
%load_ext autoreload
%autoreload 2


import sys
sys.path.append("..")


from optimus import Optimus
op = Optimus("pandas")


df = op.create.dataframe({
        ("words", "str", True): ['  I like     fish  ', '    zombies', 'simpsons   cat lady', None],
        ("num", "int", True): [1, 2, 2, 3],
        ("animals", "str", True): ['dog', 'cat', 'frog', 'eagle'],
        ("thing", "str", True): ['housé', 'tv', 'table', 'glass'],
        ("two strings", "str", True): ['cat-car', 'dog-tv', 'eagle-tv-plus', 'lion-pc'],
        ("filter", "str", True): ['a', 'b', '1', 'c'],
        ("num 2", "string", True): ['1', '2', '3', '4'],
        ("col_array", None, True): [['baby', 'sorry'], ['baby 1', 'sorry 1'], ['baby 2', 'sorry 2'], ['baby 3', 'sorry 3']],                
        ("col_int", None, True): [[1, 2, 3], [3, 4], [5, 6, 7], [7, 8]]
})

df.display()


df = df.cols.set("new_col_1", 1)
df.display(highlight="new_col_1")


df_col = op.create.dataframe({("NEW COLUMN", "str", True): ["q", "w", "e", "r"]})
df = df.cols.append(df_col)
df.display(highlight="NEW COLUMN")


df.cols.append({"col 2": 2.22, "col 3": 3}).display(highlight=["col 2", "col 3"])


df.cols.append({
    "just test": "test",
    "num 2": df['num'] * 2,
    "more numbers": [1, 2, 3, 4]
}).cols.select(["num", "just test", "num 2", "more numbers"]).display()


display(df.cols.names())
columns = ["words", 1, "animals", 3]
df.cols.select(columns).display()

['words',
 'num',
 'animals',
 'thing',
 'two strings',
 'filter',
 'num 2',
 'col_array',
 'col_int',
 'new_col_1',
 'NEW COLUMN']


df.cols.select("n.*", regex=True).display()


df.cols.select(data_type="int").display()


df.cols.rename('num', 'number').display(highlight="number")


df.cols.rename([('num', 'number'), ("animals", "gods")], str.upper).display(highlight=["NUMBER", "GODS"])


df.cols.rename(str.lower).display(highlight="new column")


df.cols.rename(str.upper).display()



  df.cols.cast(columns=[("num", "str"), ("num 2", "int")]).cols.select(["num", "num 2"]).cols.data_types()

{'num': 'object', 'num 2': 'int64'}



  df.cols.cast("num", "str").cols.data_types()

{'words': 'object',
 'num': 'object',
 'animals': 'object',
 'thing': 'object',
 'two strings': 'object',
 'filter': 'object',
 'num 2': 'string',
 'col_array': 'object',
 'col_int': 'object',
 'new_col_1': 'int64',
 'NEW COLUMN': 'object'}



  df.cols.cast("*", "str").cols.data_types()

{'words': 'object',
 'num': 'object',
 'animals': 'object',
 'thing': 'object',
 'two strings': 'object',
 'filter': 'object',
 'num 2': 'object',
 'col_array': 'object',
 'col_int': 'object',
 'new_col_1': 'object',
 'NEW COLUMN': 'object'}


df.cols.move("thing", "after", "words").display(highlight=["thing", "words"])


df.cols.sort().display()


df.cols.sort(order="desc").display()


df.cols.drop("num").display()


df.cols.drop(["num", "words"]).display()


df.display()
df \
    .cols.rename([('num', 'number')]) \
    .cols.drop(["number", "words"]) \
    .cols.append({"col_10": 10}) \
    .cols.sort(order="desc") \
    .cols.upper("two strings") \
    .display()


df.cols.select("two strings").cols.unnest("two strings", "-").display()


df.cols.select("two strings").cols.unnest("two strings", "-", index=1).display()


df.cols.select("col_array").cols.unnest("col_array").display()


df.cols.select("col_int").cols.unnest("col_int").display()


df.cols.select("two strings").cols.unnest(["two strings"], splits=3, separator="-").display()


