How to iterate through columns in a row to find the first that meets some criteria

Question

I need to iterate through columns in a row of a dataframe to find the first cell (in this given row) that is fully capitalized. I need to repeat this for all rows in the dataframe, finally outputting a dataframe with one column and each row with the corresponding first capitalized string.

As an example - this could be the input dataframe:

+-----+--------+--------+--------+------+
|  0  |   1    |   2    |   3    |  4   |
+-----+--------+--------+--------+------+
| a   | Amount | SEQ    | LTOTAL | None |
| BBc | LCALC  | None   | None   | None |
| c   | LCALC  | None   | None   | None |
| Dea | RYR    | LTOTAL | None   | None |
+-----+--------+--------+--------+------+

And I would need the output to be the following, in a separate dataframe:

+-------+
| SEQ   |
| LCALC |
| LCALC |
| RYR   |
+-------+

Answer 1

If need check all columns test values by isupper and replace non matched values to NaN s, so possible back filling missing values and seelct first column by iloc :

df = df.where(df.applymap(lambda x: x.isupper())).bfill(axis=1).iloc[:, 0].to_frame('col')
print (df)
     col
0    SEQ
1  LCALC
2  LCALC
3    RYR

EDIT:

Create df1 with columns by position of matched values, so first column are first upper values, ...:

#reshape by stack, None and NaNs columns are removed, 
#remove second level of MultiIndex
s = df.stack().reset_index(level=1, drop=True)
#filter only upper values, convert to DataFrame
df1  = s[s.str.isupper()].rename_axis('idx').reset_index(name='val')
#create counter column for count first, second... columns
df1['g'] = df1.groupby('idx').cumcount()
#reshape by pivot and if necessary add non upper rows
df1 = df1.pivot('idx','g','val').reindex(df.index)
print (df1)
g      0       1
0    SEQ  LTOTAL
1  LCALC     NaN
2  LCALC     NaN
3    RYR  LTOTAL

first = df1[0].to_frame('col')
second = df1[1].to_frame('col')
print (first)
    col
0    SEQ
1  LCALC
2  LCALC
3    RYR

print (second)
      col
0  LTOTAL
1     NaN
2     NaN
3  LTOTAL

Answer 2

def get_first_upper(row):
    for val in row:
        if val and val.isupper():
            return val
df1.apply(get_first_upper)

Answer 3

You can iterate rows with

column = list()

for _, row in df.iterrows():
    for item in row:
        if item.isupper():
            column.append(item)
            break
    else:
        column.append(numpy.nan)

new_df = pandas.DataFrame(column)

Answer 4

Use the following code to iterate through the row and break at first instance of an all caps cell in the row

import pandas as pd
l=[]
for index,row in df.iterrows():
    for i in row:
            if(i.isuppercase()):
                    l.append(i)
                    break
new_df = pandas.DataFrame(l)