CODEWITHSUNDEEP
pythonpython-3.xpandasdataframeapply
JA-pythonista
JA-pythonista

Reputation: 1323

How to use pandas apply on certain columns with args

I have a pandas dataframe with columns val_min and val_max below:

df 
   val_min  val_max
0   1001.0  1500.0
3   401.0   500.0
4   401.0   500.0
5   401.0   500.0
6   501.0   600.0

Now I am trying to create a new column using these two columns with pandas apply function and create a new column based on some certain functions defined in my function, see my attempt below:

def get_range(df, val_min, val_max, new_col):
    if df[val_min] <500 & df[val_max]<500:
        df[new_col] = "<500"
        
    elif df[val_min] >500 & df[val_max]<=1000:
        df[new_col] = "500 - 1000"
        
    elif df[val_min] >1000 & df[val_max]<=2000:
        df[new_col] = "1000 - 2000"
        
    elif df[val_min] >2000 & df[val_max]<=5000:
        df[new_col] = "2000 - 5000"
        
    elif df[val_min] >5000 & df[val_max]<=7500:
        df[new_col] = "5000 - 7500"
        
    elif df[val_min] >7500 & df[val_max]<=10000:
        df[new_col] = "7500 - 10000"
        
    elif df[val_min] >10000 & df[val_max]<=12000:
        df[new_col] = "10000 - 12000"
        
    elif df[val_min] >12000 & df[val_max]<=15000:
        df[new_col] = "12000 - 15000"
        
    elif df[val_min] >15000 & df[val_max]>15000:
        df[new_col] = ">15000"

df["new_col"] = df[["val_min", "val_max"]].apply(get_range, axis=1)

Upvotes: 1

Views: 121

Answers (4)

Balaji Ambresh
Balaji Ambresh

Reputation: 5037

Consider using np.select

import numpy as np

cases = [(df.val_min < 500) & (df.val_max < 500),
           (df.val_min > 500) & (df.val_max <= 1000),
           (df.val_min > 1000) & (df.val_max <= 2000),
           (df.val_min > 2000) & (df.val_max <= 5000),
           (df.val_min > 5000) & (df.val_max <= 7500),
           (df.val_min > 7500) & (df.val_max <= 10000),
           (df.val_min > 10000) &( df.val_max <= 12000),
           (df.val_min > 12000) &( df.val_max <= 15000),
            (df.val_min > 15000) &( df.val_max > 15000)]


values = ['<500',
          '500 - 1000',
          '1000 - 2000',
          '2000 - 5000',
          '5000 - 7500',
          '7500 - 10000',
          '10000 - 12000',
          '12000 - 15000',
          '>15000']


df['new_col'] = np.select(cases, values, default='')
print(df)

Output

   val_min  val_max      new_col
0   1001.0   1500.0  1000 - 2000
1    401.0    500.0             
2    401.0    500.0             
3    401.0    500.0             
4    501.0    600.0   500 - 1000

Upvotes: 1

Akhilesh_IN
Akhilesh_IN

Reputation: 1307

you can use apply with lambda

df = pd.DataFrame({"val_min":list(range(10,1000,100)),"val_max":list(range(20,2000,200))})


def get_range(val_min, val_max):
    if val_min <500 & val_max<500:
        new_col = "<500"
        
    elif val_min >500 & val_max<=1000:
        new_col = "500 - 1000"
        
    elif val_min >1000 & val_max<=2000:
        new_col = "1000 - 2000"
        
    elif val_min >2000 & val_max<=5000:
        new_col = "2000 - 5000"
        
    elif val_min >5000 & val_max<=7500:
        new_col = "5000 - 7500"
        
    elif val_min >7500 & val_max<=10000:
        new_col = "7500 - 10000"
        
    elif val_min >10000 & val_max<=12000:
        new_col = "10000 - 12000"
        
    elif val_min >12000 & val_max<=15000:
        new_col = "12000 - 15000"
        
    elif val_min >15000 & val_max>15000:
        new_col = ">15000"
    return new_col

df["new_col"] = df[["val_min", "val_max"]].apply(lambda row :get_range(*row), axis=1)

print(df)

   val_min  val_max     new_col
0       10       20        <500
1      110      220        <500
2      210      420        <500
3      310      620  500 - 1000
4      410      820  500 - 1000
5      510     1020  500 - 1000
6      610     1220  500 - 1000
7      710     1420  500 - 1000
8      810     1620  500 - 1000
9      910     1820  500 - 1000
[Finished in 1.8s]

Upvotes: 0

Robin
Robin

Reputation: 51

could you try this ? it might work

def get_range(df, val_min, val_max, new_col):
    if val_min <500 & val_max <500:
        return "<500"

    elif val_min >500 & val_max <=1000:
        return "500 - 1000"
[...]

df["new_col"] = df[["val_min", "val_max"]].apply(get_range, axis=1)

Upvotes: 0

MrNobody33
MrNobody33

Reputation: 6483

You should change the function's structure to this:

def get_range(x):
    val_min=x.val_min
    val_max=x.val_max
    if (val_min <500)&(val_max<500):
        return "<500"
        
    elif (val_min >500)&(val_max<=1000):
        return  "500 - 1000"
    ...

And then apply this to the dataframe:

df["new_col"] = df.apply(get_range, axis=1)

So, for example:

import pandas as pd


def get_range(x):
    val_min=x.val_min
    val_max=x.val_max
    if (val_min <500 )&(val_max<500):
        return "<500"
        
    elif (val_min>500)&(val_max<=1000):
        return  "500 - 1000"
        

df = pd.DataFrame({'val_min':[600, 34], 'val_max':[800, 340]})

df["new_col"] = df.apply(get_range, axis=1)

Output:

   val_min  val_max     new_col
0      600      800  500 - 1000
1       34      340        <500

Upvotes: 1

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