df_fill = op.create.dataframe(A=[1.0, 2.0, op.nan, 4.0, 5.0], B=[op.nan, op.nan, 3.0, 4.0, 5.0], C=[1, op.nan, None, 2, 2])
df_fill.display()


df_fill.cols.impute(["A", "B"], "continuous", "median")


df_fill.cols.impute(["C"], strategy="most_frequent")

df


def func(val, arg):
    return arg

df.set.numeric("filter", func, args="new string").display(highlight="filter")


df.set.numeric("filter", "new string").display(highlight="filter")


def func(val):
    return val + 20

df.cols.set(["num", "num 2"], func).display(highlight=["num", "num 2"])


df.rows.int("filter").display(highlight="filter")


df.rows.greater_than("num", 1).display(highlight="num")


def func(val, arg1, arg2):
    return val + arg1 + arg2

df.cols.set("num_sum", func, args=[5, 6], default="num").display(highlight="num_sum")


where = (df["num"] > 2) | (df["num 2"]> 2)
df.cols.set(["num", "num 2"], 10, where=where).display(highlight=["num", "num 2"])


df_null = op.create.dataframe(session=[1,1,1,1,1,1,1], timestamp=[1,2,3,4,5,6,6], id=[None, 5.0, op.nan, None, 10.0, op.nan, op.nan])
df_null


df_null.cols.count_nulls("id")

5


df_null.cols.count_nulls("*")

{'session': 0, 'timestamp': 0, 'id': 5}


df.cols.count_uniques("*")

{'words': 4,
 'num': 3,
 'animals': 4,
 'thing': 4,
 'two strings': 4,
 'filter': 4,
 'num 2': 4,
 'col_array': 4,
 'col_int': 4,
 'new_col_1': 1,
 'NEW COLUMN': 4}


df.display()


df_distinct = op.create.dataframe({
    ("words", "str"): ["  I like    fish  ", "    zombies", "simpsons   cat lady", None, None],
    ("num", "int"): [1,2,2,3,0],
})
df_distinct


df_distinct.rows.unique("num").print()

words                 num
(object)          (int32)
--------------  ---------
I like    fish          1
zombies                 2
                        3
                        0


df_zeros = df_distinct
df_zeros.cols.count_zeros("*")

{'words': 0, 'num': 1}



  df.cols.data_types('*')

{'words': 'object',
 'num': 'int32',
 'animals': 'object',
 'thing': 'object',
 'two strings': 'object',
 'filter': 'object',
 'num 2': 'string',
 'col_array': 'object',
 'col_int': 'object',
 'new_col_1': 'int64',
 'NEW COLUMN': 'object'}


df.display(highlight="animals")


df.cols.replace("animals", ["dog", "cat"], "animals").display(highlight="animals")


df.cols.replace(["two strings", "animals"], ["dog-tv", "cat", "eagle", "fish"], "animals").display(highlight=["animals", "two strings"])


df.cols.replace("animals", [("dog", "dog_1"), ("cat", "cat_1")]).display(highlight="animals")


df.cols.replace("animals", "dog", "pet").display(highlight="animals")


df.cols.replace("*", ["a", "b", "c"], "%")


df.cols.replace('num', [3, 2], 10, search_by="values").display(highlight="num")


df.cols.replace('num', [(3, 6), (2, 12)], search_by="values").display(highlight="num")


df.cols.replace("animals", "dog", "animal", search_by="words").display(highlight="animals")


df.cols.replace("animals", "dog", "animal", search_by="words", output_cols="friends").display(highlight=["animals", "friends"])


df.cols.replace("*", "1", "11", search_by="chars")


df.cols.replace("animals", [("dog", "dog_1"), ("cat", "cat_1")], search_by="words").display(highlight="animals")


df.cols.replace("animals", ["dog", "cat"], "animals", "words").display(highlight="animals")


df.cols.replace_regex('*', '.*[Cc]at.*', 'contains cat')


df.cols.nest(["animals", "two strings"], output_col="col_nested", separator="---").display(highlight=["animals", "two strings", "col_nested"])


df.cols.copy("num", "num_1").cols.nest(["num", "num_1"], output_col="col_nested", shape="vector").display(highlight=["num", "num_1", "col_nested"])


df.cols.nest(["animals", "two strings", "num 2"], output_col="col_nested", shape="array").display(highlight=["animals", "two strings", "num 2", "col_nested"])


df = op.load.csv("https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.csv")

df


df.cols.hist("price", 10)

