SQL Server、Python 和 OS X

SQL Server、Python 和 OS X 的数据处理之旅

python
import pyodbc
server = 'localhost'
database = 'mydatabase'
username = 'myusername'
password = 'mypassword'
conn_str = f'DRIVER={{ODBC Driver 17 for SQL Server}};SERVER={server};DATABASE={database};UID={username};PWD={password}'
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute('SELECT * FROM mytable')
for row in cursor:
    print(row)
conn.close()

python
import pandas as pd
data = {
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'gender': ['Female', 'Male', 'Male']
}
df = pd.DataFrame(data)
# 删除年龄小于 30 的记录
df = df[df['age'] >= 30]
# 将性别转换为数字编码
df['gender'] = df['gender'].map({'Female': 0, 'Male': 1})
# 输出处理后的数据
print(df)

python
import pandas as pd
import matplotlib.pyplot as plt
data = {
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'gender': ['Female', 'Male', 'Male']
}
df = pd.DataFrame(data)
# 统计不同性别的人数
gender_counts = df['gender'].value_counts()
# 绘制柱状图
plt.bar(gender_counts.index, gender_counts.values)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.title('Gender Distribution')
plt.show()