Dynamically creating nested lists of sequential numbers

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I have a dataframe which is a subset of another dataframe and contains the following indexes: 45, 46, 47, 51, 52

Example dataframe:

      price  count
45   3909.0      8
46  3908.75      8
47  3908.50      8
51  3907.75      8
52   3907.5      8

I want to make 2 lists, each being its own list of the indexes that are sequential. (Example of this data format)

list[0] = [45, 46, 47]
list[1] = [51, 52]

Problem: The following code causes this error on the second to last line:

IndexError: list assignment index out of range

        same_width_nodes = df.loc[df['count'] == width]
        i = same_width_nodes.index[0]
        seq = 0
        sequences = [[]]
        sequences[seq] = []

        for index, row in same_width_nodes.iterrows():
            if i == index:
                i += 1
                sequences[seq].append(index)
            else:
                seq += 1
                sequences[seq] = [index]
                i = index

Maybe there's a better way to achieve this, but I'd like to know why I can't create a new item in the sequences list as I am doing here, and how I should be doing it.

pandas dataframe python
4 Answers

In steps.

First we do a rolling diff on your index, anything that is greater than 1 we code as True, we then apply a cumsum to create a new group per sequence.

45    0
46    0
47    0
51    1
52    1

Next, we use the groupby method with the new sequences to create your nested list inside a list comprehension

Setup.

df = pd.DataFrame([1,2,3,4,5],columns=['A'],index=[45,46, 47, 51, 52])


 A
45  1
46  2
47  3
51  4
52  5

df['grp'] = df.assign(idx=df.index)['idx'].diff().fillna(1).ne(1).cumsum()

idx = [i.index.tolist() for _,i in df.groupby('grp')]

[[45, 46, 47], [51, 52]]
user_93751020

You can use this:

s_index=df.index.to_series()
l = s_index.groupby(s_index.diff().ne(1).cumsum()).agg(list).to_numpy()

Output:

l[0]
[45, 46, 47]

and

l[1]
[51, 52]
user_63615310

The issue is with this line

sequences[seq] = [index]

You are trying to assign the list an index which is not created. Instead do this.

sequences.append([index])
user_47900790

I use the diff to find when the index value diff changes greater than 1. I iterate the tuples and access by index their values.

index=[45,46,47,51,52]
price=[3909.0,3908.75,3908.50,3907.75,3907.5]
count=[8,8,8,8,8]

df=pd.DataFrame({'index':index,'price':price,'count':count})
df['diff']=df['index'].diff().fillna(0)
print(df)
result_list=[[]]
seq=0
for row in df.itertuples():
     index=row[1]
     diff=row[4]
     if diff<=1:
         result_list[seq].append(index)
     else:
         seq+=1
         result_list.insert(1,[index])

print(result_list)  

output:
[[45, 46, 47], [51, 52]]
user_40011770
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