Assume my df is as follows:
df = pd.DataFrame({'Order ID': [1, 1, 2, 3, 3, 3, 4, 4], 'Product': ['USB', 'Bat', 'Ball', 'USB', 'Phone', 'Toy', 'Bike', 'Apple']})
I want to group by Order ID, and then put the Product values in a list of lists depending on the frequency of their Order ID. For example -
[['USB', 'Bat'], ['Ball'], ['USB', 'Phone', 'Toy'], ['Bike', 'Apple']]
USB and Bat both have the same order ID (1), so they are in the same list.
My code, trial 1:
combo_outer = [] # outer most list
grouped = df.groupby(['Order ID', 'Product'])
for group, frame in grouped:
combo_inner = [] # for inner lists
for row_index, row in frame.iterrows():
combo_inner.append(row['Product'])
combo_outer.extend(combo_inner)
Trial 2:
df['Product'].values.tolist()
In both cases, I end up getting a single list:
['Bat', 'USB', 'Ball', 'Phone', 'Toy', 'USB', 'Apple', 'Bike']
What am I doing wrong?
In Trial 1, instead of using extend
, you should use append
. Or you can use list comprehension:
[g.values.tolist() for _, g in df.Product.groupby(df['Order ID'])]
# [['USB', 'Bat'], ['Ball'], ['USB', 'Phone', 'Toy'], ['Bike', 'Apple']]
In your first trial just do this: grouped = df.groupby(['Order ID'])
instead of grouped = df.groupby(['Order ID', 'Product'])
The second thing is, use append
in the last line instead of extend
Trail one should be something like this:
combo_outer = [] # outer most list
grouped = df.groupby(['Order ID'])
for group, frame in grouped:
combo_inner = [] # for inner lists
for row_index, row in frame.iterrows():
combo_inner.append(row['Product'])
combo_outer.append(combo_inner)
You can send the list function directly to the groupby using agg
and convert to a list.
df.Product.groupby(df['Order ID']).agg(list).tolist()
Outputs:
[['USB', 'Bat'], ['Ball'], ['USB', 'Phone', 'Toy'], ['Bike', 'Apple']]
In Trial 1, instead of using extend , you should use append . Or you can use apply function as shown below
df1['new']=df.groupby('Order ID')['Product'].apply(list)
df1['new'].values.tolist()
OUTPUT
[['USB', 'Bat'], ['Ball'], ['USB', 'Phone', 'Toy'], ['Bike', 'Apple']]
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