Python Data Science Kit for Humans.

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Project description

DSKit

DSKit (Data Science Kit) is a Python package that provides tools for solving simple Data Science tasks.

Installing

pip install dskit

Tutorial

DSKit consists of three submodules:

dskit.frame - contains a set of functions for pandas.DataFrame and pandas.Series manipulation.
dskit.pipeline - contains a PipelineFrame wrapper of pandas.DataFrame.
dskit.tensor - contains a set of functions for numpy.ndarray manipulation.

dskit.frame

astype

astype is a convenience function, which partially applies passed parameter to pd.DataFrame.astype or pd.Series.astype function.

xs = pd.Series([1, 2, 3])
print(astype(float)(xs))

# 0    1.0
# 1    2.0
# 2    3.0
# dtype: float64

ys = pd.DataFrame({"A": [1, 2], "B": ["01-01-2020", "02-01-2020"]})
print(astype({"A": float, "B": "datetime64[D]"})(ys))

#      A          B
# 0  1.0 2020-01-01
# 1  2.0 2020-02-01

create_dummifier

create_dummifier is less harmful alternative to pd.get_dummies. This function takes a Dict[str, Tuple[object, ...]] and returns Callable[[pd.DataFrame], pd.DataFrame]. Key of the dictionary is treated as a name of a column and value of the dictionary is treated as unique values of that column. Each key and value of the dictionary are passed to create_encoder function. Bases on created encoders a new function is created which takes pd.DataFrame and returns pd.DataFrame. The returned function is a "dummify" function.

frame = pd.DataFrame({"A": (1, 2, 2, 5, 5), "B": ("a", "a", "b", "c", "d")})
uniques: Dict[str, Tuple[object, ...]] = unique(frame)

dummify: Callable[[pd.DataFrame], pd.DataFrame] = create_dummifier(uniques)
print(dummify(frame))

#    A_1  A_2  A_5  B_a  B_b  B_c  B_d
# 0  1.0  0.0  0.0  1.0  0.0  0.0  0.0
# 1  0.0  1.0  0.0  1.0  0.0  0.0  0.0
# 2  0.0  1.0  0.0  0.0  1.0  0.0  0.0
# 3  0.0  0.0  1.0  0.0  0.0  1.0  0.0
# 4  0.0  0.0  1.0  0.0  0.0  0.0  1.0

other_frame = pd.DataFrame({"C": (True, True, False, True), "A": (1, 2, 3, 4)})
print(dummify(other_frame))

#    A_1  A_2  A_5      C
# 0  1.0  0.0  0.0   True
# 1  0.0  1.0  0.0   True
# 2  0.0  0.0  0.0  False
# 3  0.0  0.0  0.0   True

# notice, that columns are sorted alphabetically (C comes after A)
# you can pass sort_columns=False if you want to omit sorting

One of the reasons why create_dummifier is less harmful than pd.get_dummies is that it will not dummify new values. Thanks to that Machine Learning models will operate on data with the same number of dimensions regardless of new values presence in new portion of data.

old_frame = pd.DataFrame({"B": ("a", "a", "b")})
new_frame = pd.DataFrame({"B": ("a", "b", "c")})

uniques: Dict[str, Tuple[object, ...]] = unique(old_frame)
dummify: Callable[[pd.DataFrame], pd.DataFrame] = create_dummifier(uniques)

print(dummify(new_frame))

#    B_a  B_b
# 0  1.0  0.0
# 1  0.0  1.0
# 2  0.0  0.0

print(pd.get_dummies(new_frame))

#    B_a  B_b  B_c
# 0    1    0    0
# 1    0    1    0
# 2    0    0    1

create_encoder

create_encoder is a function used by create_dummifier. create_encoder takes a column name with a set of values and returns Callable[[Tuple[object, ...]], pd.DataFrame]. The returned function one-hot-encodes passed values.

encoded: pd.DataFrame = create_encoder("A", (1, 2, 3))((1, 2, 3, 4, np.nan))
print(encoded)

#    A_1  A_2  A_3
# 0    1    0    0
# 1    0    1    0
# 2    0    0    1
# 3    0    0    0
# 4    0    0    0

create_numpy_encoder

create_numpy_encoder is a similar function to create_encoder. The only difference is that it operates on numpy.ndarray instead of pd.DataFrame.

encoded: np.ndarray = create_numpy_encoder((1, 2, 3))((1, 2, 3, 4, np.nan))
print(encoded)

# [[1 0 0]
#  [0 1 0]
#  [0 0 1]
#  [0 0 0]
#  [0 0 0]]

maprows

maprows is a function that applies some function to each row of the pd.DataFrame.

xs = pd.DataFrame({"A": [1, 2], "B": ["01-01-2020", "02-01-2020"]})

for x in maprows(lambda x: x.A, xs):
  print(x)

# 1
# 2

It is a wrapper of pd.DataFrame.iterrows which simply removes unnecessary row indices.

name

name is simply:

def name(x: Union[pd.Series, str]) -> str:
  return x if isinstance(x, str) else (x.name or "")

