How many times have you said Pandas is slow and still kept on using it? 🐼💨
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How many times have you said Pandas is slow and still kept on using it? 🐼💨
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Get ready to say Pandas can be fast but it's expensive 😂
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Get ready to say Pandas can be fast but it's expensive 😂
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🙌 Original Limitations:
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🙌 Original Limitations:
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💻 CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded😰), leading to slower processing of large datasets.
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💻 CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded😰), leading to slower processing of large datasets.
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🧠 Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.
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🧠 Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.
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𝌣 Achievements with 
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𝌣 Achievements with 
@nvidia
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nvidia
 RAPIDS cuDF:
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 RAPIDS cuDF:
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🚀 GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code. 
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🚀 GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code. 
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🔄 Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary. 
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🔄 Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary. 
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📈 Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.
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📈 Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.
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😅New Limitations: 
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😅New Limitations: 
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🎮 GPU Availability: Requires a GPU (not everything should need a GPU) 
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🎮 GPU Availability: Requires a GPU (not everything should need a GPU) 
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🔄 Library Compatibility: Currently in the initial stages, all the functionality cannot be ported 
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🔄 Library Compatibility: Currently in the initial stages, all the functionality cannot be ported 
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🐢 Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU. 
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🐢 Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU. 
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🤔 User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist
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🤔 User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist
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Blog: 
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Blog: 
https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/
https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/
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For Jupyter Notebooks:
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For Jupyter Notebooks:
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%load_ext cudf.pandas
import pandas as pd
python
```python
%load_ext cudf.pandas
import pandas as pd
```
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For python scripts:
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For python scripts:
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python -m cudf.pandas script.py
shell
```shell
python -m cudf.pandas script.py
```
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How many times have you said Pandas is slow and still kept on using it? 🐼💨

Get ready to say Pandas can be fast but it's expensive 😂

🙌 Original Limitations:

💻 CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded😰), leading to slower processing of large datasets.

🧠 Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.

𝌣 Achievements with @nvidia RAPIDS cuDF:

🚀 GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code. 

🔄 Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary. 

📈 Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.

😅New Limitations: 

🎮 GPU Availability: Requires a GPU (not everything should need a GPU) 

🔄 Library Compatibility: Currently in the initial stages, all the functionality cannot be ported 

🐢 Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU. 

🤔 User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist

Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/

For Jupyter Notebooks:

```python
%load_ext cudf.pandas
import pandas as pd
```

For python scripts:

```shell
python -m cudf.pandas script.py
```


❤️
prabhatkr Rohitkhatri75436 Rybens dev7halo
🚀
GPT007