In pandas, I'm creating a dataframe like:

```
    df = pd.read_csv(file_path)[['timestamp', 'close']]
    df['close'] = df['close'].astype(float)
    df = df.set_index('timestamp')

```

The data looks like:

```
                    close
timestamp                 
2019-04-18          203.86
2019-04-17          203.13
2019-04-16          199.25
2019-04-15          199.23
2019-04-12          198.87

```

Now, I would like to fill in the missing `timestamp` and `close` values with linear interpolation from the nearest neighbors.

I created a list of the missing dates with:

```
dates = pd.date_range(start=df['timestamp'].min(), end=df['timestamp'].max())

```

Then reindexed with:

```
df = df.reindex(dates).iloc[::-1]

```

But this produced:

```
                      close
timestamp
2019-04-18             NaN
2019-04-17             NaN
2019-04-16             NaN
2019-04-15             NaN
2019-04-14             NaN

```

I was hoping this would at least copy over the previous values (although, I've yet to find a good way to get the missing close value interpolation handled smoothly.)
How would I phrase this in pandas?