如果列之间存在任何 NaN 值，如何处理脚本在 python 中 [英] How to process the script in case of any NaN values between the column In python

问题描述

我正在尝试处理我正在尝试查找列之间的月份的脚本，该脚本工作正常，但只要任何字段为空，就会出现错误.

I am trying to process the an script Where i am Trying to find the Months between the column , The script is working fine but whenever any field is blank it Gives an error.

如果中间有任何 NaN 值，它必须跳过并移动到下一行.

If any NaN values comes in between It must skips and move to Next rows.

如何解决错误:

输入数据:

Month1    Month2     Month_list
Mar2020   Dec2020
Nov2020   Jan2021
NaN       NaN
Sep2020   Feb2021
Oct2020   Dec2020
NaN       NaN
Dec2020   Mar2021

预期输出:

预期输出

Month1    Month2     Month_list

Mar2020   Sep2020    Mar2020,Apr2020,May2020,Jun2020,Jul2020,Aug2020,Sep2020
Nov2020   Jan2021    Nov2020,Dec2020,Jan2021
NaN       NaN        NaN
Sep2020   Feb2021    Sep2020,Oct2020,Nov2020,Dec2020,Jan2021,Feb2021
Oct2020   Dec2020    Oct2020,Nov2020,Dec2020
NaN       NaN        NaN
Dec2020   Mar2021    Dec2020,Jan2021,Feb2021,Mar2021

代码:

def get_date_list(x):
    return ",".join(
        item.strftime("%b %Y")
        for item in pd.date_range(x['Month1'], x['Month2'], freq="MS")
    )
    
df['Month_list'] = df.apply(lambda x: get_date_list(x), axis=1)

错误:ValueError:start 和 end 都不能是 NaT

Error: ValueError: Neither start nor end can be NaT

推荐答案

IIUC，你的函数需要一个 if else 块:

IIUC, your function needs a if else block:

def get_date_list(x):
    if not pd.isna(x['Month1']):
        return ",".join(
        item.strftime("%b %Y")
        for item in pd.date_range(x['Month1'], x['Month2'], freq="MS") 
        )
    return np.nan
df['Month_list'] = df.apply(lambda x: get_date_list(x), axis=1)

---

print(df)

    Month1   Month2                                         Month_list
0  Mar2020  Dec2020  Mar 2020,Apr 2020,May 2020,Jun 2020,Jul 2020,A...
1  Nov2020  Jan2021                         Nov 2020,Dec 2020,Jan 2021
2      NaN      NaN                                                NaN
3  Sep2020  Feb2021  Sep 2020,Oct 2020,Nov 2020,Dec 2020,Jan 2021,F...
4  Oct2020  Dec2020                         Oct 2020,Nov 2020,Dec 2020
5      NaN      NaN                                                NaN
6  Dec2020  Mar2021                Dec 2020,Jan 2021,Feb 2021,Mar 2021

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