751 - 800

Question 1

maths.Standard Deviation

Answer 1

is a measure of how spread out numbers are. Its symbol is σ (the greek letter sigma). it is the square root of the Variance.

Example: (600, 470, 170, 430, 300)

1) Mean: (600 + 470 + 170 + 430 + 300) / 5 = 394

2) Difference from Mean: (600 - 394, 470 - 394, 170 - 394, 430 - 394, 300 - 394) 
(206, 76, -224, 36, -94)

3) Variance = (206^2 + 76^2 + (-224^2) + 36^2 + (-94^2)) / 5 = 21704

4) Standard Deviation: √21704 = 147,32

maths.Standard Deviation

Question 2

maths.Normal distribution

Answer 2

is an arrangement of a data set in which most values cluster in the middle of the range and the rest taper off symmetrically toward either extreme.

maths.Normal distribution

Question 3

maths.Statistical Distribution or maths.Probability Distribution

Answer 3

is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment

maths.Statistical Distribution or maths.Probability Distribution
maths.Statistical Distribution or maths.Probability Distribution

Question 4

string.removesuffix(suffix, /)

🎯 suffix - строка-суффикс, который необходимо удалить.

Answer 4

заканчивается строкой суффикса suffix, то метод возвращает копию строки без суффикса. Если суффикс suffix в исходной строке str не обнаружен, то метод возвращает копию исходной строки str.

line = 'TmpDirMixin'

print(line.removesuffix('Tests'))
👉 TmpDirMixin

print(line.removesuffix('x'))
👉 TmpDirMixin

print(line.removesuffix('xin'))
👉 TmpDirMi

string.removesuffix

Question 5

maths.Line Graph

Answer 5

a graph that shows information connected in some way (usually as it changes over time).

maths.Line Graph

Question 6

maths.Scatter Plot

Answer 6

graph of plotted points showing the relationship between two data sets.

maths.Scatter Plot

Question 7

maths.Bar Graph and maths.Bar Chart

Answer 7

graphical display of data using bars of different heights. It is a really good way to show relative sizes . Bar Graphs are good when your data is in categories

maths.Bar Graph and maths.Bar Chart

Question 8

heapq.heappush(heap, item)

🎯 heap - список с кучей,
🎯 item - добавляемый элемент

Кучи - это двоичные деревья, для которых каждый родительский узел имеет значение, меньшее или равное любому из его дочерних элементов. Интересным свойством кучи является то, что ее наименьшим элементом всегда является корень heap[0].

Answer 8

добавляет значение элемента item в кучу heap, сохраняя инвариант кучи и сортирует от меньшего к болшьшему.

from heapq import heappush

h = []
heappush(h, (5, 'write code'))
heappush(h, (7, 'release product'))
heappush(h, (1, 'write spec'))
heappush(h, (3, 'create tests'))
print(h)

👉 [(1, 'write spec'), (3, 'create tests'), (5, 'write code'), (7, 'release product')]

heapq.heappush

Question 9

heapq.nlargest and nsmallest(n, iterable, key=None)

🎯 key — определяет функцию с одним аргументом, которая используется для извлечения ключа
сравнения из каждого элемента в итерируемой последовательности iterable, например key=str.lower.

Answer 9

возвращает список с n самыми большими или наименьшими элементами из набора данных, определенного с помощью итерируемой последовательности iterable.

import heapq
seq = [100, 2, 400, 500, 400]

heapq.nsmallest(2, seq)
👉 [2, 100]

seq = [100, 2, 400, 500, 400]
heapq.nsmallest(2, enumerate(seq), key=lambda x: x[1])

👉 [(1, 2), (0, 100)]

seq = [100, 2, 400, 500, 400]
heapq.nlargest(2, seq)

👉 [500, 400]

seq = [100, 2, 400, 500, 400]
heapq.nlargest(2, enumerate(seq), key=lambda x: x[1])

👉 [(3, 500), (2, 400)]

heapq.nlargestheapq.nlargest and nsmallest
heapq.nlargestheapq.nlargest and nsmallest

Question 10

heapq.merge(*iterables, key=None, reverse=False)

Answer 10

объединяет несколько отсортированных последовательностей *iterables в один отсортированный итератор.

first_list = sorted([45, 12, 63, 95])
second_list = sorted([42, 13, 69, 54, 15])
final_list = list(heapq.merge(first_list, second_list))

👉 [12, 13, 15, 42, 45, 54, 63, 69, 95]

heapq.merge

Question 11

heapq.heapify(x)

🎯 x - список элементов.

