modifying numpy ndarray by index-CodePudding

I'm stuck on trying to add values to the numbers column.

import pandas as pd

def twod_array(num):
    data = {"group": [-1, 0, 1, 2],
            'numbers': [[2], [14, 15], [16, 17], [19, 20, 21]],
            }
    df = pd.DataFrame(data=data)
    print(df)
    return 0

Currently it prints this:

   group       numbers
0     -1           [2]
1      0      [14, 15]
2      1      [16, 17]
3      2  [19, 20, 21]

What I'd like to do is to add a value based on the passed input, so for example if I pass 14.5 as a num, this is the output I'd like to see:

   group       numbers
0     -1           [2]
1      0      [14,14.5 15]
2      1      [16, 17]
3      2  [19, 20, 21]

I'm hoping someone can help with this. This is what I have so far but it fails at the insert line with the error "numpy.ndarray" object has no attribute 'insert'.

df = pd.DataFrame({"group": [-1, 0, 1, 2],
        'numbers': [[2], [14, 15], [16, 17], [19, 20, 21]],
        })
arr = df['numbers'].to_list()
num = 14.5

for i, sub_arr in enumerate(arr):
      for j, n in enumerate(sub_arr):
            if arr[i][j]>num:
                  if j!=0: 
                    arr[i].insert(j,num)
                  else: arr[i-1].insert(-1 ,num)

df['numbers'] = arr

CodePudding user response：

Iterate through the df and see if num lies between the first and last value of the numbers column. If it does, use the bisect module to insert num in a sorted fashion.

import bisect
for i in range(len(df)):
  if num >= df.loc[i,'numbers'][0] and num<= df.loc[i,'numbers'][-1]:
    bisect.insort(df.loc[i,'numbers'],num)
    
print(df)

   group         numbers
0     -1             [2]
1      0  [14, 14.5, 15]
2      1        [16, 17]
3      2    [19, 20, 21]

CodePudding user response：

num = 14.5
mask = (df.numbers.apply(min).lt(num) &
        df.numbers.apply(max).gt(num))
index = mask[mask].index[0]
df.numbers.at[index].append(num)
df.numbers.at[index].sort()
print(df)

# Output:
   group         numbers
0     -1             [2]
1      0  [14, 14.5, 15]
2      1        [16, 17]
3      2    [19, 20, 21]