如何使用.loc在for循环中将数据帧行附加到另一行？

Question

Let's say I have the following dataframes:

df_t1 = pd.DataFrame([["AAA", 1 ,2],["BBB", 0, 3],["CCC", 1, 2],["DDD", 0, 0],["EEE", 0, 3]], columns=list('ABC'))

    A   B   C
0   AAA 1   2
1   BBB 0   3
2   CCC 1   2
3   DDD 0   0
4   EEE 0   3

and

df_t2 = pd.DataFrame([["XXX", 4, 1],["YYY", 5 ,6],["ZZZ", 0, 3]], columns=list('ABC'))

    A   B   C
0   XXX 4   1
1   YYY 5   6
2   ZZZ 0   3

I can locate the rows in df_t1 that meet a certain condition using the code below:

df_t1.loc[(df_t1['B'] <= 2) & (df_t1['C'] > 2)]

A   B   C
1   BBB 0   3
4   EEE 0   3

df_t1.loc[(df_t1['B'] <= 3) & (df_t1['C'] > 3)]

    A   B   C
[Empty Dataframe]

I can create a for loop that returns those same results:

for i in value_check:
    print(df_t1.loc[(df_t1['B'] <= i) & (df_t1['C'] > i)])

A  B  C
1  BBB  0  3
4  EEE  0  3
Empty DataFrame
Columns: [A, B, C]
Index: []

But when I try to use that code to attach those values to df_t2:

value_check = [2,3]

for i in value_check:
    df_t2.append(df_t1.loc[(df_t1['B'] <= i) & (df_t1['C'] > i)])

df_t2 is unchanged

Answer 1

From the docs, the append method: "Appends rows of other to the end of this frame, returning a new object". You have to use assign df_t2 in your loop:

value_check = [2,3]

for i in value_check:
    df_t2 = df_t2.append(df_t1.loc[(df_t1['B'] <= i) & (df_t1['C'] > i)])

That said, concatenating is more efficient.

Edit: Here is a implementation using concatenation and list comprehension

df_t2 = pd.concat([df_t2] + [df_t1.loc[(df_t1['B'] <= i) & (df_t1['C'] > i)] for i in value_check], axis=0)

Answer 2

Using pd.concat

df_t1 = pd.DataFrame([["AAA", 1 ,2],["BBB", 0, 3],["CCC", 1, 2],["DDD", 0, 0],["EEE", 0, 3]], columns=list('ABC'))

df_t2 = pd.DataFrame([["XXX", 4, 1],["YYY", 5 ,6],["ZZZ", 0, 3]], columns=list('ABC'))

value_check = [2, 3]

for i in value_check:
    condition = (df_t1['B'] <= i) & (df_t1['C'] > i)
    row_to_add = df_t1.loc[condition]

    df_t2 = pd.concat([df_t2, row_to_add], axis=0)