{'hist': {'price': [{'lower': 1.0, 'upper': 2.0, 'count': 1},
   {'lower': 2.0, 'upper': 3.0, 'count': 1},
   {'lower': 3.0, 'upper': 4.0, 'count': 4},
   {'lower': 4.0, 'upper': 5.0, 'count': 1},
   {'lower': 5.0, 'upper': 6.0, 'count': 2},
   {'lower': 6.0, 'upper': 7.0, 'count': 0},
   {'lower': 7.0, 'upper': 8.0, 'count': 0},
   {'lower': 8.0, 'upper': 9.0, 'count': 6},
   {'lower': 9.0, 'upper': 10.0, 'count': 4}]}}


df.cols.frequency("billingId")

{'frequency': {'billingId': {'values': [{'value': 672, 'count': 1},
    {'value': 323, 'count': 1},
    {'value': 912, 'count': 1},
    {'value': 423, 'count': 1},
    {'value': 521, 'count': 1},
    {'value': 234, 'count': 1},
    {'value': 875, 'count': 1},
    {'value': 812, 'count': 1},
    {'value': 111, 'count': 1},
    {'value': 624, 'count': 1},
    {'value': 467, 'count': 1},
    {'value': 634, 'count': 1},
    {'value': 992, 'count': 1},
    {'value': 553, 'count': 1},
    {'value': 116, 'count': 1},
    {'value': 551, 'count': 1},
    {'value': 123, 'count': 1},
    {'value': 886, 'count': 1},
    {'value': 735, 'count': 1}]}}}


display(df.cols.min("billingId"))
display(df.cols.percentile(['billingId', 'price'], [0.05, 0.25, 0.5, 0.75, 0.95]))
display(df.cols.max("billingId"))
display(df.cols.median(["billingId", "price"]))
display(df.cols.range(["billingId", "price"]))
display(df.cols.std(["billingId", "price"]))

111

{'billingId': {0.05: 115.5,
  0.25: 373.0,
  0.5: 553.0,
  0.75: 773.5,
  0.95: 919.9999999999998},
 'price': {0.05: 1.9, 0.25: 3.0, 0.5: 8.0, 0.75: 8.0, 0.95: 10.0}}

992

{'billingId': 553.0, 'price': 8.0}

{'billingId': {'min': 111.0, 'max': 992.0}, 'price': {'min': 1.0, 'max': 10.0}}

{'billingId': 280.19735108590083, 'price': 2.9528457876452054}


df.cols.min("*")

{'id': 1,
 'firstName': '(((   Heinrich )))))',
 'lastName': '             GALiLEI',
 'billingId': 111,
 'product': '110790',
 'price': 1,
 'birth': '1899/01/01',
 'dummyCol': '#'}


display(df.cols.kurtosis("billingId"))
display(df.cols.mean("billingId"))
display(df.cols.skew("billingId"))
display(df.cols.sum("billingId"))
display(df.cols.var("billingId"))
display(df.cols.mad("billingId"))

-0.9809401924348062

556.0

-0.23247289799632914

10564.0

78510.55555555556

230.0


df.cols.mad("price")

2.0


df.cols.mad("price", more=True)

{'price': {'mad': 2.0, 'median': 8.0}}


print(df.cols.percentile(['price'], [0.05, 0.25, 0.5, 0.75, 0.95]))

{'price': {0.05: 1.9, 0.25: 3.0, 0.5: 8.0, 0.75: 8.0, 0.95: 10.0}}


print(df.cols.mode(["price", "billingId"]))

