Untitled26 (1).py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import numpy as numpy,pandas as pd, matplotlib.pyplot as plt, seaborn as sns
patient_data=pd.read_csv("/Users/kimayashringarpure/Downloads/Patient_Data_skewed.csv")
pd.set_option("display.max_columns" and "display.max_rows",None)


new_column_names = ['Date','Name','Patient ID','City Of Residence','Phone number','Age','Gender','Department','Help Desk Services' ,'Hospital Processes','Waiting Time for OPD','Waiting time for admission and discharge procedures','Availability of Doctors',
                    'Punctuality of doctors','Quality of doctors','Availability of specialists','Accuracy of diagnosis','Quality of infrastructure',
                    'Quality and Availability of medical equipment','General staff behaviour','Nursing staff response time','Overall efficiency of nursing staff','Quality of Diagnostic Services','Availability of drugs','Digital Health and HMIS','Pre and post operation/treatment care and guidance',
                    'Emergency Handling','Efficiency of EMR system','Use of telemedicine','Hygiene and cleanliness in the facility','Quality and Quantity of food provided',
                    'Amenities for family and relatives','Overall experience in the healthcare facility','Possibility of you visiting again']

if len(new_column_names) != len(patient_data.columns):
    raise ValueError("The number of new column names should match the number of columns in the CSV file.")

patient_data.columns = new_column_names
patient_data=patient_data.drop(['Name','Phone number','Patient ID'],axis=1,inplace=False)

#PIE CHARTS
import pandas as pd
import matplotlib.pyplot as plt


column_name="Gender"
category_counts=patient_data[column_name].value_counts()
plt.figure(figsize=(8, 8))  # Optional: Set the figure size
plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140)
#autopct stands for "automatic percentage."Setting it to '%1.1f%%' formats the labels on the pie chart to display the percentage of each category with one decimal place.
#For example, if you have a category that represents 25% of the pie, it will be labeled as "25.0%" in the chart.
plt.title(f"Pie Chart for {column_name}")
plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

# Show the plot
plt.show()
import pandas as pd
import matplotlib.pyplot as plt

column_name="Department"
category_counts=patient_data[column_name].value_counts()
plt.figure(figsize=(8, 8))  # Optional: Set the figure size
plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140)
plt.title(f"Pie Chart for {column_name}")
plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

# Show the plot
plt.show()
import pandas as pd
import matplotlib.pyplot as plt

column_name="City Of Residence"
category_counts=patient_data[column_name].value_counts()
plt.figure(figsize=(8, 8))  # Optional: Set the figure size
plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140)
plt.title(f"Pie Chart for {column_name}")
plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

# Show the plot
plt.show()
import pandas as pd
import matplotlib.pyplot as plt

# Define age groups or bins
age_bins = [0, 18, 30, 40, 50, 60, 100]
age_labels = ["0-18", "19-30", "31-40", "41-50", "51-60", "61+"]

# Create age group categories
patient_data['AgeGroup'] = pd.cut(patient_data['Age'], bins=age_bins, labels=age_labels)

# Count the occurrences of each age group
age_group_counts = patient_data['AgeGroup'].value_counts()

plt.figure(figsize=(8, 8)) 
plt.pie(age_group_counts, labels=age_group_counts.index, autopct='%1.1f%%', startangle=140)

# Optional: Customize the plot
plt.title("Pie Chart for Age Groups")
plt.axis('equal')

# Show the plot
plt.show()


#DESCRIPTIVE STATS
Statistical_Data=pd.DataFrame(patient_data.describe())
print(Statistical_Data)

#DEPARTMENT WISE ANALYSIS
import seaborn as sns
import matplotlib.pyplot as plt

# Assuming 'patient_data' is your DataFrame

fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 12))

# Boxplot for 'Availability of specialists'
box_plot1 = sns.boxplot(x="Department", y="Availability of specialists", data=patient_data, ax=axes[0])

ax = box_plot1.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot1.figure.tight_layout()
box_plot1.set_xticklabels(box_plot1.get_xticklabels(), rotation=90)

# Boxplot for 'Accuracy of diagnosis'
box_plot2 = sns.boxplot(x="Department", y="Accuracy of diagnosis", data=patient_data, ax=axes[1])

ax = box_plot2.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot2.figure.tight_layout()
box_plot2.set_xticklabels(box_plot2.get_xticklabels(), rotation=90)