Answer 11

преобразовывает список x в кучу на месте за линейное время.

import heapq
h = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
heapq.heapify(h)
print(h)

👉 [0, 1, 2, 6, 3, 5, 4, 7, 8, 9]

heapq.heapify

Question 12

heapq.heapreplace(heap, item)

🎯 heap - список с кучей,
🎯 item - добавляемый элемент.

Answer 12

сначала удаляет и возвращает наименьший элемент из кучи heap, а потом добавляет новый элемент item. Размер кучи heap не меняется.

h = [(3, 'three'), (1, 'one'), (7, 'seven'), (5, 'five'), (9, 'nine')]
heapq.heapify(h)

heapq.heapreplace(h, (0, 'zero'))
print(h)
👉 [(0, 'zero'), (3, 'three'), (7, 'seven'), (5, 'five'), (9, 'nine')]

heapq.heapreplace(h, (222, 'zero'))
print(h)
👉 [(3, 'three'), (5, 'five'), (7, 'seven'), (222, 'zero'), (9, 'nine')]

heapq.heapreplace

Question 13

heapq.heappushpop(heap, item)

🎯 heap - список с кучей,
🎯 item - добавляемый элемент.

Answer 13

добавляет значение элемента item в кучу heap, затем возвращает и удаляет самый маленький элемент из кучи heap.

h = [(3, 'three'), (1, 'one'), (7, 'seven'), (5, 'five'), (9, 'nine')]
heapq.heapify(h)

heapq.heappushpop(h, (10, 'zero'))
print(h)
👉 [(3, 'three'), (5, 'five'), (7, 'seven'), (10, 'zero'), (9, 'nine')]

heapq.heappushpop(h, (1, 'zero'))
print(h)
👉 [(3, 'three'), (5, 'five'), (7, 'seven'), (10, 'zero'), (9, 'nine')]

heapq.heappushpop(h, (3, 'ttttttttttttttttttt'))
print(h)
👉 [(3, 'ttttttttttttttttttt'), (5, 'five'), (7, 'seven'), (10, 'zero'), (9, 'nine')]

heapq.heappushpop

Question 14

heapq.heappop(heap)

Answer 14

возвращает и удаляет наименьший элемент из кучи heap, сохраняя инвариант кучи.

h = [(3, 'three'), (1, 'one'), (7, 'seven'), (5, 'five'), (9, 'nine')]
heapq.heapify(h)

heapq.heappop(h)
print(h)
👉 [(3, 'three'), (5, 'five'), (7, 'seven'), (9, 'nine')]

heapq.heappop

Question 15

string.capwords(s, sep=None)

🎯 s - произвольная строка.
🎯 sep=None - строка, используется для разделения и объединения слов.

Answer 15

разделяет строку s на слова с помощью метода str.split(), далее используя метод строки str.capitalize() преобразует каждое слово с заглавной буквы и соединяет полученные слова используя метод str.join().

s = 'The quick brown fox jumped over the lazy dog.'

print(string.capwords(s))
👉 The Quick Brown Fox Jumped Over The Lazy Dog.

string.capwords

Question 16

string.removeprefix(prefix, /)

🎯 prefix - строка-префикс, который необходимо удалить.

Answer 16

возвращает копию строки без префикса. Если префикс prefix в исходной строке str не обнаружен, то метод возвращает копию исходной строки str.

line = 'BaseTestCase'
print(line.removeprefix('Test'))
👉 BaseTestCase

print(line.removeprefix('a'))
👉 BaseTestCase

print(line.removeprefix('Base'))
👉 TestCase

string.removeprefix

Question 17

maths.Histogram

🎯 weight
🎯 height
🎯 how much time

Answer 17

a graphical display of data using bars of different heights. It is similar to a Bar Chart, but a histogram groups numbers into ranges. The height of each bar shows how many fall into each range. And you decide what ranges to use!

maths.Histogram

Question 18

eval(expression, globals=None, locals=None)

🎯 expression - строка-выражение, которую требуется исполнить. Либо объект кода, что возвращает compile()
🎯 globals=None - словарь глобального пространства, относительно которого следует исполнить выражение
🎯 locals=None - переменные локального пространства, в котором следует исполнить выражение.

Answer 18

выполняет строку-выражение, переданную ей в качестве обязательного аргумента и возвращает результат выполнения этой строки.

x = "print('Привет')"
eval(x)
👉 Привет

y = 'print("5 + 10 =", (5+10))'
eval(y)
👉 5 + 10 = 15

s=3
eval('s==3')
👉 True

s=3
eval('s + 1')
👉 4

s=3
eval('s')
👉 3

s=3
eval('str(s)+"test"')
👉 '3test'

eval

Question 19

maths.Correlation(corr(X,Y) or ρX,Y)

Answer 19

When two sets of data are strongly linked together we say they have a High Correlation.

maths.Correlation

Question 20

maths.Central Value

Answer 20

with just 2 numbers the answer is easy: go half-way between.

