[Proficient in Python in 100 days] Day51: Python data analysis_ data analysis basics and Anaconda environment construction

Python scientific computing and data analysis is the field of scientific research, data processing, and analysis using the Python programming language. They provide scientists, engineers, data analysts, and researchers with powerful tools and libraries for processing, analyzing, and visualizing all kinds of data to extract valuable insights and information from the data.

Python scientific computing and data analysis can be applied in a variety of fields, including:

Business and Market Analysis : Help companies make decisions and optimize sales strategies, marketing and customer relationship management.

Biology and Medicine : For genomics research, drug discovery, disease prediction, and medical image analysis.

Physics and Engineering : For simulations, data analysis, design of experiments, and signal processing.

Social Science : For survey research, social network analysis, public opinion analysis, and psychological research.

Finance : For risk assessment, portfolio optimization, quantitative trading and market forecasting. etc,

2. Data collection and preparation

The first step in data analysis is data collection and preparation. Data can come from a variety of sources, including experiments, surveys, sensors, files, databases, and networks. At this stage, data typically needs to be cleaned, deduplicated, missing values handled, and transformed into a format suitable for analysis.

2.1 Data Collection

Data collection is the process of obtaining data from different sources, which can include databases, files, APIs, sensors, web crawlers, etc. The following are some common data collection methods:

2.1.1 File import :

Import data from local files such as CSV, Excel, text files. This is easily achieved in Python using the Pandas library.

import pandas as pd

# 从CSV文件导入数据
data = pd.read_csv('data.csv')

2.1.2 Database connection :

Get data from a relational database through a database connection. You can use a library such as SQLAlchemy or a dedicated database library to connect to the database.

from sqlalchemy import create_engine

# 创建数据库连接
engine = create_engine('sqlite:///mydatabase.db')

# 查询数据库
data = pd.read_sql_query('SELECT * FROM mytable', engine)

2.1.3 API requests :

Get data through API requests. Use Python's requests library to send HTTP requests and get data.

import requests

url = 'https://api.example.com/data'
response = requests.get(url)
data = response.json()

2.1.4 Web crawler :

Use crawling tools such as BeautifulSoup or Scrapy to scrape data from web pages.

from bs4 import BeautifulSoup
import requests

url = 'https://example.com/page'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
# 提取数据

2.2 Data cleaning

Once data is collected, data cleaning is often required to ensure data quality and consistency. Data cleaning tasks include:

2.2.1 Handling missing values :

Detect and handle missing values in data, you can fill missing values, delete rows or columns containing missing values.

# 填充缺失值
data['column_name'].fillna(value, inplace=True)

# 删除包含缺失值的行
data.dropna(inplace=True)

2.2.2 Remove duplicate values :

Detect and remove duplicate data rows.

data.drop_duplicates(inplace=True)

2.2.3 Data type conversion :

Convert the data to the correct data type, such as converting a string to a number.

data['column_name'] = data['column_name'].astype(float)

2.2.4 Outlier processing :

To detect and deal with outliers, you can use statistical methods or visualization tools to identify outliers based on the distribution of your data.

2.2.5 Date and time processing :

If the data contains dates and times, they can be parsed and processed appropriately.

data['date_column'] = pd.to_datetime(data['date_column'])

2.2.6 Data format normalization :

Ensure data is in a consistent format across datasets.

Data collection and preparation are the foundation of data analysis, ensuring that you have high-quality data for subsequent analysis and modeling work. These steps are often time-intensive, but they are critical to obtaining accurate and reliable analysis results.

3. Data Analysis Tools

Python is a very powerful programming language with a wealth of data analysis tools and libraries. Here are some commonly used Python data analysis tools and libraries:

NumPy : NumPy (Numerical Python) is the fundamental library for numerical computation. It provides multidimensional array objects and a set of mathematical functions that enable you to perform numerical operations efficiently.
Pandas : Pandas is a library for data processing and analysis, providing high-performance, easy-to-use data structures, such as Series and DataFrame, and data manipulation tools. It is a common tool in data analysis for tasks such as data cleaning, transformation, grouping, and aggregation.
Matplotlib : Matplotlib is a library for drawing various types of charts and graphs. It is used for data visualization and can create line charts, scatter plots, histograms, pie charts, etc.
Seaborn : Seaborn is an advanced data visualization library built on top of Matplotlib. It provides prettier default styles and a simpler interface for creating statistical graphics and information visualizations.
Scipy : Scipy is a library for scientific computing, including a series of advanced mathematical, scientific and engineering computing functions, such as optimization, interpolation, integration, linear algebra, etc.
Scikit-Learn : Scikit-Learn is a library for machine learning and data mining that provides various algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection.
Statsmodels : Statsmodels is a library for statistical modeling and hypothesis testing, enabling linear models, nonlinear models, time series analysis, and more.
NLTK (Natural Language Toolkit): NLTK is a library for natural language processing that provides tools for processing text data, analyzing language structure, and performing sentiment analysis.
Beautiful Soup : Beautiful Soup is a library for parsing HTML and XML documents, often used in web crawlers and data scraping.
Jupyter Notebook : Jupyter Notebook is an interactive computing environment that allows you to create and share documents in your browser, which can contain code, graphics, text, and mathematical equations.
Pillow : Pillow is an image processing library for opening, manipulating, and saving various image files.

These tools and libraries can help you with various data analysis tasks, including data cleaning, visualization, statistical analysis, machine learning and deep learning, etc. Depending on your specific needs and projects, you can choose the right tool to get the job done. These libraries have rich documentation and community resources that can help you learn and apply them in depth.

4. Data analysis process

Data analysis is a systematic process aimed at extracting useful information, insights and patterns from data.

Data Exploration : Understand the basic characteristics of data, including descriptive statistics, visualization, data distribution, and correlation analysis.

Feature Engineering : Selecting and transforming features in data for use in modeling and analysis.

Modeling : Choose an appropriate analysis technique, such as regression, classification, clustering, or time series analysis, and train the model.

Evaluate and Validate : Evaluate the performance of the model, verify the accuracy of the model using cross-validation, metrics, and graphs.

Interpretation of Results : Interpreting analysis results to enable decision making in a business or research context.

4.1 Define the problem and goals

Before starting data analysis, the problem and the goals of the analysis must first be defined. This could be answering a specific business question, identifying market trends, forecasting sales, or performing scientific research. For example, let's say we're an e-commerce company and our question is: "How can I improve my website's shopping cart conversion rate?"

4.2 Data Collection

Once the questions and goals are defined, the next step is to collect relevant data. Data can come from multiple sources, including databases, files, APIs, sensors, and more. In the example, we will use a simulated e-commerce website dataset:

import pandas as pd

# 从CSV文件导入数据
data = pd.read_csv('ecommerce_data.csv')

4.3 Data cleaning

Data cleaning is a critical step in ensuring data quality and availability. It includes tasks like handling missing values, deduplication, outlier handling, etc.

Handle missing values:

# 检查缺失值
missing_values = data.isnull().sum()

# 填充缺失值或删除缺失值所在的行/列
data['column_name'].fillna(value, inplace=True)
data.dropna(inplace=True)

Remove duplicates:

# 检查和删除重复值
data.drop_duplicates(inplace=True)

4.4 Data Exploration and Visualization

The goal of data exploration is to understand the underlying characteristics, distributions, and relationships of data. This usually involves analyzing the data using statistical metrics and visualization tools.

Descriptive statistics:

# 查看数据的基本统计信息
summary_stats = data.describe()

# 计算相关系数
correlation_matrix = data.corr()

data visualization:

import matplotlib.pyplot as plt
import seaborn as sns

# 创建直方图
plt.hist(data['column_name'], bins=20)

# 创建散点图
sns.scatterplot(x='column1', y='column2', data=data)

4.5 Feature Engineering

Feature engineering involves selecting, building, and transforming features in data for modeling and analysis. This might include creating new features, encoding categorical variables, etc.