{'price': 8.0, 'billingId': {0: 111.0, 1: 116.0, 2: 123.0, 3: 234.0, 4: 323.0, 5: 423.0, 6: 467.0, 7: 521.0, 8: 551.0, 9: 553.0, 10: 624.0, 11: 634.0, 12: 672.0, 13: 735.0, 14: 812.0, 15: 875.0, 16: 886.0, 17: 912.0, 18: 992.0}}

df


df \
    .cols.trim("lastName") \
    .cols.lower("lastName") \
    .cols.upper(["product", "firstName"]) \
    .cols.reverse("firstName") \
    .display()


df.cols.iqr("price")

5.0


df.cols.iqr("price", more=True)

{'iqr': 5.0, 'q1': 3.0, 'q2': 8.0, 'q3': 8.0}


df.cols.years_between("birth", date_format="%Y/%m/%d", round=True).display(highlight="birth")


df.cols.remove("*", ["&", "%"]).display()


df.cols.normalize_chars("lastName").display(highlight="lastName")


df.cols.remove_special_chars("lastName").display(highlight="lastName")


df.cols.clip("billingId", 100, 200).display(highlight="billingId")


df_abs = op.create.dataframe({
    ("words", "str"): ['  I like     fish  ', '    zombies', 'simpsons   cat lady', None],
    ("num", "int"): [-1, -2, -2, 3],
    ("animals", "str"): ['dog', 'cat', 'frog', 'eagle'],
    ("thing", "str"): ['housé', 'tv', 'table', 'glass'],
    ("two strings", "str"): ['cat-car', 'dog-tv', 'eagle-tv-plus', 'lion-pc'],
    ("filter", "str"): ['a', 'b', '1', 'c'],
    ("num 2", "string"): ['-1', '-2', '3', '4'],
    ("col_array", None): [['baby', 'sorry'],
        ['baby 1', 'sorry 1'],
        ['baby 2', 'sorry 2'],
        ['baby 3', 'sorry 3']],
    ("col_int", None): [[1, 2, 3], [3, 4], [5, 6, 7], [7, 8]],
})

df_abs


df_abs.cols.abs(["num", "num 2"]).display(highlight=["num", "num 2"])


df.cols.qcut("billingId", quantiles=5).display(highlight="billingId")

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]
⋅⋅⋅⋅zombies	2	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1
⋅⋅⋅⋅zombies	2	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)	col 2 12 (float64)	col 3 13 (int64)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q	2.22	3
⋅⋅⋅⋅zombies	2	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w	2.22	3
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e	2.22	3
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r	2.22	3

words 1 (object)	number 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

col_array 1 (object)	col_array_0 2 (object)	col_array_1 3 (object)
['baby',⋅'sorry']	['baby',	'sorry']
['baby⋅1',⋅'sorry⋅1']	['baby	1',⋅'sorry⋅1']
['baby⋅2',⋅'sorry⋅2']	['baby	2',⋅'sorry⋅2']
['baby⋅3',⋅'sorry⋅3']	['baby	3',⋅'sorry⋅3']

col_int 1 (object)	col_int_0 2 (object)	col_int_1 3 (object)
[1,⋅2,⋅3]	[1,	2,⋅3]
[3,⋅4]	[3,	4]
[5,⋅6,⋅7]	[5,	6,⋅7]
[7,⋅8]	[7,	8]

words 1 (object)	num 2 (int32)
⋅⋅I⋅like⋅⋅⋅⋅fish⋅⋅	1
⋅⋅⋅⋅zombies	2
simpsons⋅⋅⋅cat⋅lady	2
None	3
None	0