# Boxplot for 'Quality of doctors'
box_plot3 = sns.boxplot(x="Department", y="Quality of doctors", data=patient_data, ax=axes[2])

ax = box_plot3.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot3.figure.tight_layout()
box_plot3.set_xticklabels(box_plot3.get_xticklabels(), rotation=90)

plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# Assuming 'patient_data' is your DataFrame

# Calculate the Satisfaction_Score
satisfaction_cols = patient_data.columns[8:]
patient_data_1["Satisfaction_Score"] = patient_data[satisfaction_cols].mean(axis=1)

# Create subplots
fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(10, 16))

# Boxplot for 'Department'
box_plot4 = sns.boxplot(x="Department", y="Satisfaction_Score", data=patient_data_1, ax=axes[0])

ax = box_plot4.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot4.figure.tight_layout()
box_plot4.set_xticklabels(box_plot4.get_xticklabels(), rotation=90)

# Boxplot for 'Gender'
box_plot5 = sns.boxplot(x="Gender", y="Satisfaction_Score", data=patient_data_1, ax=axes[1])

ax = box_plot5.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot5.figure.tight_layout()

# Boxplot for 'City Of Residence'
box_plot6 = sns.boxplot(x="City Of Residence", y="Satisfaction_Score", data=patient_data_1, ax=axes[2])

ax = box_plot6.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot6.figure.tight_layout()
box_plot6.set_xticklabels(box_plot6.get_xticklabels(), rotation=90)

# Boxplot for 'Age Group'
patient_data_1.loc[patient_data_1['Age']<18, 'age_group'] = '<18'
patient_data_1.loc[patient_data_1['Age'].between(18,30), 'age_group'] = '18-30'
patient_data_1.loc[patient_data_1['Age'].between(30,40), 'age_group'] = '30-40'
patient_data_1.loc[patient_data_1['Age'].between(40,50), 'age_group'] = '40-50'
patient_data_1.loc[patient_data_1['Age'].between(50,60), 'age_group'] = '50-60'
patient_data_1.loc[patient_data_1['Age'].between(60,70), 'age_group'] = '60-70'
patient_data_1.loc[patient_data_1['Age']>70, 'age_group'] = '>70'
box_plot7 = sns.boxplot(x="age_group", y="Satisfaction_Score", data=patient_data_1, ax=axes[3])

ax = box_plot7.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot7.figure.tight_layout()
box_plot7.set_xticklabels(box_plot7.get_xticklabels(), rotation=90)

plt.show()

#heatmap
correlation_matrix = patient_data.corr()
plt.figure(figsize=(20, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.show()


#SCORES:

#OVERALL HOSPITAL SCORE
# Assuming 'Statistical_Data' is your DataFrame

# Extract the relevant row of data
Statistical_Data_1 = Statistical_Data.iloc[1, 2:]

# Calculate the cumulative mean score
cumulative_mean = Statistical_Data_1.mean()

# Example usage:
float_rating_overall = cumulative_mean

# Ensure the rating is between 0 and 10
float_rating_overall = max(0, min(10, float_rating_overall))

# Calculate the number of filled stars
filled_stars_overall = round(float_rating_overall)

# Calculate the number of empty stars
empty_stars_overall = 10 - filled_stars_overall

# Create the visual representation
visual_rating_overall = '★' * filled_stars_overall + '☆' * empty_stars_overall

# Print the result for Overall Hospital Score
print('Overall Hospital Score =', float_rating_overall, '->', visual_rating_overall)

# Rating for Facilities
facilities_scores = patient_data[['Quality of infrastructure', 'Quality and Quantity of food provided',
                                  'Efficiency of EMR system', 'Hygiene and cleanliness in the facility',
                                  'Quality and Availability of medical equipment', 'Use of telemedicine']].mean()
facilities_average_score = facilities_scores.mean()

# Example usage:
float_rating_facilities = facilities_average_score

# Ensure the rating is between 0 and 10
float_rating_facilities = max(0, min(10, float_rating_facilities))

# Calculate the number of filled stars
filled_stars_facilities = round(float_rating_facilities)

# Calculate the number of empty stars
empty_stars_facilities = 10 - filled_stars_facilities

# Create the visual representation
visual_rating_facilities = '★' * filled_stars_facilities + '☆' * empty_stars_facilities

# Print the result for Facilities
print('Rating of Facilities =', float_rating_facilities, '->', visual_rating_facilities)

# Rating for Staff
staff_scores = patient_data[['Availability of Doctors', 'Punctuality of doctors', 'Quality of doctors',
                             'Accuracy of diagnosis', 'General staff behaviour', 'Nursing staff response time',
                             'Emergency Handling']].mean()
staff_average_score = staff_scores.mean()

