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An essential step in the digitization process of cultural heritage is the collection, structuring, and publication of data. Without real, coherent, and accessible data, any analysis or digital application would remain at a purely theoretical level.
The National Heritage Institute directly contributes to this process by publishing open datasets on the governmental platform data.gov.ro, providing public access to official information about cultural institutions in Romania. Among these is the dataset regarding museums in Romania, which contains information such as museum names, administrative location, founding year, geographical coordinates, and descriptive data.
In this problem, you will perform an exploratory analysis for this dataset.
The dataset is provided in CSV (Comma-Separated Values) format and contains information about museums in Romania, resulting from the conversion of a JSON format published on the data.gov.ro platform.
Each row in the CSV file corresponds to a museum, and each column describes a specific attribute of it.
The dataset contains, among others, the following columns:
_id – internal identifier of the recordjudețul – the county where the museum is locateddenumirea (română) – the name of the museum in Romanianlocalitatea – the locality where the museum is locatedanul înființării – the year the museum was foundedcategoria (română) – the category of the museumlatitudine – the geographical coordinate of latitudelongitudine – the geographical coordinate of longitudedescrierea (română) – textual description of the museumURL – the museum's website (if available)Missing values are marked by empty fields.
Determine the total number of museums in the dataset.
Determine the number of museums for which we find the value București in the județul column.
Determine the number of columns that contain at least one missing value (NaN).
Determine the year in which the most museums were founded, based on the information specified in the anul înființării column.
Determine for each county that appears in this dataset how many museums exist in that county.
For each museum, calculate the percentage of completed columns (non-NaN) out of the total columns. The calculation of this score is performed using the following formula:

Determine the average completeness score for the entire dataset. For this, we will add the scores obtained in requirement 6 and divide the total by the number of museums.
Determine the percentage of museums that have the maximum completeness score. We define the maximum completeness score as the maximum value among those determined in task 6.
For automatic evaluation, you must upload a file in csv format with the following structure:
id – row identifier (corresponding to the one in test.csv)
ididididid_id column from the dataset)ididsubtaskID – requirement identifier:
1 for total number of records (task 1)2 for number of museums in Bucharest (task 2)3 for number of columns with missing values (task 3)4 for year with the most museums (task 4)5 for distribution of museums by counties (task 5)6 for percentage of completed values (task 6)7 for average completeness score (task 7)8 for museums with maximum completeness (task 8)answer – the answer corresponding to each task
for task 1, will contain the total number of museums in the dataset (integer value)
for task 2, will contain the number of museums in Bucharest (integer value)
for task 3, will contain the number of columns that contain at least one missing value (NaN) (integer value)
for task 4, will contain the year in which the most museums were founded (integer value)
for task 5, will contain the number of museums in each county (integer value, one row for each county)
for task 6, will contain the percentage of completed fields for each museum, calculated as:
(number of completed fields / total number of columns) × 100(float value, recommended rounded to 2 decimal places)
for task 7, will contain the average completeness score for the entire dataset (float value, recommended rounded to 2 decimal places)
for task 8, will contain the percentage of museums that have the maximum completeness score (float value, recommended rounded to 2 decimal places)