Custom colored lines in matplotlib but legend won't update

I am trying to plot lines out of a pandas dataframe, a pivot table of my data. There's 12 lines, one for each month of the year, and I don't want any duplicate colors. So I decided to set the colors manually. In the plot, the color of the lines is updated, but when I create a legend, it still displays the old colors.

Below is a snippet of my code. I want to change the colors for 4 out of 12 subplots:

fig, axs = plt.subplots(4, 3)

for i, country in enumerate(countries):
    
    pivot_yearly = pd.pivot_table(df[country].to_frame(), index=df.index.month, columns=df.index.year)
    pivot_monthly = pd.pivot_table(df[country].to_frame(), index=df.index.weekday, columns=df.index.month) # The problem is with this one.
    pivot_dyyr = pd.pivot_table(df[country].to_frame(), index=df.index.weekday, columns=df.index.year)
    
    axs[i,0].plot(pivot_yearly)

    cm = plt.get_cmap('gist_rainbow')
    cNorm = colors.Normalize(vmin=0, vmax=11)
    scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)

    axs[i,1].set_prop_cycle(color=[scalarMap.to_rgba(j) for j in range(12)])
    axs[i,1].plot(pivot_monthly)

    axs[i,3].plot(pivot_dyyr)

labels = "Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec".split()
leg1 = fig.legend(pivot_yearly.columsn.get_level_values('Date'), ncols=3)
leg2 = fig.legend(pivot_monthly.columns.get_level_values('Date'), labels=labels, ncols=3)
leg3 = fig.legend(pivot_dyyr.columns.get_level_values('Date'), ncols=3)

plt.tight_layout()
plt.show()

Unfortunately I cannot share my data, but it concerns 4 countries and the index 'Date' as a DateTimeIndex.

When I run the line axs[i,1].set_prop_cycle() the line colors are updated, but the legend is not.

As you have not provided any data, I took the healthexp dataset because it has 6 countries and year. Post that, converted it to what I assume is the format you have by changing each country to a column and plotted it. As there are just 6 countries in this dataset, I have changed you 4x3 plot to 3x2. Please check if this works for you.

fig, axs = plt.subplots(2, 3, figsize=(12,5))

## My data
df=sns.load_dataset('healthexp')
df['Month'] = np.random.randint(1,13, size=(len(df)))  ## Create random month
df['Year']= pd.to_datetime(df['Year'].astype(str)+df['Month'].astype(str), format='%Y%m') #Change year to datetime
df=df.pivot_table(index="Year", columns="Country", values="Spending_USD", fill_value=0) ## Change each Country as column
countries=df.columns  ## Guessing you want one plot per country

for i, country in enumerate(countries):
    pivot = pd.pivot_table(df[country].to_frame(), index=df.index.weekday, columns=df.index.month)
    cm = plt.get_cmap('gist_rainbow')
    cNorm = colors.Normalize(vmin=0, vmax=12)
    scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
    axs[i%2,int(i/2)].set_prop_cycle(color=[scalarMap.to_rgba(j) for j in range(12)])  ## Note I changed axs to 3x2
    axs[i%2,int(i/2)].plot(pivot)  ## Note I changed axs to 3x2

labels = "Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec".split()
leg = fig.legend(pivot.columns.get_level_values('Year'), labels=labels, ncol=3,bbox_to_anchor=(.65,0)) ##ncol as I om on older version of matplotlib

plt.show()