# Example usage:
float_rating_staff = staff_average_score

# Ensure the rating is between 0 and 10
float_rating_staff = max(0, min(10, float_rating_staff))

# Calculate the number of filled stars
filled_stars_staff = round(float_rating_staff)

# Calculate the number of empty stars
empty_stars_staff = 10 - filled_stars_staff

# Create the visual representation
visual_rating_staff = '★' * filled_stars_staff + '☆' * empty_stars_staff

# Print the result for Staff
print('Rating of Staff =', float_rating_staff, '->', visual_rating_staff)

# Rating for Administration
admin_scores = patient_data[['Help Desk Services', 'Waiting time for admission and discharge procedures',
                             'Waiting Time for OPD', 'Pre and post operation/treatment care and guidance']].mean()
admin_average_score = admin_scores.mean()

# Example usage:
float_rating_admin = admin_average_score

# Ensure the rating is between 0 and 10
float_rating_admin = max(0, min(10, float_rating_admin))

# Calculate the number of filled stars
filled_stars_admin = round(float_rating_admin)

# Calculate the number of empty stars
empty_stars_admin = 10 - filled_stars_admin

# Create the visual representation
visual_rating_admin = '★' * filled_stars_admin + '☆' * empty_stars_admin

# Print the result for Administration
print('Rating of Administration =', float_rating_admin, '->', visual_rating_admin)










# In[2]:


dept=patient_data['Department']
dept
qual=patient_data['Quality of doctors']
df2=pd.DataFrame()
df2['Department']=dept
df2['Quality']=qual
df2.boxplot(x='Department', y='Quality')



# In[15]:


box_plot1 = sns.boxplot(x="Department", y="Accuracy of diagnosis", data=patient_data)

ax = box_plot1.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    # every 4th line at the interval of 6 is median line
    # 0 -> p25 1 -> p75 2 -> lower whisker 3 -> upper whisker 4 -> p50 5 -> upper extreme value
    y = round(lines[4+cat*6].get_ydata()[0],1) 

    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot1.figure.tight_layout()
box_plot1.set_xticklabels(box_plot1.get_xticklabels(), rotation=90)


# In[5]:


box_plot1 = sns.boxplot(x="Department", y="Availability of specialists", data=patient_data)

ax = box_plot1.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    # every 4th line at the interval of 6 is median line
    # 0 -> p25 1 -> p75 2 -> lower whisker 3 -> upper whisker 4 -> p50 5 -> upper extreme value
    y = round(lines[4+cat*6].get_ydata()[0],1) 

    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot1.figure.tight_layout()
box_plot1.set_xticklabels(box_plot1.get_xticklabels(), rotation=90)


# In[17]:


#SATISFACTION PLOTS:
# Using map function

patient_data_1 = patient_data.loc[: , patient_data.columns != "Digital Health and HMIS"]


patient_data_1["Satisfaction_Score"]=patient_data.iloc[:, 8:].mean(axis=1)
patient_data_1
gender_satisfaction = patient_data_1.groupby('Gender')['Satisfaction_Score'].mean().reset_index()
gender_satisfaction


age_satisfaction = patient_data_1.groupby('Age')['Satisfaction_Score'].mean().reset_index()

age_satisfaction

location_satisfaction = patient_data_1.groupby('City Of Residence')['Satisfaction_Score'].mean().reset_index()
location_satisfaction
box_plot4 = sns.boxplot(x="Department", y="Satisfaction_Score", data=patient_data_1)

ax = box_plot4.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    # every 4th line at the interval of 6 is median line
    # 0 -> p25 1 -> p75 2 -> lower whisker 3 -> upper whisker 4 -> p50 5 -> upper extreme value
    y = round(lines[4+cat*6].get_ydata()[0],1) 

    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot4.figure.tight_layout()
box_plot4.set_xticklabels(box_plot.get_xticklabels(), rotation=90)


# In[20]:


box_plot5 = sns.boxplot(x="Gender", y="Satisfaction_Score", data=patient_data_1)

ax = box_plot5.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    # every 4th line at the interval of 6 is median line
    # 0 -> p25 1 -> p75 2 -> lower whisker 3 -> upper whisker 4 -> p50 5 -> upper extreme value
    y = round(lines[4+cat*6].get_ydata()[0],1) 

    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot5.figure.tight_layout()


