向量化操作改进数据分析工作流的Pandas Numpy示例分析

import pandas as pd
 
 data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
 df = pd.DataFrame(data)
 # Using vectorization to add columns 'A' and 'B'
 df['C'] = df['A'] + df['B']
 print(df['C'])
 
 Output:
 0    5
 1    7
 2    9

import pandas as pd
 
 data = {'A': [1, 2, 3]}
 df = pd.DataFrame(data)
 # Define a custom function
 def square(x):
     return x ** 2
 
 # Applying the 'square' function to the 'A' column
 df['A_squared'] = df['A'].apply(square)
 print(df['A_squared'])
 
 Output:
 0    1
 1    4
 2    9

import pandas as pd
 
 data = {'A': [1, 2, 3]}
 df = pd.DataFrame(data)
 
 # Creating a new column 'D' based on a condition in column 'A'
 df['D'] = df['A'].apply(lambda x: 'Even' if x % 2 == 0 else 'Odd')
 
 print(df)
 
 Output:
    A     D
 0  1   Odd
 1  2  Even
 2  3   Odd

list1 = [1, 2, 3, 4, 5]
 list2 = [6, 7, 8, 9, 10]
 result = []
 
 for i in range(len(list1)):
     result.append(list1[i] + list2[i])
 print(result)
 
 Output:
 [7, 9, 11, 13, 15]

array1 = np.array([1, 2, 3, 4, 5])
 array2 = np.array([6, 7, 8, 9, 10])
 result = array1 + array2
 print(result)
 
 Output:
 [ 7  9 11 13 15]

import numpy as np
 import timeit
 
 # Create two NumPy arrays and two lists for the comparison
 array1 = np.random.randint(1, 100, size=1000000)
 array2 = np.random.randint(1, 100, size=1000000)
 list1 = list(array1)
 list2 = list(array2)
 
 # Vectorized processing with NumPy
 def numpy_vectorized():
     result = array1 + array2
 
 # Traditional loop-based processing
 def loop_based():
     result = []
     for i in range(len(list1)):
         result.append(list1[i] + list2[i])
 
 # Measure execution time for NumPy vectorized approach
 numpy_time = timeit.timeit(numpy_vectorized, number=100)
 
 # Measure execution time for traditional loop-based approach
 loop_time = timeit.timeit(loop_based, number=100)
 
 print(f"NumPy Vectorized Approach: {numpy_time:.5f} seconds")
 print(f"Traditional Loop-Based Approach: {loop_time:.5f} seconds")
 
 
 Output:
 NumPy Vectorized Approach: 0.30273 seconds
 Traditional Loop-Based Approach: 17.91837 seconds