Similar to R and Pandas in Python, Julia provides a simple yet efficient interface with SQLite database. In addition, it is extremely handy to use sqldf() function, which is almost identical to the sqldf package in R, in SQLite package for data munging.

julia> # LOADING SQLITE PACKAGE

julia> using SQLite

julia> # CONNECT TO THE SQLITE DB FILE 

julia> db = SQLite.connect("/home/liuwensui/Documents/db/sqlitedb/csdata.db")

julia> # SHOW TABLES IN THE DB 

julia> query("select name from sqlite_master where type = 'table'")
1x1 DataFrame
|-------|-----------|
| Row # | name      |
| 1     | tblcsdata |

julia> # PULL DATA FROM THE TABLE

julia> # THE DATA WOULD BE AUTOMATICALLY SAVED AS A DATAFRAME

julia> df1 = query("select * from tblcsdata");

julia> head(df1, 2)
6x12 DataFrame
|-------|---------|----------|-----------|---------|-----------|----------|-----|-----------|-------|-------|-------|-------|
| Row # | LEV_LT3 | TAX_NDEB | COLLAT1   | SIZE1   | PROF2     | GROWTH2  | AGE | LIQ       | IND2A | IND3A | IND4A | IND5A |
| 1     | 0.0     | 0.530298 | 0.0791719 | 13.132  | 0.0820164 | 1.16649  | 53  | 0.385779  | 0     | 0     | 1     | 0     |
| 2     | 0.0     | 0.370025 | 0.0407454 | 12.1326 | 0.0826154 | 11.092   | 54  | 0.224123  | 1     | 0     | 0     | 0     |

julia> # SELECT DATA FROM THE TABLE WITH SQLDF() FUNCTION 

julia> df2 = sqldf("select * from df1 where AGE between 25 and 30");

julia> # SUMMARIZE DATA WITH SQLDF() FUNCTION 

julia> df3 = sqldf("select age, avg(LEV_LT3) as avg_lev from df2 group by age")
6x2 DataFrame
|-------|-----|-----------|
| Row # | AGE | avg_lev   |
| 1     | 25  | 0.0923202 |
| 2     | 26  | 0.0915009 |
| 3     | 27  | 0.0579876 |
| 4     | 28  | 0.104191  |
| 5     | 29  | 0.0764582 |
| 6     | 30  | 0.0806471 |