CODEWITHSUNDEEP
dataframedatetimejulia
Cade Karrenberg
Cade Karrenberg

Reputation: 53

Can I groupby day or month in Julia using DataFrames

I'm working with a DataFrame where column1 is a DateTime type and all other columns are Float64 and represent hourly water use. The DataFrame spans 2 years of water use recorded at hourly intervals for every day and looks something like this.

Date                 | UserID1 | UserID2 | UserID3 | ...
DateTime             | Float64 | Float64 | Float64 | ...
---------------------------------------------------------
2017-01-01 00:00:00  | 1.5     | 22.5    | 5.5     | ...
2017-01-01 01:00:00  | 4.5     | 3.2     | 9.12    | ...
.
.
.
2019-12-31 22:00:00 |  4.2     |  7.6    | 8.9     | ...
2029-12-31 23:00:00 |  3.2     |  0.9    | 11.2    | ...

I'd like to use groupby to sum water use for all users over each month. In Pandas I could easily do something like df.groupby(df.index.month).sum()

I can't figure out an analogy for Julia DataFrames. Im using CSV, DataFrames, and Dates packages. I can loop through each entry in the Date column like

for i in df.Date
   if day(i) == 1
.
.
.

But I'd really like to use groupby because its such a large DataFrame and I have several other functions I'd like to perform other than sum

Upvotes: 2

Views: 1576

Answers (1)

Przemyslaw Szufel
Przemyslaw Szufel

Reputation: 42194

Consider the following DataFrame:

julia> df = DataFrame(date=Date.(2020,rand(1:5,10), rand(1:28, 10)),val=rand(1:20,10))
10×2 DataFrame
│ Row │ date       │ val   │
│     │ Date       │ Int64 │
├─────┼────────────┼───────┤
│ 1   │ 2020-01-07 │ 17    │
│ 2   │ 2020-04-17 │ 2     │
│ 3   │ 2020-01-18 │ 18    │
│ 4   │ 2020-01-01 │ 11    │
│ 5   │ 2020-04-25 │ 16    │
│ 6   │ 2020-05-08 │ 5     │
│ 7   │ 2020-04-10 │ 4     │
│ 8   │ 2020-02-12 │ 10    │
│ 9   │ 2020-04-16 │ 1     │
│ 10  │ 2020-03-16 │ 15    │

This can be grouped using the following function:

julia> groupby(transform(df, :date => x->yearmonth.(x)),:date_function)
GroupedDataFrame with 5 groups based on key: date_function
First Group (3 rows): date_function = (2020, 1)
│ Row │ date       │ val   │ date_function │
│     │ Date       │ Int64 │ Tuple…        │
├─────┼────────────┼───────┼───────────────┤
│ 1   │ 2020-01-07 │ 17    │ (2020, 1)     │
│ 2   │ 2020-01-18 │ 18    │ (2020, 1)     │
│ 3   │ 2020-01-01 │ 11    │ (2020, 1)     │
⋮
Last Group (1 row): date_function = (2020, 3)
│ Row │ date       │ val   │ date_function │
│     │ Date       │ Int64 │ Tuple…        │
├─────┼────────────┼───────┼───────────────┤
│ 1   │ 2020-03-16 │ 15    │ (2020, 3)     │

or with using Query:

julia> df |>
          @groupby(yearmonth(_.date)) |>
          @map({YearMonth=key(_), Sum=sum(_.val)}) |>
          DataFrame
5×2 DataFrame
│ Row │ YearMonth │ Sum   │
│     │ Tuple…    │ Int64 │
├─────┼───────────┼───────┤
│ 1   │ (2020, 1) │ 46    │
│ 2   │ (2020, 4) │ 23    │
│ 3   │ (2020, 5) │ 5     │
│ 4   │ (2020, 2) │ 10    │
│ 5   │ (2020, 3) │ 15    │

Upvotes: 4

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