List down the use of pandas and numpy in django

Data cleaning and preprocessing: Use Pandas and NumPy to clean and preprocess data before storing it in the Django database.

Data analysis: Perform data analysis tasks using Pandas and NumPy to gain insights from the data stored in Django models.

Data visualization: Utilize Pandas and NumPy to create visualizations such as charts and plots to represent data stored in Django models.

Data manipulation: Perform various data manipulation operations like filtering, sorting, merging, and aggregating using Pandas and NumPy.

Import and export data: Read data from external sources, such as CSV files or Excel spreadsheets, into Django models using Pandas. Export Django model data to different file formats using Pandas.

Handling missing data: Use Pandas to handle missing data in Django models by filling or removing the missing values.

Time series analysis: Perform time series analysis on data stored in Django models using Pandas, which includes tasks like resampling, rolling calculations, and date/time indexing.

Statistical analysis: Utilize Pandas and NumPy to perform statistical analysis on data stored in Django models, such as calculating mean, median, standard deviation, and correlation.

Data filtering and querying: Filter and query Django models based on specific conditions using Pandas and NumPy.

Data transformation: Apply various transformations to data stored in Django models using Pandas and NumPy, such as scaling, normalization, and encoding.

Data sampling: Sample data from Django models using Pandas for tasks like data exploration or model training.

Feature engineering: Create new features or derive additional insights from existing data in Django models using Pandas and NumPy.

Data merging and joining: Merge and join data from multiple Django models using Pandas based on common keys or columns.

Handling large datasets: Use Pandas and NumPy to efficiently handle large datasets in Django models by optimizing memory usage and performance.

Machine learning integration: Integrate Pandas and NumPy with machine learning libraries in Django to preprocess and prepare data for model training.

Time zone conversion: Use Pandas to convert and manipulate time zones for datetime data stored in Django models.

Data summarization: Summarize and aggregate data stored in Django models using Pandas and NumPy, such as calculating totals, averages, or counts.

Data profiling and exploration: Use Pandas and NumPy to explore and profile data stored in Django models, including descriptive statistics, data distributions, and data quality checks.

Text data processing: Apply text processing techniques to textual data in Django models using Pandas and NumPy, such as tokenization, stemming, and sentiment analysis.

Financial analysis: Perform financial analysis tasks on data stored in Django models using Pandas and NumPy, including calculating returns, analyzing portfolios, and conducting risk assessments.