Pandas使用新长度插入数据帧

Question

I have a dataframe with columns of Datetime, lat, lon, z. I am reading the data in from a csv file so setting the period for the datetimes do not work. The times are in 6 hour intervals but I want to linearly interpolate the data to hourly intervals.

Go from

       'A'              'B'    'C'   'D'
0   2010-09-13 18:00:00 16.3 -78.5    1
1   2010-09-14 00:00:00 16.6 -79.8    6 
2   2010-09-14 06:00:00 17.0 -81.1    12

To

       'A'              'B'    'C'   'D'
1   2010-09-13 18:00:00 16.3  -78.5   1      
2   2010-09-13 19:00:00 16.35 -78.7   2
3   2010-09-13 20:00:00 16.4  -78.9   3
4   2010-09-13 21:00:00 16.45 -79.1   4
5   2010-09-13 22:00:00 16.5  -79.3   5
....

I have tried using the interpolate command but there are no arguments for a new length of the dataframe.

df.interpolate(method='linear')

I was thinking that I could use .loc to include 5 rows of NANs between each line in the data frame and then use the interpolation function but that seems like a bad workaround.

Solution Using DatetimeIndex eliminates the association with the other columns if your initial column was not imported as datetime.

i = pd.DatetimeIndex(start=df['A'].min(), end=df['A'].max(),    freq='H')
df = df.reindex(i).interpolate()
print(df)

Gives the correct answer.

Answer 1

i = pd.DatetimeIndex(start=df.index.min(), end=df.index.max(), freq='H')
df = df.reindex(i).interpolate()
print(df)

outputs

2010-09-13 18:00:00  16.300000 -78.500000
2010-09-13 19:00:00  16.350000 -78.716667
2010-09-13 20:00:00  16.400000 -78.933333
2010-09-13 21:00:00  16.450000 -79.150000
2010-09-13 22:00:00  16.500000 -79.366667

1. Create a new index with the desired frequency using DatetimeIndex ( docs ).

2. reindex ( docs ) with this new index. By default values for new indices will be np.nan .

3. interpolate ( docs ) to fill in these missing values. You can supply the method kwarg to determine how interpolation is done.