What's the best way to handle large datasets in Jupyter Notebook?

Step 1: Use Efficient Data Structures
The first step in handling large datasets in Jupyter Notebook is to use efficient data structures. Pandas is a powerful data manipulation library in Python that provides data structures like DataFrame and Series which are efficient for handling large datasets.

Step 2: Load Data in Chunks
If your dataset is too large to fit into memory, you can load it in chunks. Pandas provides a function called read_csv() which has a parameter called chunksize. You can specify the number of rows to be read at a time, thus allowing you to work with a part of the dataset at a time.

Step 3: Use Efficient Data Types
Pandas automatically decides the data type of the data while loading it. However, it might not always choose the most memory efficient data type. You can manually specify the data types of the columns while loading the data to save memory.

Step 4: Use Categorical Data Type
If a column in your dataset has a limited number of unique values, you can convert its data type to category to save memory.

Step 5: Use Sparse Data Structures
Pandas provides sparse data structures which can be used to store data that's mostly missing or constant. These data structures use less memory.

Step 6: Use Dask Library
Dask is a parallel computing library that integrates with Pandas. It allows you to work with larger than memory datasets.

Step 7: Use Sampling
If your dataset is too large, you can use sampling techniques to reduce its size. You can either use random sampling or stratified sampling depending on your needs.

Step 8: Use Incremental Learning
If your dataset is too large to fit into memory and you want to build a machine learning model, you can use incremental learning techniques. These techniques allow you to train a model on a part of the dataset at a time.

Step 9: Use Disk-Based Data Storage
If your dataset is too large to fit into memory, you can use disk-based data storage formats like HDF5.

Step 10: Use Cloud-Based Solutions
If your dataset is too large to handle on your local machine, you can use cloud-based solutions like Google Colab which provide more computational resources.