如何遍历数据框中的行，并为每一行剪切每 3 个值并垂直堆叠这些值？

Question

I have a huge data frame that have 100 rows and 126900 columns. Each row in this dataframe represents a road trip of a vehicle. The columns are repetitive like this: longitude, latitude, timestamp, longitude, latitude, timestamp, longitude, latitude, timestamp, etc. Each row shows the a sequence of instant locations of the vehicle in each second. Now for each row (each trip), I want to kind of split the repetitive columns and stack the values vertically. So the transformed dataframe should be a 3-columns dataframe.

I am sorry I am new to Stackoverflow and do not know how to insert table like this. Please see the screenshot here

I am thinking to use a double for loop with iterrow() method like this:

for index, row in df.iterrows(): for i in range(len(df)):

But I am stuck here and do not know what to do next. And it seems that looping through rows/columns in a huge dataframe might be generally a bad practice. Can anyone help me on this issue please?

Answer 1

Try this:

new_df = pd.DataFrame()
for c in range (0, len(df.columns)-1, 3):
    new_df = pd.concat([new_df, df[df.columns[c:3]]])