python如何做数据清洗？

import pandas as pd

import numpy as np

# 创建空的df，保存测试数据

test_df = pd.DataFrame({'K1':['C1','C1','C2','C3','C4','C2','C1'],'K2':['A','A','B','C','D',np.NaN,np.NaN]})

# 按K1列进行分组，组内进行unique操作（去除重复元素，返回元组或列表）

test_df_unique = pd.DataFrame(test_df.groupby(['K1'])['K2'].agg('unique'))

# 自定义函数判断元组中是否含有nan

def has_nan(list):

    flag = False

    for x in list:

        if x is np.NaN:

            flag = True

            break

    return flag

# 自定义函数判断元组中是否不含有nan

def no_nan(list):

    flag = True

    for x in list:

        if x is np.NaN:

            flag = False

            break

    return flag

# 获取k2列含有nan的数据

test_df_unique_has_nan = test_df_unique[test_df_unique['K2'].apply(has_nan)]

# 获取k2列不含有nan的数据

test_df_unique_no_nan = test_df_unique[test_df_unique['K2'].apply(no_nan)]

# 管理测试数据，获取源数据

test_df_get = test_df[test_df['K1'].isin(test_df_unique_has_nan.index.tolist())]

test_df_alone = test_df[test_df['K1'].isin(test_df_unique_no_nan.index.tolist())]

# 去除含nan的重复数据

test_df_get_nonan = test_df_get[~test_df_get['K2'].isna()]

# 组合数据

result = test_df_get_nonan.append(test_df_alone)

# 去重，得到最终结果

result_save = result.drop_duplicates(subset=['K1','K2'],keep='last')

# 结果落地

result_save.to_excel('C:/Users/zhen/Desktop/数据清洗之去重.xlsx')