Python 如何保护 DataFrame 中每列的值

发布于07月25日

我的输入是DataFrame和Dictionnary:

import pandas as pd

df = pd.DataFrame({'A': ['foo', None, '2'],
 'B': ['baz', '1', '4'],
 'C': [None, 'bar', None],
 'D': [None, None, None]})

#      A    B    C   D
# 0  foo  baz  NaN NaN
# 1  NaN    1  bar NaN
# 2    2    4  NaN NaN

dict_ = {
    "A": "CONDTIONNAL",
    "B": "MANDATORY",
    "C": "MANDATORY",
    "D": "OPTIONAL"
}

我希望将多字符串作为输出:

"""
    Column A is OK
    Column B is OK
    Column C is not OK / values missing at [0, 2]
    Column D is OK
"""

我首先判断值是否为空，但不幸的是，值被折叠了:

df.isnull().any().to_dict()

{'A': True, 'B': False, 'C': True, 'D': True}

你能帮我弄清楚复数弦吗？理想情况下，如果我们可以创建一个自定义函数，返回一个布尔值:如果所有列都有效，则返回True，否则返回False，我会非常高兴.

推荐答案

使用布尔运算:

# flag invalid rows when value is MANDATORY
invalids = df.isnull() & pd.Series(dict_).eq('MANDATORY')

# get invalid indices
idx, cols = np.where(invalids)
# aggregate as list
rows = pd.Series(idx, index=df.columns[cols]).groupby(level=0).agg(list)

# for each column, display report
for i, (col, invalid) in enumerate(invalids.any().items()):
    suffix = f'not OK / values missing at {rows[col]}' if invalid else 'OK'
    print(f'    Column {col} is {suffix}')

输出:

    Column A is OK
    Column B is OK
    Column C is not OK / values missing at [0, 2]
    Column D is OK

对于多字符串:

'\n'.join([f'    Column {col} is {f"not OK / values missing at {rows[col]}" if invalid else "OK"}'
           for i, (col, invalid) in enumerate(invalids.any().items())])