Python uses Pandas to implement data selection and filtering

Before using Pandas for data selection and filtering, we need to do some preparatory work. 1. Environment setup: Firstly, ensure that Python is installed and the Pandas class library is installed. You can use the pip command for installation, which is: ` pip install pandas` 2. Dependent class libraries: In addition to Pandas, we also use Numpy and Matplotlib class libraries. Similarly, you can use the pip command for installation, which is: 'pip install numpy' and 'pip install matplotlib'` 3. Dataset introduction: In this example, we will use the Titanic dataset. This is a commonly used dataset that contains information about the passengers on the Titanic, including their identity, age, gender, ticket prices, and more. The dataset can be downloaded from the following website:` https://www.kaggle.com/c/titanic/data ` After the preparation work is completed, we can start writing Python code. python #Import the required class libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt #Read Dataset data = pd.read_csv('titanic.csv') #View the first few rows of the dataset print(data.head()) #Data selection #Select single column data age = data['Age'] print(age.head()) #Select multiple columns of data columns = ['Name', 'Sex', 'Age'] subset = data[columns] print(subset.head()) #Data filtering #Filter row data female_passengers = data[data['Sex'] == 'female'] print(female_passengers.head()) #Combine multiple filter conditions male_passengers = data[(data['Sex'] == 'male') & (data['Age'] > 30)] print(male_passengers.head()) #Visualization data #Draw a histogram data['Age'].plot(kind='hist', bins=20, color='c') plt.title('Age Distribution') plt.xlabel('Age') plt.ylabel('Frequency') plt.show() In the above code, we first imported the required class libraries: Pandas, Numpy, and Matplotlib. Then, use 'pd.read'_ The csv() function reads a dataset named "titanic. csv" and stores it in the 'data' variable. Next, we showed how to select single column and multi column data, using the methods of 'data' column name 'and' data [column name list] 'respectively. Then, we showed how to perform data filtering by using Boolean conditions to select specific row data. We demonstrate by selecting passengers with a gender of 'female' and male passengers over the age of 30. Finally, we used Matplotlib to draw histograms to visualize the distribution of age data. Please modify and extend the above code according to your own needs to meet specific data selection and filtering needs.