I have a df like this:
col1 col2
[1,3,4,5] [3,3,6,2]
[1,4,5,5] [3,8,4,3]
[1,3,4,8] [8,3,7,2]
Trying to divide the elements in the lists in col1 and col2 together to get what's in the result column:
col1 col2 result
[1,3,4,5] [3,3,6,2] [.33,1,.66,2.5]
[1,4,5,5] [3,8,4,3] [.33,.5,1.25,1.66]
[1,3,4,8] [8,3,7,2] [.33,1,.57,4]
Tried a lot of different approaches - but always get an error.
Attempts :
#attempt1
df['col1'].div(df['col2'], axis=0)
#attempt2
from operator import truediv
for i in df.col1:
a = np.array(df['col1'])
for t in df.col2:
b = np.array(df['col2'])
x = a/b
print(x)
#attempt3
for i in df.index:
a = col1
b = col2
x = map(truediv, a, b)
#attempt4
a = col1
b = col2
result = [x/y for x, y in zip(a, b)]
#then apply to df
#attempt5
a = col1
b = col2
result = a/b
print(percent_matched)
#then #apply to df
>>>TypeError: unsupported operand type(s) for /: 'list' and 'list'
Any ideas?
.applymap
to convert the columns to np.array
s.div
to divide the columnsresult
must be rounded, tack on .apply(lambda x: np.round(x, 3))
, when calculating that column.
np.round()
df['result'] = df.col1.div(df.col2).apply(lambda x: np.round(x, 3))
import numpy as np
import pandas as pd
data = {'col1': [[1,3,4,5], [1,4,5,5], [1,3,4,8]], 'col2': [[3,3,6,2], [3,8,4,3], [8,3,7,2]]}
df = pd.DataFrame(data)
# convert columns to arrays
df = df.applymap(np.array)
# divide the columns
df['result'] = df.col1.div(df.col2)
You can use list comprehension with apply, this is conditional on both the lists being of same length
df['result'] = df.apply(lambda x: [np.round(x['col1'][i]/x['col2'][i], 2) for i in range(len(x['col1']))], axis = 1)
col1 col2 result
0 [1, 3, 4, 5] [3, 3, 6, 2] [0.33, 1.0, 0.67, 2.5]
1 [1, 4, 5, 5] [3, 8, 4, 3] [0.33, 0.5, 1.25, 1.67]
2 [1, 3, 4, 8] [8, 3, 7, 2] [0.12, 1.0, 0.57, 4.0]
Edit: As @TrentonMcKinney suggested, this can be done without using LC. This solution capitalized on Numpy's vectorized operations,
df.apply(lambda x: np.round(np.array(x[0]) / np.array(x[1]), 3), axis=1)
df=df.apply(pd.Series.explode)#
df['result']=(df.col1.div(df.col2))
df.groupby(level=0)['result'].agg(list).reset_index()
If these are large, you're far better off converting these to np.arrays and then doing the divide:
df["col1"] = df["col1"].apply(np.array)
df["col2"] = df["col2"].apply(np.array)
df["output"] = df.col1/df.col2
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