How to select certain values based on a condition in a data frame?

Question

I have a dataframe called df that looks like this:

Date        Reading1 Reading2 Reading3 Reading4
2000-05-01     15        13        14       11
2000-05-02     15        14        18        9
2000-05-03     14        12        15        8
2000-05-04     17        11        16       13

I used df.setindex('Date') to make the date the index. I have 3 questions.

1) How do I display the number of days that had a reading greater than 13 in the entire data frame not just in a single column?

I tried df.[(df.Reading1:df.Reading4>13)].shape[0] but obviously the syntax is wrong.

2) How do I display the values that happened on 2000-05-03 for columns Readings 1, 3, and 4?

I tried df.loc[["20000503"],["Reading1","Reading3,"Reading4"]]

but i got the error "None of the Index(['20000503'],dtype='object')] are in the [index]"

3) How do find do I display the dates for which the values for the column Readings 1 are twice as much as those in column Readings 2? And how do I display those values (the ones in Reading 1 that are twice as big) as well?

I have no idea where to even start this one.

Answer 1

Try this:

1. (df > 13).any(axis=1).sum()
Create a boolean dataframe then check to see if any value is True along the row and sum rows to get number of days.

2. df.loc['2000-05-03', ['Reading1', 'Reading3', 'Reading4']]
Use partial string indexing on DatetimeIndex to get a day, then column filtering with a list of column header.

3. df.loc[df['Reading1']  > (df['Reading2'] * 2)].index
   df.loc[df['Reading1']  > (df['Reading2'] * 2)].to_numpy().tolist()
Create a boolean series to do boolean indexing and get the index to return date.  Next convert the dataframe to numpy array then tolist to get values.