pythonとtableauでscotch whiskyの味の特徴を分析、可視化

import pandas as pd
data_url = 'https://outreach.mathstat.strath.ac.uk/outreach/nessie/datasets/whiskies.txt'
df = pd.read_csv(data_url) 

body_cols = ['Body']
winey_cols = ['Winey', 'Nutty']
central_cols = ['Malty']
feinty_cols = ['Honey', 'Tobacco']
peaty_cols = ['Medicinal', 'Smoky']
floral_cols = ['Floral']
fruity_cols = ['Fruity', 'Sweetness']
woody_cols = ['Spicy']

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.decomposition import FactorAnalysis as FA

# 因子分析
def fa_and_plot(X, target, n_components=2):
    ss = StandardScaler()
    fa = FA(n_components=n_components, max_iter=500)

    pl = Pipeline([("scaler", ss), ("fa", fa)])
    x = pl.fit_transform(X)

    return pl['fa'], x
   
val_cols = ['winey', 'woody', 'body',
             'central', 'feinty', 'peaty',
             'floral', 'fruity']
             
fa, x_fa = fa_and_plot(df[val_cols], df['Distillery'].values, n_components=2)

# 因子負荷量
plt.figure(figsize=(10, 6))
plt.subplot(2, 1, 1)
plt.bar(list(df[val_cols].columns), fa.components_[0])
plt.title('fa loading 1')
plt.subplot(2, 1, 2)
plt.title('fa loading 2')
plt.bar(list(df[val_cols].columns), fa.components_[1])
plt.show()

import seaborn as sns
sns.heatmap(df[val_cols].corr(), vmax=1, vmin=-1, center=0)
plt.show()

# 出力データ加工用
id_cols = ['RowID', 'Distillery', 'Postcode', ' Latitude', ' Longitude', 'x0_fa', 'x1_fa']

# tableauでレーダーチャートに表示するためのカラム
df_fa2 = df_fa.copy()
df_fa2['z_body'] = df_fa2['body']
df_melt = df_fa2.melt(value_vars=val_cols+['z_body'], id_vars=id_cols, var_name='key')

# tableauで計算するためにカテゴリごとにidをつける
df_melt['key_id'] = df_melt['key'].astype('category').cat.codes + 1

from google.colab import auth
from oauth2client.client import GoogleCredentials
import gspread
import gspread_dataframe as gs_df

# 認証処理
auth.authenticate_user()
gc = gspread.authorize(GoogleCredentials.get_application_default())

# 一つ目のワークシートへ書き込み
worksheet = gc.open('class_whiskey').get_worksheet(0)
gs_df.set_with_dataframe(worksheet, df_melt)