DataFrame Processing Operations API - ArcticDB (original) (raw)

Build a query to process read results with. Syntax is designed to be similar to Pandas:

q = adb.QueryBuilder()
q = q[q["a"] < 5] (equivalent to q = q[q.a < 5] provided the column name is also a valid Python variable name)
dataframe = lib.read(symbol, query_builder=q).data

For Group By and Aggregation functionality please see the documentation for the groupby. For projection functionality, see the documentation for the apply method.

Supported arithmetic operations when projection or filtering:

• Binary arithmetic: +, -, *, /
• Unary arithmetic: -, abs

Supported filtering operations:

• isna, isnull, notna, and notnull - return all rows where a specified column is/is not NaN or None. isna is equivalent to isnull, and notna is equivalent to notnull, i.e. no distinction is made between NaN and None values in column types that support both (e.g. strings). For example:
• Binary comparisons: <, <=, >, >=, ==, !=
• Unary NOT: ~
• Binary combinators: &, |, ^
• List membership: isin, isnotin (also accessible with == and !=)

isin/isnotin accept lists, sets, frozensets, 1D ndarrays, or *args unpacking. For example:

l = [1, 2, 3]
q.isin(l)

is equivalent to...

q.isin(1, 2, 3)

Boolean columns can be filtered on directly:

q = adb.QueryBuilder()
q = q[q["boolean_column"]]

and combined with other operations intuitively:

q = adb.QueryBuilder()
q = q[(q["boolean_column_1"] & ~q["boolean_column_2"]) & (q["numeric_column"] > 0)]

Arbitrary combinations of these expressions is possible, for example:

q = q[(((q["a"] * q["b"]) / 5) < (0.7 * q["c"])) & (q["b"] != 12)]

See tests/unit/arcticdb/version_store/test_filtering.py for more example uses.

Timestamp filtering¶

pandas.Timestamp, datetime.datetime, pandas.Timedelta, and datetime.timedelta objects are supported. Note that internally all of these types are converted to nanoseconds (since epoch in the Timestamp/datetime cases). This means that nonsensical operations such as multiplying two times together are permitted (but not encouraged).

Restrictions¶

String equality/inequality (and isin/isnotin) is supported for printable ASCII characters only. Although not prohibited, it is not recommended to use ==, !=, isin, or isnotin with floating point values.

Exceptions¶

inf or -inf values are provided for comparison Column involved in query is a Categorical Symbol is pickled Column involved in query is not present in symbol Query involves comparing strings using <, <=, >, or >= operators Query involves comparing a string to one or more numeric values, or vice versa Query involves arithmetic with a column containing strings

apply ¶

Apply enables new columns to be created using supported QueryBuilder numeric operations. See the documentation for the QueryBuilder class for more information on supported expressions - any expression valid in a filter is valid when usingapply.

Examples:

`>>> df = pd.DataFrame( { "VWAP": np.arange(0, 10, dtype=np.float64), "ASK": np.arange(10, 20, dtype=np.uint16), "VOL_ACC": np.arange(20, 30, dtype=np.int32), }, index=np.arange(10), )

lib.write("expression", df) q = adb.QueryBuilder() q = q.apply("ADJUSTED", q["ASK"] * q["VOL_ACC"] + 7) lib.read("expression", query_builder=q).data VOL_ACC ASK VWAP ADJUSTED 0 20 10 0.0 207 1 21 11 1.0 238 2 22 12 2.0 271 3 23 13 3.0 306 4 24 14 4.0 343 5 25 15 5.0 382 6 26 16 6.0 423 7 27 17 7.0 466 8 28 18 8.0 511 9 29 19 9.0 558 `

date_range ¶

date_range(date_range: DateRangeInput)

DateRange to read data for. Applicable only for Pandas data with a DateTime index. Returns only the part of the data that falls within the given range. If this is the only processing clause being applied, then the returned data object will use less memory than passing date_range directly as an argument to the read method, at the cost of possibly being slightly slower.

Examples:

`>>> q = adb.QueryBuilder()

q = q.date_range((pd.Timestamp("2000-01-01"), pd.Timestamp("2001-01-01"))) `

groupby ¶

Group symbol by column name. GroupBy operations must be followed by an aggregation operator. Currently the following five aggregation operators are supported:

• "mean" - compute the mean of the group
• "sum" - compute the sum of the group
• "min" - compute the min of the group
• "max" - compute the max of the group
• "count" - compute the count of group

For usage examples, see below.

