Getting last non na value across rows in a pandas dataframe

Question

I have a dataframe of shape (40,500). Each row in the dataframe has some numerical values till some variable column number k, and all the entries after that are nan.

I am trying to get the value of last non-nan column in each row. Is there a way to do this without looping through all the rows of the dataframe?

Sample Dataframe:

2016-06-02 7.080 7.079 7.079 7.079 7.079 7.079   nan   nan   nan
2016-06-08 7.053 7.053 7.053 7.053 7.053 7.054   nan   nan   nan  
2016-06-09 7.061 7.061 7.060 7.060 7.060 7.060   nan   nan   nan   
2016-06-14   nan   nan   nan   nan   nan   nan   nan   nan   nan  
2016-06-15 7.066 7.066 7.066 7.066   nan   nan   nan   nan   nan  
2016-06-16 7.067 7.067 7.067 7.067 7.067 7.067 7.068 7.068   nan  
2016-06-21 7.053 7.053 7.052   nan   nan   nan   nan   nan   nan  
2016-06-22 7.049 7.049   nan   nan   nan   nan   nan   nan   nan  
2016-06-28 7.058 7.058 7.059 7.059 7.059 7.059 7.059 7.059 7.059  

Reqd output

2016-06-02 7.079 
2016-06-08 7.054
2016-06-09 7.060
2016-06-14   nan 
2016-06-15 7.066
2016-06-16 7.068 
2016-06-21 7.052 
2016-06-22 7.049
2016-06-28 7.059  

Answer 1

You need last_valid_index with custom function, because if all values are NaN it return KeyError:

def f(x):
    if x.last_valid_index() is None:
        return np.nan
    else:
        return x[x.last_valid_index()]

df['status'] = df.apply(f, axis=1)
print (df)
                1      2      3      4      5      6      7      8      9  \
0                                                                           
2016-06-02  7.080  7.079  7.079  7.079  7.079  7.079    NaN    NaN    NaN   
2016-06-08  7.053  7.053  7.053  7.053  7.053  7.054    NaN    NaN    NaN   
2016-06-09  7.061  7.061  7.060  7.060  7.060  7.060    NaN    NaN    NaN   
2016-06-14    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   
2016-06-15  7.066  7.066  7.066  7.066    NaN    NaN    NaN    NaN    NaN   
2016-06-16  7.067  7.067  7.067  7.067  7.067  7.067  7.068  7.068    NaN   
2016-06-21  7.053  7.053  7.052    NaN    NaN    NaN    NaN    NaN    NaN   
2016-06-22  7.049  7.049    NaN    NaN    NaN    NaN    NaN    NaN    NaN   
2016-06-28  7.058  7.058  7.059  7.059  7.059  7.059  7.059  7.059  7.059   

            status  
0                   
2016-06-02   7.079  
2016-06-08   7.054  
2016-06-09   7.060  
2016-06-14     NaN  
2016-06-15   7.066  
2016-06-16   7.068  
2016-06-21   7.052  
2016-06-22   7.049  
2016-06-28   7.059  

Alternative solution - fillna with method ffill and select last column by iloc:

df['status'] = df.ffill(axis=1).iloc[:, -1]
print (df)
            status  
0                   
2016-06-02   7.079  
2016-06-08   7.054  
2016-06-09   7.060  
2016-06-14     NaN  
2016-06-15   7.066  
2016-06-16   7.068  
2016-06-21   7.052  
2016-06-22   7.049  
2016-06-28   7.059  

Answer 2

use agg('last')

df.groupby(['status'] * df.shape[1], 1).agg('last')

---

'last' within agg produces that last valid value within group. I passed a list of length equal to the number of columns. Each value of this list is 'status'. That means that I'm grouping by one group. The result is a dataframe with one column named 'status'

Answer 3

Here's a NumPy based solution -

In [113]: a
Out[113]: 
array([[ 17.,  53.,  nan,  63.,  66.,  nan,  nan,  nan,  nan,  nan],
       [ 54.,  96.,  71.,  20.,  70.,  58.,  91.,  nan,  nan,  nan],
       [ 58.,  26.,  72.,  93.,  58.,  29.,  44.,  28.,  36.,  88.],
       [ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan],
       [ 94.,  23.,  nan,  nan,  92.,  81.,  40.,  30.,  84.,  nan]])

In [114]: m = ~np.isnan(a)

In [115]: a[np.arange(m.shape[0]), m.shape[1]-m[:,::-1].argmax(1)-1]
Out[115]: array([ 66.,  91.,  88.,  nan,  84.])

To port this for dataframe, first off we can extract the values as an array : a = df.values and finally make the output dataframe :

vals = a[np.arange(m.shape[0]), m.shape[1]-m[:,::-1].argmax(1)-1]
df_out = pd.DataFrame(vals,index=df.index)