Updating a multi-indexed dataframe with a list (or series) of values

Question

I want to be able to update the values in a multi-indexed dataframe, using the output of a separate function that performs calculations on another existing dataframe.

Let's say for example, I have the following:

import numpy as np, pandas as pd

names = ['Johnson','Jackson','Smith']
attributes = ['x1','x2','x3','x4','x5']
categories = ['y1','y2','y3','y4','y5','y6']

index = pd.MultiIndex.from_product([names, attributes])
placeholders = np.zeros((len(names)*len(attributes), len(categories)), dtype=int)

df = pd.DataFrame(placeholders, index=index, columns=categories)

Which generates the corresponding dataframe:

            y1  y2  y3  y4  y5  y6
Johnson x1   0   0   0   0   0   0
        x2   0   0   0   0   0   0
        x3   0   0   0   0   0   0
        x4   0   0   0   0   0   0
        x5   0   0   0   0   0   0
Jackson x1   0   0   0   0   0   0
        x2   0   0   0   0   0   0
        x3   0   0   0   0   0   0
        x4   0   0   0   0   0   0
        x5   0   0   0   0   0   0
Smith   x1   0   0   0   0   0   0
        x2   0   0   0   0   0   0
        x3   0   0   0   0   0   0
        x4   0   0   0   0   0   0
        x5   0   0   0   0   0   0

Now, I have another function that generates a series of values that I want to then use to update this dataframe. For example:

x1 = pd.Series([2274, 556, 1718, 1171, 183, 194], index=categories)
x2 = pd.Series([627, 154, 473, 215, 68, 77], index=categories)

How would I go about updating the series values for ('Johnson','x1') ?

The vectors x1 and x2 are generated by calling the function inside of two nested for loops. I can't seem to figure out how to update the dataframe, the values just remain all zeros:

for i in names:
    for j in attributes:
        x1 = generate_data_list('x1')
        df.loc[i,j].update(x1)

Appreciate any help!

Answer 1

Just assign x1 to df.loc[i, j] :

df.loc['Johnson', 'x1'] = x1

Or:

df.loc[('Johnson', 'x1')] = x1

df
#              y1   y2    y3    y4   y5   y6
#Johnson x1  2274  556  1718  1171  183  194
#        x2     0    0     0     0    0    0
#        x3     0    0     0     0    0    0
#        x4     0    0     0     0    0    0
#        x5     0    0     0     0    0    0
#Jackson x1     0    0     0     0    0    0
#        x2     0    0     0     0    0    0
#        x3     0    0     0     0    0    0
#        x4     0    0     0     0    0    0
#        x5     0    0     0     0    0    0
#Smith   x1     0    0     0     0    0    0
#        x2     0    0     0     0    0    0
#        x3     0    0     0     0    0    0
#        x4     0    0     0     0    0    0
#        x5     0    0     0     0    0    0

Answer 2

You can create the information in the right format then using update

x1 = pd.DataFrame(data=[[2274, 556, 1718, 1171, 183, 194]], index=pd.MultiIndex.from_arrays([['Johnson'],['x1']]),columns=categories)
x1
              y1   y2    y3    y4   y5   y6
Johnson x1  2274  556  1718  1171  183  194
df.update(x1)
df
                y1     y2      y3      y4     y5     y6
Johnson x1  2274.0  556.0  1718.0  1171.0  183.0  194.0
        x2     0.0    0.0     0.0     0.0    0.0    0.0
        x3     0.0    0.0     0.0     0.0    0.0    0.0
        x4     0.0    0.0     0.0     0.0    0.0    0.0
        x5     0.0    0.0     0.0     0.0    0.0    0.0
Jackson x1     0.0    0.0     0.0     0.0    0.0    0.0
        x2     0.0    0.0     0.0     0.0    0.0    0.0
        x3     0.0    0.0     0.0     0.0    0.0    0.0
        x4     0.0    0.0     0.0     0.0    0.0    0.0
        x5     0.0    0.0     0.0     0.0    0.0    0.0
Smith   x1     0.0    0.0     0.0     0.0    0.0    0.0
        x2     0.0    0.0     0.0     0.0    0.0    0.0
        x3     0.0    0.0     0.0     0.0    0.0    0.0
        x4     0.0    0.0     0.0     0.0    0.0    0.0
        x5     0.0    0.0     0.0     0.0    0.0    0.0