Example: what is the central value for 3 and 7?
Answer: Half-way between, which is 5.

We can calculate it by adding 3 and 7 and then dividing the result by 2: (3+7) / 2 = 5

Example: what is the central value of 3, 7 and 8?

Answer: We calculate it by adding 3, 7 and 8 and then dividing the results by 3 
(because there are 3 numbers): (3+7+8) / 3 = 18/3 = 6

maths.Central Value

Question 21

maths.Median(x̄)

Answer 21

is the “middle” of a sorted list of numbers. With an even amount of numbers things are slightly different. In that case we find the middle pair of numbers, and then adding them together and dividing by two.

A quick way to find the middle: Count how many numbers, add 1, then divide by 2

Median of (12, 3, 5)
Put them in order: 3, 5, 12

👉 The middle is 5, so the median is 5.

Example: (3, 13, 7, 5, 21, 23, 39, 23, 40, 23, 14, 12, 56, 23, 29)
When we put those numbers in order we have: 3, 5, 7, 12, 13, 14, 21, 23, 23, 23, 23, 29, 39, 40, 56

👉 The median value of this set of numbers is 23.

Example: (3, 13, 7, 5, 21, 23, 23, 40, 23, 14, 12, 56, 23, 29)

When we put those numbers in order we have: 3, 5, 7, 12, 13, 14, 21, 23, 23, 23, 23, 29, 40, 56

👉 To find the value halfway between them, add them together and divide by 2:
21 + 23 = 44 then 44 ÷ 2 = 22

maths.Median

Question 22

maths.Mode(Mo)

Answer 22

is simply the number which appears most often.

In {6, 3, 9, 6, 6, 5, 9, 3} the Mode is 6, as it occurs most often.

maths.Mode

Question 23

maths.Harmonic Mean

Answer 23

the reciprocal of the average of the reciprocals

Harmonic mean of (1, 2, 4)?

The reciprocals of 1, 2 and 4 are: (1/1) = 1, (1/2) = 0.5, (1/4) = 0.25

Now add them up: 1 + 0,5 + 0,25 = 1,75

Average: 1,75 / 3

The reciprocal of that average: 3 / 1,75 = 1,714

maths.Harmonic Mean
maths.Harmonic Mean

Question 24

maths.mean | maths.arithmetic mean | maths.average

Answer 24

просто среднеарифметическое.

6, 11, 7
Add the numbers: 6 + 11 + 7 = 24
Divide by how many numbers (there are 3 numbers): 24 / 3 = 8
The Mean is 8

3, −7, 5, 13, −2
The sum of these numbers is 3 − 7 + 5 + 13 − 2 = 12
There are 5 numbers.
The mean is equal to 12 ÷ 5 = 2,4
The mean of the above numbers is 2,4

Here is how to do it one line: Mean = (3 − 7 + 5 + 13 − 2) / 5 = 12 / 5 = 2,4

maths.mean | maths.arithmetic mean | maths.average

Question 25

numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order=’K’, casting=’safe’, op_axes=None, itershape=None, buffersize=0)

Answer 25

Efficient multi-dimensional iterator object to iterate over arrays. To get started using this object, see the introductory guide to array iteration.

a = geek.arange(12)
# shape array with 3 rows and 
# 4 columns
a = a.reshape(3,4)
 
print('Original array is:')
print(a)
print()
print('Modified array is:')

for x in geek.nditer(a):
    print(x)

Original array is:
[[ 0 1 2 3]
 [ 4 5 6 7]
 [ 8 9 10 11]]

Modified array is:
0 1 2 3 4 5 6 7 8 9 10 11

a = geek.arange(12)
# shape array with 3 rows and 
# 4 columns 
a = a.reshape(3,4)
print('Original array is:')
print(a)
print()
# modifying array values
for x in geek.nditer(a, op_flags = ['readwrite']):
    x[...] = 5*x
print('Modified array is:')
print(a)

Original array is:
[[ 0 1 2 3]
 [ 4 5 6 7]
 [ 8 9 10 11]]

Modified array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

numpy.nditer
numpy.nditer

Question 26

seaborn.set_style(style=None, rc=None)