Example of name usage:

print(name(pd.Series([1, 2, 3])) == "") # True
print(name(pd.Series([1, 2, 3], name="ABC"))) # ABC

Notice that, default name of any pd.Series is None.

transformrows

transformrows is a function similar to maprows. The difference between transformrows and maprows is that transformrows takes a function Callable[[pd.Series], pd.Series] instead of Callable[[pd.Series], Y]. After applying passed function to each row, transformrows will concatenate resulting rows into a new frame. The concatenation is done by using concatenate function which is passed as third parameter. By default partial(pd.concat, axis="columns") function is used.

xs = pd.DataFrame({"A": [1, 2], "B": ["01-01-2020", "02-01-2020"]})
ys: pd.DataFrame = transformrows(lambda x: 2 * x, xs)

print(ys)

#    A                     B
# 0  2  01-01-202001-01-2020
# 1  4  02-01-202002-01-2020

unique

unique is a function which takes pd.DataFrame and returns Dict[str, Tuple[object, ...]]. Key of the dictionary is a name of a column of the passed frame and value of the dictionary is unique values of that column.

frame = pd.DataFrame({"A": (1, 2, 2, 5, 5), "B": ("a", "a", "b", "c", "d")})
uniques: Dict[str, Tuple[object, ...]] = unique(frame)

print(uniques)

# {'A': (1, 2, 5), 'B': ('a', 'b', 'c', 'd')}

dskit.pipeline

PipelineFrame

You can create a PipelineFrame by passing some pd.DataFrame:

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})
pipeline = PipelineFrame(xs)

Additionally, you can pass some concatenation function along with pd.DataFrame:

pipeline = PipelineFrame(xs, concatenate_columns=inner)
# inner is simply: partial(pd.concat, axis="columns", join="inner")

You will see later how passing custom concatenation function might benefit. For now you can stick with a default concatenation function partial(pd.concat, axis="columns").

Under the hood PipelineFrame stores passed pd.DataFrame as Iterable[pd.DataFrame]. Thanks to that, some functions applied to underlining pd.DataFrame are lazily evaluated. Following PipelineFrame methods are lazy:

map
apply
filter
select

Those methods also return PipelineFrame (the same object you operate on). I call those methods non-terminal methods.

The rest of the PipelineFrame methods are not lazy:

aggregate
foreach
get

I call those methods terminal methods, because they do not return a PipelineFrame, so you can not apply other PipelineFrame methods to the result.

map

map allows you to map the underlining pd.DataFrame using some function. map can take different types of parameters. Based on parameter's type map behaves differently. Supported types and behaviours include:

Callable[[pd.DataFrame], pd.DataFrame] - apply passed function to underlining pd.DataFrame.
Tuple[Callable[[pd.DataFrame], pd.DataFrame], ...] - apply each function to underlining pd.DataFrame and concatenate resulting frames using passed concatenate_columns function.
List[Callable[[pd.Series], pd.Series]] - apply each function to each row of underlining pd.DataFrame and concatenate resulting frames using passed concatenate_columns function.

The last type is slightly more complicated. It has following definition: Iterable[Tuple[Union[pd.Series, str], Union[Callable[[pd.DataFrame], pd.Series], Callable[[pd.Series], pd.Series]]]]. It is an Iterable of pairs. The first value of a pair is either a pd.Series or a str. If it is a pd.Series, it will be mapped to a str using a name function. If the first value of the pair is a name of the underlining pd.DataFrame column, map will assume, that the second value is a Callable[[pd.Series], pd.Series]. If the first value of the pair is any other string, map will assume, that the second value is a Callable[[pd.DataFrame], pd.Series]. So in the first case map will take an existing column from the underlining pd.DataFrame and apply passed function to that column, and in the second case map will take the whole underlining pd.DataFrame and apply passed function to it. The result of that function will be stored in the underlining pd.DataFrame with a name passed as the first value of the pair.

Let's take a look at some examples:

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})

# the first behaviour
PipelineFrame(xs).map(lambda ys: 2 * ys).foreach(print)

# sidenote: foreach is a terminal method which applies a function

#    A   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB

# the second behaviour
PipelineFrame(xs).map((lambda ys: 2 * ys, lambda ys: ys)).foreach(print)

#    A   B  A  B
# 0  2  AA  1  A
# 1  2  BB  1  B
# 2  4  AA  2  A
# 3  6  BB  3  B
# 4  6  BB  3  B

# the third behaviour
PipelineFrame(xs).map([lambda ys: 2 * ys, lambda ys: ys]).foreach(print)

#    A   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB
# 0  1   A
# 1  1   B
# 2  2   A
# 3  3   B
# 4  3   B

# the fourth behaviour
PipelineFrame(xs).map(
  (
    ("A", lambda ys: ys + 1),
    (xs.B, lambda ys: 2 * ys),
    ("C", lambda ys: ys.A.astype(str) + ys.B)
  )
).foreach(print)

#    A   B   C
# 0  2  AA  1A
# 1  2  BB  1B
# 2  3  AA  2A
# 3  4  BB  3B
# 4  4  BB  3B

You can see, that by using the fourth behaviour of the map you can add new columns to a pd.DataFrame. You might also want to use operators instead of calling methods explicitly:

