Xtykutl

While iterating through the rows of a specific column in a Pandas DataFrame, I would like to add a new row below the currently iterated row, if the cell in the currently iterated row meets a certain condition.

Say for example:

df = pd.DataFrame(data = {'A': [0.15, 0.15, 0.7], 'B': [1500, 1500, 7000]})

DataFrame:

      A     B

0  0.15  1500

1  0.15  1500

2  0.70  7000

Attempt:

y = 100                             #An example scalar



i = 1



for x in df['A']:

    if x is not None:               #Values in 'A' are filled atm, but not necessarily.

        df.loc[i] = [None, x*y]     #Should insert None into 'A', and product into 'B'.

        df.index = df.index + 1     #Shift index? According to this S/O answer: https://stackoverflow.com/a/24284680/4909923

    i = i + 1



df.sort_index(inplace=True)         #Sort index?

I haven't been able to succeed so far; getting a shifted index numbering that doesn't start at 0, and rows seem not to be inserted in an orderly way:

      A     B

3  0.15  1500

4   NaN    70

5  0.70  7000

I tried various variants of this, trying to use applymap with a lambda function, but was not able to get it working.

Desired result:

      A     B

0  0.15  1500

1  None  15

2  0.15  1500

3  None  15

4  0.70  7000

5  None  70

I believe you can use:

df = pd.DataFrame(data = {'A': [0.15, 0.15, 0.7], 

                          'B': [1500, 1500, 7000],

                          'C': [100, 200, 400]})



v = 100

L = 

for i, x in df.to_dict('index').items():

    print (x)

    #append dictionary

    L.append(x)

    #append new dictionary, for missing keys ('B, C') DataFrame constructor add NaNs 

    L.append({'A':x['A'] * v})



df = pd.DataFrame(L)

print (df)

       A       B      C

0   0.15  1500.0  100.0

1  15.00     NaN    NaN

2   0.15  1500.0  200.0

3  15.00     NaN    NaN

4   0.70  7000.0  400.0

5  70.00     NaN    NaN

It doesn't seem you need a manual loop here:

df = pd.DataFrame(data = {'A': [0.15, 0.15, 0.7], 'B': [1500, 1500, 7000]})



y = 100



# copy slice of dataframe

df_extra = df.loc[df['A'].notnull()].copy()



# assign A and B series values

df_extra = df_extra.assign(A=np.nan, B=(df_extra['A']*y).astype(int))



# increment index partially, required for sorting afterwards

df_extra.index += 0.5



# append, sort index, drop index

res = df.append(df_extra).sort_index().reset_index(drop=True)



print(res)



      A     B

0  0.15  1500

1   NaN    15

2  0.15  1500

3   NaN    15

4  0.70  7000

5   NaN    70