iretate over columns in df and calculate euclidean distance with one column in pandas

[Pit292]

Hi, I have a dataset with several columns (Time Series) and I would like to synchronize them - the 'col2' should be the reference.



Here is my df:



With the code below I am able to synchronize the only two columns 'col3' according to 'col2' (time series).

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import pandas as pd
import numpy as np
# pip install fastdtw  

df=pd.DataFrame({'ID':range(0,25), 'col2':np.random.randn(25)+3, 'col3':np.random.randn(25)+3,'col4':np.random.randn(25)+3,'col5':np.random.randn(25)+3})
from fastdtw import *
from scipy.spatial.distance import *

x = np.array(df['col2'].fillna(0))
y = np.array(df['col3'].fillna(0))

distance, path = fastdtw(x, y, dist=euclidean)

result = []

for i in range(0,len(path)):
    result.append([df['ID'].iloc[path[i][0]],
    df['col2'].iloc[path[i][0]],
    df['col3'].iloc[path[i][1]]])
    
df_synchronized = pd.DataFrame(data=result,columns=['ID','col2','col3']).dropna()
df_synchronized = df_synchronized.drop_duplicates(subset=['ID'])
df_synchronized = df_synchronized.sort_values(by='ID')
df_synchronized = df_synchronized.reset_index(drop=True)
df_synchronized.head(n=3) 

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Here is the df_synchronized:


I would like to iterate over all columns in DataFrame and do the same for 'col4' and 'col5' as was for 'col3' being done. Simply, 'col3' needs to be replaced in a loop with 'col4' and 'col5'. The goal would be to have the df_synchronized with all columns from df.

Is there any way, how to make it done?

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distance, path = fastdtw(x, y, dist=euclidean)

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can't be change to distance, path = fastdtw(x, y, z, aa, dist=euclidean). 'Synchronization' needs to be done on one column, then save into df_synchronized, then with next column...

Yoriz write May-09-2021, 06:52 PM:
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