Combine DataFrame Rows to eliminate NaNs-CodePudding

I have a DataFrame created from a data logger, where each point of data has it's own timestamp that looks like this:

df_orig = pd.DataFrame(
    {
        "val1": [ 1, np.nan, np.nan, 11, np.nan, np.nan, 21, np.nan, np.nan, ],
        "val2": [ np.nan, 2, np.nan, np.nan, 12, np.nan, np.nan, 22, np.nan, ],
        "val3": [ np.nan, np.nan, 3, np.nan, np.nan, 13, np.nan, np.nan, 23, ],
    },
    index=pd.to_datetime( [
        "2021-01-01 00:00", "2021-01-01 00:00:01", "2021-01-01 00:00:02",
        "2021-01-01 00:01", "2021-01-01 00:01:01", "2021-01-01 00:01:02",
        "2021-01-01 00:02", "2021-01-01 00:02:01", "2021-01-01 00:02:02",
    ] )
)

                     val1  val2  val3
2021-01-01 00:00:00   1.0   NaN   NaN
2021-01-01 00:00:01   NaN   2.0   NaN
2021-01-01 00:00:02   NaN   NaN   3.0
2021-01-01 00:01:00  11.0   NaN   NaN
2021-01-01 00:01:01   NaN  12.0   NaN
2021-01-01 00:01:02   NaN   NaN  13.0
2021-01-01 00:02:00  21.0   NaN   NaN
2021-01-01 00:02:01   NaN  22.0   NaN
2021-01-01 00:02:02   NaN   NaN  23.0

I don't actually need the precision of when each single data point was logged. I would like to condense the DataFrame by eliminating the NaNs and merging the lines that are very close together. the result should look like this:

                     val1  val2  val3
2021-01-01 00:00:00     1     2     3
2021-01-01 00:01:00    11    12    13
2021-01-01 00:02:00    21    22    23

Is there a way to do this?

CodePudding user response：

If possible simplify solution for resample per minutes with max or min or first use:

df = df_orig.resample('Min').max()
print (df)
                     val1  val2  val3
2021-01-01 00:00:00   1.0   2.0   3.0
2021-01-01 00:01:00  11.0  12.0  13.0
2021-01-01 00:02:00  21.0  22.0  23.0