如何跳过 csv 文件 python 中的非数字行

Question

I have sample csv file with string values like below:

1234, san@mail, IN, 001
, ram@mail, IN, 003
1235, john@mail, IN, 004 
san-ba, luios@mail, IN, 005 
undefined, thomas@mail, IN, 006

I need to skip the rows that having empty and non numeric in row[0] form the file.

Expected result:

1234, san@mail, IN, 001
1235, john@mail, IN, 004 

Answer 1

You could try to convert the value into a float, and if it fails then you skip it:

for row in data:
    first_val = row[0]
    try:
        float(first_val)
    except ValueError:
        continue
    # here you use the row, knowing the first value is numerical
    print("this row has a numerical value in index 0")

Answer 2

You can convert categorial values to NaN, then drop NaNs

import pandas as pd
import numpy as np

def categorial_to_nan(val):
    if str(val).isdigit():return val
    else:return np.NAN

Here is your dataset

          id         email    x   y
0       1234      san@mail   IN   1
1        NaN      ram@mail   IN   3
2       1235     john@mail   IN   4
3     san-ba    luios@mail   IN   5
4  undefined   thomas@mail   IN   6

df['id'] = df['id'].map(categorial_to_nan)
df = df.dropna()
print('After')
print(df)

The Result

     id       email    x   y
0  1234    san@mail   IN   1
2  1235   john@mail   IN   4