Is there a way to iterate through rows of a DataFrame and append some to a separate DataFrame?-CodePudding

What I want to do is something like:

for i, row in df.iterrows():
    if row.at['Column'] == some_value:
        some_other_df.append(row)

I can't seem to get this to work with the row object. It won't append like this, and it won't concatenate right with pd.concat()

Iterating through the rows creates a Series object, which is different from a DataFrame object, so it makes sense that it won't concatenate properly. Anyways, I'm looking for some method to be able to accomplish what the above pseudocode should.

CodePudding user response：

if you post the data as a code, it'll help in sharing the result from the solution.

this should help. try it out

some_other_df = df[df['Column'].eq(some_value)]

CodePudding user response：

You should probably filter or query instead of iterating.

import pandas as pd


df1 = pd.DataFrame(
    [
        [1, "Tom", 36, 3],
        [2, "Bob", 30, 2]
    ],
    columns=["ID", "Name", "Age", "Children"]
)
print(f"{df1}\n")

df2 = pd.DataFrame(
    [
        [3, "Joe", 23, 1],
        [4, "Mike", 27, 4]
    ],
    columns=["ID", "Name", "Age", "Children"]
)
print(f"{df2}\n")

filtered_df = df1.query("Name == 'Tom'")
final_df = pd.concat([df2, filtered_df], ignore_index=True)
print(final_df)