# 创建新特征
data['total_revenue'] = data['quantity'] * data['price']

# 对分类变量进行独热编码
data = pd.get_dummies(data, columns=['category'])

4.6 Modeling and Analysis

After selecting an appropriate analysis technique, modeling and analysis can begin. This might include regression, classification, clustering, time series analysis, etc.

from sklearn.linear_model import LinearRegression

# 创建线性回归模型
model = LinearRegression()
model.fit(data[['feature1', 'feature2']], data['target'])

4.7 Model Evaluation and Validation

After modeling, the performance and accuracy of the model needs to be evaluated. This can be done through cross-validation, metrics (such as mean squared error, precision, recall), and visualization.

from sklearn.metrics import mean_squared_error

# 用测试数据集评估模型
predictions = model.predict(test_data[['feature1', 'feature2']])
mse = mean_squared_error(test_data['target'], predictions)

4.8 Interpretation and reporting of results

Finally, interpret analysis results and produce reports to communicate results and insights to relevant stakeholders.

# 解释模型系数
coefficients = model.coef_

# 制作数据分析报告

This is a general data analysis process, and each step requires careful thought and customization to meet specific questions and goals. Data analysis is an iterative process that often requires multiple explorations, modeling, and validations to gradually improve the accuracy and interpretability of the model.

5 Installation and environment setting of data science tools

5.1 Install Python

First, install Python. You can download the required version of python installer from Python official website. Download Python | Python.org The official home of the Python Programming Language https://www.python.org/downloads/ and follow the instructions in the official documentation to install it. Note that if you have already installed Anaconda, you usually do not need to install Python separately, because Anaconda includes Python.

Reference: [100 days proficient in python] Day1: Getting started with python_Get to know python first, build a python environment, and run the first python applet_Python entry applet_LeapMay's blog-CSDN blog

5.2 Install Anaconda

If you want to use Anaconda to manage the Python environment and libraries, you can install Anaconda as follows:

访问 Anaconda 官方网站 Free Download | AnacondaAnaconda's open-source Distribution is the easiest way to perform Python/R data science and machine learning on a single machine.https://www.anaconda.com/download
Download the Anaconda distribution for your operating system (usually Anaconda or Miniconda).
After downloading, follow the instructions in the official documentation to run the installer and select the "Add Anaconda to my PATH environment variable" option during the installation to be able to use Anaconda from the command line.

5.3 Configure the virtual environment

With Anaconda, you can easily create and manage virtual environments. Virtual environments help you isolate the libraries and dependencies required by different projects. Here is an example of creating a virtual environment:

(1) Create a virtual environment called "myenv":

conda create --name myenv

(2) Activate the virtual environment:

On Windows:

conda activate myenv

On macOS and Linux:

source activate myenv

(3) Install libraries and dependencies (in a virtual environment):

conda install numpy pandas matplotlib

Your virtual environment is now configured and includes NumPy, Pandas, and Matplotlib.

5.4 Using a virtual environment

Every time you start a new project, you can create a new virtual environment and install the libraries required by the project in it. This keeps libraries isolated between projects and avoids version conflicts.

(1) Create a project virtual environment

conda create --name myprojectenv

(2) Activate the project virtual environment

conda activate myprojectenv #在 Windows 上

source activate myprojectenv #在 macOS 和 Linux 上

(3) Install the libraries required by the project in the project virtual environment

conda install numpy pandas matplotlib requests

The library can also be pipinstalled using:

pip install package_name

(4) Run the project in a virtual environment

After installing the required libraries in the virtual environment, you can run your project in it, using the virtual environment's Python interpreter and libraries. When you're done working, you can exit the virtual environment.

Enter the project directory :

Use the terminal to go to your project directory, the folder containing the project files

cd /path/to/your/project

Run the project :

Once inside the project directory, you can use the Python interpreter in the virtual environment to run the project's scripts or applications.

python your_project_script.py

This will run the Python scripts in the project, and any required dependencies will be loaded from the virtual environment.

(5) Exit the virtual environment

conda deactivate  # 使用 conda

In this way, you can maintain different projects in different virtual environments and freely switch virtual environments as needed.

Through the above steps, you can install Python, Anaconda, configure a virtual environment, and install scientific computing libraries to start working on data science and data analysis. In real projects, you may also need to install other libraries and tools, depending on your needs. When installing packages and libraries, you can use Anaconda's environment management features to make the whole process easier and more controllable.