In the field of data science, Python has become the preferred programming language for data analysts and data scientists due to its simple syntax, powerful capabilities, and rich community resources. To help everyone better conduct data analysis, the following will introduce the 7 commonly used open source libraries in Python data analysis.
1. NumPy
NumPy (Numerical Python) is the foundational library for scientific computing in Python, providing high-performance multidimensional array objects called “ndarray” and a rich library of mathematical functions. The core of NumPy is the “ndarray” object, which supports efficient array and matrix operations, avoiding the inefficiency of Python loops, thereby greatly improving computation speed. Additionally, NumPy supports broadcasting, allowing operations between arrays of different shapes. NumPy offers a large number of mathematical functions, including trigonometric functions, exponential functions, logarithmic functions, statistical functions, etc., which can be directly applied to array elements. In data analysis, NumPy is commonly used in numerical computation, scientific computing, image processing, and other fields.
2. Pandas
Pandas provides high-performance, easy-to-use data structures and data analysis tools, making it the core library for data analysis. Pandas is built on NumPy but offers more advanced data structures and operations, such as “Series” (one-dimensional array with labels) and “DataFrame” (two-dimensional table composed of multiple Series). Pandas supports data cleaning and preprocessing, such as handling missing values, duplicates, and data type conversions. It also provides flexible data selection and slicing methods, allowing the use of labels, positions, and boolean indexing. Pandas supports data grouping and aggregation operations, as well as functions and tools specifically for handling time series data. Moreover, Pandas conveniently reads and writes various data formats (CSV, Excel, JSON, SQL databases, etc.), making data import and export very simple.
3. Matplotlib
Matplotlib is the most commonly used plotting library in Python, providing a wealth of plotting tools to create various static, dynamic, and interactive charts. Matplotlib supports various chart types, including line charts, scatter plots, bar charts, pie charts, histograms, box plots, etc. Users can customize various properties of the charts, such as titles, labels, colors, line styles, legends, etc. Matplotlib supports subplot drawing, allowing multiple subplots to be drawn on the same chart, facilitating data comparison. In data analysis, Matplotlib is commonly used for data visualization, data exploration, and result presentation.
4. Seaborn
Seaborn is an advanced visualization library based on Matplotlib, providing more aesthetically pleasing and advanced chart types that are easier to use. Seaborn focuses on statistical data visualization, offering various statistical graphics, such as distribution plots, relationship plots, and categorical plots. Seaborn comes with multiple beautiful chart styles, making it easy to create professional-level charts. Additionally, Seaborn integrates seamlessly with Pandas, allowing for easy plotting using Pandas’ DataFrame, resulting in more concise code. In data analysis, Seaborn is commonly used for data visualization, statistical analysis, and exploratory data analysis.
5. SciPy
SciPy is a scientific computing library based on NumPy, providing various scientific computing tools, including optimization, integration, interpolation, signal processing, and statistics. SciPy offers various optimization algorithms, such as optimization, minimization, and root-finding. It also provides numerical integration algorithms and various interpolation methods for data fitting and prediction. Additionally, SciPy provides signal processing tools, such as filtering and spectral analysis. In statistics, SciPy offers various statistical functions and probability distributions. SciPy is commonly used in scientific computing, engineering calculations, signal processing, and image processing.
6. Statsmodels
Statsmodels provides various statistical models and statistical testing tools, focusing on statistical modeling and inference. Statsmodels supports linear models, such as linear regression, generalized linear models (GLM), and mixed-effect models. It also offers time series analysis models, such as stationarity tests, autocorrelation, and partial autocorrelation functions. Additionally, Statsmodels provides various statistical test methods, such as t-tests, chi-squared tests, and F-tests. In data analysis, Statsmodels is commonly used for statistical modeling, econometrics, and time series analysis.
7. Scikit-learn
Scikit-learn is the most commonly used machine learning library in Python, providing various machine learning algorithms and tools. Scikit-learn supports classification algorithms, such as support vector machines (SVM), decision trees, and random forests. It also supports regression algorithms, such as linear regression and ridge regression. Additionally, Scikit-learn provides clustering algorithms, such as K-means clustering and DBSCAN. In dimensionality reduction, Scikit-learn supports principal component analysis (PCA) and linear discriminant analysis (LDA). Scikit-learn also offers model selection and evaluation tools, such as cross-validation and grid search. In data analysis, Scikit-learn is commonly used for machine learning, data mining, and pattern recognition.
These 7 open source libraries play different roles in data analysis, and they often need to be used together to complete complex data analysis tasks. Mastering these libraries can effectively facilitate various data analysis work.
