I currently have a csv imported into Jupyter lab. Pandas has been imported, the data frame is 7845 rows x 14 columns. I have two specific columns one named “source_app_packets” and the other is “source_app_packets.1”. The two columns are almost identical. The main difference is any data missing from “source_app_packets” is present on “source_app_packets.1” and vice versa. My question is there any way to combine these two?
If you also import
numpy you could use something like this, which assumes your data is in
import numpy as np # code to import data # update source_app_packets column df["source_app_packets"] = np.where( df["source_app_packets"].isnull(), df["source_app_packets.1"], df["source_app_packets"], ) df.drop(["source_app_packets.1"], axis=1, inplace=True)