如何在Pandas中使用GroupBy对负值和正值进行求和

在这篇文章中，我们将讨论如何使用Pandas的GroupBy方法来计算DataFrame中所有负数和正数的总和。

要使用groupby()方法，请使用下面的语法。

语法: df.groupby(column_name)

实现步骤

第1步：创建lambda函数来计算正和与负和的值。

pos = lambda col : col[col > 0].sum()
neg = lambda col : col[col < 0].sum()

第2步：我们将使用groupby()方法并应用lambda函数来计算总和。

d = df.groupby(df['Alphabet'])
print(d['Frequency'].agg([('negative_values', neg),
                         ('positive_values', pos)
                         ]))
print(d['Bandwidth'].agg([('negative_values', neg),
                         ('positive_values', pos)
                         ]))

示例

示例 1:

计算两列（即频率和带宽）的a、b、c的所有正值和负值之和。

# Import Necessary Libraries
import pandas as pd
import numpy as np
  
# Creating a DataFrame with 
# random values
df = pd.DataFrame({'Alphabet': ['a', 'b', 'c', 'c',
                                'a', 'a', 'c', 'b'],
                     
                   'Frequency': [-10, 29, -12, -190,
                                 72, -98, -12, 0],
                     
                   'BandWidth': [10, 34, 23, -10, -87,
                                 -76, 365, 10]})
  
print(df)
  
# Group By dataframe on categorical
# values
d = df.groupby(df['Alphabet'])
  
# creating lambda function to calculate
# positive as well as negative values
def pos(col): 
  return col[col > 0].sum()
  
def neg(col): 
  return col[col < 0].sum()
  
  
# Apply lambda function to particular 
# column
print(d['Frequency'].agg([('negative_values', neg),
                          ('positive_values', pos)
                          ]))
  
print(d['Bandwidth'].agg([('negative_values', neg),
                          ('positive_values', pos)
                          ]))

输出:

示例 2:

计算两列（即X和Y）的a、b的所有正值和负值之和。

# Import Necessary Libraries
import pandas as pd
import numpy as np
  
# Creating a DataFrame with random values
df = pd.DataFrame({'Function': ['F(x)', 'F(x)', 'F(y)',
                                'F(x)', 'F(y)', 'F(x)',
                                'F(x)', 'F(y)'],
                     
                   'X': [-10, 29, -12, -190, 72, -98,
                         -12, 0],
                     
                   'Y': [10, 34, 23, -10, -87, -76, 
                         365, 10]})
  
print(df)
  
# Group By dataframe on categorical values
d = df.groupby(df['Function'])
  
# creating lambda function to calculate
# positive as well as negative values
def pos(col): 
  return col[col > 0].sum()
  
def neg(col): 
  return col[col < 0].sum()
  
# Apply lambda function to particular 
# column
print(d['X'].agg([('negative_values', neg),
                  ('positive_values', pos)
                  ]))
  
print(d['Y'].agg([('negative_values', neg),
                  ('positive_values', pos)
                  ]))

输出:

DataFrame

X 输出

Y 输出

示例 3:

计算每个名字的所有正值以及负值的总和，即Marks。下一步是制作lambda函数来计算总和。在最后一步，我们将根据名字对数据进行分组，并调用lambda函数来计算数值之和。

# Import Necessary Libraries
import pandas as pd
import numpy as np
  
# Creating a DataFrame with random values
df = pd.DataFrame({'Name': ['Aryan', 'Nityaa', 'Dhruv',
                            'Dhruv', 'Nityaa', 'Aryan',
                            'Nityaa', 'Aryan', 'Aryan', 
                            'Dhruv', 'Nityaa', 'Dhruv', 
                            'Dhruv'],
                   'Marks': [90, 93, 78, 56, 34, 12, 67, 
                             45, 78, 92, 29, 88, 81]})
print(df)
  
# Group By dataframe on categorical values
d = df.groupby(df['Name'])
  
# creating lambda function to calculate
# positive as well as negative values
def pos(col): 
  return col[col > 0].sum()
  
def neg(col): 
  return col[col < 0].sum()
  
  
# Apply lambda function to particular
# column
print(d['Marks'].agg([('negative_values', neg),
                      ('positive_values', pos)
                      ]))

输出:

Names

Marks