I am currently trying to create a new column (lets call it col C) within a dataframe that adds one to each row value if that same row has a value in another column that is greater than or less than a specified criteria.

So if the criteria is value in column A >2 and value in column B >= 4 column C looks as follows:

Ive tried creating separate dataframes of the rows that meet each criteria and then dropping the ones that don't from the dataframe but there has to be a much simpler way.

CodePudding user response：

You can try the below either of the 2 snippets:

Mostly prefer 2nd snippet

import pandas as pd
import numpy as np

df = pd.DataFrame(data= [[1,4],[3,1],[4,5],[1,1]], columns=['a','b'])

df['c'] = np.where(((df['a']>2) & (df['b']>=4)), 2, 0)
df['c'] = np.where((((df['a']>2) | (df['b']>=4))&(df['c']==0)), 1, df['c'])
print(df)

OR

import pandas as pd
import numpy as np

df = pd.DataFrame(data= [[1,4],[3,1],[4,5],[1,1]], columns=['a','b'])
df['c'] = 0
df['c'] = np.where((df['a']>2), df['c'] 1, df['c'])
df['c'] = np.where((df['b']>=4), df['c'] 1, df['c'])
print(df)

CodePudding user response：

you can also do something like this:

df = pd.DataFrame(data= [[1,4],[3,1],[4,5],[1,1]], columns=['a','b'])

df = df.assign(c=(df.a>2).astype(int) (df.b>=4).astype(int))

>>> df
'''
   a  b  c
0  1  4  1
1  3  1  1
2  4  5  2
3  1  1  0