[Python] Can't get frequency data right for Seaborn histogram

I have a list containing thousands of survey answers ranging from 'A' to 'I', with the addition of None for blanks:

Raw data list example:

[None, 'A', 'G', None, None, 'I', 'B', ...<snip>... , 'C']


I would like to produce a Seaborn histogram showing the frequency of each response (in alphabetical order), something similar to this Excel chart:

Desired outcome (Excel version)

Desired outcome (Numbers are different here than in test data below)

The closest I get is this, with all columns incorrectly equal to 1:

Histogram with correct axes but wrong column heights

I tried histplot, catplot and displot as per some random tutorials online.

I am unsure of how to process the list of raw values into the right format for Seaborn. I have followed many different tutorials doing things like sns.histplot(data=df, x="Frequency") but can't get the dataframe right before this step.

Main method I tried for frequency (from some tutorials): df = pd.DataFrame.from_dict(Counter(values), orient='index').reset_index()

Any suggestions will be humbly received.

Much appreciation in advance.

Minimum reproducible example:

import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
import pandas as pd


# Test data - real data is much larger and extracted as a list from an SQLite3 DB.
values = ['E', None, 'B', 'H', 'I', 'A', None, None, 'D',
'C', 'E', 'I', 'C', 'B', None, 'A', None, 'E',
'F', 'H', 'A', 'D', 'A', 'A', 'F', 'A', 'C',
'C', 'H', 'E', None, 'B', 'E', 'I', 'G', 'A',
'I', 'A', 'B', 'I']

sns.set_theme(style="darkgrid")
df = pd.DataFrame.from_dict(Counter(values), orient='index').reset_index()
df.columns = ['Choice', 'Frequency']
sns.histplot(data=df, x='Choice')
plt.show()

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