Pandas how to place an array in a single dataframe cell?

Question

So I currently have a dataframe that looks like:

And I want to add a completely new column called "Predictors" with only one cell that contains an array.

So [0, 'Predictors'] should contain an array and everything below that cell in the same column should be empty.

Here's my attempt, I tried to create a separate dataframe that just contained the "Predictors" column, and tried appending it to the current dataframe, but I get: 'Length mismatch: Expected axis has 3 elements, new values have 4 elements.'

How do I append a single cell containing an array to my dataframe?

# create a list and dataframe to hold the names of predictors
dataframe=dataframe.drop(['price','Date'],axis=1)  
predictorsList = dataframe.columns.get_values().tolist()
predictorsList = np.array(predictorsList, dtype=object)

# Combine actual and forecasted lists to one dataframe
combinedResults = pd.DataFrame({'Actual': actual, 'Forecasted': forecasted})

predictorsDF = pd.DataFrame({'Predictors': [predictorsList]})

# Add Predictors to dataframe
#combinedResults.at[0, 'Predictors'] = predictorsList
pd.concat([combinedResults,predictorsDF], ignore_index=True, axis=1)

Answer 1

You could fill the rest of the cells in the desired column with NaN, but they will not "empty". To do that, use pd.merge on both indexes:

Setup

import pandas as pd
import numpy as np

df = pd.DataFrame({
     'Actual': [18.442, 15.4233, 20.6217, 16.7, 18.185], 
     'Forecasted': [19.6377, 13.1665, 19.3992, 17.4557, 14.0053]
})

arr = np.zeros(3)
df_arr = pd.DataFrame({'Predictors': [arr]})

Merging df and df_arr

result = pd.merge(
    df,
    df_arr,
    how='left',
    left_index=True, # Merge on both indexes, since right only has 0...
    right_index=True # all the other rows will be NaN
)

Results

>>> print(result)
    Actual  Forecasted       Predictors
0  18.4420     19.6377  [0.0, 0.0, 0.0]
1  15.4233     13.1665              NaN
2  20.6217     19.3992              NaN
3  16.7000     17.4557              NaN
4  18.1850     14.0053              NaN

>>> result.loc[0, 'Predictors']
array([0., 0., 0.])

>>> result.loc[1, 'Predictors'] # actually contains a NaN value
nan 

Answer 2

You need to change the object type of the column (in your case Predictors) first

import pandas as pd
import numpy as np


df=pd.DataFrame(np.arange(20).reshape(5,4), columns=list('abcd'))
df=df.astype(object)  # this line allows the signment of the array
df.iloc[1,2] = np.array([99,99,99])
print(df)

gives

    a   b             c   d
0   0   1             2   3
1   4   5  [99, 99, 99]   7
2   8   9            10  11
3  12  13            14  15
4  16  17            18  19