pandas DataFrame的迭代子集及计算方法

import pandas as pd

# 创建一个示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

# 使用iterrows()方法遍历DataFrame
for index, row in df.iterrows():
    print(f"Index: {index}")
    print(f"Name: {row['Name']}")
    print(f"Age: {row['Age']}")
    print(f"City: {row['City']}")
    print()

import pandas as pd

# 创建一个示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

# 使用itertuples()方法遍历DataFrame
for row in df.itertuples():
    print(f"Index: {row.Index}")
    print(f"Name: {row.Name}")
    print(f"Age: {row.Age}")
    print(f"City: {row.City}")
    print()

import pandas as pd

# 创建一个示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

# 使用iteritems()方法遍历DataFrame
for column, values in df.iteritems():
    print(f"Column: {column}")
    print(f"Values: {values.tolist()}")
    print()

import pandas as pd

# 创建一个示例DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

# 计算年龄的平均值和最大值
mean_age = df['Age'].mean()
max_age = df['Age'].max()

print(f"Mean Age: {mean_age}")
print(f"Max Age: {max_age}")

import pandas as pd
import numpy as np

# 创建一个示例DataFrame
data = {'A': [1, -2, 3],
        'B': [4, 5, -6]}
df = pd.DataFrame(data)

# 计算A列的绝对值和B列的平方根
abs_A = np.abs(df['A'])
sqrt_B = np.sqrt(df['B'])

print(f"Absolute A: {abs_A.tolist()}")
print(f"Square Root B: {sqrt_B.tolist()}")

import pandas as pd

# 创建一个示例DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

# 判断A列是否全部大于0
all_positive = (df['A'] > 0).all()

# 判断B列是否存在小于0的值
has_negative = (df['B'] < 0).any()

print(f"All Positive: {all_positive}")
print(f"Has Negative: {has_negative}")