Pandas Scaling to Large Datasets

Pandas Scaling to Large Datasets.

Pandas provides data structures for in-memory analytics, which makes using pandas to analyze datasets that are larger than memory datasets somewhat tricky. Even datasets that are a sizable fraction of memory become unwieldy, as some pandas operations need to make intermediate copies.

When working with large datasets in pandas, performance can become a major bottleneck. Here are some strategies to scale pandas effectively:

Use Efficient Data Types
- Convert object columns to categorical or string
- Use float32/int32 instead of float64/int64 if possible
- Use pd.to_datetime() for dates to optimize performance
```
df['category_column'] = df['category_column'].astype('category')
df['int_column'] = df['int_column'].astype('int32')
```

Load Data Efficiently
a) Read Only Required Columns

            df = pd.read_csv("large_file.csv", usecols=['col1', 'col2'])

b) Load Data in Chunks

            
            chunksize = 100000  # Process 100K rows at a time
            for chunk in pd.read_csv("large_file.csv", chunksize=chunksize):
            process(chunk)

c) Use dtype Parameter to Reduce Memory

                df = pd.read_csv("large_file.csv", dtype={'col1': 'int32', 'col2': 'float32'})

Use modin.pandas for Parallel Processing
Replace:
```
import pandas as pd
            
```
With:
```
import modin.pandas as pd    
            
```
This will automatically speed up operations by using multiple CPU cores.

Use Dask for Out-of-Core Processing
Dask allows handling large data without loading everything into memory.

import dask.dataframe as dd
ddf = dd.read_csv("large_file.csv")
ddf.groupby('col1').mean().compute()  # Compute when needed

Use SQLite or Parquet Instead of CSV
a) Store & Query Large Datasets with SQLite

    import sqlite3
conn = sqlite3.connect("database.db")
df = pd.read_sql_query("SELECT * FROM table_name", conn)

b) Use Parquet for Faster I/O

            df.to_parquet("large_data.parquet")
            df = pd.read_parquet("large_data.parquet")

Vectorized Operations & NumPy
Always prefer vectorized operations over loops. Bad:

df['new_col'] = df['col1'] * 2  # OK
for i in range(len(df)):  # Slow
df.loc[i, 'new_col'] = df.loc[i, 'col1'] * 2

Better:

            df['new_col'] = df['col1'].mul(2)  # Faster

Drop Unused Data (Garbage Collection)

            import gc
            del df
            gc.collect()

Cloud & Distributed Solutions
- Google BigQuery + pandas: pandas-gbq
- Spark with pandas: pyspark.pandas
- Snowflake, DuckDB, ClickHouse for large-scale data processing

Final Thought
For datasets < 1GB, optimize pandas with modin, dtypes, and vectorization.
For datasets > 1GB, use Dask, SQLite, or Spark.