Notes

Yes — I read your CSV ✅
But one important thing first:

⚠️ This CSV is NOT one single table

It is a merged extract of 12 different tables, combined into one file using a column called:

✅ source_table

So the correct way is:

Split this CSV into 12 tables

Then create SQL CREATE TABLE code for each one

✅ What’s inside your file (Summary)

Your file has:

710 rows

60 columns

Data from 12 tables

These tables are:

assign_patients

manage_appointments

chief_complaints

clinic_policy_acknowledgements

consent_to_treatments

hipaa_consents

family_history_answers

patient_medical_histories

patient_surgical_histories

patient_allergies

patient_social_histories

patient_habits

✅ How the data works (Proper Explanation)

Each row belongs to one table only, identified by:

source_table

Example:

If source_table = manage_appointments, then that row contains appointment data.

If source_table = patient_allergies, then that row contains allergy data.

So your dataset is like:

📌 One CSV = many tables mixed together

✅ SQL Database Table Code (MySQL)

Below is clean SQL code you can use directly.

1) assign_patients
CREATE TABLE assign_patients (
id BIGINT PRIMARY KEY,
specialist_id BIGINT,
patient_id BIGINT NOT NULL,
assigned_by BIGINT,
status VARCHAR(50),
assigned_at DATETIME,
ended_at DATETIME,
notes TEXT,
created_at DATETIME,
updated_at DATETIME
);

2) manage_appointments
CREATE TABLE manage_appointments (
id BIGINT PRIMARY KEY,
specialist_id BIGINT,
patient_id BIGINT NOT NULL,
assign_patient_id BIGINT,
appointment_title VARCHAR(255),
appointment_notes TEXT,
appointment_date DATETIME,
duration_minutes BIGINT,
status VARCHAR(50),
checked_in_at DATETIME,
created_by BIGINT,
updated_by BIGINT,
created_at DATETIME,
updated_at DATETIME
);

3) chief_complaints
CREATE TABLE chief_complaints (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
complaint_text TEXT,
created_at DATETIME,
updated_at DATETIME
);

4) clinic_policy_acknowledgements
CREATE TABLE clinic_policy_acknowledgements (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
missed_appointment_policy_agreed TINYINT,
patient_signature TEXT,
signature_date DATETIME,
created_at DATETIME,
updated_at DATETIME
);

5) consent_to_treatments
CREATE TABLE consent_to_treatments (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
patient_signature TEXT,
signature_date DATETIME,
printed_name VARCHAR(255),
agreed TINYINT,
created_at DATETIME,
updated_at DATETIME
);

6) hipaa_consents
CREATE TABLE hipaa_consents (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
patient_signature TEXT,
signature_date DATETIME,
agreed TINYINT,
authorized_individuals TEXT,
release_to VARCHAR(255),
created_at DATETIME,
updated_at DATETIME
);

7) family_history_answers
CREATE TABLE family_history_answers (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
medical_id BIGINT,
relative VARCHAR(255),
value DOUBLE,
sibling_group BIGINT,
custom_medical_history TEXT,
created_at DATETIME,
updated_at DATETIME
);

8) patient_medical_histories
CREATE TABLE patient_medical_histories (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
medical_id BIGINT,
cancer VARCHAR(255),
heart_attack VARCHAR(255),
created_at DATETIME,
updated_at DATETIME
);

9) patient_surgical_histories
CREATE TABLE patient_surgical_histories (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
surgical_id BIGINT,
created_at DATETIME,
updated_at DATETIME
);

10) patient_allergies
CREATE TABLE patient_allergies (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
allergy_id BIGINT,
reaction TEXT,
created_at DATETIME,
updated_at DATETIME
);

11) patient_social_histories
CREATE TABLE patient_social_histories (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,
social_id BIGINT,
occupation VARCHAR(255),
created_at DATETIME,
updated_at DATETIME
);

12) patient_habits
CREATE TABLE patient_habits (
id BIGINT PRIMARY KEY,
patient_id BIGINT NOT NULL,
appointment_id BIGINT,

never_smoked TINYINT,
smoked_since BIGINT,
previously_smoked_packs_per_day DOUBLE,
currently_smoke_packs_per_day DOUBLE,
stopped_smoking BIGINT,
smoked_for_years DOUBLE,
e_smoker_cartridges_per_day DOUBLE,
want_to_quit_smoking TINYINT,

chewing_status VARCHAR(255),
chewing_oz_per_week DOUBLE,
previously_chewed_oz_per_week DOUBLE,
quit_tobacco_when BIGINT,

drinking_type VARCHAR(255),
liquor_per_day DOUBLE,
beer_per_day DOUBLE,
wine_per_day DOUBLE,

created_at DATETIME,
updated_at DATETIME
);

✅ How you can use this in your code (Correct Approach)
Step 1: Filter data per table

Example:

assign_patients_df = df[df["source_table"] == "assign_patients"]
appointments_df = df[df["source_table"] == "manage_appointments"]

Step 2: Insert into real DB tables

You can insert each dataframe into MySQL using:

to_sql() (SQLAlchemy)

or manual INSERT

If you want, I can also generate:

✅ Python code that automatically splits the CSV into 12 DataFrames
✅ and writes them into MySQL tables automatically.

Just tell me your DB type:
MySQL / PostgreSQL / SQLite