I have a dataframe with these values in column A:

df = pd.DataFrame(A,columns =['A'])

    A
0   0
1   5
2   1
3   7
4   0
5   2
6   1
7   3
8   0

I need to create a new column (called B) and populate it using next conditions:

Condition 1: If the value of A is equal to 0 then, the value of B must be 0.

Condition 2: If the value of A is not 0 then I compare its value to the previous value of B. If A is higher than the previous value of B then I take A, otherwise I take B. The result should be this:

    A   B
0   0   0
1   5   5
2   1   5
3   7   7
4   0   0
5   2   2
6   1   2
7   3   3

The dataset is huge and using loops would be too slow. I would need to solve this without using loops and the pandas “Loc” function. Anyone could help me to solve this using the Apply function? I have tried different things without success.

Thanks a lot.

CodePudding user response：

Use .shift() to shift your one cell down and check if the previous value is smaller and it is not 0. Then use .mask() to replace the values with the previous if the condition stands.

df['B'] = df['A'].mask(cond=((df['A'].shift(1) > df['A']) & df['A'] != 0), other=df['A'].shift(1))
print(df)

Output:

  A  B
0  0  0
1  5  5
2  1  5
3  7  7
4  0  0
5  2  2
6  1  2
7  3  3
8  0  0

CodePudding user response：

Try this:

df['B'] = df['A'].shift()
df['B'] = df.apply(lambda x:0 if x.A == 0 else x.A if x.A > x.B else x.B, axis=1)

CodePudding user response：

One way to do this I guess could be the following

def do_your_stuff(row):
    global value
    # fancy stuff here
    value = row["b"]
    [...]

value = df.iloc[0]['B']
df["C"] = df.apply(lambda row: do_your_stuff(row), axis=1)