如何根据熊猫中的某些条件将df1中的一行与df2中的其他行进行比较？

Question

I have two files(some rows could be same and some could be different) which have data like this-

PID,          STARTED,%CPU,%MEM,COMMAND
1,Wed Sep 12 10:10:21 2018, 0.0, 0.0,init
2,Wed Sep 12 10:10:21 2018, 0.0, 0.0,kthreadd

Now, I want to perform following operations on these dataframes-

1. select one row(say R1) from df1
2. iterate all the rows from df2 and check for mathces with R1.
3. if it found matches then store it in a seperate dataframe, if it doesn't match ignore it.

Since file has 10000 rows. so I am implementing it via python pandas but not getting the proper way. Any help would be appreciable.

Answer 1

Raw data

First dataframe:

df = pd.DataFrame({
    'Started': [*np.repeat(pd.Timestamp(2018, 9, 12, 12, 12, 21), 2)],
    '%CPI': [0.0, 0.0],
    '%MEM': [0.0, 0.0],
    'COMMAND': ['init', 'kthreadd']
})

Output:

    Started %CPI    %MEM    COMMAND
0   2018-09-12 12:12:21 0.0 0.0 init
1   2018-09-12 12:12:21 0.0 0.0 kthreadd

Second dataframe:

df2 = pd.DataFrame({
    'Started': [pd.Timestamp(2018, 9, 12, 12, 12, 21), pd.Timestamp(2020, 9, 12, 12, 12, 21)],
    '%CPI': [0.0, 1.0],
    '%MEM': [0.0, 1.0],
    'COMMAND': ['init', 'different']
})

Output (row 0 the same, row 1 different):

    Started %CPI    %MEM    COMMAND
0   2018-09-12 12:12:21 0.0 0.0 init
1   2020-09-12 12:12:21 1.0 1.0 different

---

Answer

Create new dataframe with only matching rows:

columns = df.columns.tolist()

matches = pd.merge(df, df2, left_on=columns, right_on=columns)

Output:

    Started %CPI    %MEM    COMMAND
0   2018-09-12 12:12:21 0.0 0.0 init