Positive and negative value sum separately in Pandas-CodePudding

Find and sum all negative values Find and sum all positive values

DATA

ID   value

A    -1
B    -5
AA    1
TT    3
UV    4
QA    50
WQ   -40
QC    10

DESIRED

positive  68

negative -46

DOING

df.groupby(df['value'].agg([('value' , lambda x : x[x < 0].sum()) , ('positive' , lambda x : x[x > 0].sum())])

Any suggestion is appreciated

CodePudding user response：

First, you have to make a dataframe of your data using pandas.

import pandas as pd

df = pd.DataFrame({'ID': ['A', 'B', 'AA', 'TT', 'UV', 'QA', 'WQ', 'QC'],
                   'value': [-1, -5, 1, 3, 4, 50, -40, 10]})

Then, sum the values greater than 0 and less than 0.

positive_sum = df[df['value'] > 0]['value'].sum()
negative_sum = df[df['value'] < 0]['value'].sum()

And, print out the results.

print("positive: ", positive_sum)
print("negative: ", negative_sum)

Output

CodePudding user response：

Maybe

import pandas as pd

df = pd.DataFrame({'ID': ['A', 'B', 'AA', 'TT', 'UV', 'QA', 'WQ', 'QC'],
               'value': [-1, -5, 1, 3, 4, 50, -40, 10]})

negative_sum = df[df['value'] < 0]['value'].sum()
positive_sum = df[df['value'] > 0]['value'].sum()

print("negative sum:", negative_sum)
print("positive sum:", positive_sum)