I have a dataframe as follow:

I would like to check each row in the columns for the below conditions and store the results (0 or 1) in a new column called FC.

If mea1 is between 0.3 and 0.4 then FC = 1, else 0

If mea2 is between 0.10 and 0.11 and FC != 0, then FC = 1, else 0

If mea3 is between 0.01 and 0.05 and FC != 0, then FC = 1, else 0

Result: (1): met and (0): failed

I was able to achieve the result by using nested for-loops but the program took forever to run on a table with 10,000 entries.

Here is my latest attempt at using the lambda function without checking for FC. col = mea1 etc...:

df['FC'] = df.apply(lambda x: 1 if ((x[x[col] >= lower_limit]) & (x[x[col] <= upper_limit])) else 0 , axis = 1 )

Thanks!

CodePudding user response：

I think you can do it like this:

df['FC'] = (
             (df['mea1'] >=0.3) & (df['mea1'] <= 0.4)
             & (df['mea2'] >= 0.1) & (df['mea2'] <= 0.11) 
             & (df['mea3']>=0.01) & (df['mea3']<=0.05)
          ) * 1

CodePudding user response：

"Iteration" and pandas don't really mix. If you think the only way to do what you're thinking of doing is through iteration, you're probably wrong. Most of the time there is a fast, vectorized, solution.

df['FC'] = (df.mea1.between(0.3, 0.4, inclusive='both')
           & df.mea2.between(0.1, 0.11, inclusive='both')
           & df.mea3.between(0.01, 0.05, inclusive='both')).astype(int)

Output:

   mea1  mea2  mea3  FC
0  0.38  0.11  0.02   1
1  0.32  0.12  0.03   0

CodePudding user response：

def assignFC(row: pd.Series):
    fc = 0
    if row[“mea1”] >= 0.3 and row[“mea1”] <= 0.4:
        fc = 1
    if row[“mea2”] >= 0.1 and row[“mea2”] <= 0.11 and fc==1:
        fc = 1
    else:
        fc = 0
    if row[“mea3”] >= 0.01 and row[“mea3”] <= 0.05 and fc==1:
        fc = 1
    else:
        fc = 0
    return fc

df[“FC”] = df.apply(assignFC, axis=1)