All combinations of two dataframes
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2
Having two dataframes:
id Country Channel Week Value
0 ES Train 2018-48 1000
1 ES Train 2018-49 1000
2 ES Train 2018-50 1000
3 ES Train 2018-51 1000
4 ES Train 2018-52 1000
and
Week Product1 Product2 Product3
2018-48 25% 50% 25%
2018-49 25% 50% 25%
2018-50 25% 50% 25%
2018-51 25% 50% 25%
2018-52 25% 50% 25%
How can I create a combination of both where I add each product to every line of the first frame and use the value multiplied by the product itself?
E.g. for the first two weeks, this would result in:
id Country Channel Week Value Product
0 ES Train 2018-48 250 Product1
1 ES Train 2018-48 500 Product2
2 ES Train 2018-48 250 Product3
3 ES Train 2018-49 250 Product1
4 ES Train 2018-49 500 Product2
5 ES Train 2018-49 250 Product3
6 ...
python pandas
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
add a comment |
2
Having two dataframes:
id Country Channel Week Value
0 ES Train 2018-48 1000
1 ES Train 2018-49 1000
2 ES Train 2018-50 1000
3 ES Train 2018-51 1000
4 ES Train 2018-52 1000
and
Week Product1 Product2 Product3
2018-48 25% 50% 25%
2018-49 25% 50% 25%
2018-50 25% 50% 25%
2018-51 25% 50% 25%
2018-52 25% 50% 25%
How can I create a combination of both where I add each product to every line of the first frame and use the value multiplied by the product itself?
E.g. for the first two weeks, this would result in:
id Country Channel Week Value Product
0 ES Train 2018-48 250 Product1
1 ES Train 2018-48 500 Product2
2 ES Train 2018-48 250 Product3
3 ES Train 2018-49 250 Product1
4 ES Train 2018-49 500 Product2
5 ES Train 2018-49 250 Product3
6 ...
python pandas
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
add a comment |
2
2
2
Having two dataframes:
id Country Channel Week Value
0 ES Train 2018-48 1000
1 ES Train 2018-49 1000
2 ES Train 2018-50 1000
3 ES Train 2018-51 1000
4 ES Train 2018-52 1000
and
Week Product1 Product2 Product3
2018-48 25% 50% 25%
2018-49 25% 50% 25%
2018-50 25% 50% 25%
2018-51 25% 50% 25%
2018-52 25% 50% 25%
How can I create a combination of both where I add each product to every line of the first frame and use the value multiplied by the product itself?
E.g. for the first two weeks, this would result in:
id Country Channel Week Value Product
0 ES Train 2018-48 250 Product1
1 ES Train 2018-48 500 Product2
2 ES Train 2018-48 250 Product3
3 ES Train 2018-49 250 Product1
4 ES Train 2018-49 500 Product2
5 ES Train 2018-49 250 Product3
6 ...
python pandas
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
Having two dataframes:
id Country Channel Week Value
0 ES Train 2018-48 1000
1 ES Train 2018-49 1000
2 ES Train 2018-50 1000
3 ES Train 2018-51 1000
4 ES Train 2018-52 1000
and
Week Product1 Product2 Product3
2018-48 25% 50% 25%
2018-49 25% 50% 25%
2018-50 25% 50% 25%
2018-51 25% 50% 25%
2018-52 25% 50% 25%
How can I create a combination of both where I add each product to every line of the first frame and use the value multiplied by the product itself?
E.g. for the first two weeks, this would result in:
id Country Channel Week Value Product
0 ES Train 2018-48 250 Product1
1 ES Train 2018-48 500 Product2
2 ES Train 2018-48 250 Product3
3 ES Train 2018-49 250 Product1
4 ES Train 2018-49 500 Product2
5 ES Train 2018-49 250 Product3
6 ...
