问题
I have a dataframe with 12 column. I would like to extract the rows of a column depending on the values of another column.
Sample of my dataframe
order_id order_type order_items
45 Lunch [('Burger', 5), ('Fries', 6)]
12 Dinner [('Shrimp', 10), ('Fish&Chips', 7)]
44 Lunch [('Salad', 9), ('Steak', 9)]
23 Breakfast [('Coffee', 2), ('Eggs', 3)]
I would like to extract the breakfast, lunch and dinner menu by extracting the first item of each tuple
.
and extract the number of orders from the next item in the tuple.
Each item is type string according to this line of code
print(type(df['order_items'][0]))
>> <class 'str'>
I tried to apply a filter to extract the breakfast menu:
BreakfastLst=df.loc[df['order_type'] == 'Breakfast']['order_items']
but the output looks like this, and I can't use a for loop
to iterate through sublists and access the tuples.
2 [('Coffee', 4), ('Eggs', 7)]
7 [('Coffee', 2), ('Eggs', 3)]
8 [('Cereal', 7), ('Pancake', 8), ('Coffee', 4),...
9 [('Cereal', 3), ('Eggs', 1), ('Coffee', 1), ('...
I also tried to convert to lists
:
orderTypeLst = df(['order_type'])['order_items'].apply(list)
and then extract the lists by doing this:
breakFast=orderTypeLst['Breakfast']
lunch=orderTypeLst['Lunch']
dinner=orderTypeLst['Dinner']
but the output is a string. And I can't iterate through that either.
["[('Coffee', 4), ('Eggs', 7)]",
"[('Coffee', 2), ('Eggs', 3)]",
"[('Cereal', 7), ('Pancake', 8), ('Coffee', 4), ('Eggs', 8)]"]
As for dictionaries
I tried the below, but the output is duplicated:
pd.Series(outlierFile.order_type.values,index=outlierFile.order_items).to_dict()
output sample
"[('Fries', 1), ('Steak', 6), ('Salad', 8), ('Chicken', 10)]": 'Lunch',
"[('Cereal', 6), ('Pancake', 8), ('Eggs', 3)]": 'Breakfast',
"[('Shrimp', 9), ('Salmon', 9)]": 'Dinner',
"[('Pancake', 3), ('Coffee', 5)]": 'Breakfast',
"[('Eggs', 1), ('Pancake', 1), ('Coffee', 5), ('Cereal', 5)]": 'Breakfast'
my desired output is a clean version of each order_type (list or dictionary) so I can iterate through the tuples and extract the needed items.
Any input would be helpful Thanks,
回答1:
IIUC, try using pandas.DataFrame.groupby
after evaluation:
my_dict = df.groupby('order_type')['order_items'].apply(lambda x: sum(x, [])).to_dict()
print(my_dict)
Output:
{'Breakfast': [('Coffee', 2), ('Eggs', 3)],
'Dinner': [('Shrimp', 10), ('Fish&Chips', 7)],
'Lunch': [('Burger', 5), ('Fries', 6), ('Salad', 9), ('Steak', 9)]}
来源:https://stackoverflow.com/questions/58151118/extracting-tuples-from-a-list-in-pandas-dataframe