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})

# the first behaviour
PipelineFrame(xs) / (lambda ys: 2 * ys) & print

#    A   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB

# the second behaviour
PipelineFrame(xs) / (lambda ys: 2 * ys, lambda ys: ys) & print

#    A   B  A  B
# 0  2  AA  1  A
# 1  2  BB  1  B
# 2  4  AA  2  A
# 3  6  BB  3  B
# 4  6  BB  3  B

# the third behaviour
PipelineFrame(xs) / [lambda ys: 2 * ys, lambda ys: ys] & print

#    A   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB
# 0  1   A
# 1  1   B
# 2  2   A
# 3  3   B
# 4  3   B

# the fourth behaviour
(
  PipelineFrame(xs)
  / (
      ("A", lambda ys: ys + 1),
      (xs.B, lambda ys: 2 * ys),
      ("C", lambda ys: ys.A.astype(str) + ys.B)
    )
  & print
)

#    A   B   C
# 0  2  AA  1A
# 1  2  BB  1B
# 2  3  AA  2A
# 3  4  BB  3B
# 4  4  BB  3B

Notice that, evaluation happens only after using a terminal method (in this case foreach):

def f(ys: pd.DataFrame) -> pd.DataFrame:
  print("A")
  return ys

pipeline = PipelineFrame(xs) / f

print("B")
pipeline & print

# B
# A
#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

apply

apply behaves nearly the same way as map, but with single minor difference. map guarantees that, the passed pd.DataFrame remains unmodified (unless passed function modifies it), and apply does not. apply will modify the passed pd.DataFrame only in the context of the fourth behaviour of the map function, and only when you pass exclusively existing column names.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})
PipelineFrame(xs).apply((("A", lambda x: x + 1),)).foreach(print)

#    A  B
# 0  2  A
# 1  2  B
# 2  3  A
# 3  4  B
# 4  4  B

print(xs)

#    A  B
# 0  2  A
# 1  2  B
# 2  3  A
# 3  4  B
# 4  4  B

# alternatively

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})
PipelineFrame(xs) // (("A", lambda x: x + 1),) & print

#    A  B
# 0  2  A
# 1  2  B
# 2  3  A
# 3  4  B
# 4  4  B

In any other case apply will behave exactly the same way map does. Use it only if you know what you are doing.

filter

filter allows you to filter the underlining pd.DataFrame. Similar to map method, filter also behaves differently based on a type of the passed parameter:

Callable[[pd.DataFrame], np.ndarray] - apply passed function to the underlining pd.DataFrame and filter columns based on the resulting np.ndarray. Under the hood, simple xs.iloc[:, fs(xs)] is done, where fs is a passed function and xs is the underlining pd.DataFrame.
Tuple[Callable[[pd.DataFrame], np.ndarray], ...] - apply passed functions to the underlining pd.DataFrame, fold the results using partial(np.all, axis=0) function and filter columns based on the resulting np.ndarray. Under the hood, simple xs.iloc[:, ys] is done, where ys is the result of a partial(np.all, axis=0) function and xs is the underlining pd.DataFrame.
List[Callable[[pd.Series], bool]] - create a function lambda x: all(map(lambda f: f(x), fs)), where fs is the list of passed functions, apply that function to each row and filter rows based on the result.

As with map method, the last behaviour has a slightly more complex associated parameter type: Iterable[Tuple[Union[pd.Series, str], Callable[[pd.Series], np.ndarray]]]. It is an Iterable of pairs. The first value of a pair is either a pd.Series or a str. If it is a pd.Series, it will be mapped to a str using a name function. The second value is a function which is applied to a column of the underlining pd.DataFrame. The names of that column is the first value of a pair. After each function of an Iterable of pairs is applied, the results are folded using partial(np.all, axis=0) function. Based on the result zs of that function application, the underlining pd.DataFrame is filtered using expression xs.iloc[zs], where xs is the underlining pd.DataFrame.

Let's take a look at some examples:

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"], "dates": ["2020-02-02", "2020-01-01", "2020-02-01", "2020-02-01", "2020-01-02"]})

# the first behaviour
PipelineFrame(xs).filter(lambda ys: ys.columns == "B").foreach(print)

#    B
# 0  A
# 1  B
# 2  A
# 3  B
# 4  B

# the second behaviour
PipelineFrame(xs).filter((lambda ys: ys.columns.str.isupper(), lambda ys: ys.dtypes == object)).foreach(print)

#    B
# 0  A
# 1  B
# 2  A
# 3  B
# 4  B

# the third behaviour
PipelineFrame(xs).filter([lambda ys: ys.A == 1, lambda ys: ys.B == "B"]).foreach(print)

#    A  B       dates
# 1  1  B  2020-01-01

# the fourth behaviour
PipelineFrame(xs).filter(
  (
    ("A", lambda ys: ys > 1),
    (xs.B, lambda ys: ys == "B"),
    ("dates", lambda ys: ys == "2020-01-02")
  )
).foreach(print)

