插值函数不填充数据框中的 nan 值

Question

晚上好，我在填充数据框中缺失的数据时遇到了问题。

如果缺失值的数量在 1 到 6 之间，我想用函数 interpolate 填充每个公司的 ESG 缺失数据，如果缺失值超过 6 个，则删除该列。

代码运行，但它没有填充 Nan 值。

import pandas as pd

#import the file
excel_file_path = 'insert your path'
sp500_esg = pd.read_excel(excel_file_path)

#rename date column for esg
wrongindex2 = sp500_esg.columns[0]
sp500_esg = sp500_esg.rename(columns = {wrongindex2:"Date"})

#set date column as index
sp500_esg = sp500_esg.set_index('Date', drop = True)

#make a copy
try1 = sp500_esg.copy()

#resample from daily to yearly
try1 = try1.resample('Y').mean()

#count total NaN in each column
print(try1.isnull().sum())

def interpolate_func(try1):
    for column in try1.columns:
            # Count the number of missing values in the column
            missing_count = try1[column].isna().sum()

            # If the number of missing values is between 1 and 6, interpolate to fill missing values
            if 1 <= missing_count <= 6:
                try1[column] = try1[column].interpolate()

            # If the number of missing values is greater than 6, drop the column
            elif missing_count > 6:
                try1 = try1.drop(column, axis=1)

    return try1

有什么想法吗？

谢谢！

我尝试指定插值函数的参数，没有结果

Answer 1

我看不到原始数据，但我没有看到您的代码有任何问题。这是一个小代码，可以满足您的需要：

import pandas as pd
df = pd.DataFrame({'a': [1,2,None, None]*4, 'b':[3,4,None,5]*4, 'c':[6,7,8,9]*4})
print(df)
for column in df.columns:
    n = df[column].isna().sum()
    if n > 6:
        df.drop(columns=column, inplace=True)
    elif 1 <  n < 6:
        df[column] = df[column].interpolate()      
print(df)

      a    b  c
0   1.0  3.0  6
1   2.0  4.0  7
2   NaN  NaN  8
3   NaN  5.0  9
4   1.0  3.0  6
5   2.0  4.0  7
6   NaN  NaN  8
7   NaN  5.0  9
8   1.0  3.0  6
9   2.0  4.0  7
10  NaN  NaN  8
11  NaN  5.0  9
12  1.0  3.0  6
13  2.0  4.0  7
14  NaN  NaN  8
15  NaN  5.0  9
      b  c
0   3.0  6
1   4.0  7
2   4.5  8
3   5.0  9
4   3.0  6
5   4.0  7
6   4.5  8
7   5.0  9
8   3.0  6
9   4.0  7
10  4.5  8
11  5.0  9
12  3.0  6
13  4.0  7
14  4.5  8
15  5.0  9