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	animals	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	animals	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	animals-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	animals	tv	animals	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	frog	table	animals-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	animals	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog_1	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	cat_1	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (object)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (object)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (object)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	%%t-%%r	%	1	['%%%y',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zom%ies	2	%%t	tv	dog-tv	%	2	['%%%y⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅%%t⋅l%dy	2	frog	t%%le	e%gle-tv-plus	1	3	['%%%y⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	e%gle	gl%ss	lion-p%	%	4	['%%%y⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	animal	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (int32)	animals 3 (object)	thing 4 (object)	two strings 5 (object)	filter 6 (object)	num 2 7 (string)	col_array 8 (object)	col_int 9 (object)	new_col_1 10 (int64)	NEW COLUMN 11 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	dog	housé	contains⋅cat	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	contains⋅cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
contains⋅cat	2	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

words 1 (object)	num 2 (int32)	num_1 3 (int32)	col_nested 4 (object)	animals 5 (object)	thing 6 (object)	two strings 7 (object)	filter 8 (object)	num 2 9 (string)	col_array 10 (object)	col_int 11 (object)	new_col_1 12 (int64)	NEW COLUMN 13 (object)
⋅⋅I⋅like⋅⋅⋅⋅⋅fish⋅⋅	1	1	[1,⋅1]	dog	housé	cat-car	a	1	['baby',⋅'sorry']	[1,⋅2,⋅3]	1	q
⋅⋅⋅⋅zombies	2	2	[2,⋅2]	cat	tv	dog-tv	b	2	['baby⋅1',⋅'sorry⋅1']	[3,⋅4]	1	w
simpsons⋅⋅⋅cat⋅lady	2	2	[2,⋅2]	frog	table	eagle-tv-plus	1	3	['baby⋅2',⋅'sorry⋅2']	[5,⋅6,⋅7]	1	e
None	3	3	[3,⋅3]	eagle	glass	lion-pc	c	4	['baby⋅3',⋅'sorry⋅3']	[7,⋅8]	1	r

num 1 (int32)	just test 2 (object)	num 2 3 (float64)	more numbers 4 (int64)
1	test	2.0	1
2	test	4.0	2
2	test	4.0	3
3	test	6.0	4

A 1 (float64)	B 2 (float64)	C 3 (float64)
1.0	nan	1.0
2.0	nan	nan
nan	3.0	nan
4.0	4.0	2.0
5.0	5.0	2.0

A 1 (float64)	B 2 (float64)	C 3 (float64)
1.0	4.0	1.0
2.0	4.0	nan
3.0	3.0	nan
4.0	4.0	2.0
5.0	5.0	2.0

session 1 (int64)	timestamp 2 (int64)	id 3 (float64)
1	1	nan
1	2	5.0
1	3	nan
1	4	nan
1	5	10.0
1	6	nan
1	6	nan

id 1 (int64)	firstName 2 (object)	lastName 3 (object)	billingId 4 (int64)	product 5 (object)	price 6 (int64)	birth 7 (object)	dummyCol 8 (object)
1	Luis	Alvarez$$%!	123	Cake	10	1980/07/07	never
2	André	Ampère	423	piza	8	1950/07/08	gonna
3	NiELS	Böhr//((%%	551	pizza	8	1990/07/09	give
4	PAUL	dirac$	521	pizza	8	1954/07/10	you
5	Albert	Einstein	634	pizza	8	1990/07/11	up
6	Galileo	⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI	672	arepa	5	1930/08/12	never
7	CaRL	Ga%%%uss	323	taco	3	1970/07/13	gonna
8	David	H$$$ilbert	624	taaaccoo	3	1950/07/14	let
9	Johannes	KEPLER	735	taco	3	1920/04/22	you
10	JaMES	M$$ax%%well	875	taco	3	1923/03/12	down
11	Isaac	Newton	992	pasta	9	1999/02/15	never⋅

id 1 (int64)	firstName 2 (object)	lastName 3 (object)	billingId 4 (int64)	product 5 (object)	price 6 (int64)	birth 7 (object)	dummyCol 8 (object)
1	SIUL	alvarez$$%!	123	CAKE	10	1980/07/07	never
2	ÉRDNA	ampère	423	PIZA	8	1950/07/08	gonna
3	SLEIN	böhr//((%%	551	PIZZA	8	1990/07/09	give
4	LUAP	dirac$	521	PIZZA	8	1954/07/10	you
5	TREBLA	einstein	634	PIZZA	8	1990/07/11	up
6	OELILAG	galilei	672	AREPA	5	1930/08/12	never
7	LRAC	ga%%%uss	323	TACO	3	1970/07/13	gonna
8	DIVAD	h$$$ilbert	624	TAAACCOO	3	1950/07/14	let
9	SENNAHOJ	kepler	735	TACO	3	1920/04/22	you
10	SEMAJ	m$$ax%%well	875	TACO	3	1923/03/12	down
11	CAASI	newton	992	PASTA	9	1999/02/15	never⋅

