将函数应用于 Pandas DataFrame 中的每一行

Python是用于执行数据分析任务的出色语言。它提供了大量的类和函数，有助于以更简单的方式分析和操作数据。
可以使用 apply()函数将函数应用于给定数据帧中的每一行。让我们看看我们可以如何完成这项任务。
示例 #1：

Python3

# Import pandas package
import pandas as pd
 
# Function to add
def add(a, b, c):
    return a + b + c
 
def main():
     
    # create a dictionary with
    # three fields each
    data = {
            'A':[1, 2, 3],
            'B':[4, 5, 6],
            'C':[7, 8, 9] }
     
    # Convert the dictionary into DataFrame
    df = pd.DataFrame(data)
    print("Original DataFrame:\n", df)
     
    df['add'] = df.apply(lambda row : add(row['A'],
                     row['B'], row['C']), axis = 1)
  
    print('\nAfter Applying Function: ')
    # printing the new dataframe
    print(df)
  
if __name__ == '__main__':
    main()

输出：

示例 #2：
您也可以使用 numpy函数作为数据框的参数。

Python3

import pandas as pd
import numpy as np
  
def main():
     
    # create a dictionary with
    # five fields each
    data = {
            'A':[1, 2, 3],
            'B':[4, 5, 6],
            'C':[7, 8, 9] }
     
    # Convert the dictionary into DataFrame
    df = pd.DataFrame(data)
    print("Original DataFrame:\n", df)
     
    # applying function to each row in the dataframe
    # and storing result in a new column
    df['add'] = df.apply(np.sum, axis = 1)
  
    print('\nAfter Applying Function: ')
    # printing the new dataframe
    print(df)
  
if __name__ == '__main__':
    main()

输出：

示例 #3：规范化数据

Python3

# Import pandas package
import pandas as pd
 
def normalize(x, y):
    x_new = ((x - np.mean([x, y])) /
             (max(x, y) - min(x, y)))
     
    # print(x_new)
    return x_new
 
def main():
     
    # create a dictionary with three fields each
    data = {
        'X':[1, 2, 3],
        'Y':[45, 65, 89] }
     
    # Convert the dictionary into DataFrame
    df = pd.DataFrame(data)
    print("Original DataFrame:\n", df)
     
    df['X'] = df.apply(lambda row : normalize(row['X'],
                                  row['Y']), axis = 1)
  
    print('\nNormalized:')
    print(df)
  
if __name__ == '__main__':
    main()

输出：

示例 #4：生成范围

Python3

import pandas as pd
import numpy as np
  
pd.options.mode.chained_assignment = None
 
# Function to generate range
def generate_range(n):
     
    # printing the range for eg:
    # input is 67 output is 60-70
    n = int(n)
     
    lower_limit = n//10 * 10
    upper_limit = lower_limit + 10
     
    return str(str(lower_limit) + '-' + str(upper_limit))
      
def replace(row):
    for i, item in enumerate(row):
         
        # updating the value of the row
        row[i] = generate_range(item)
    return row
          
  
def main():
    # create a dictionary with
    # three fields each
    data = {
            'A':[0, 2, 3],
            'B':[4, 15, 6],
            'C':[47, 8, 19] }
     
    # Convert the dictionary into DataFrame
    df = pd.DataFrame(data)
  
    print('Before applying function: ')
    print(df)
      
    # applying function to each row in
    # dataframe and storing result in a new column
    df = df.apply(lambda row : replace(row))
      
  
    print('After Applying Function: ')
    # printing the new dataframe
    print(df)
  
if __name__ == '__main__':
    main()

输出：