Answer 26

Set the parameters that control the general style of the plots. The style parameters control properties like the color of the background and whether a grid is enabled by default.

sns.set_style("whitegrid")
sns.barplot(x=["A", "B", "C"], y=[1, 3, 2])

sns.set_style("darkgrid", {"grid.color": ".6", "grid.linestyle": ":"})
sns.lineplot(x=["A", "B", "C"], y=[1, 3, 2])

# load the tips dataset present by default in seaborn
tips = sns.load_dataset('tips')
sns.set_style('white')

make a countplot
sns.countplot(x ='sex', data = tips)

tips = sns.load_dataset('tips')
sns.lmplot(x ='total_bill', y ='tip', size = 2, aspect = 4, data = tips)

seaborn.set_style
seaborn.set_style

Question 27

seaborn.set_context(context=None, font_scale=1, rc=None)

Answer 27

Set the parameters that control the scaling of plot elements. This affects things like the size of the labels, lines, and other elements of the plot, but not the overall style.

sns.set_context("notebook")
sns.lineplot(x=[0, 1, 2], y=[1, 3, 2])

sns.set_context("notebook", font_scale=1.25)
sns.lineplot(x=[0, 1, 2], y=[1, 3, 2])

sns.set_context("notebook", rc={"lines.linewidth": 3})
sns.lineplot(x=[0, 1, 2], y=[1, 3, 2])

seaborn.set_context

Question 28

seaborn.despine(fig=None, ax=None, top=True, right=True, left=False, bottom=False, offset=None, trim=False)

Answer 28

Remove the top and right spines from plot(s).

seaborn.despine

Question 29

seaborn.PairGrid(**kwargs)

Answer 29

Subplot grid for plotting pairwise relationships in a dataset.This object maps each variable in a dataset onto a column and row in a grid of multiple axes.

penguins = sns.load_dataset("penguins")
g = sns.PairGrid(penguins)

g = sns.PairGrid(penguins)
g.map(sns.scatterplot)

g = sns.PairGrid(penguins)
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot)

df = seaborn.load_dataset('tips')
# PairGrid object with hue
graph = seaborn.PairGrid(df, hue ='day')
# type of graph for diagonal
graph = graph.map_diag(plt.hist)
# type of graph for non-diagonal
graph = graph.map_offdiag(plt.scatter)
# to add legends
graph = graph.add_legend()
# to show
plt.show()

seaborn.PairGrid
seaborn.PairGrid

Question 30

seaborn.swarmplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor=’gray’, linewidth=0, ax=None, **kwargs)

Answer 30

Draw a categorical scatterplot with non-overlapping points.

sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.swarmplot(x=tips["total_bill"])

ax = sns.swarmplot(x="day", y="total_bill", data=tips)

ax = sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips)

seaborn.set(style='whitegrid')
fmri = seaborn.load_dataset("fmri")
seaborn.swarmplot(x="timepoint", y="signal", data=fmri)

seaborn.set(style='whitegrid')
fmri = seaborn.load_dataset("fmri")
seaborn.swarmplot(x="timepoint", y="signal", hue="region", data=fmri)

seaborn.swarmplot
seaborn.swarmplot

Question 31

seaborn.clustermap(data, *, pivot_kws=None, method=’average’, metric=’euclidean’, z_score=None, standard_scale=None, figsize=(10, 10), cbar_kws=None, row_cluster=True, col_cluster=True, row_linkage=None, col_linkage=None, row_colors=None, col_colors=None, mask=None, dendrogram_ratio=0.2, colors_ratio=0.03, cbar_pos=(0.02, 0.8, 0.05, 0.18), tree_kws=None, **kwargs)

Answer 31

Plot a matrix dataset as a hierarchically-clustered heatmap.

iris = sns.load_dataset("iris")
species = iris.pop("species")
g = sns.clustermap(iris)

g = sns.clustermap(iris, figsize=(7, 5), row_cluster=False, dendrogram_ratio=(.1, .2), cbar_pos=(0, .2, .03, .4))

lut = dict(zip(species.unique(), "rbg"))
row_colors = species.map(lut)
g = sns.clustermap(iris, row_colo
rs=row_colors)

data = sns.load_dataset('flights')
# Categorical encoding
frequency_encoding(data, 'month')
# Clustering data row-wise and
# changing color of the map.
sns.clustermap(data, figsize=(7, 7)

seaborn.clustermap
seaborn.clustermap

Question 32

seaborn.FaceGrid()

Answer 32

Multi-plot grid for plotting conditional relationships.