# In[23]:


box_plot6 = sns.boxplot(x="City Of Residence", y="Satisfaction_Score", data=patient_data_1)

ax = box_plot6.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    # every 4th line at the interval of 6 is median line
    # 0 -> p25 1 -> p75 2 -> lower whisker 3 -> upper whisker 4 -> p50 5 -> upper extreme value
    y = round(lines[4+cat*6].get_ydata()[0],1) 

    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot6.figure.tight_layout()
box_plot6.set_xticklabels(box_plot6.get_xticklabels(), rotation=90)



# In[24]:


patient_data_1.loc[patient_data_1['Age']<18, 'age_group'] = '<18'
patient_data_1.loc[patient_data_1['Age'].between(18,30), 'age_group'] = '18-30'
patient_data_1.loc[patient_data_1['Age'].between(30,40), 'age_group'] = '30-40'
patient_data_1.loc[patient_data_1['Age'].between(40,50), 'age_group'] = '40-50'
patient_data_1.loc[patient_data_1['Age'].between(50,60), 'age_group'] = '50-60'
patient_data_1.loc[patient_data_1['Age'].between(60,70), 'age_group'] = '60-70'
patient_data_1.loc[patient_data_1['Age']>70, 'age_group'] = '>70'

box_plot7 = sns.boxplot(x="age_group", y="Satisfaction_Score", data=patient_data_1)

ax = box_plot7.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    # every 4th line at the interval of 6 is median line
    # 0 -> p25 1 -> p75 2 -> lower whisker 3 -> upper whisker 4 -> p50 5 -> upper extreme value
    y = round(lines[4+cat*6].get_ydata()[0],1) 

    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot7.figure.tight_layout()
box_plot7.set_xticklabels(box_plot7.get_xticklabels(), rotation=90)


# In[12]:


#heatmap
correlation_matrix = patient_data.corr()
plt.figure(figsize=(20, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.show()


# In[13]:


patient_data['Possibility of you visiting again'].mean()


# In[14]:





# In[38]:


#SCORES:

#OVERALL HOSPITAL SCORE
# Assuming 'Statistical_Data' is your DataFrame

# Extract the relevant row of data
Statistical_Data_1 = Statistical_Data.iloc[1, 2:]

# Calculate the cumulative mean score
cumulative_mean = Statistical_Data_1.mean()

# Example usage:
float_rating_overall = cumulative_mean

# Ensure the rating is between 0 and 10
float_rating_overall = max(0, min(10, float_rating_overall))

# Calculate the number of filled stars
filled_stars_overall = round(float_rating_overall)

# Calculate the number of empty stars
empty_stars_overall = 10 - filled_stars_overall

# Create the visual representation
visual_rating_overall = '★' * filled_stars_overall + '☆' * empty_stars_overall

# Print the result for Overall Hospital Score
print('Overall Hospital Score =', float_rating_overall, '->', visual_rating_overall)



#FACULTY SCORE
#FACILITIES SCORE
#ADMINISTRATION SCORE


# In[37]:


# Rating for Facilities
facilities_scores = patient_data[['Quality of infrastructure', 'Quality and Quantity of food provided',
                                  'Efficiency of EMR system', 'Hygiene and cleanliness in the facility',
                                  'Quality and Availability of medical equipment', 'Use of telemedicine']].mean()
facilities_average_score = facilities_scores.mean()

# Example usage:
float_rating_facilities = facilities_average_score

# Ensure the rating is between 0 and 10
float_rating_facilities = max(0, min(10, float_rating_facilities))

# Calculate the number of filled stars
filled_stars_facilities = round(float_rating_facilities)

# Calculate the number of empty stars
empty_stars_facilities = 10 - filled_stars_facilities

# Create the visual representation
visual_rating_facilities = '★' * filled_stars_facilities + '☆' * empty_stars_facilities

# Print the result for Facilities
print('Rating of Facilities =', float_rating_facilities, '->', visual_rating_facilities)

# Rating for Staff
staff_scores = patient_data[['Availability of Doctors', 'Punctuality of doctors', 'Quality of doctors',
                             'Accuracy of diagnosis', 'General staff behaviour', 'Nursing staff response time',
                             'Emergency Handling']].mean()
staff_average_score = staff_scores.mean()