Examples:

Average (mean) over two groups:

`>>> df = pd.DataFrame( { "grouping_column": ["group_1", "group_1", "group_1", "group_2", "group_2"], "to_mean": [1.1, 1.4, 2.5, np.nan, 2.2], }, index=np.arange(5), )

q = adb.QueryBuilder() q = q.groupby("grouping_column").agg({"to_mean": "mean"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

           to_mean
 group_1  1.666667
 group_2       2.2

Max over one group:

`>>> df = pd.DataFrame( { "grouping_column": ["group_1", "group_1", "group_1"], "to_max": [1, 5, 4], }, index=np.arange(3), )

q = adb.QueryBuilder() q = q.groupby("grouping_column").agg({"to_max": "max"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

         to_max
group_1  5

Max and Mean:

`>>> df = pd.DataFrame( { "grouping_column": ["group_1", "group_1", "group_1"], "to_mean": [1.1, 1.4, 2.5], "to_max": [1.1, 1.4, 2.5] }, index=np.arange(3), )

q = adb.QueryBuilder() q = q.groupby("grouping_column").agg({"to_max": "max", "to_mean": "mean"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

         to_max   to_mean
group_1     2.5  1.666667

Min and max over one column, mean over another:

`>>> df = pd.DataFrame( { "grouping_column": ["group_1", "group_1", "group_1", "group_2", "group_2"], "agg_1": [1, 2, 3, 4, 5], "agg_2": [1.1, 1.4, 2.5, np.nan, 2.2], }, index=np.arange(5), )

q = adb.QueryBuilder() q = q.groupby("grouping_column") q = q.agg({"agg_1_min": ("agg_1", "min"), "agg_1_max": ("agg_1", "max"), "agg_2": "mean"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

         agg_1_min  agg_1_max     agg_2
group_1          1          3  1.666667
group_2          4          5       2.2

head ¶

Filter out all but the first n rows of data. If n is negative, return all rows except the last n rows.

optimise_for_memory ¶

Reduce peak memory usage during the query, at the expense of some performance.

Optimisations applied:

• Memory used by strings that are present in segments read from storage, but are not required in the final dataframe that will be presented back to the user, is reclaimed earlier in the processing pipeline.

optimise_for_speed ¶

Process query as fast as possible (the default behaviour)

prepend ¶

Applies processing specified in other before any processing already defined for this QueryBuilder.

resample ¶

resample( rule: Union[str, DateOffset], closed: Optional[str] = None, label: Optional[str] = None, offset: Optional[Union[str, Timedelta]] = None, origin: Union[str, Timestamp] = "epoch", )

Resample a symbol on the index. The symbol must be datetime indexed. Resample operations must be followed by an aggregation operator. Currently, the following 7 aggregation operators are supported:

• "mean" - compute the mean of the group
• "sum" - compute the sum of the group
• "min" - compute the min of the group
• "max" - compute the max of the group
• "count" - compute the count of group
• "first" - compute the first value in the group
• "last" - compute the last value in the group

Note that not all aggregators are supported with all column types:

• Numeric columns - support all aggregators
• Bool columns - support all aggregators
• String columns - support count, first, and last aggregators
• Datetime columns - support all aggregators EXCEPT sum

Note that time-buckets which contain no index values in the symbol will NOT be included in the returned DataFrame. This is not the same as Pandas default behaviour. Resampling is currently not supported with:

• Dynamic schema where an aggregation column is missing from one or more of the row-slices.
• Sparse data.

The resample results match pandas resample with origin="epoch". We plan to add an 'origin' argument in a future release and will then change the default value to '"start_day"' to match the Pandas default. This will change the results in cases where the rule is not a multiple of 24 hours.

Examples:

Resample two hours worth of minutely data down to hourly data, summing the column 'to_sum':

`>>> df = pd.DataFrame( { "to_sum": np.arange(120), }, index=pd.date_range("2024-01-01", freq="min", periods=120), )

q = adb.QueryBuilder() q = q.resample("h").agg({"to_sum": "sum"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

                     to_sum
2024-01-01 00:00:00    1770
2024-01-01 01:00:00    5370

As above, but specifying that the closed boundary of each time-bucket is the right hand side, and also to label the output by the right boundary:

`>>> q = adb.QueryBuilder()

q = q.resample("h", closed="right", label="right").agg({"to_sum": "sum"}) lib.read("symbol", query_builder=q).data `

                     to_sum
2024-01-01 00:00:00       0
2024-01-01 01:00:00    1830
2024-01-01 02:00:00    5310

Nones, NaNs, and NaTs are omitted from aggregations:

`>>> df = pd.DataFrame( { "to_mean": [1.0, np.nan, 2.0], }, index=pd.date_range("2024-01-01", freq="min", periods=3), )

q = adb.QueryBuilder() q = q.resample("h").agg({"to_mean": "mean"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

                     to_mean
2024-01-01 00:00:00      1.5

Output column names can be controlled through the format of the dict passed to agg:

`>>> df = pd.DataFrame( { "agg_1": [1, 2, 3, 4, 5], "agg_2": [1.0, 2.0, 3.0, np.nan, 5.0], }, index=pd.date_range("2024-01-01", freq="min", periods=5), )

q = adb.QueryBuilder() q = q.resample("h") q = q.agg({"agg_1_min": ("agg_1", "min"), "agg_1_max": ("agg_1", "max"), "agg_2": "mean"}) lib.write("symbol", df) lib.read("symbol", query_builder=q).data `

                     agg_1_min  agg_1_max     agg_2
2024-01-01 00:00:00          1          5      2.75

row_range ¶

row_range(row_range: Tuple[int, int])

Row range to read data for. Inclusive of the lower bound, exclusive of the upper bound. Should behave the same as df.iloc[start:end], including in the handling of negative start/end values.

tail ¶

Filter out all but the last n rows of data. If n is negative, return all rows except the first n rows.

then ¶

Applies processing specified in other after any processing already defined for this QueryBuilder.