'A': range(1, 11), 'B': np.random.randn(10)}) print df AB 0 1 0.769028 1 2 -0.392471 2 3 0.153051 3 4 -0.379848 4 5 -0.665426 5 6 0.880684 6 7 1.126381 7 8 -0.559828 8 9 0.862935 9 10 -0.909402 df = df.assign(ln_A = lambda x: np.log(xA)) print df AB ln_A 0 1 0.769028 0.000000 1 2 -0.392471 0.693147 2 3 0.153051 1.098612 3 4 -0.379848 1.386294 4 5 -0.665426 1.609438 5 6 0.880684 1.791759 6 7 1.126381 1.945910 7 8 -0.559828 2.079442 8 9 0.862935 2.197225 9 10 -0.909402 2.302585 

Or apply as Lu Qi commented.

Sometimes the lambda function is useful:

 import pandas as pd import numpy as np df = pd.DataFrame({'A': range(1, 11), 'B': np.random.randn(10)}) df['ln_A'] = df['A'].apply(np.log) df['round'] = df['B'].apply(lambda x: np.round(x, 2)) print df AB ln_A round 0 1 -0.982828 0.000000 -0.98 1 2 2.306111 0.693147 2.31 2 3 0.967858 1.098612 0.97 3 4 -0.286280 1.386294 -0.29 4 5 -2.026937 1.609438 -2.03 5 6 0.061735 1.791759 0.06 6 7 -0.506620 1.945910 -0.51 7 8 -0.309438 2.079442 -0.31 8 9 -1.261842 2.197225 -1.26 9 10 1.079921 2.302585 1.08 

Answer 2

Since pandas is on top of numpy. You can easily apply the function to numpy.array . The following example may help. You can transfer the list (or column) to numpy.array, and then do a vector calculation.

 import numpy as np import pandas as pd data = pd.DataFrame([[1,2],[3,4]],columns=['a','b']) def square(x): return x ** 2 data['c'] = square(np.array(data.a)) 

Answer 3

The following is similar to the general concept of list comprehension

 def some_fn(x): # return some_other_fn(x.Colname1, x.Colname2, ...) return xa + xb df = pd.DataFrame({'a' : [1, 2], 'b' : [3, 4]}) df['c'] = [some_fn(row) for ind, row in df.iterrows()]