比较 pandas DataFrame 中的两个日期列以验证第三列

Question

Background info
I'm working on a DataFrame where I have successfully joined two different datasets of football players using fuzzymatcher. These datasets did not have keys for an exact match and instead had to be done by their names. An example match of the name column from two databases to merge as one is the following

long_name       name
L. Messi        Lionel Andrés Messi Cuccittini

As part of the validation process of a 18,000 row database, I want to check the two date of birth columns in the merged DataFrame - df , ensuring that the columns match like the example below

dob             birth_date
1987-06-24      1987-06-24

Both date columns have been converted from strings to dates using pd.to_datetime() , eg

df['birth_date'] = pd.to_datetime(df['birth_date'])

My question
My query, I have another column called 'value'. I want to update my pandas DataFrame so that if the two date columns match, the entry is unchanged. However, if the two date columns don't match, I want the data in this value column to be changed to null. This is something I can do quite easily in Excel with a date_diff calculation but I'm unsure in pandas.

My current code is the following:

df.loc[(df['birth_date'],= df['dob']).'value'] = np.nan

Reason for this step (feel free to skip)
The reason for this code is that it will quickly show me fuzzy matches that are inaccurate (approx 10% of total database) and allow me to quickly fix those.

Ideally I need to also work on the matching algorithm to ensure a perfect date match, however, my current algorithm currently works quite well in it's current state and the project is nearly complete. Any advice on this however I'd be happy to hear, if this is something you know about

Many thanks in advance!

Answer 1

IICU: Please Try np.where . Works as follows;

np.where(if condition, assign x, else assign y)

if condition =df.loc[(df['birth_date'],= df['dob'], x =np.nan and y = prevailing df.value

df['value']= np.where(df.loc[(df['birth_date'] != df['dob']),'value'], np.nan, df['value'])