id 1 (object)	firstName 2 (object)	lastName 3 (object)	billingId 4 (object)	product 5 (object)	price 6 (object)	birth 7 (object)	dummyCol 8 (object)
1	Luis	Alvarez$$!	123	Cake	10	1980/07/07	never
2	André	Ampère	423	piza	8	1950/07/08	gonna
3	NiELS	Böhr//((	551	pizza	8	1990/07/09	give
4	PAUL	dirac$	521	pizza	8	1954/07/10	you
5	Albert	Einstein	634	pizza	8	1990/07/11	up
6	Galileo	⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI	672	arepa	5	1930/08/12	never
7	CaRL	Gauss	323	taco	3	1970/07/13	gonna
8	David	H$$$ilbert	624	taaaccoo	3	1950/07/14	let
9	Johannes	KEPLER	735	taco	3	1920/04/22	you
10	JaMES	M$$axwell	875	taco	3	1923/03/12	down
11	Isaac	Newton	992	pasta	9	1999/02/15	never⋅

id 1 (int64)	firstName 2 (object)	lastName 3 (object)	billingId 4 (int64)	product 5 (object)	price 6 (int64)	birth 7 (object)	dummyCol 8 (object)
1	Luis	Alvarez	123	Cake	10	1980/07/07	never
2	André	Ampère	423	piza	8	1950/07/08	gonna
3	NiELS	Böhr	551	pizza	8	1990/07/09	give
4	PAUL	dirac	521	pizza	8	1954/07/10	you
5	Albert	Einstein	634	pizza	8	1990/07/11	up
6	Galileo	⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI	672	arepa	5	1930/08/12	never
7	CaRL	Gauss	323	taco	3	1970/07/13	gonna
8	David	Hilbert	624	taaaccoo	3	1950/07/14	let
9	Johannes	KEPLER	735	taco	3	1920/04/22	you
10	JaMES	Maxwell	875	taco	3	1923/03/12	down
11	Isaac	Newton	992	pasta	9	1999/02/15	never⋅

id 1 (int64)	firstName 2 (object)	lastName 3 (object)	billingId 4 (float64)	product 5 (object)	price 6 (int64)	birth 7 (object)	dummyCol 8 (object)
1	Luis	Alvarez$$%!	123.0	Cake	10	1980/07/07	never
2	André	Ampère	200.0	piza	8	1950/07/08	gonna
3	NiELS	Böhr//((%%	200.0	pizza	8	1990/07/09	give
4	PAUL	dirac$	200.0	pizza	8	1954/07/10	you
5	Albert	Einstein	200.0	pizza	8	1990/07/11	up
6	Galileo	⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI	200.0	arepa	5	1930/08/12	never
7	CaRL	Ga%%%uss	200.0	taco	3	1970/07/13	gonna
8	David	H$$$ilbert	200.0	taaaccoo	3	1950/07/14	let
9	Johannes	KEPLER	200.0	taco	3	1920/04/22	you
10	JaMES	M$$ax%%well	200.0	taco	3	1923/03/12	down
11	Isaac	Newton	200.0	pasta	9	1999/02/15	never⋅

