python - nested loops robust to nulls/blanks-CodePudding

Here's a slice of my data:

[
['2002','','28','102','5'],
['2003','32','15','88','2'],
]

A year followed by four possible types: A,B,C,D, I want to loop through each of these so that I get a 'long' form of data, ex:

{'year':'2002','type':B}x28 (28 of these)
{'year':'2002','type':C}x102
{'year':'2002','type':D}x5

And so forth; note that the first occurrence of A should be skipped because it has a blank.

I got:

Invalid literal int() with base 10:''

Which is odd because I made sure to cast the readout into an int using int():

master_array = []
for i in range(len(tsv_data)):
    for j in range(int(tsv_data[i][1])):
        temp = {}
        temp['type']='A'
        master_array.append(temp)

I just moved on and tried:

master_array = []
for i in range(len(tsv_data)):
    try:
        for j in range(int(tsv_data[i][1])):
            temp = {}
            temp['type']='A'
            temp['year']=tsv_data[i][0]
            master_array.append(temp)
    except:
        try:
            for j in range(int(tsv_data[i][2])):
                temp = {}
                temp['type']='B'
                temp['year']=tsv_data[i][0]
                master_array.append(temp)
        except:
            try:
                for j in range(int(tsv_data[i][3])):
                    temp = {}
                    temp['type']='C'
                    temp['year']=tsv_data[i][0]
                    master_array.append(temp)
            except:
                for j in range(int(tsv_data[i][4])):
                    temp = {}
                    temp['type']='D'
                    temp['year']=tsv_data[i][0]
                    master_array.append(temp)

Which ran, but it gave me a tiny fraction of what the actually list should be. The behavior I was expect was for try/except to skip all the ones with blanks and iterate over the cells with values.

Question

How can I read the values in each cell and use them as iteration cues for my master_array and robust to blanks?

The result should be one item for each year and type configuration, based on whatever the value was.

CodePudding user response：

Only take the range if the value is actual digits. This does that and is a more simple and readable implementation.

master_array = []
for y, a, b, c, d in tsv_data:
    if a.isdigit():
        master_array.extend([{"year": y, "type": "A"} for _ in range(int(a))])
    if b.isdigit():
        master_array.extend([{"year": y, "type": "B"} for _ in range(int(b))])
    if c.isdigit():
        master_array.extend([{"year": y, "type": "C"} for _ in range(int(c))])
    if d.isdigit():
        master_array.extend([{"year": y, "type": "D"} for _ in range(int(d))])

You could do this with the simpler truth check, but it is better to check it is a digit before the conversion.

    if a:  # Will work, but better to check int.
        master_array.extend([{"year": y, "type": "A"} for _ in range(int(a))])