Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error **ValueError: Series lengths must match to compare when matching dates in Pandas** **in python**. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Table of Contents

## How ValueError: Series lengths must match to compare when matching dates in Pandas Error Occurs?

Today I get the following error **ValueError: Series lengths must match to compare when matching dates in Pandas** **in python**.

## How To Solve ValueError: Series lengths must match to compare when matching dates in Pandas Error ?

**How To Solve ValueError: Series lengths must match to compare when matching dates in Pandas Error ?**To Solve ValueError: Series lengths must match to compare when matching dates in Pandas Error (Note that there are more efficient patterns if you have a lot of

`some_id`

and`some_date`

values to look up, but that's a separate issue.)**ValueError: Series lengths must match to compare when matching dates in Pandas**To Solve ValueError: Series lengths must match to compare when matching dates in Pandas Error (Note that there are more efficient patterns if you have a lot of

`some_id`

and`some_date`

values to look up, but that's a separate issue.)

## Solution 1

You say:

some_date = df.iloc[1:2]['Date'] # gives 2016-01-01

but that’s *not* what it gives. It gives a Series with one element, not simply a value — when you use `[1:2]`

as your slice, you don’t get a single element, but a container with one element:

>>> some_date 1 2016-01-01 Name: Date, dtype: datetime64[ns]

Instead, do

>>> some_date = df.iloc[1]['Date'] >>> some_date Timestamp('2016-01-01 00:00:00')

after which

>>> df[(df['ID']==some_id) & (df['Date'] == some_date)] ID Date 0 1 2016-01-01

(Note that there are more efficient patterns if you have a lot of `some_id`

and `some_date`

values to look up, but that’s a separate issue.)

## Solution 2

As mentioned by DSM, some_date is a series and not a value. When you use boolean masking, and checking if value of a column is equal to some variable or not, we have to make sure that the variable is a value, not a container. One possible way of solving the problem is mentioned by DSM, there is also another way of solving your problem.

df[(df['ID']==some_id) & (df['Date'] == some_date.values[0])]

We have just replaced the some_date with some_date.values[0]. some_date.values returns an array with one element. We are interested in the value in the container, not the container, so we index it by [0] to get the value.

**Summery**

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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