id 1 (int64)	firstName 2 (object)	lastName 3 (object)	billingId 4 (category)	product 5 (object)	price 6 (int64)	birth 7 (object)	dummyCol 8 (object)
1	Luis	Alvarez$$%!	(110.999,⋅287.4]	Cake	10	1980/07/07	never
2	André	Ampère	(287.4,⋅527.0]	piza	8	1950/07/08	gonna
3	NiELS	Böhr//((%%	(527.0,⋅632.0]	pizza	8	1990/07/09	give
4	PAUL	dirac$	(287.4,⋅527.0]	pizza	8	1954/07/10	you
5	Albert	Einstein	(632.0,⋅837.2]	pizza	8	1990/07/11	up
6	Galileo	⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI	(632.0,⋅837.2]	arepa	5	1930/08/12	never
7	CaRL	Ga%%%uss	(287.4,⋅527.0]	taco	3	1970/07/13	gonna
8	David	H$$$ilbert	(527.0,⋅632.0]	taaaccoo	3	1950/07/14	let
9	Johannes	KEPLER	(632.0,⋅837.2]	taco	3	1920/04/22	you
10	JaMES	M$$ax%%well	(837.2,⋅992.0]	taco	3	1923/03/12	down
11	Isaac	Newton	(837.2,⋅992.0]	pasta	9	1999/02/15	never⋅

Optimus column operations¶

Create dataframe¶

Create Columns¶

Create a column with a constant value¶

Append one or multiples columns from dataframes¶

Create multiple columns with a constant value¶

Create multiple columns with a constant string, a new column with existing columns value and an array¶

Select columns¶

Select columns with a Regex¶

Select all the columns of type string¶

Rename Column¶

Rename multiple columns and uppercase all the columns¶

Convert to lower case¶

Convert to uppercase¶

Cast a columns¶

Cast a column to string¶

Cast all columns to string¶

Move columns¶

Sorting Columns¶

Sort in Alphabetical order¶

Sort in Reverse Alphabetical order¶

Drop columns¶

Drop one column¶

Drop multiple columns¶

Chaining¶

Unnest Columns¶

Only get the first element¶

Unnest array of string¶

Unnest and array of ints¶

Spits in 3 parts¶

Impute¶

Fill missing data¶

Set values using user defined functions¶

Set a value only to numeric values in filter¶

Sum a numeric value (20 in this case) to two columns¶

Select rows where filter is an integer¶

Create an abstract dataframe to filter rows where the value of num is greater than 1¶

Create an UDF with two arguments and pass it to df.cols.set¶

Set a value where the values of num and num 2 are both greater than 2¶

Count Nulls¶

Count uniques¶

Unique¶

Count Zeros¶

Column Data Types¶

Replace¶

Replace "dog" and "cat" in animals by the string "animals"¶

Replace "dog-tv", "cat", "eagle" and "fish" in columns two strings and animals by the string "animals"¶

Replace "dog" by "dog_1" and "cat" by "cat_1" in animals¶

Replace "dog" by "pet" in animals¶

Replace "a", "b" and "c" by "%" in all columns¶

Replace 3 and 2 by 10 in a numeric column¶

Replace 3 by 6 and 2 by 12 in a numeric column¶

Replace as words¶

Replace using a regular expression¶

Nest¶

Merge two columns in a string column¶

Merge two columns in a column vector¶

Merge three columns in an array¶

Histograms¶

Statistics¶

Quantile Statistics¶

Descriptive Statistics¶

Calculate Median Absolute deviation¶

Calculate precentiles¶

Calculate Mode¶

String Operations¶

Calculate the interquartile range¶

Cleaning and Date Operations Operations¶

Years between a date and today (No other date is passed)¶

Set a value only to numeric values in `filter`¶

Select rows where `filter` is an integer¶

Create an abstract dataframe to filter rows where the value of `num` is greater than 1¶

Create an UDF with two arguments and pass it to `df.cols.set`¶

Set a value where the values of `num` and `num 2` are both greater than 2¶

Replace `"dog"` and `"cat"` in `animals` by the string `"animals"`¶

Replace `"dog-tv"`, `"cat"`, `"eagle"` and `"fish"` in columns `two strings` and `animals` by the string `"animals"`¶

Replace `"dog"` by `"dog_1"` and `"cat"` by `"cat_1"` in `animals`¶

Replace `"dog"` by `"pet"` in `animals`¶

Replace `"a"`, `"b"` and `"c"` by `"%"` in all columns¶

Replace `3` and `2` by `10` in a numeric column¶

Replace `3` by `6` and `2` by `12` in a numeric column¶