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Consider the following dataframe:
item_id hour when date quantity
110 0YrKNYeEoa 1 before 2015-01-26 247286
111 0UMNiXI7op 1 before 2015-01-26 602001
112 0QBtIMN3AH 1 before 2015-01-26 981630
113 0GuKXLiWyV 1 after 2015-01-26 2203913
114 0SoFbjvXTs 1 after 2015-01-26 660183
115 0UkT257SXj 1 before 2015-01-26 689332
116 0RPjXnkiGx 1 after 2015-01-26 283090
117 0FhJ9RGsLT 1 before 2015-01-26 2024256
118 0FhGJ4MFlg 1 before 2015-01-26 74524
119 0FQhHZRXhB 1 before 2015-01-26 0
120 0FsSdJQlTB 1 before 2015-01-26 0
121 0FrrAzTFHE 1 before 2015-01-26 0
122 0FfkgBdMHi 1 before 2015-01-26 0
123 0FOnJNexRn 1 before 2015-01-26 0
124 0FcWhIdBds 1 before 2015-01-26 0
125 0F2lr0cL9t 1 before 2015-01-26 1787659
I would like to pivot it to get the table arranged as:
Index before after
(item_id, hour, date) quantityB quantityA
When I try with:
df.pivot(index=['item_id', 'hour', 'date'], columns='when', values='quanty')
I get:
ValueError: Wrong number of items passed 8143, placement implies 3
Why?
233
DataFrame - pivot() functionThe pivot() function is used to reshaped a given DataFrame organized by given index / column values. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. Column to use to make new frame's index. If None, uses existing index.
The process of stacking pivots a level of column labels to the row index. Unstacking performs the opposite, pivoting a level of the row index into the column index.
Levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame. Parameters: data : DataFrame. values : column to aggregate, optional. index: column, Grouper, array, or list of the previous.
How to make a pivot table? Use the pd. pivot_table() function and specify what feature should go in the rows and columns using the index and columns parameters respectively. The feature that should be used to fill in the cell values should be specified in the values parameter.
If I understand what you are asking I think what you want is pandas.pivot_table(...) which you can use like so:
table = pd.pivot_table(df, index=['item_id', 'hour', 'date'], columns='when', values='quantity')
which with a sample data frame of
item_id hour when date quantity
0 a 1 before 2015-01-26 25
1 b 1 before 2015-01-26 14
2 a 1 after 2015-01-26 4
3 d 1 before 2015-01-26 43
4 b 1 after 2015-01-26 30
5 d 1 after 2015-01-26 12
produces
when after before
item_id hour date
a 1 2015-01-26 4 25
b 1 2015-01-26 30 14
d 1 2015-01-26 12 43
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