#    A  B       dates
# 4  3  B  2020-01-02

# alternatively

# the first behaviour
PipelineFrame(xs) % (lambda ys: ys.columns == "B") & print

#    B
# 0  A
# 1  B
# 2  A
# 3  B
# 4  B

# the second behaviour
PipelineFrame(xs) % (lambda ys: ys.columns.str.isupper(), lambda ys: ys.dtypes == object) & print

#    B
# 0  A
# 1  B
# 2  A
# 3  B
# 4  B

# the third behaviour
PipelineFrame(xs) % [lambda ys: ys.A == 1, lambda ys: ys.B == "B"] & print

#    A  B       dates
# 1  1  B  2020-01-01

# the fourth behaviour
(
  PipelineFrame(xs)
  % (
      ("A", lambda ys: ys > 1),
      (xs.B, lambda ys: ys == "B"),
      ("dates", lambda ys: ys == "2020-01-02")
    )
  & print
)

#    A  B       dates
# 4  3  B  2020-01-02

select

select allows you to select columns from the underlining pd.DataFrame. select takes either an Iterable[Union[pd.Series, str]] or a ColumnFilter. If an Iterable[Union[pd.Series, str]] is passed, select at first will map each pd.Series in the Iterable using a name function, and then it will pass resulting Iterable[str] to column function to construct a ColumnFilter. The resulting fs of type ColumnFilter will be used for selecting like so xs.loc[:, fs(xs)], where xs is the underlining pd.DataFrame. If a ColumnFilter is passed, select will immediately use it for selecting like so xs.loc[:, fs(xs)], where fs is the passed ColumnFilter and xs is the underlining pd.DataFrame.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"], "dates": ["2020-02-02", "2020-01-01", "2020-02-01", "2020-02-01", "2020-01-02"]})

PipelineFrame(xs).select(("A", "B")).foreach(print)

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

PipelineFrame(xs).select(column("A", "B")).foreach(print)

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

PipelineFrame(xs).select(~column("A")).foreach(print)

#    B       dates
# 0  A  2020-02-02
# 1  B  2020-01-01
# 2  A  2020-02-01
# 3  B  2020-02-01
# 4  B  2020-01-02

# alternatively

PipelineFrame(xs) * ("A", "B") & print

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

PipelineFrame(xs) * column("A", "B") & print

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

PipelineFrame(xs) * ~column("A") & print

#    B       dates
# 0  A  2020-02-02
# 1  B  2020-01-01
# 2  A  2020-02-01
# 3  B  2020-02-01
# 4  B  2020-01-02

aggregate

aggregate allows you to map columns from the underlining pd.DataFrame to anything. Similar to map method, aggregate also behaves differently based on a type of the passed parameter:

Callable[[pd.Series], object] - apply passed function to each column of the underlining pd.DataFrame.
Tuple[Callable[[pd.Series], object], ...] - apply passed functions to each column of the underlining pd.DataFrame.

As with map method, the last behaviour has a slightly more complex associated parameter type: Iterable[Tuple[Union[pd.Series, str], Union[Callable[[pd.Series], object], Iterable[Callable[[pd.Series], object]]]]]. It is an Iterable of pairs. The first value of a pair is either a pd.Series or a str. If it is a pd.Series, it will be mapped to a str using a name function. The second value is either a single function which is applied to a column of the underlining pd.DataFrame or multiple functions which are applied to a column of the underlining pd.DataFrame. The names of that column is the first value of a pair.

aggregate will always store results of passed functions in a dictionary. A key in that dictionary is a column name. A value in that dictionary is a functions results.

Let's take a look at some examples:

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})

# the first behaviour
print(PipelineFrame(xs).aggregate(np.unique))

# {'A': array([1, 2, 3]), 'B': array(['A', 'B'], dtype=object)}

# the second behaviour
for column, ys in PipelineFrame(xs).aggregate((np.unique, pd.Series.sum)).items():
  print(f"{column}: {tuple(ys)}")

# A: (array([1, 2, 3]), 10)
# B: (array(['A', 'B'], dtype=object), 'ABABB')

# notice, that we had to create tuple(ys), because ys is an Iterable

# the third behaviour
print(
  PipelineFrame(xs)
  .aggregate(
    (
      ("A", sum),
      (xs.B, np.unique),
    )
  )
)

# {'A': 10, 'B': array(['A', 'B'], dtype=object)}

# alternatively

# the first behaviour
print(PipelineFrame(xs) << np.unique)

# {'A': array([1, 2, 3]), 'B': array(['A', 'B'], dtype=object)}

# the second behaviour
for column, ys in (PipelineFrame(xs) << (np.unique, pd.Series.sum)).items():
  print(f"{column}: {tuple(ys)}")

# A: (array([1, 2, 3]), 10)
# B: (array(['A', 'B'], dtype=object), 'ABABB')

# the third behaviour
print(
  PipelineFrame(xs)
  << (
      ("A", sum),
      (xs.B, np.unique),
    )
)

# {'A': 10, 'B': array(['A', 'B'], dtype=object)}

foreach

foreach allows you to apply some function to the underlining pd.DataFrame. The result of the function will not be used anyhow.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})

PipelineFrame(xs).map(lambda ys: 2 * ys).foreach(print)

