Questions:
Data is present is in following structure:
s.No| Item Name | Source1 | Price1 | Source 2| Price 2| ....
1 | coffee | website1| 3.5 | website2| 3.5 |
2 | Tea | website3| 4.5 | website1| 4.5 |
3 | Soft Drink| website1| 1.5 | website2| 2.5 |
Desired Ouput wanted either using excel or python-pandas
ItemName| website1 | website2| website3
coffee | 3.5 | 3.5 | na
Tea | 4.5 | na | 4.5
Soft Drink| 1.5 | 2.5 | na
Process of tabulating is taking a lot of manual effort and is hugely error prone.
Could someone please help me write code for either excel VB script or with python–pandas please
How to&Answers:
Here’s a solution:
pvt1 = df.pivot(index='Item_Name', columns='Source1', values='Price1').reset_index()
pvt2 = df.pivot(index='Item_Name', columns='Source2', values='Price2').reset_index()
pvt = pd.merge(pvt1, pvt2, on='Item_Name')
which gives us:
Item_Name website1_x website3 website1_y website2
0 Soft_Drink 1.5 NaN NaN 2.5
1 Tea NaN 4.5 4.5 NaN
2 coffee 3.5 NaN NaN 3.5
Then, this is the code that currently handles website1, but needs to be fixed so it acts on all such columns:
pvt['website1'] = pvt['website1_x'].combine_first(pvt['website1_y'])
pvt.drop(['website1_x', 'website1_y'], axis=1, inplace=True)
Output:
Item_Name website3 website2 website1
0 Soft_Drink NaN 2.5 1.5
1 Tea 4.5 NaN 4.5
2 coffee NaN 3.5 3.5
Answer：
Using pandas, zip and tuple unpacking:
prices = pd.DataFrame(index=df['Item Name'])
for idx, s_no, item, *row in df.itertuples():
# print(item, row)
iters = [iter(row)] * 2
for source, price in zip(*iters):
# print(source, price)
prices.loc[item, source] = price
Item Name website1 website2 website3 coffee 3.5 3.5 na Tea 4.5 na 4.5 Soft Drink 1.5 2.5 na
If s.No is the index, remove the idx from the for-loop
Tags: dynamic, pythonpython
