# Welcome to denstatbank’s documentation!¶

denstatbank is a python wrapper to Statistics Denmark’s Databank API. The package allows you to easily gather data on a variety of topics made available by Statistics Denmark.

## A walkthrough example¶

Let us walkthrough a quick example of how to query for data on a specific topic. The first step is to instantiate the client. This is easily done with the following two lines of code.

```
>>> from denstatbank import StatBankClient
>>> sbc = StatBankClient(lang='en')
```

Now, let’s find a table to get data from the databank. The tables method provides a list of all tables containing data currently available in the databank. Let’s go with the very first one.

```
>>> tdf = sbc.tables()
>>> tdf.iloc[0]
id FOLK1A
text Population at the first day of the quarter
unit number
updated 2020-02-11T08:00:00
firstPeriod 2008Q1
latestPeriod 2020Q1
active True
variables [region, sex, age, marital status, time]
```

All data tables have values associated with certain variables specific to the table. The population table that we shall look at has five such variables with the names you see above. The variables themselves have a list of valid values. One quick way of finding acceptable values for the variables is by using the tableinfo method as follows:

```
>>> vdf = sbc.tableinfo('folk1a', variables_df=True)
>>> years = vdf[vdf['variable']=='time']['id'].tolist()
```

We have now extracted the list of all acceptable values for the variable ‘time’. Now, we need to put this inside a dictionary where the dictionary key is the variable name (in Danish). The variable_dict method that you can call with the client does this for you.

```
>>> tid = sbc.variable_dict(code='tid', values=years)
```

Finally, we query the data with the table id and pass the variables dictionary inside of a list. You must use a list here since more than one variable can be passed.

```
>>> df = sbc.data(table_id='folk1a', variables=[tid])
>>> df.head()
Population at the first day of the quarter by Indhold and time
tid
2008Q1 5475791
2008Q2 5482266
2008Q3 5489022
2008Q4 5505995
2009Q1 5511451
```

And there we have the population data. Let us quickly plot it to get a feel for the data.

```
>>> df.plot(style='o-', figsize=(10, 6))
```

denstatbank uses the pandas python library to facilitate the handling of data. Pandas is a fast, popular and powerful library used for data analysis and manipulation. It is therefore well suited to be used with this package. There are plenty of resources available to learn from if you are new to pandas. I would highly recommend this book by the creator of the pandas himself.

## Code Documentation¶

That was just a quick demonstration of what you can do. To learn more, have a look at the detailed documentation of the client methods which details the different parameter options and includes examples.

## Statistical Analysis¶

I hope to include a couple of examples demonstrating statistical and time series analysis that you can perform on the data available via the API. You will be able to find them on the package github page.

## Other Links¶

The documentation for the Databank API can be found here.

Here, is the official website of Statistics Denmark.