# Example usage:
float_rating_staff = staff_average_score

# Ensure the rating is between 0 and 10
float_rating_staff = max(0, min(10, float_rating_staff))

# Calculate the number of filled stars
filled_stars_staff = round(float_rating_staff)

# Calculate the number of empty stars
empty_stars_staff = 10 - filled_stars_staff

# Create the visual representation
visual_rating_staff = '★' * filled_stars_staff + '☆' * empty_stars_staff

# Print the result for Staff
print('Rating of Staff =', float_rating_staff, '->', visual_rating_staff)

# Rating for Administration
admin_scores = patient_data[['Help Desk Services', 'Waiting time for admission and discharge procedures',
                             'Waiting Time for OPD', 'Pre and post operation/treatment care and guidance']].mean()
admin_average_score = admin_scores.mean()

# Example usage:
float_rating_admin = admin_average_score

# Ensure the rating is between 0 and 10
float_rating_admin = max(0, min(10, float_rating_admin))

# Calculate the number of filled stars
filled_stars_admin = round(float_rating_admin)

# Calculate the number of empty stars
empty_stars_admin = 10 - filled_stars_admin

# Create the visual representation
visual_rating_admin = '★' * filled_stars_admin + '☆' * empty_stars_admin

# Print the result for Administration
print('Rating of Administration =', float_rating_admin, '->', visual_rating_admin)





# In[36]:


staff_average_score=patient_data[[ 'Availability of Doctors', 'Punctuality of doctors' ,  'Quality of doctors',    'Accuracy of diagnosis',
                                   'General staff behaviour',  'Nursing staff response time',  'Nursing staff response time',  'Emergency Handling' ]].mean()
staff_average_score=staff_average_score.mean()
staff_average_score
# Example usage:
float_rating = staff_average_score

# Ensure the rating is between 0 and 10
float_rating = max(0, min(10, float_rating))

# Calculate the number of filled stars
filled_stars = round(float_rating)

# Calculate the number of empty stars
empty_stars = 10 - filled_stars

# Create the visual representation
visual_rating = '★' * filled_stars + '☆' * empty_stars

# Print the result
print(f"Rating: {float_rating} -> {visual_rating}")




# In[ ]:





# In[17]:


admin_average_score=patient_data[[ 'Help Desk Services', 'Waiting time for admission and discharge procedures', 'Waiting Time for OPD', 'Pre and post operation/treatment care and guidance',   ]].mean()

admin_average_score=admin_average_score.mean()
admin_average_score
# Example usage:
float_rating = admin_average_score

# Ensure the rating is between 0 and 10
float_rating = max(0, min(10, float_rating))

# Calculate the number of filled stars
filled_stars = round(float_rating)

# Calculate the number of empty stars
empty_stars = 10 - filled_stars

# Create the visual representation
visual_rating = '★' * filled_stars + '☆' * empty_stars

# Print the result
print(f"Rating: {float_rating} -> {visual_rating}")




# In[32]:


# Boxplot
plt.figure(figsize=(8, 6))
boxplot = df2.boxplot(column='Quality', by='Department', showmeans=True, meanline=True, grid=False)

# Add labels and title
plt.title('Boxplot of Numeric Value by Category')
plt.xlabel('Category')
plt.ylabel('Numeric Value')

# Add mean values as text annotations
for i, box in enumerate(boxplot['boxes']):
    # Get the mean value
    mean_val = df2[df2['Department'] == box.get_label()]['Quality'].mean()
    
    # Add text annotation with mean value
    boxplot.annotate(f'Mean: {mean_val:.2f}', xy=(i + 1, mean_val), ha='center', va='center', color='red')

# Show the plot
plt.show()


# In[6]:


# Boxplot with mean values
plt.figure(figsize=(8, 6))
boxplot = df2.boxplot(column='Quality', by='Department', grid=False, showmeans=True, meanline=True)
plt.xticks(rotation=90)


# In[7]:


import seaborn as sns
import matplotlib.pyplot as plt

# Assuming 'patient_data' is your DataFrame

fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(10, 8))

# Boxplot for 'Availability of specialists'
box_plot1 = sns.boxplot(x="Department", y="Availability of specialists", data=patient_data, ax=axes[0])

ax = box_plot1.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot1.figure.tight_layout()
box_plot1.set_xticklabels(box_plot1.get_xticklabels(), rotation=90)

# Boxplot for 'Accuracy of diagnosis'
box_plot2 = sns.boxplot(x="Department", y="Accuracy of diagnosis", data=patient_data, ax=axes[1])

ax = box_plot2.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot2.figure.tight_layout()
box_plot2.set_xticklabels(box_plot2.get_xticklabels(), rotation=90)

plt.show()


