Python / Pandas：用顺序填充 NaN - 线性插值 --> ffill --> bfill

Question

I have a df :

     company  year      revenues
0  company 1  2019   1,425,000,000
1  company 1  2018   1,576,000,000
2  company 1  2017   1,615,000,000
3  company 1  2016   1,498,000,000
4  company 1  2015   1,569,000,000
5  company 2  2019             nan
6  company 2  2018   1,061,757,075
7  company 2  2017             nan
8  company 2  2016     573,414,893
9  company 2  2015     599,402,347

I would like to fill the nan values, with an order. I want to linearly interpolate first, then forward fill and then backward fill. I currently have:

f_2_impute = [x for x in cl_data.columns if cl_data[x].dtypes != 'O' and 'total' not in x and 'year' not in x]

def ffbf(x):
    return x.ffill().bfill()

group_with = ['company']

for x in cl_data[f_2_impute]:
    cl_data[x] = cl_data.groupby(group_with)[x].apply(lambda fill_it: ffbf(fill_it))

which performs ffill() and bfill() . Ideally I want a function that tries first to linearly intepolate the missing values, then try forward filling them and then backward filling them.

Any quick ways of achieving it? Thanking you in advance.

Answer 1

I believe you need first convert columns to floats if , there:

df = pd.read_csv(file, thousands=',')

Or:

df['revenues'] = df['revenues'].replace(',','', regex=True).astype(float)

and then add DataFrame.interpolate :

def ffbf(x):
    return x.interpolate().ffill().bfill()