I think learning Python is a process from shallow to deep. Learning Python is divided into stages, and the difficulties and problems faced at different stages are also different. Let's take my own process of learning python for data analysis as an example to describe the obstacles I encountered when learning Python in stages.
The first stage is to learn basic mathematical knowledge
Data processing involves professional knowledge in mathematics, and the main learning content focuses on advanced mathematics and linear algebra. I think that if you want to be proficient in data analysis, it is not enough to know programming and data processing, and basic data knowledge is also indispensable. Then in the learning process at this stage, the most common difficulty is to understand some complex mathematical relationships, and then deduce mathematical formulas, such as the partial derivatives of complex functions, the meaning of matrix eigenvectors, etc.
The second stage of learning the programming foundation of Python
At this stage, I mainly learn Python's integrated development environment and virtual environment Python variables, assignments, simple operations, control statements in Python, and loop statements. The four major data structures in Python (lists, sets, dictionaries, tuples), Python Functions, classes, objects (object-oriented programming), Python exception handling for reading and writing files. Because I have studied Java and C++ before, I have a certain foundation for this, and the learning process is relatively simple, but for Xiaobai who has not been exposed to programming before, it may be in the professional knowledge of object-oriented programming and the four major data structures. It's a little difficult. This requires you to search for more relevant knowledge on the Internet to understand and learn.
The third stage is to learn third-party libraries commonly used in Python data analysis
The main ones are numpy, pandas, Matplotlib and SciPy. Let me first introduce the functions of these libraries:
1. numpy: This library is an open source numerical computing extension of Python. This tool can be used to store and process large matrices, which is much more efficient than Python's own nested list structure (this structure can also be used to represent matrices), and supports a large number of dimension arrays and matrices In addition, it also provides a large number of mathematical function libraries for array operations.
2. Pandas: Pandas can import data from various file formats such as CSV, JSON, SQL, and Microsoft Excel. Pandas can perform operations on various data, such as merging, reshaping, selection, and data cleaning and data processing features. A tool based on NumPy, pandas is one of the important factors that make Python a powerful and efficient data analysis environment.
3. Matplotlib: It is a drawing library for Python, which allows users to easily visualize data and provide a variety of output formats. Matplotlib can be used to draw various static, dynamic, and interactive charts. A lot of data can be presented more intuitively in the form of charts with Matplotlib. The case of Matplotlib drawing is as follows:
4. SciPy: I think SciPy is the core library of data analysis. It contains modules such as optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal processing and image processing, Ordinary differential equation solving and other calculations commonly used in science and engineering. In the process of learning SciPy, the biggest obstacle I encountered is that these libraries involve a wide range, so there are many functions. Unless your memory is amazing, otherwise it is difficult for you to remember everything by artificial memory. So my suggestion is to remember a few of the most commonly used functions, and then put their official documents in your favorites, and then look for related functions when necessary.
The fourth stage combines real data case practice
There are many data cases on each website. These data cases often have actual requirements, and the data sets can be downloaded locally. If you want to actually implement data analysis, you can go to the website to find the data you are interested in and try it yourself. This stage is actually the most difficult, because you need to implement data analysis without the guidance of others. Everything is difficult at the beginning, but when you do more analysis of data cases, you will gradually get started.