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This article introduces the relevant knowledge of "what are the knowledge points of Python data analysis?" in the operation of actual cases, many people will encounter such a dilemma, and then let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Many students who study data analysis suffer from the lack of basic data, resulting in their own simple learning of theoretical knowledge in the learning process, but can not carry out practical exercises. To solve this problem, the erudite Valley teachers combined with the actual available data, focused on using Pandas for data preparation, cleaning, finishing, calculation and visualization, taught the most professional data cleaning and processing tools OpenRefine, the most mainstream data visualization frameworks Seaborn and Echarts, and used Bokeh to achieve dynamic visualization charts and control panels.

1. EDA exploratory data analysis method.

Integrate operation algorithms and charts to explore the distribution and relationship of data, so as to find the rules of data.

2. Sharp tools for data cleaning and processing

Explain in detail the industry mainstream data cleaning tool OpenRefine, which can quickly screen data, clean data, arrange weight, analyze the distribution and trend of time dimension, etc., the function is very powerful, and the work efficiency is greatly improved! Hands-on and master OpenRefine in actual combat projects.

3. Sharp tools for data visualization

Explain in detail the industry's mainstream data visualization tool Seaborn and the domestic popular Echarts visualization framework, drawing advanced reports is not to mention!

4. Five comprehensive actual combat projects

According to the different data types, several typical data analysis projects are designed, covering a variety of data analysis methods, including statistical analysis methods, time series analysis methods, geographic analysis methods and so on. Projects include: chipotle takeout order data analysis, UouTube video trend data analysis, 2012 US presidential election donation data analysis, Beijing air quality data analysis, Chicago criminal record data analysis from 2015 to 2017.

5. Business intelligence expansion

Taking the New York City Taxi data Analysis project as an example, this paper uses Bokeh to realize the dynamic visualization of data analysis results and the realization of business intelligence control panel.

Should we focus on learning the third-party libraries of Python in the data analysis practice? At present, Python is recognized as the best programming language for data analysis. in the third-party library of Python, there are a large number of modules to realize data science, so which Python data analysis libraries are used in the practical cases in this course?

1. Numpy: support a large number of advanced dimensional array and matrix operations, running efficiency colleges and universities, which is the basis for dealing with a large number of array class structures and machine learning frameworks.

2. Pandas: at present, the most popular data analysis library, high-performance, easy-to-use data structure and data analysis tools, the use of data is simple and intuitive.

3. Matplotlib: quickly draw line chart, histogram, pie chart, scatter chart, etc., suitable for simple, simple data presentation,

4. Seaborn: specializing in statistical visualization and advanced Matplotlib, you can make more attractive pictures.

5. Bokeh: easy to use, naturally supports Python and Pandas, suitable for a variety of occasions.

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