#    A   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB

# alternatively

PipelineFrame(xs) / (lambda ys: 2 * ys) & print

#    A   B
# 0  4  AA
# 1  4  BB
# 2  6  AA
# 3  8  BB
# 4  8  BB

get

get allows you to get the underlining pd.DataFrame.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})
ys = (PipelineFrame(xs).map(lambda ys: 2 * ys)).get()

print(ys)

#    A   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB

# get method does not has an operator form

append

append allows you to append new columns or rows to a PipelineFrame created by functions instead of substituting an original frame wrapped by PipelineFrame with the result of concatenation of those columns or rows. append can take different types of parameters. Based on parameter's type append provides different outputs. Supported types and outputs are following:

Callable[[pd.DataFrame], pd.DataFrame] - returns the same output as append((fs,)), where fs is a passed parameter.
Tuple[Callable[[pd.DataFrame], pd.DataFrame], ...] - returns a Tuple[Callable[[pd.DataFrame], pd.DataFrame], ...] which has an identity function as a first function. Other functions are simply functions of a passed Tuple parameter.
List[Callable[[pd.Series], pd.Series]] - returns a List[Callable[[pd.Series], pd.Series]] which has an identity function as a first function. Other functions are simply functions of a passed List parameter.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})

(
  PipelineFrame(xs)
  / select("A")(lambda ys: ys * 2)
  & print
)

#    A
# 0  2
# 1  2
# 2  4
# 3  6
# 4  6

# notice that, original "A" and "B" columns are not present in the resulting frame

# the first type of the parameter
(
  PipelineFrame(xs)
  / append(select("A")(lambda ys: ys * 2))
  & print
)

#    A  B  A
# 0  1  A  2
# 1  1  B  2
# 2  2  A  4
# 3  3  B  6
# 4  3  B  6

# this time we have another column "A" appended
# if you want to change the name of that column, you could use rename, prefix or sufix functions

(
  PipelineFrame(xs)
  / append(rename(lambda x: "test_" + x)(select("A")(lambda ys: ys * 2)))
  & print
)

#    A  B  test_A
# 0  1  A       2
# 1  1  B       2
# 2  2  A       4
# 3  3  B       6
# 4  3  B       6

# or you can write

(
  PipelineFrame(xs)
  / append(prefix("test_")(select("A")(lambda ys: ys * 2)))
  & print
)

#    A  B  test_A
# 0  1  A       2
# 1  1  B       2
# 2  2  A       4
# 3  3  B       6
# 4  3  B       6

# the second type of the parameter
(
  PipelineFrame(xs)
  / append(
      prefix("new_")(
        (
          select("A")(lambda ys: ys * 2),
          select("B")(lambda ys: "A" + ys)
        )
      )
    )
  & print
)

#    A  B  new_A new_B
# 0  1  A      2    AA
# 1  1  B      2    AB
# 2  2  A      4    AA
# 3  3  B      6    AB
# 4  3  B      6    AB

# the third type of the parameter
(
  PipelineFrame(xs)
  / append([lambda ys: 2 * ys])
  & print
)

#    A   B
# 0  1   A
# 1  1   B
# 2  2   A
# 3  3   B
# 4  3   B
# 0  2  AA
# 1  2  BB
# 2  4  AA
# 3  6  BB
# 4  6  BB

column

column allows you to specify what columns you want to select. column is basically a convenient way to create a value of type ColumnFilter. A ColumnFilter is a type, that supports + (add) and ~ (invert) operators.

When you use x + y on two ColumnFilter values, you get another ColumnFilter value that has columns from x and then columns from y value.
When you use ~x on single ColumnFilter value, you get another ColumnFilter value that has all columns except columns in x value.

A value of type ColumnFilter also behaves as a function (supports call) of type Callable[[pd.DataFrame], List[str]]. This function takes a pd.DataFrame and returns a list of column names.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"], "dates": ["2020-02-02", "2020-01-01", "2020-02-01", "2020-02-01", "2020-01-02"]})

print(column("A")(xs))
# ['A']

print(column("A", "B")(xs))
# ['A', 'B', 'dates']

print((column("A") + column("B") + column("dates"))(xs))
# ['A', 'B', 'dates']

print((~column("A"))(xs))
# ['B', 'dates']

print((~column("A") + column("B") + column("A"))(xs))
# ['B', 'dates', 'B', 'A']

(
  PipelineFrame(xs)
  * (column("A") + ~column("A", "B"))
  & print
)

#    A       dates
# 0  1  2020-02-02
# 1  1  2020-01-01
# 2  2  2020-02-01
# 3  3  2020-02-01
# 4  3  2020-01-02

inner

inner is simply:

inner: Callable[[Iterable[Union[pd.Series, pd.DataFrame]]], pd.DataFrame] = partial(pd.concat, axis="columns", join="inner")

prefix

prefix is simply:

def prefix(x: str) -> Callable[
    [Union[FrameFunction[pd.DataFrame], Tuple[FrameFunction[pd.DataFrame], ...]]],
    Tuple[FrameFunction[pd.DataFrame], ...]
    ]:

  return rename(lambda y: x + y)

removecolumns

removecolumns allows you to separate some columns from the rest of frame's columns. removecolumns takes column sets to be separated and returns a function which takes a pd.DataFrame and returns a Tuple[pd.DataFrame, ...]. Number of resulting frames depends on number of column sets to be separated. The resulting Tuple[pd.DataFrame, ...] always has the rest of frame's columns as the last element.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"], "C": ["2020-02-02", "2020-01-01", "2020-02-01", "2020-02-01", "2020-01-02"]})

ys, rest = removecolumns(("A",))(xs)

print(ys)

