<jupyter_start><jupyter_text>Participants profile<jupyter_code>par_df = pd.concat([df['Basic Info'], df['Class']], axis=1)
par_df.head()
grouped_by_parti = par_df.groupby("id")
participants_count = len(grouped_by_parti)
print("Total No of participants : ", participants_count)
participants_profile = []
for ind , grp in grouped_by_parti:
    ag = grp['gender'].to_numpy()
    assert (ag[0] == ag).all()
    ac = grp['class'].to_numpy()
    assert (ac[0] == ac).all()
    participants_profile.append((ind, len(grp), ag[0], ac[0]))

participants_profile = pd.DataFrame(participants_profile, columns=['Participant ID', 'Instance Count', 'Gender', 'Parkinson-s disease'])
participants_profile.head()
participants_profile['Instance Count'].describe()
plt.figure(figsize=(10,10))

gender_count = participants_profile['Gender'].value_counts()
labels = ['Female', 'Male']
sizes = (gender_count[0], gender_count[1])
ax1 = plt.subplot(121)
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
ax1.set_title('Gender')

pk_count = participants_profile['Parkinson-s disease'].value_counts()
labels = ['Yes', 'No']
sizes = (pk_count[1], pk_count[0])
ax2 = plt.subplot(122)
ax2.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
ax2.set_title('Parkinson-s disease')
plt.show()<jupyter_output><empty_output><jupyter_text>Feature profile<jupyter_code>feature_df = pd.DataFrame(list(df.columns.values), columns=['Feature Type', 'Features'])
feature_df.head()
grouped = feature_df.groupby('Feature Type')
feature_count = {grp_name: len(ids) for grp_name, ids in grouped.groups.items()}
del feature_count['Basic Info'], feature_count['Class']
feature_count
f, ax = plt.subplots(figsize=(16,5))
ax.bar(feature_count.keys(), feature_count.values(), width=.5, color='g')<jupyter_output><empty_output><jupyter_text>Baseline Features**PPE** - Pitch Period Entropy<jupyter_code>baseline_features = df['Baseline Features']
baseline_features.head()
baseline_features.describe()
plt.figure(figsize=(20,10))
sns.heatmap(baseline_features.corr(), annot=True)
bandwidth_features = df['Bandwidth Parameters']
bandwidth_features.describe()
plt.figure(figsize=(5,2))
sns.heatmap(bandwidth_features.corr(), annot=True)
Formant_features = df['Formant Frequencies']
Formant_features.describe()
plt.figure(figsize=(5,2))
sns.heatmap(Formant_features.corr(), annot=True)
Intensity_features = df['Intensity Parameters']
Intensity_features.describe()
plt.figure(figsize=(5,2))
sns.heatmap(Intensity_features.corr(), annot=True)
Vocal_features = df['Vocal Fold']
Vocal_features.describe()
plt.figure(figsize=(20,10))
sns.heatmap(Vocal_features.corr(), annot=True)
# Wavelet Features
Wavelet_features = df['Wavelet Features']
Wavelet_features.describe()
# plt.figure(figsize=(50,40))
# sns.heatmap(Wavelet_features.corr(), annot=True)<jupyter_output><empty_output>
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