# In[8]:


import seaborn as sns
import matplotlib.pyplot as plt

# Assuming 'patient_data' is your DataFrame

fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 12))

# Boxplot for 'Availability of specialists'
box_plot1 = sns.boxplot(x="Department", y="Availability of specialists", data=patient_data, ax=axes[0])

ax = box_plot1.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot1.figure.tight_layout()
box_plot1.set_xticklabels(box_plot1.get_xticklabels(), rotation=90)

# Boxplot for 'Accuracy of diagnosis'
box_plot2 = sns.boxplot(x="Department", y="Accuracy of diagnosis", data=patient_data, ax=axes[1])

ax = box_plot2.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot2.figure.tight_layout()
box_plot2.set_xticklabels(box_plot2.get_xticklabels(), rotation=90)

# Boxplot for 'Quality of doctors'
box_plot3 = sns.boxplot(x="Department", y="Quality of doctors", data=patient_data, ax=axes[2])

ax = box_plot3.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot3.figure.tight_layout()
box_plot3.set_xticklabels(box_plot3.get_xticklabels(), rotation=90)

plt.show()



# In[28]:


#SATISFACTION PLOTS:
# Using map function

patient_data_1 = patient_data.loc[: , patient_data.columns != "Digital Health and HMIS"]


patient_data_1["Satisfaction_Score"]=patient_data.iloc[:, 8:].mean(axis=1)
patient_data_1

box_plot4 = sns.boxplot(x="Department", y="Satisfaction_Score", data=patient_data_1, ax=axes[2])

ax = box_plot4.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4+cat*6].get_ydata()[0], 1)
    ax.text(
        cat, 
        y, 
        f'{y}', 
        ha='center', 
        va='center', 
        fontweight='bold', 
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot4.figure.tight_layout()
box_plot4.set_xticklabels(box_plot4.get_xticklabels(), rotation=90)

plt.show()


# In[31]:


import seaborn as sns
import matplotlib.pyplot as plt

# Assuming 'patient_data' is your DataFrame

# Calculate the Satisfaction_Score
satisfaction_cols = patient_data.columns[8:]
patient_data_1["Satisfaction_Score"] = patient_data[satisfaction_cols].mean(axis=1)

# Create subplots
fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(10, 16))

# Boxplot for 'Department'
box_plot4 = sns.boxplot(x="Department", y="Satisfaction_Score", data=patient_data_1, ax=axes[0])

ax = box_plot4.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot4.figure.tight_layout()
box_plot4.set_xticklabels(box_plot4.get_xticklabels(), rotation=90)

# Boxplot for 'Gender'
box_plot5 = sns.boxplot(x="Gender", y="Satisfaction_Score", data=patient_data_1, ax=axes[1])

ax = box_plot5.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot5.figure.tight_layout()

# Boxplot for 'City Of Residence'
box_plot6 = sns.boxplot(x="City Of Residence", y="Satisfaction_Score", data=patient_data_1, ax=axes[2])

ax = box_plot6.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot6.figure.tight_layout()
box_plot6.set_xticklabels(box_plot6.get_xticklabels(), rotation=90)

# Boxplot for 'Age Group'
patient_data_1.loc[patient_data_1['Age']<18, 'age_group'] = '<18'
patient_data_1.loc[patient_data_1['Age'].between(18,30), 'age_group'] = '18-30'
patient_data_1.loc[patient_data_1['Age'].between(30,40), 'age_group'] = '30-40'
patient_data_1.loc[patient_data_1['Age'].between(40,50), 'age_group'] = '40-50'
patient_data_1.loc[patient_data_1['Age'].between(50,60), 'age_group'] = '50-60'
patient_data_1.loc[patient_data_1['Age'].between(60,70), 'age_group'] = '60-70'
patient_data_1.loc[patient_data_1['Age']>70, 'age_group'] = '>70'
box_plot7 = sns.boxplot(x="age_group", y="Satisfaction_Score", data=patient_data_1, ax=axes[3])

ax = box_plot7.axes
lines = ax.get_lines()
categories = ax.get_xticks()

for cat in categories:
    y = round(lines[4 + cat * 6].get_ydata()[0], 1)
    ax.text(
        cat,
        y,
        f'{y}',
        ha='center',
        va='center',
        fontweight='bold',
        size=10,
        color='white',
        bbox=dict(facecolor='#445A64'))

box_plot7.figure.tight_layout()
box_plot7.set_xticklabels(box_plot7.get_xticklabels(), rotation=90)

plt.show()


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