Let us see how to find the sum of negative and positive values. At first, create a dataframe with positive and negative values −
dataFrame = pd.DataFrame({'Place': ['Chicago', 'Denver', 'Atlanta', 'Chicago', 'Dallas', 'Denver','Dallas', 'Atlanta'], 'Temperature': [-2, 30, -5, 10, 30, -5, 20, -10]})
Next, use groupby to group on the basis of Place column −
groupRes = dataFrame.groupby(dataFrame['Place'])
Use lambda function to return the positive and negative values. We have also added the positive and negative values individually −
# lambda function def plus(val): return val[val > 0].sum() def minus(val): return val[val < 0].sum()
Example
Following is the complete code −
import pandas as pd # create a DataFrame with temperature in celsius dataFrame = pd.DataFrame({'Place': ['Chicago', 'Denver', 'Atlanta', 'Chicago', 'Dallas', 'Denver','Dallas', 'Atlanta'], 'Temperature': [-2, 30, -5, 10, 30, -5, 20, -10]}) print(dataFrame) # using groupby to group on the basis of place groupRes = dataFrame.groupby(dataFrame['Place']) # lambda function def plus(val): return val[val > 0].sum() def minus(val): return val[val < 0].sum() print(groupRes['Temperature'].agg([('negTemp', minus), ('posTemp', plus)]))
Output
This will produce the following code −
Place Temperature 0 Chicago -2 1 Denver 30 2 Atlanta -5 3 Chicago 10 4 Dallas 30 5 Denver -5 6 Dallas 20 7 Atlanta -10 negTemp posTemp Place Atlanta -15 0 Chicago -2 10 Dallas 0 50 Denver -5 30