CODEWITHSUNDEEP
pandaspivot-tablepython-3.6
Abhilash
Abhilash

Reputation: 445

How to get value in a column based on condition in pandas data frame pivot table?

I have a MySQL table as shown below:

     ID   | article | price | promo_price | delivery_days | stock |  received_on
    17591   03D/6H   3082.00    1716.21         30            0      2019-03-20
    29315   03D/6H   3082.00    1716.21         26            0      2019-03-24
    47796   03D/6H   3082.00    1716.21         24            0      2019-03-25

    22016   L1620S    685.00    384.81           0            3      2019-03-20
    35043   L1620S    685.00    384.81           0            2      2019-03-24
    53731   L1620S    685.00    384.81           0            2      2019-03-25

I created a pivot table to monitor the stock data.

md = df.pivot_table(
        values='stock', 
        index=['article','price', 'promo_price','delivery_days'], 
        columns='received_on', 
        aggfunc=np.sum)

dates = md.columns.tolist()
dates.sort(reverse=True)

md = md[dates]

This is the resuslt

    +---------------------------------+--------------+--------------+--------------+
    |                                 |   2019-03-25 |   2019-03-24 |   2019-03-20 |
    |---------------------------------+--------------+--------------+--------------|
    | ('03D/6H', 3082.0, 1716.21, 24) |            0 |          nan |          nan |
    | ('03D/6H', 3082.0, 1716.21, 26) |          nan |            0 |          nan |
    | ('03D/6H', 3082.0, 1716.21, 30) |          nan |          nan |            0 |
    | ('L1620S-KD', 685.0, 384.81, 0) |            2 |            2 |            3 |
    +---------------------------------+--------------+--------------+--------------+

How do I filter the rows and get the price, promo_price and delivery days of an article based on the recent stock received date?

For ex: I want the stock info for all the days but price, promo_price and delivery days of only 2019-03-25 as shown below

    +---------------------------------+--------------+--------------+--------------+
    |                                 |   2019-03-25 |   2019-03-24 |   2019-03-20 |
    |---------------------------------+--------------+--------------+--------------|
    | ('03D/6H', 3082.0, 1716.21, 24) |            0 |          nan |          nan |
    | ('L1620S', 685.0, 384.81, 0)    |            2 |            2 |            3 |
    +---------------------------------+--------------+--------------+--------------+

EDIT:

If there is no change in price, promo_price and delivery days, I am getting the result as expected. But if there is any change in the values, then I am getting multiple rows for the same article.

Article L1620S data is as expected. But article 03D/6H resulted in three rows.

Upvotes: 1

Views: 1034

Answers (1)

jezrael
jezrael

Reputation: 862481

You can use:

df['received_on'] = pd.to_datetime(df['received_on'])

md = df.pivot_table(
        values='stock', 
        index=['article','price', 'promo_price','delivery_days'], 
        columns='received_on', 
        aggfunc=np.sum)

#sorting columns in descending order
md = md.sort_index(axis=1, ascending=False)

#remove missing rows in first column
md = md.dropna(subset=[md.columns[0]])
#another solution 
#md = md[md.iloc[:, 0].notna()]
print (md)
received_on                               2019-03-25  2019-03-24  2019-03-20
article price  promo_price delivery_days                                    
03D/6H  3082.0 1716.21     24                    0.0         NaN         NaN
L1620S  685.0  384.81      0                     2.0         2.0         3.0

EDIT: First filter by first level and then by position - first row:

md = md.sort_index(axis=1, ascending=False)

idx = pd.IndexSlice
md1 = md.loc[idx['03D/6H',:,:],:].iloc[[0]]
print (md1)
received_on                               2019-03-25  2019-03-24  2019-03-20
article price  promo_price delivery_days                                    
03D/6H  3082.0 1716.21     24                    0.0         NaN         NaN

Upvotes: 1

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