#    A
# 0  1
# 1  1
# 2  2
# 3  3
# 4  3

print(rest)

#    B           C
# 0  A  2020-02-02
# 1  B  2020-01-01
# 2  A  2020-02-01
# 3  B  2020-02-01
# 4  B  2020-01-02

ys, zs, rest = removecolumns(column("A", "B"), ("C",))(xs)

print(ys)

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

print(zs)

#             C
# 0  2020-02-02
# 1  2020-01-01
# 2  2020-02-01
# 3  2020-02-01
# 4  2020-01-02

print(rest)

# Empty DataFrame
# Columns: []
# Index: [0, 1, 2, 3, 4]

rename

rename allows you to apply some function Callable[[str], str] to each column name. rename returns a function which takes a Tuple of functions (or a single function) of type Callable[[pd.DataFrame], pd.DataFrame] and returns a Tuple of functions of the same type. That function will apply Callable[[str], str] to each column name of each function's pd.DataFrame output.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})

# a single function as a parameter
(
  PipelineFrame(xs)
  / rename(lambda x: "_" + x)(lambda xs: xs.loc[:, ["B"]])
  & print
)

#   _B
# 0  A
# 1  B
# 2  A
# 3  B
# 4  B

# a tuple of functions as a parameter
(
  PipelineFrame(xs)
  / rename(lambda x: "_" + x)((
      select("A")(),
      select("B")(lambda ys: 2 * ys)
    ))
  & print
)

#    _A  _B
# 0   1  AA
# 1   1  BB
# 2   2  AA
# 3   3  BB
# 4   3  BB

# if you will not pass any function as a parameter, an identity function will be used
(
  PipelineFrame(xs)
  / rename(lambda x: "_" + x)()
  & print
)

#   _A _B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

sufix

sufix is simply:

def sufix(x: str) -> Callable[
    [Union[FrameFunction[pd.DataFrame], Tuple[FrameFunction[pd.DataFrame], ...]]],
    Tuple[FrameFunction[pd.DataFrame], ...]
    ]:

  return rename(lambda y: y + x)

select

select allows you to map some subset of columns. select is basically a convenient way to create a value of type Selector. A value of type Selector, behaves like a function of type Callable[[Callable[[pd.DataFrame], pd.DataFrame]], Callable[[pd.DataFrame], pd.DataFrame]] (call) or like a function of type Callable[[Callable[[np.ndarray], np.ndarray]], Callable[[pd.DataFrame], pd.DataFrame]] (numpy).

select function takes a list (varargs) of Union[str, ColumnFilter] values and returns a value of type Selector. Each value of type str is converted to ColumnFilter by using column function and then a list of ColumnFilter values is folded into single ColumnFilter by using + as an operator and value of ColumnFilter type, which returns no columns, as an accumulator. That ColumnFilter value will be used by Selector value to get a subset of some frame columns.

When you use Selector value as a Callable[[Callable[[pd.DataFrame], pd.DataFrame]], Callable[[pd.DataFrame], pd.DataFrame]] function (by using call), your passed function to it will be decorated in a way that the subset of columns will be passed to your function (instead of the whole frame).
When you use Selector value as a Callable[[Callable[[np.ndarray], np.ndarray]], Callable[[pd.DataFrame], pd.DataFrame]] function (by using numpy), your passed function to it will be decorated in a way that subset of columns will first be converted to np.ndarray and then passed to your function. The resulting np.ndarray of your function will be wrapped into pd.DataFrame. If the result of your function will have the same number of columns as the subset of columns, your np.ndarray after wrapping into pd.DataFrame will have column names of those columns in the subset. The same behaviour applies to indices. You can turn off indices copying by setting copy_index=False.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"], "C": ["2020-02-02", "2020-01-01", "2020-02-01", "2020-02-01", "2020-01-02"]})

# by default, if you do not pass any function, an identity function is used
print(select("A")()(xs))

#    A
# 0  1
# 1  1
# 2  2
# 3  3
# 4  3

def f(xs: pd.DataFrame) -> pd.DataFrame:
  print(xs.columns)
  print(xs.shape)

  return xs.B

print(select("B", "C")(f)(xs))

# Index(['B', 'C'], dtype='object')
# (5, 2)
# 0    A
# 1    B
# 2    A
# 3    B
# 4    B
# Name: B, dtype: object

(
  PipelineFrame(xs)
  / select("A", "B")()
  & print
)

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

# same could be achieved with

(
  PipelineFrame(xs)
  * column("A", "B")
  & print
)

