Python data analysis: data-driven decision-making artifact

pythonData AnalysisData ScienceVisualizationMachine Learning

Data preparation and cleaning

Pythonprovides varioustools, such aspandasand NumPy, for loading, cleaning and transforming data. These tools can handle missing values, duplicates, and data type conversions to ensure accuracy in data analysis.

import pandas as pd # 加载数据 data = pd.read_csv("data.csv") # 清理丢失值 data = data.dropna() # 转换数据类型 data["Age"] = data["Age"].astype(int)

Data Exploration and Visualization

Python's powerful visualization libraries, such as Matplotlib and Seaborn, make data exploration and presentation easy. These libraries allow the creation of a variety of charts and graphs to help analysts understand data distributions, trends, and patterns.

import matplotlib.pyplot as plt # 创建直方图 plt.hist(data["Age"]) plt.xlabel("Age") plt.ylabel("Frequency") plt.show()

Statistical Analysis

Python provides a wide range of modules for performing statistical analysis. Libraries such as Scipy and Statsmodels provide various functions for calculating frequency, mean, variance and other statistical measures. These metrics are critical to understanding the overall characteristics of the data.

from scipy import stats # 计算频率 frequencies = stats.itemfreq(data["Gender"]) # 计算均值 mean_age = data["Age"].mean()

Machine Learning and Prediction

Python is powerful in machinelearningand can be used to build predictive models. The Scikit-learn library provides a wide range of machine learning algorithms that can be used for classification, regression, and other prediction tasks. These models enable organizations to leverage data to make informed decisions.

from sklearn.linear_model import LinearRegression # 创建线性回归模型 model = LinearRegression() # 训练模型 model.fit(data[["Age", "Gender"]], data["Salary"]) # 预测工资 predicted_salary = model.predict([[30, "Male"]])