python pandas
python pandas
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
asked Nov 21 '18 at 23:18
Bishonen_PLBishonen_PL
13910
13910
add a comment |
add a comment |
1 Answer
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1
Assuming you start with dataframes df1 and df2, first melt df2 (wide-to-long), convert your percentages to numeric, then merge with df1:
df2 = pd.melt(df2, id_vars='Week', value_vars=['Product1', 'Product2', 'Product3'])
df2['value'] = pd.to_numeric(df2['value'].str[:-1])
res = df1.merge(df2)
.eval('Value = Value * value / 100')
.drop('value', 1)
print(res)
id Country Channel Week Value variable
0 0 ES Train 2018-48 250.0 Product1
1 0 ES Train 2018-48 500.0 Product2
2 0 ES Train 2018-48 250.0 Product3
3 1 ES Train 2018-49 250.0 Product1
4 1 ES Train 2018-49 500.0 Product2
5 1 ES Train 2018-49 250.0 Product3
6 2 ES Train 2018-50 250.0 Product1
7 2 ES Train 2018-50 500.0 Product2
8 2 ES Train 2018-50 250.0 Product3
9 3 ES Train 2018-51 250.0 Product1
10 3 ES Train 2018-51 500.0 Product2
11 3 ES Train 2018-51 250.0 Product3
12 4 ES Train 2018-52 250.0 Product1
13 4 ES Train 2018-52 500.0 Product2
14 4 ES Train 2018-52 250.0 Product3
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
Assuming you start with dataframes df1 and df2, first melt df2 (wide-to-long), convert your percentages to numeric, then merge with df1:
df2 = pd.melt(df2, id_vars='Week', value_vars=['Product1', 'Product2', 'Product3'])
df2['value'] = pd.to_numeric(df2['value'].str[:-1])
res = df1.merge(df2)
.eval('Value = Value * value / 100')
.drop('value', 1)
print(res)
id Country Channel Week Value variable
0 0 ES Train 2018-48 250.0 Product1
1 0 ES Train 2018-48 500.0 Product2
2 0 ES Train 2018-48 250.0 Product3
3 1 ES Train 2018-49 250.0 Product1
4 1 ES Train 2018-49 500.0 Product2
5 1 ES Train 2018-49 250.0 Product3
6 2 ES Train 2018-50 250.0 Product1
7 2 ES Train 2018-50 500.0 Product2
8 2 ES Train 2018-50 250.0 Product3
9 3 ES Train 2018-51 250.0 Product1
10 3 ES Train 2018-51 500.0 Product2
11 3 ES Train 2018-51 250.0 Product3
12 4 ES Train 2018-52 250.0 Product1
13 4 ES Train 2018-52 500.0 Product2
14 4 ES Train 2018-52 250.0 Product3
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
add a comment |
1
Assuming you start with dataframes df1 and df2, first melt df2 (wide-to-long), convert your percentages to numeric, then merge with df1:
df2 = pd.melt(df2, id_vars='Week', value_vars=['Product1', 'Product2', 'Product3'])
df2['value'] = pd.to_numeric(df2['value'].str[:-1])
res = df1.merge(df2)
.eval('Value = Value * value / 100')
.drop('value', 1)
print(res)
id Country Channel Week Value variable
0 0 ES Train 2018-48 250.0 Product1
1 0 ES Train 2018-48 500.0 Product2
2 0 ES Train 2018-48 250.0 Product3
3 1 ES Train 2018-49 250.0 Product1
4 1 ES Train 2018-49 500.0 Product2
5 1 ES Train 2018-49 250.0 Product3
6 2 ES Train 2018-50 250.0 Product1
7 2 ES Train 2018-50 500.0 Product2
8 2 ES Train 2018-50 250.0 Product3
9 3 ES Train 2018-51 250.0 Product1
10 3 ES Train 2018-51 500.0 Product2
11 3 ES Train 2018-51 250.0 Product3
12 4 ES Train 2018-52 250.0 Product1
13 4 ES Train 2018-52 500.0 Product2
14 4 ES Train 2018-52 250.0 Product3
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
add a comment |
1
1
1
Assuming you start with dataframes df1 and df2, first melt df2 (wide-to-long), convert your percentages to numeric, then merge with df1:
df2 = pd.melt(df2, id_vars='Week', value_vars=['Product1', 'Product2', 'Product3'])
df2['value'] = pd.to_numeric(df2['value'].str[:-1])
res = df1.merge(df2)
.eval('Value = Value * value / 100')
.drop('value', 1)
print(res)
id Country Channel Week Value variable
0 0 ES Train 2018-48 250.0 Product1
1 0 ES Train 2018-48 500.0 Product2
2 0 ES Train 2018-48 250.0 Product3
3 1 ES Train 2018-49 250.0 Product1
4 1 ES Train 2018-49 500.0 Product2
5 1 ES Train 2018-49 250.0 Product3
6 2 ES Train 2018-50 250.0 Product1
7 2 ES Train 2018-50 500.0 Product2
8 2 ES Train 2018-50 250.0 Product3
9 3 ES Train 2018-51 250.0 Product1
10 3 ES Train 2018-51 500.0 Product2
11 3 ES Train 2018-51 250.0 Product3
12 4 ES Train 2018-52 250.0 Product1
13 4 ES Train 2018-52 500.0 Product2
14 4 ES Train 2018-52 250.0 Product3
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
Assuming you start with dataframes df1 and df2, first melt df2 (wide-to-long), convert your percentages to numeric, then merge with df1:
df2 = pd.melt(df2, id_vars='Week', value_vars=['Product1', 'Product2', 'Product3'])
df2['value'] = pd.to_numeric(df2['value'].str[:-1])
res = df1.merge(df2)
.eval('Value = Value * value / 100')
.drop('value', 1)
print(res)
id Country Channel Week Value variable
0 0 ES Train 2018-48 250.0 Product1
1 0 ES Train 2018-48 500.0 Product2
2 0 ES Train 2018-48 250.0 Product3
3 1 ES Train 2018-49 250.0 Product1
4 1 ES Train 2018-49 500.0 Product2
5 1 ES Train 2018-49 250.0 Product3
6 2 ES Train 2018-50 250.0 Product1
7 2 ES Train 2018-50 500.0 Product2
8 2 ES Train 2018-50 250.0 Product3
9 3 ES Train 2018-51 250.0 Product1
10 3 ES Train 2018-51 500.0 Product2
11 3 ES Train 2018-51 250.0 Product3
12 4 ES Train 2018-52 250.0 Product1
13 4 ES Train 2018-52 500.0 Product2
14 4 ES Train 2018-52 250.0 Product3
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
answered Nov 21 '18 at 23:25
jppjpp
102k2166116
102k2166116
add a comment |
add a comment |
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