#    A  B
# 0  1  A
# 1  1  B
# 2  2  A
# 3  3  B
# 4  3  B

(
  PipelineFrame(xs)
  / select(~column("A"))(lambda ys: 2 * ys)
  / (select("B")(lambda ys: 3 * ys), select("C")(lambda ys: 2 * ys))
  & print
)

#         B                                         C
# 0  AAAAAA  2020-02-022020-02-022020-02-022020-02-02
# 1  BBBBBB  2020-01-012020-01-012020-01-012020-01-01
# 2  AAAAAA  2020-02-012020-02-012020-02-012020-02-01
# 3  BBBBBB  2020-02-012020-02-012020-02-012020-02-01
# 4  BBBBBB  2020-01-022020-01-022020-01-022020-01-02

# now let's try to use numpy
# it is especially useful when combined with some sklearn.preprocessing estimators

scaler = StandardScaler()
encoder = OneHotEncoder(sparse=False)

(
  PipelineFrame(xs)
  / (
      select("A").numpy(scaler.fit_transform),
      *prefix("encoded_")(select("B").numpy(encoder.fit_transform)),
      select("C")()
    )
  / astype({"C": "datetime64[D]"})
  & print
)

#           A  encoded_0  encoded_1          C
# 0 -1.118034        1.0        0.0 2020-02-02
# 1 -1.118034        0.0        1.0 2020-01-01
# 2  0.000000        1.0        0.0 2020-02-01
# 3  1.118034        0.0        1.0 2020-02-01
# 4  1.118034        0.0        1.0 2020-01-02

# we used select("C")() on the end to include "C" column
# notice that, we used * before prefix("encoded_")(select("B").numpy(encoder.fit_transform))

# if you want to append columns, you can write

scaler = StandardScaler()
encoder = OneHotEncoder(sparse=False)

(
  PipelineFrame(xs)
  / append((
      *prefix("scaled_")(select("A").numpy(scaler.fit_transform)),
      *prefix("encoded_")(select("B").numpy(encoder.fit_transform))
    ))
  / astype({"C": "datetime64[D]"})
  & print
)

#    A  B          C  scaled_A  encoded_0  encoded_1
# 0  1  A 2020-02-02 -1.118034        1.0        0.0
# 1  1  B 2020-01-01 -1.118034        0.0        1.0
# 2  2  A 2020-02-01  0.000000        1.0        0.0
# 3  3  B 2020-02-01  1.118034        0.0        1.0
# 4  3  B 2020-01-02  1.118034        0.0        1.0

zipframe

zipframe is a convenience function, which allows you to concatenate columns using some concatenate_columns function. The difference between pd.concat and zipframe is that pd.concat takes some Iterable of columns and returns the result of concatenation immediately, whereas zipframe takes some Iterable of columns and returns a function which takes another set of columns and concatenates passed earlier Iterable of columns with that set.

xs = pd.DataFrame({"A": [1, 1, 2, 3, 3], "B": ["A", "B", "A", "B", "B"]})
ys = pd.DataFrame({"C": ["2020-02-02", "2020-01-01", "2020-02-01", "2020-02-01", "2020-01-02"]})

(
  PipelineFrame(xs)
  / zipframe(ys)
  & print
)

#    A  B           C
# 0  1  A  2020-02-02
# 1  1  B  2020-01-01
# 2  2  A  2020-02-01
# 3  3  B  2020-02-01
# 4  3  B  2020-01-02

dskit.tensor

create_batches

create_batches is a function which takes Iterable[Tuple[np.ndarray, ...]] with length of batches and returns an Iterable[Tuple[np.ndarray, ...]] of created batches.

xs = np.arange(15).reshape(-1, 3)
ys = np.arange(10).reshape(-1, 2)

print(xs)

# [[ 0  1  2]
#  [ 3  4  5]
#  [ 6  7  8]
#  [ 9 10 11]
#  [12 13 14]]

print(ys)

# [[0 1]
#  [2 3]
#  [4 5]
#  [6 7]
#  [8 9]]

for sub_xs, sub_ys in create_batches(zip(xs, ys), length=3):
  print(sub_xs)
  print(sub_ys)

  print()

# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]
# [[0 1]
#  [2 3]
#  [4 5]]
#
# [[ 9 10 11]
#  [12 13 14]]
# [[6 7]
#  [8 9]]

cycle_tensor

cycle_tensor is a "cycle" function used for tensors. This function takes a np.ndarray with Tuple[int, ...] and returns "cycled" np.ndarray.

xs = np.arange(4).reshape(-1, 2)
print(xs)

# [[0 1]
#  [2 3]]

cycled_xs = cycle_tensor(xs, (3, 3))
print(cycled_xs)

# [[0 1 0 1 0 1]
#  [2 3 2 3 2 3]
#  [0 1 0 1 0 1]
#  [2 3 2 3 2 3]
#  [0 1 0 1 0 1]
#  [2 3 2 3 2 3]]

zeros = cycle_tensor(0, (2, 2, 3))
print(zeros)

