Converting a DateTime Index value to an Index Number

Question

If I have a dataframe that has a datetime index and I get the first valid index by using series.first_valid_index - It returns a the date time of the first non nan which is what I'm looking for however:

Is there a way to get the index number that the datetime value corresponds to. For example, it returns 2018-07-16 but I'd like to know that's the 18th row of the dataframe?

If not, is there a way to count the rows from the beginning of the dataframe to that index value?

Answer 1

TLDR: If you're asking for a way to map a given index value (in this case a DatetimeIndex) to its integer equivalent, you are asking for get_loc, if you just want to find the integer index from the Series, use argmax with the underlying numpy array.

Setup

np.random.seed(3483203)

df = pd.DataFrame(
    np.random.choice([0, np.nan], 5),
    index=pd.date_range(start='2018-01-01', freq='1D', periods=5)
)

              0
2018-01-01  NaN
2018-01-02  NaN
2018-01-03  0.0
2018-01-04  NaN
2018-01-05  NaN

---

Use pandas.Index.get_loc here, which is a general function to return an integer index for a given label:

>>> idx = df[0].first_valid_index()
>>> idx
Timestamp('2018-01-03 00:00:00', freq='D')
>>> df.index.get_loc(idx)
2

If you want to avoid finding the datetime index at all, you may use argmax on the underlying numpy array:

>>> np.argmax(~np.isnan(df[0].values))
2

Answer 2

I would try following (untested):

x = len(df)
num_index = range(0,x,1)
df =  df.reset_index()
df = df.set_index(num_index)