Python 当列名在某个日期范围内时，dataframe将NaN替换为0

我有这样一个数据帧：

time A      time B      2017-11  2017-12  2018-01  2018-02          
2017-01-24  2020-01-01  NaN      NaN      NaN      NaN
2016-11-28  2020-01-01  NaN      4.0      2.0      2.0
2017-03-18  2017-12-21  NaN      NaN      NaN      NaN

当列名称介于时间A和时间B之间时，我希望将所有NaN替换为0。例如，对于第三行，时间范围为2017-03-18到2017-12-21，因此第三行的数据的列名称介于此范围之间，如果是NaN，则将其替换为0，否则保持不变。希望它清楚。谢谢

请尝试以下代码：

newdf=df[(df.date>some_date) & (df.date<somedate)]
newdf.fillna(0)

---

newdf=df[（df.date>some_date）&（df.date也许不是最好的解决方案，但它仍然有效

这是我的测试样本：

d = pd.DataFrame([
    {"time A": "2017-01-24", "time B": np.nan,       "2016-11": np.nan, "2016-12": np.nan, "2017-01": np.nan, "2017-02": np.nan},
    {"time A": "2016-11-28", "time B": np.nan,       "2016-11": np.nan, "2016-12": 4,      "2017-01": 2,      "2017-02": 2},
    {"time A": "2016-12-18", "time B": "2017-01-01", "2016-11": np.nan, "2016-12": np.nan, "2017-01": np.nan, "2017-02": np.nan},
])

d["time B"].fillna("2020-01-01", inplace=True)
d.set_index(["time A", "time B"], inplace=True)

初始表格：

time A      time B      2016-11  2016-12  2017-01  2017-02          
2017-01-24  2020-01-01  NaN      NaN      NaN      NaN
2016-11-28  2020-01-01  NaN      4.0      2.0      2.0
2016-12-18  2017-01-01  NaN      NaN      NaN      NaN

    time A      time B      month       value
0   2017-01-24  2020-01-01  2016-11-01  NaN
1   2017-01-24  2020-01-01  2016-12-01  NaN
2   2017-01-24  2020-01-01  2017-01-01  NaN
3   2017-01-24  2020-01-01  2017-02-01  0.0

time A      time B      2016-12  2017-01  2017-02
2016-11-28  2020-01-01  4.0      2.0      2.0
2016-12-18  2017-01-01  NaN      0.0      NaN
2017-01-24  2020-01-01  NaN      NaN      0.0

看起来
时间A
是开放日期，
时间B
是关闭日期，或类似的smth。因此，为了方便起见，我用任何未来日期填充了缺少的
时间B
，例如
'2020-01-01'

我不喜欢使用数据透视表，所以我习惯于对其进行堆栈并格式化日期列：

d_stack = d.stack(dropna=False).reset_index()
d_stack.columns = ["time A", "time B", "month", "value"]

for col in ["time A", "time B"]:
    d_stack[col] = pd.to_datetime(d_stack[col], format="%Y-%m-%d", errors="ignore")

d_stack["month"] = pd.to_datetime(d_stack["month"], format="%Y-%m", errors="ignore")

现在填充缺少的值更方便了

def fill_existing(x):
    if (x["time A"] <= x["month"] <= x["time B"] and
            np.isnan(x["value"])):
        return 0
    else:
        return x["value"]

d_stack["value"] = d_stack.apply(fill_existing, axis=1)

最后，将
month
格式化并返回到初始表格格式：

d_stack["month"] = d_stack["month"].apply(lambda x: x.strftime("%Y-%m"))
pd.pivot_table(d_stack, columns="month", index=["time A", "time B"],
               values="value", aggfunc=np.sum)

结果：

time A      time B      2016-11  2016-12  2017-01  2017-02          
2017-01-24  2020-01-01  NaN      NaN      NaN      NaN
2016-11-28  2020-01-01  NaN      4.0      2.0      2.0
2016-12-18  2017-01-01  NaN      NaN      NaN      NaN

    time A      time B      month       value
0   2017-01-24  2020-01-01  2016-11-01  NaN
1   2017-01-24  2020-01-01  2016-12-01  NaN
2   2017-01-24  2020-01-01  2017-01-01  NaN
3   2017-01-24  2020-01-01  2017-02-01  0.0

time A      time B      2016-12  2017-01  2017-02
2016-11-28  2020-01-01  4.0      2.0      2.0
2016-12-18  2017-01-01  NaN      0.0      NaN
2017-01-24  2020-01-01  NaN      NaN      0.0

请编辑您的文章，用您的数据样本替换图像，您的问题不清楚