# [[[0 0 0]
#   [0 0 0]]
#
#  [[0 0 0]
#   [0 0 0]]]

expand_tail

expand_tail is simply:

def expand_tail(xs: Tuple[X, ...], length: int, filler: X) -> Tuple[X, ...]:
  count = max(length - len(xs), 0)

  fillers = repeat(filler)
  sliced_fillers = islice(fillers, count)

  expanded = chain(xs, sliced_fillers)
  return tuple(expanded)

Example of expand_tail usage:

xs = expand_tail((1, 2), length=5, filler=3)
print(xs) # (1, 2, 3, 3, 3)

expand_tail_dimensions

expand_tail_dimensions is simply:

def expand_tail_dimensions(tensor: np.ndarray, length: int) -> np.ndarray:
  expanded_shape: Shape = expand_tail(tensor.shape, length, filler=1)
  return tensor.reshape(expanded_shape)

Example of expand_tail_dimensions usage:

xs = np.arange(27).reshape(-1, 3, 3)
ys = expand_tail_dimensions(xs, 5)

print(ys.shape) # (3, 3, 3, 1, 1)

gridrange

gridrange is a function similar to Python's range function. The difference between gridrange and range is that gridrange operates on Tuple[int, ...] instead of int.

for x in gridrange((2, 3)):
  print(x)

# (0, 0)
# (0, 1)
# (0, 2)
# (1, 0)
# (1, 1)
# (1, 2)

for x in gridrange((1, 1), (3, 4)):
  print(x)

# (1, 1)
# (1, 2)
# (1, 3)
# (2, 1)
# (2, 2)
# (2, 3)

for x in gridrange((1, 1), (10, 20), (5, 5)):
  print(x)

# (1, 1)
# (1, 6)
# (1, 11)
# (1, 16)
# (6, 1)
# (6, 6)
# (6, 11)
# (6, 16)

iteraxis

iteraxis is a function which takes np.ndarray and returns Iterable[np.ndarray] along passed axis. This function is similar to np.apply_along_axis. The difference between iteraxis and np.apply_along_axis is that np.apply_along_axis applies some function to arrays and iteraxis returns those arrays.

xs = np.arange(27).reshape(-1, 3, 3)

for x in iteraxis(xs, axis=-1):
  print(x)

# [0 1 2]
# [3 4 5]
# [6 7 8]
# [ 9 10 11]
# [12 13 14]
# [15 16 17]
# [18 19 20]
# [21 22 23]
# [24 25 26]

move_tensor

move_tensor is simply:

def move_tensor(source: np.ndarray, destination: np.ndarray, coordinate: Optional[Coordinate] = None) -> np.ndarray:
  tensor = destination.copy()
  move_tensor_inplace(source, tensor, coordinate)

  return tensor

Example of move_tensor usage:

xs = np.arange(4).reshape(-1, 2)
ys = np.zeros((3, 3), dtype=np.uint)

moved = move_tensor(xs, ys, coordinate=(1, 1))
print(moved)

# [[0 0 0]
#  [0 0 1]
#  [0 2 3]]

move_tensor_inplace

move_tensor_inplace is a procedure which moves source np.ndarray to destination np.ndarray at coordinate Tuple[int, ...]. Only destination np.ndarray is modified. The coordinate is optional.

xs = np.arange(4).reshape(-1, 2)
ys = np.zeros((3, 3), dtype=np.uint)

move_tensor_inplace(xs, ys)
print(ys)

# [[0 1 0]
#  [2 3 0]
#  [0 0 0]]

next_batch

next_batch is a function used by create_batches. next_batch takes an Iterable[Tuple[np.ndarray, ...]] with length of batch and returns a next batch. The next batch might have smaller length than the passed one.

xs = np.arange(15).reshape(-1, 3)
ys = np.arange(10).reshape(-1, 2)

print(xs)

# [[ 0  1  2]
#  [ 3  4  5]
#  [ 6  7  8]
#  [ 9 10 11]
#  [12 13 14]]

print(ys)

# [[0 1]
#  [2 3]
#  [4 5]
#  [6 7]
#  [8 9]]

sub_xs, sub_ys = next_batch(zip(xs, ys), length=3)

print(sub_xs)
print(sub_ys)

# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]
# [[0 1]
#  [2 3]
#  [4 5]]

slices

slices is simply:

RawSlice = Union[
  Tuple[Optional[int]],
  Tuple[Optional[int], Optional[int]],
  Tuple[Optional[int], Optional[int], Optional[int]]
]

def slices(xs: Iterable[RawSlice]) -> Tuple[slice, ...]:
  ys = starmap(slice, xs)
  return tuple(ys)

Example of slices usage:

xs = np.arange(9).reshape(-1, 3)
ys = (1, None), (0, 1)

print(xs[slices(ys)])

# [[3]
#  [6]]

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dskit 0.0.10

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DSKit

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dskit.frame

astype

create_dummifier

create_encoder

create_numpy_encoder

maprows

name

transformrows

unique

dskit.pipeline

PipelineFrame

map

apply

filter

select

aggregate

foreach

get

append

column

inner

prefix

removecolumns

rename

sufix

select

zipframe

dskit.tensor

create_batches

cycle_tensor

expand_tail

expand_tail_dimensions

gridrange

iteraxis

move_tensor

move_tensor_inplace

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slices

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