Python Functions & Libraries Reference Guide
Reference guide to Python libraries and functions for numerical computations, data analysis, simulations, and automation
Python Libraries
| Library | Description | Key Functions |
|---|---|---|
| NumPy | Efficient numerical computation with arrays, matrices, and linear algebra. | array(), linspace(), arange(), mean(), sum(), dot(), reshape(), random() |
| pandas | Data manipulation and analysis; handling CSV, Excel, and tabular data. | read_csv(), DataFrame(), head(), describe(), merge(), groupby(), pivot_table() |
| scipy | Scientific computing for optimization, integration, interpolation, and signal processing. | integrate.quad(), optimize.minimize(), interpolate.interp1d(), signal.convolve() |
| matplotlib | 2D plotting and chart visualization library. | plot(), scatter(), bar(), hist(), subplot(), figure(), xlabel(), ylabel(), title() |
| seaborn | Statistical data visualization built on matplotlib. | heatmap(), pairplot(), boxplot(), countplot(), distplot(), regplot() |
| SymPy | Symbolic mathematics for algebra, calculus, and equation solving. | symbols(), diff(), integrate(), solve(), simplify(), Eq() |
| SimPy | Discrete-event simulation for modeling processes and systems. | Environment(), Process(), Resource(), run(), timeout() |
| control | Control systems analysis and design, transfer functions, state-space, and plotting. | tf(), ss(), step_response(), impulse_response(), bode(), nyquist(), rlocus() |
| openpyxl | Excel automation: read, write, and manipulate spreadsheets. | load_workbook(), Workbook(), active, cell(), save(), append() |
| os | Automate file system operations and environment tasks. | listdir(), mkdir(), remove(), path.exists(), getcwd(), chdir() |
| shutil | High-level file operations for copying, moving, and archiving files. | copy(), move(), rmtree(), make_archive(), unpack_archive() |
Core Math & Algebra Functions
| Function | Description | Required Library | Example |
|---|---|---|---|
| abs() | Absolute value of numbers or arrays. | Built-in / NumPy (np) | abs(-5) np.abs([-1,-2]) |
| sqrt() | Square root of a number or array elements. | math / NumPy (np) | math.sqrt(25) np.sqrt([4,9,16]) |
| pow() | Exponentiation of numbers. | Built-in | pow(2, 3) |
| exp() | Exponential function e^x. | NumPy (np) | np.exp(2) |
| log() | Natural or base-n logarithm. | math / NumPy (np) | math.log(10) np.log10(1000) |
| solve() | Solve algebraic equations. | SymPy | x = symbols('x') solve(x**2 - 4, x) |
Linear Algebra & Matrix Functions
| Function | Description | Required Library | Example |
|---|---|---|---|
| array() | Create a NumPy array. | NumPy (np) | np.array([1,2,3]) |
| dot() | Dot product of two vectors/matrices. | NumPy (np) | np.dot([1,2],[3,4]) |
| matmul() | Matrix multiplication. | NumPy (np) | np.matmul(A,B) |
| inv() | Matrix inverse. | NumPy.linalg (la) | la.inv(A) |
| eig() | Eigenvalues and eigenvectors. | NumPy.linalg (la) | la.eig(A) |
| det() | Determinant of a matrix. | NumPy.linalg (la) | la.det(A) |
Statistics & Probability Functions
| Function | Description | Required Library | Example |
|---|---|---|---|
| mean() | Arithmetic mean of data. | NumPy (np) | np.mean([1,2,3,4]) |
| std() | Standard deviation. | NumPy (np) | np.std([1,2,3,4]) |
| var() | Variance of array elements. | NumPy (np) | np.var([1,2,3,4]) |
| random() | Generate random values. | NumPy (np) | np.random.rand(3,2) |
| normal() | Generate values from a normal distribution. | NumPy (np) | np.random.normal(0,1,1000) |
Visualization & Plotting Functions
| Function | Description | Required Library | Example |
|---|---|---|---|
| plot() | Line plot of x,y data. | Matplotlib (plt) | plt.plot(x, y) plt.show() |
| scatter() | Scatter plot of data points. | Matplotlib (plt) | plt.scatter(x, y) plt.show() |
| bar() | Bar chart of categories vs. values. | Matplotlib (plt) | plt.bar(categories, values) plt.show() |
| hist() | Histogram of numeric data distribution. | Matplotlib (plt) | plt.hist(data, bins=10) plt.show() |
| heatmap() | Heatmap visualization of matrix/data correlations. | Seaborn (sns) / Matplotlib (plt) | sns.heatmap(df.corr(), annot=True) plt.show() |
Simulation & Control System Functions
| Function | Description | Required Library | Example |
|---|---|---|---|
| integrate.quad() | Numerical integration. | SciPy | integrate.quad(lambda x: x**2, 0, 1) |
| odeint() | Solve ODE systems numerically. | SciPy | odeint(model,y0,t) |
| step_response() | Step response of control system. | Control | sys = control.tf([1],[1,1]) control.step_response(sys) |
| impulse_response() | Impulse response of control system. | Control | control.impulse_response(sys) |
| rlocus() | Root locus plot. | Control | control.rlocus(sys) |
About Python Functions and Libraries Reference Guide
This comprehensive Python reference provides detailed insights into the most essential Python libraries, functions, and tools for numerical computation, data analysis, simulation, and automation. It is designed for engineers, researchers, and students who want to efficiently solve technical problems, model systems, and process large datasets using Python.
Applications
- Numerical Computation – Perform efficient mathematical operations, linear algebra, matrix manipulations, and algorithmic computations using
NumPyandSciPy. - Data Analysis & Processing – Import, clean, and analyze structured datasets using
pandasandNumPy. - Visualization & Plotting – Generate professional 2D and 3D plots using
MatplotlibandSeabornfor visual data insights and reporting. - Symbolic Mathematics – Solve algebraic and calculus problems symbolically with
SymPy. - Simulation & Control Systems – Model dynamic systems, run discrete-event simulations, and design control systems using
SimPyandControl. - Automation & File Handling – Automate repetitive engineering tasks, file operations, and Excel workflows using
os,shutil, andopenpyxl.
By understanding these Python libraries and functions, engineers and scientists can accelerate computational workflows, automate processes, and improve the accuracy and reliability of analysis and simulations. This reference serves as a quick reference for professionals and students looking for common Python functions and features for technical problem-solving, simulation, and data analysis.