In this article, we will discuss how to calculate the sum of all negative numbers and positive numbers in DataFrame using the GroupBy method in Pandas.
To use the groupby() method use the given below syntax.
Syntax: df.groupby(column_name)
Stepwise Implementation
Step 1: Creating lambda functions to calculate positive-sum and negative-sum values.
pos = lambda col : col[col > 0].sum() neg = lambda col : col[col < 0].sum()
Step 2: We will use the groupby() method and apply the lambda function to calculate the sum.
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)
]))
Examples
Example 1:
Calculate the sum of all positive as well as negative values of a, b, c for both columns i.e., Frequency and bandwidth
Python3
# 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) ])) |
Output:
Example 2:
Calculate the sum of all positive as well as negative values of a, b for both columns i.e., X and Y
Python3
# 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) ])) |
Output:
DataFrame
X Output
Y Output
Example 3:
Calculate the sum of all positive as well as negative values of every name i.e., Marks. The next step is to make the lambda function to calculate the sum. In the last step, we will group the data according to the names and call the lambda functions to calculate the sum of the values.
Python3
# 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) ])) |
Output:
Names
Marks
