Both NumPy and pandas are popular Python libraries used for data analysis and manipulation, but they are designed for different purposes and have distinct features:
1. Primary Purpose
- NumPy:
- Focuses on numerical computing.
- Provides support for large, multi-dimensional arrays and matrices, along with mathematical operations on these arrays.
- Serves as the foundation for many other libraries (e.g., pandas, SciPy, and scikit-learn).
- pandas:
- Focuses on data manipulation and analysis.
- Provides high-level data structures like
DataFrameandSeriesfor working with structured and labeled data. - Simplifies handling of missing data, time-series data, and relational-style data.
2. Data Structures
- NumPy:
- Main data structure:
ndarray(N-dimensional array). - Data is homogeneous, meaning all elements in an array must be of the same type.
- Main data structure:
- pandas:
- Main data structures:
Series(1D labeled array) andDataFrame(2D labeled array). - Data can be heterogeneous, meaning columns in a DataFrame can have different data types (e.g., integers, floats, strings).
- Main data structures:
3. Operations and Functionality
- NumPy:
- Optimized for numerical computations and vectorized operations.
- Includes linear algebra, Fourier transforms, and random number generation.
- pandas:
- Offers robust tools for data wrangling, cleaning, and exploration (e.g., filtering, grouping, pivoting).
- Provides easy handling of missing values, merging/joining datasets, and reshaping data.
4. Ease of Use
- NumPy:
- Lower-level library with more manual handling required for data manipulation.
- Better for mathematical computations or when working with raw numerical data.
- pandas:
- Higher-level library, user-friendly for data manipulation tasks.
- Built on top of NumPy, so it leverages NumPy’s performance but offers simpler APIs for working with tabular data.
5. Performance
- NumPy:
- Generally faster for numerical computations on raw numerical arrays due to lower overhead.
- Uses contiguous blocks of memory for efficient computation.
- pandas:
- Slightly slower for numerical operations due to its added functionalities and support for heterogeneous data types.
- Designed for flexibility rather than raw speed.
6. Typical Use Cases
- NumPy:
- Scientific computing.
- Performing low-level array-based operations.
- Developing algorithms requiring heavy matrix computations.
- pandas:
- Data cleaning, transformation, and analysis.
- Working with structured datasets like CSV, Excel, or SQL tables.
- Handling time-series data and datasets with missing or categorical values.
Example
import numpy as np
import pandas as pd
# NumPy example
array = np.array([[1, 2], [3, 4]])
print(array.mean()) # Compute mean of all elements
# pandas example
data = {'A': [1, 2], 'B': [3, 4]}
df = pd.DataFrame(data)
print(df.mean()) # Compute mean of each column
Output:
# NumPy
2.5
# pandas
A 1.5
B 3.5
dtype: float64
In summary, use NumPy for raw numerical computations and pandas for working with structured, labeled datasets.