I have an array of arrays that I want to combine with a data frame.
arrays=[np.array(i) for i in [[1,2],[5,6,7],[]]] #let me illustrate the arrays like this
df=pd.DataFrame({'Col':['x','y','z']})
Each array element corresponds to a row in the df. This is my desired output:
Here is a way you could duplicate the df rows to accomodate the array elements going in:
df.loc[df.index.repeat([max(1,len(i)) for i in arrays])]
Thank you
You could do the following:
import numpy as np
import pandas as pd
from itertools import product
arrays = [np.array(i) for i in [[1, 2], [5, 6, 7], []]]
df = pd.DataFrame({'Col': ['x', 'y', 'z']})
# this creates a mesh (cross-product) Dataframe
mesh = pd.DataFrame([pair for co in zip(df['Col'], arrays) for pair in product(*co)],
columns=['Col', 'n'])
# merge with the original Dataframe
result = df.merge(mesh, on='Col', how='left').fillna(0)
Output
Col n
0 x 1.0
1 x 2.0
2 y 5.0
3 y 6.0
4 y 7.0
5 z 0.0
df=pd.DataFrame({'Col':['x','y','z']})
arrays=[i for i in [[1,2],[5,6,7],[]]] #let me illustrate the arrays like this
df['newcol']=arrays
df.explode('newcol')