Ensuring years and months are running as part of data cleaning - pandas

Question

I have 2 datasets:
rainfall per month (mm) from 1982-01 to 2022-08
no. of rainy days per month per year from 1982-01 to 2022-08.
month no_of_rainy_days
0 1982-01 10
1 1982-02 5
2 1982-03 11
3 1982-04 14
4 1982-05 10
month total_rainfall
0 1982-01 107.1
1 1982-02 27.8
2 1982-03 160.8
3 1982-04 157.0
4 1982-05 102.2
Qn 1: As part of ensuring data integrity, how do I ensure that the dates are running consecutively? i.e 1982-01 and next is 1982-02 and not a skip to 1982-03?
I am unsure how to perform the checking and have done a search online. Is it common practice to assume that the years and months are running?

Answer 1

First, separate the year from the month.
df.rename(columns={"month": "ym"}, inplace=True)
df[["year", "month"]] = df["ym"].astype(str).str.split("-", expand=True)
Then you can group the dataframe by year and count the number of observations per year (counts number of rows per year).
observations_per_year = df["year"]\
.groupby(df["year"])\
.agg("count")\
.reset_index(name="observations")
observations_per_year[observations_per_year["observations"] < 12]
Assuming you have any years with less than 12 observations, they will be displayed like so:
year observations
0 1982 11
4 1986 11
5 1987 11
6 1988 10
11 1993 11
Given the lack of detail and no sample data provided, I made some assumptions about your data:
Each data set will not have more than one row for any month of the year (i.e., a maximum of 12 rows/observations per year).
Each dataframe contains a single observation per row, as shown in your examples (so you would do this for each dataframe prior to merging them). As such, counting rows per year-month is an accurate means of counting the number of observations for any given month.
The sorted order of the data is irrelevant (you can later sort by year-month if needed).