What libraries are needed for python data analysis?
is Python open source numerical computation extension tool , provides Python support for multi-dimensional arrays , can support advanced dimensional array and matrix operations . In addition, for array operations also provides a large library of mathematical functions , Numpy is the basis for most of the Python scientific computing with many features.
2. The Pandas library
is a Numpy-based data analysis package created to solve data analysis tasks.Pandas incorporates a large number of libraries and standard data models that provide functions and methods needed to efficiently manipulate large data sets, enabling users to quickly and easily process data.
3. The Matplotlib library
is a 2D graphics library for drawing arrays in Python. Although it originated as an imitation of the MATLAB graphics commands, it is independent of MATLAB, and can be used in a Pythonic and object-oriented way, making it an excellent plotting library in Python. Written primarily in the pure Python language, it makes heavy use of Numpy and other extension code to provide good performance even for large arrays.
4. Seaborn library
is a Matplotlib-based data visualization tool in Python, providing many high-level encapsulated functions to help data analysts to quickly plot beautiful data graphs, thus avoiding many additional parameter configuration problems.
5. The NLTK library
Known as the Z-best tool for teaching and computational linguistics work with Python, and a fantastic library for games in natural language, NLTK is a leading platform for building Python programs that use human language data, and it provides easy-to-use interfaces to more than 50 corpora and lexical resources, and also provides a suite of text processing libraries for classification, tokenization, stemming, parsing and semantic reasoning, wrappers for NLP libraries, and an active discussion community.
What is pandas
Pandas is a tool for data analysis and processing, based on the Python programming language.
Pandas consists of two basic data structures: Series and DataFrame, Series is an array-like data structure, consisting of a set of data and a set of corresponding labels (indexes). operations, data cleaning, data filtering and data analysis.
Pandas can be used in the fields of finance, business, science and engineering, etc. The main application scenarios include data cleaning, data analysis and data visualization. In terms of data cleaning, Pandas can help us quickly and efficiently clean, convert, format and count data; in terms of data analysis, Pandas provides a wealth of functions and tools that can help us easily perform operations such as data aggregation, grouping, pivoting, sorting, merging and analyzing.
In terms of data visualization, Pandas can be used in conjunction with other data visualization libraries, so that we can be more intuitive and clear presentation of the data.Pandas is a very powerful data analysis tool that can help us analyze the data faster, more efficiently, more accurately, and is very easy to learn and use, it is one of the must-have tools for data analysis.
Pandas can be well integrated with other Python libraries and frameworks, such as NumPy, SciPy, scikit-learn, etc., expanding its functionality and application scope. With powerful time series analysis and manipulation capabilities, it can easily handle time series data. It also supports parallel computing of data, which can significantly improve the efficiency of data processing. Finally, data can be stored and read in a variety of ways, such as CSV, Excel, SQL, HDF5 and other formats, which is very convenient for data sharing and exchange.
Role of Python Programming Language
1, Web Development: Python is one of the hottest programming languages in web development, such as Django, Flask and other frameworks can easily create websites, web applications, social networks and so on.
2, data science: Python’s data science libraries (e.g., NumPy, Pandas, Matplotlib, etc.) support advanced data visualization, data analysis, modeling, machine learning, and other functions, and are the tools of choice for data scientists.
3, Artificial Intelligence: Python is widely used in the field of artificial intelligence and machine learning, such as TensorFlow, Keras and other tools can easily build neural networks, deep learning models and so on.
4, computer vision: Python can be applied to the field of computer vision and image processing, such as OpenCV and other tools can easily realize the capture, processing, analysis and recognition of images and videos.