Select rows from Python DataFrame

Question

I have got a Python DataFrame called "x" like this:

363108 05:01:00

363107 05:02:00

363106 05:03:00

363105 05:04:00

363104 05:05:00

        ...   

4 16:57:00

3 16:58:00

2 16:59:00

1 17:00:00

0 17:01:00

The "time" column is string type.

I want to create a new DataFrame called "m" from all the rows in "x" such that the minute is "00".

I have tried m = x.loc[x["time"][3:5] == "00"] but I get "IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."

Does anybody know how to do this please?

Answer 1

One way can be that you can create a new column in the existing dataframe that has the minutes field, which you can slice from the time column

df['minutes']=df['time'][-2:]
other_df=df.loc[df['minutes']=="00"]

Answer 2

You should use "apply" for the condition.

x.loc[x["time"].apply(lambda s: s[3:5] == "00")]

*In your code you are getting the range [3:5] on time Series(row 3 to 5)