Pandas：在迭代行时有条件地将行插入DataFrame

Question

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

Answer 1

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

Answer 2

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