I have these values in dataset in a pandas dataframe column
col1
0.74
0.77
0.72
0.65
0.24
0.07
0.21
0.05
0.09
I want to get a new column of six elements as list in new columns as rows (by shifting one values at a time in list)
This is the col that I want to get.
col2
[0.74,0.77,0.72,0.65,0.24,0.07]
[0.77,0.72,0.65,0.24,0.07,0.21]
[0.72,0.65,0.24,0.07,0.21,0.05]
[0.65,0.24,0.07,0.21,0.05,0.09]
[0.24,0.07,0.21,0.05,0.09,NaN]
[0.07,0.21,0.05,0.09,NaN,NaN]
Using the data you have given me i came up with this solution
import pandas as pd
import numpy as np
df = pd.DataFrame({'col1': [0.74,0.77,0.72,0.65,0.24,0.07,0.21,0.05,0.09]})
df["col2"] = ""
for i in range(len(df)):
lst = df["col1"].iloc[i:i+6].to_list()
length = len(lst)
# Only if you need the list to be the same length
while length<6:
lst.append(np.nan)
length +=1
print(lst)
df.at[i, 'col2'] = lst
Unsure if there is a faster way of doing it using list comprehension
I would use numpy's sliding_window_view:
import numpy as np
from numpy.lib.stride_tricks import sliding_window_view as swv
window = 6
extra = window-1
df['col2'] = swv(np.pad(df['col1'], (0, extra), constant_values=np.nan),
window).tolist()
output:
col1 col2
0 0.74 [0.74, 0.77, 0.72, 0.65, 0.24, 0.07]
1 0.77 [0.77, 0.72, 0.65, 0.24, 0.07, 0.21]
2 0.72 [0.72, 0.65, 0.24, 0.07, 0.21, 0.05]
3 0.65 [0.65, 0.24, 0.07, 0.21, 0.05, 0.09]
4 0.24 [0.24, 0.07, 0.21, 0.05, 0.09, nan]
5 0.07 [0.07, 0.21, 0.05, 0.09, nan, nan]
6 0.21 [0.21, 0.05, 0.09, nan, nan, nan]
7 0.05 [0.05, 0.09, nan, nan, nan, nan]
8 0.09 [0.09, nan, nan, nan, nan, nan]
s = pd.Series([0.74,
0.77,
0.72,
0.65,
0.24,
0.07,
0.21,
0.05,
0.09])
arr = []
index = 6
for i in range(index):
arr.append(s.values[i:index+i])
print (arr)
output:
[array([0.74, 0.77, 0.72, 0.65, 0.24, 0.07]), array([0.77, 0.72, 0.65, 0.24, 0.07, 0.21]), array([0.72, 0.65, 0.24, 0.07, 0.21, 0.05]), array([0.65, 0.24, 0.07, 0.21, 0.05, 0.09]), array([0.24, 0.07, 0.21, 0.05, 0.09]), array([0.07, 0.21, 0.05, 0.09])]
