I have a dataframe df1 as:
col1 col2 Val1 Val2
A g 4 6
A d 3 8
B h 5 10
B p 7 14
I have another dataframe df2 as:
col1 Val1 Val2
A 2 3
B 1 4
I want to divide df1 by df2 based on col1, val1 and val2 so that row A
from df2 divides both rows A
from df1
.
My final output of df1.div(df2)
is as follows:
col1 col2 Val1 Val2
A g 2 2
A d 1.5 2
B h 5 2.5
B p 7 3.5
Convert col1
and col2
to MultiIndex
, also convert col1
in second DataFrame
to index and then use DataFrame.div
:
df = df1.set_index(['col1', 'col2']).div(df2.set_index('col1')).reset_index()
#alternative with specify level of index
#df = df1.set_index(['col1', 'col2']).div(df2.set_index('col1'), level=0).reset_index()
print (df)
col1 col2 Val1 Val2
0 A g 2.0 2.000000
1 A d 1.5 2.666667
2 B h 5.0 2.500000
3 B p 7.0 3.500000
I think there is a slight mistake in your example. For col Val2, 2nd row - 8/3 should be 2.67. So the final output df1.div(df2)
should be :
col1 col2 Val1 Val2
0 A g 2.0 2.000000
1 A d 1.5 2.666667
2 B h 5.0 2.500000
3 B p 7.0 3.500000
Anyways here is a possible solution:
import pandas as pd
df1 = pd.DataFrame(data={'col1':['A','A','B','B'], 'col2': ['g','d','h','p'], 'Val1': [4,3,5,7], 'Val2': [6,8,10,14]}, columns=['col1','col2','Val1','Val2'])
df2 = pd.DataFrame(data={'col1':['A','B'], 'Val1': [2,1], 'Val2': [3,4]}, columns=['col1','Val1','Val2'])
print (df1)
print (df2)
Output:
>>>
col1 col2 Val1 Val2
0 A g 4 6
1 A d 3 8
2 B h 5 10
3 B p 7 14
col1 Val1 Val2
0 A 2 3
1 B 1 4
Now we can just do an INNER JOIN
of df1
and df2
on col: col1
. If you are not familiar with SQL joins have a look at this: sql-join . We can do join in pandas using the merge()
method
## join df1, df2
merged_df = pd.merge(left=df1, right=df2, how='inner', on='col1')
print (merged_df)
Output:
>>>
col1 col2 Val1_x Val2_x Val1_y Val2_y
0 A g 4 6 2 3
1 A d 3 8 2 3
2 B h 5 10 1 4
3 B p 7 14 1 4
Now that we have got the corresponding columns of df1
and df2
, we can simply compute the division and delete the redundant columns:
# Val1 = Val1_x/Val1_y, Val2 = Val2_x/Val2_y
merged_df['Val1'] = merged_df['Val1_x']/merged_df['Val1_y']
merged_df['Val2'] = merged_df['Val2_x']/merged_df['Val2_y']
# delete the cols: Val1_x,Val1_y,Val2_x,Val2_y
merged_df.drop(columns=['Val1_x', 'Val1_y', 'Val2_x', 'Val2_y'], inplace=True)
print (merged_df)
Final Output:
col1 col2 Val1 Val2
0 A g 2.0 2.000000
1 A d 1.5 2.666667
2 B h 5.0 2.500000
3 B p 7.0 3.500000
I hope this solves your question :)
You can use the pandas.merge()
function to execute a database-like join between dataframes , then use the result to divide column values:
# merge against col1 so we get a merged index
merged = pd.merge(df1[["col1"]], df2)
df1[["Val1", "Val2"]] = df1[["Val1", "Val2"]].div(merged[["Val1", "Val2"]])
This produces:
col1 col2 Val1 Val2
0 A g 2.0 2.000000
1 A d 1.5 2.666667
2 B h 5.0 2.500000
3 B p 7.0 3.500000
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