Step 1: Create a Django project and app
First, create a new Django project and app using the following commands:
django-admin startproject data_analysis_project
cd data_analysis_project
python manage.py startapp data_analysis_app
Step 2: Define the data model
In the models.py file inside the data_analysis_app directory, define the data model to represent the dataset you'll be working with. For example, let's assume you have a dataset of students with their names, ages, and grades. Define the model as follows:
from django.db import models
class Student(models.Model):
name = models.CharField(max_length=100)
age = models.IntegerField()
grade = models.CharField(max_length=10)
Run the migrations to create the necessary database tables:
python manage.py makemigrations
python manage.py migrate
Step 3: Load data into the database (Optional)
If you already have data in the database, you can skip this step. Otherwise, you can create a script to load your dataset into the database. Here's an example using a script:
import csv
from data_analysis_app.models import Student
def load_data():
with open('path/to/your/dataset.csv', 'r') as file:
reader = csv.reader(file)
next(reader) # Skip the header row
for row in reader:
name = row[0]
age = int(row[1])
grade = row[2]
# Create a new Student object and save it in the database
student = Student(name=name, age=age, grade=grade)
student.save()
Call the load_data function to load the dataset
load_data()
Run the script to load the dataset into the database:
Step 4: Perform preliminary analysis to identify null values
In your Django views, you can perform preliminary analysis to identify null values in the data. Here are some ways to achieve that:
Check for null values in a specific field:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_null_name = Student.objects.filter(name__isnull=True)
# ... rest of your code
Count the number of null values in a specific field:
from data_analysis_app.models import Student
def analyze_data(request):
null_age_count = Student.objects.filter(age__isnull=True).count()
# ... rest of your code
Check for null values across all fields:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_null_values = Student.objects.filter(name__isnull=True, age__isnull=True, grade__isnull=True)
# ... rest of your code
Step 5: Perform preliminary analysis to identify duplicate data
In your Django views, you can perform preliminary analysis to identify duplicate data in the dataset. Here's an example:
Find duplicate records based on a specific field:
from django.db.models import Count
from data_analysis_app.models import Student
def analyze_data(request):
duplicate_names = Student.objects.values('name').annotate(count=Count('id')).filter(count__gt=1)
# ... rest of your code
Check for missing values in a specific field:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_name = Student.objects.filter(name='')
# ... rest of your code
Count the number of missing values in a specific field:
from data_analysis_app.models import Student
def analyze_data(request):
missing_age_count = Student.objects.filter(age__exact=None).count()
# ... rest of your code
Check for missing values across all fields:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_values = Student.objects.filter(name='', age__exact=None, grade='')
# ... rest of your code
Identify records with missing values in any field:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_values = Student.objects.exclude(name__isnull=False, age__isnull=False, grade__isnull=False)
# ... rest of your code
Calculate the percentage of missing values in a specific field:
from data_analysis_app.models import Student
def analyze_data(request):
total_students = Student.objects.count()
missing_age_count = Student.objects.filter(age__exact=None).count()
missing_age_percentage = (missing_age_count / total_students) * 100
# ... rest of your code
Identify records with missing values in multiple fields:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_values = Student.objects.exclude(name__isnull=False, age__isnull=False).exclude(grade__isnull=False)
# ... rest of your code
Check for missing values in numeric fields:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_age = Student.objects.filter(age__exact=None)
# ... rest of your code
Check for missing values in string fields:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_name = Student.objects.filter(name='')
students_with_missing_grade = Student.objects.filter(grade='')
# ... rest of your code
Identify records with missing values in at least one field:
from data_analysis_app.models import Student
def analyze_data(request):
students_with_missing_values = Student.objects.filter(models.Q(name='') | models.Q(age__exact=None) | models.Q(grade=''))
# ... rest of your code
Calculate the percentage of missing values across all fields:
from data_analysis_app.models import Student
def analyze_data(request):
total_students = Student.objects.count()
students_with_missing_values = Student.objects.filter(models.Q(name='') | models.Q(age__exact=None) | models.Q(grade=''))
missing_values_percentage = (students_with_missing_values.count() / total_students) * 100
# ... rest of your code
Calculate the average age of the students:
from django.db.models import Avg
from data_analysis_app.models import Student
def analyze_data(request):
average_age = Student.objects.aggregate(Avg('age'))
# ... rest of your code
Find the highest grade achieved by a student:
from django.db.models import Max
from data_analysis_app.models import Student
def analyze_data(request):
highest_grade = Student.objects.aggregate(Max('grade'))
# ... rest of your code
Group students by grade and count the number of students in each grade:
from django.db.models import Count
from data_analysis_app.models import Student
def analyze_data(request):
students_by_grade = Student.objects.values('grade').annotate(count=Count('id'))
# ... rest of your code
Filter out students who are below a certain age threshold:
from data_analysis_app.models import Student
def analyze_data(request):
filtered_students = Student.objects.filter(age__gte=18)
# ... rest of your code
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