image_output_4.png image_output_3.png image_output_2.png image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})

df_clinic.isna().sum()

df_clinic.nunique()

df_clinic.groupby('patient_id').size().describe()

import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16, 30))
features = ['updrs_1', 'updrs_2', 'updrs_3', 'updrs_4']
for i in range(len(features)):
    fig.add_subplot(9, 5, i + 1)
    sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()