Python 重新格式化 Dataframe

Question

I am trying to iterate through the dataframe and if the row's value 'Age' column is empty, it will move the value in 'Company' column to the 'Location' column of the previous row. Is there a quick way to do this?

As-Is

As-Is Dataframe

To-Be

To-Be Dataframe

Answer 1

Here is a solution that works by iterating over the rows and copying the row's Name value to the previous row's Location value. Note this will break if the first row's Age is empty.

I'm pretty sure there's a more efficient way of dropping the empty rows at the end, so I welcome edits!

d = {'Name': ["Amber", "North", "Max", "South", "Jackson", "East"], 'Age': ["21", "", "23", "", "38", ""], "Location":["","","","","",""]}
df = pandas.DataFrame(data=d)

rows_to_drop = []

for index, values in df.iterrows():

    if not values["Age"]:

        df["Location"][index-1] = df["Name"][index]
        rows_to_drop.append(index)

df = df.drop(rows_to_drop)

Answer 2

You can use numpy :

arr = df.to_numpy()
arr[::2, -1] = arr[1::2,0]
df = pd.DataFrame(arr[::2], columns=df.columns)

Output:

      Name Age Location
0    Amber  21    North
1      Max  23    South
2  Jackson  38     East