How to create a new column based on conditions in the existing columns in a dataframe in python?-CodePudding

I have a dataframe like the below:

df:

PAN_NO  COST_VALUE
AAA       -0.001          
BBB       2938080         
CCC      49224091         
DDD        100            
EEE     50236272.32

I am trying to create a new column based on the below condition:

If df['cost_value'] >=0.001 and df['cost_value'] <= 299985.0 then cost_value_group should be 1

If df['cost_value'] > 299985.0 and df['cost_value'] <= 2938082.40 then cost_value_group should be 2

If df['cost_value'] > 2938082.40 and df['cost_value'] <= 17399130.0 then cost_value_group should be 3

If df['cost_value'] > 17399130.0 and df['cost_value'] <= 49224091.375 then cost_value_group should be 4

If df['cost_value'] > 49224091.375 cost_value_group should be 5

Else it should be 6


EXPECTED OUTPUT:
PAN_NO  COST_VALUE COST_VALUE_Group
AAA       -0.001          1
BBB       2938080         2
CCC      49224091         5
DDD        100            1
EEE     50236272.32       6

I tried doing :

def cost_value(x):
   if df['cost_value'] >= -0.001 and df['cost_value'] <= 299985.0:
   return 1
   elif df['cost_value'] > 299985.0 and df['cost_value'] <= 2938082.40:
   return 2
   elif df['cost_value'] > 2938082.40 and df['cost_value'] <= 17399130.0:
   return 3
   elif df['cost_value'] > 17399130.0 and df['cost_value'] <= 49224091.375:
   return 4
  elif df['cost_value'] > 49224091.375:
   return 5
  else:
   return 6
df['cost_value_group] = df['cost_value].apply(cost_value)

I am getting a value error that the true value of a series is ambiguous.

Can someone please assist me in this.

CodePudding user response：

You are on the right path.

Try this:

df['cost_value_group'] = df['cost_value'].apply(lambda x: cost_value(x))