df = seaborn.load_dataset('tips')
# Form a facetgrid using columns with a hue
graph = seaborn.FacetGrid(df, col ="sex",  hue ="day")
# map the above form facetgrid with some attributes
graph.map(plt.scatter, "total_bill", "tip", edgecolor ="w").add_legend()
# show the object
plt.show()

df = seaborn.load_dataset('tips')
# Form a facetgrid using columns with a hue
graph = seaborn.FacetGrid(df, row ='smoker', col ='time')
# map the above form facetgrid with some attributes
graph.map(plt.hist, 'total_bill', bins = 15, color ='orange')
# show the object
plt.show()

df = seaborn.load_dataset('tips')
# Form a facetgrid using columns with a hue
graph = seaborn.FacetGrid(df, col ='time', hue ='smoker')
# map the above form facetgrid with some attributes
graph.map(seaborn.regplot, "total_bill", "tip").add_legend()
# show the object
plt.show()

seaborn.FaceGrid
seaborn.FaceGrid

Question 33

numpy.random.seed()

Answer 33

используется для установки начального числа для алгоритма генератора псевдослучайных чисел в Python. Идея заключается в том, что мы всегда будем получать один и тот же набор случайных чисел для одного и того же начального числа на любой машине.

import numpy as np
np.random.seed(1)
array = np.random.rand(5)
np.random.seed(1)
array2 = np.random.rand(5)
print(array)
print(array2)

[4.17022005e-01 7.20324493e-01 1.14374817e-04 3.02332573e-01 1.46755891e-01]
[4.17022005e-01 7.20324493e-01 1.14374817e-04 3.02332573e-01 1.46755891e-01]

numpy.random.seed
numpy.random.seed

Question 34

seaborn.violinplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, bw=’scott’, cut=2, scale=’area’, scale_hue=True, gridsize=100, width=0.8, inner=’box’, split=False, dodge=True, orient=None, linewidth=None, color=None, palette=None, saturation=0.75, ax=None, **kwargs)

Answer 34

similar activity box plot do. As it shows several quantitative data across one or more categorical variables. It can be an effective and attractive way to show multiple data at several units.

sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.violinplot(x=tips["total_bill"])

ax = sns.violinplot(x="day", y="total_bill", hue="smoker", data=tips, palette="muted")

seaborn.set(style = 'whitegrid')
fmri = seaborn.load_dataset("fmri")
seaborn.violinplot(x ="timepoint",y ="signal", data = fmri)

seaborn.set(style = 'whitegrid')
fmri = seaborn.load_dataset("fmri")
seaborn.violinplot(x ="timepoint", y ="signal", hue ="region", style ="event", data = fmri)

seaborn.set(style = 'whitegrid') 
tip = seaborn.load_dataset('tips')
seaborn.violinplot(x ='day', y ='tip', data = tip)

seaborn.violinplot
seaborn.violinplot

Question 35

seaborn.displot(data=None, *, x=None, y=None, hue=None, row=None, col=None, weights=None, kind=’hist’, rug=False, rug_kws=None, log_scale=None, legend=True, palette=None, hue_order=None, hue_norm=None, color=None, col_wrap=None, row_order=None, col_order=None, height=5, aspect=1, facet_kws=None, **kwargs)

Answer 35

Figure-level interface for drawing distribution plots onto a FacetGrid.

penguins = sns.load_dataset("penguins")
sns.displot(data=penguins, x="flipper_length_mm")

sns.displot(data=penguins, x="flipper_length_mm", kind="kde")

sns.displot(data=penguins, x="flipper_length_mm", y="bill_length_mm")

seaborn.displot

Question 36

seaborn.jointplot(*, x=None, y=None, data=None, kind=’scatter’, color=None, height=6, ratio=5, space=0.2, dropna=False, xlim=None, ylim=None, marginal_ticks=False, joint_kws=None, marginal_kws=None, hue=None, palette=None, hue_order=None, hue_norm=None, **kwargs)

Answer 36

draw jointplot with

Draw a plot of two variables with bivariate and univariate graphs.

penguins = sns.load_dataset("penguins")
sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm")

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species")

data = sns.load_dataset("attention")
# hex kind
sns.jointplot(x = "solutions", y = "score", kind = "hex", data = data)
# show the plot
plt.show()

data = sns.load_dataset("exercise")
# draw jointplot with   # kde kind
sns.jointplot(x = "id", y = "pulse", kind = "kde", data = data)
# Show the plot
plt.show()

seaborn.jointplot
seaborn.jointplot

Question 37

seaborn.rugplot(x=None, *, height=0.025, axis=None, ax=None, data=None, y=None, hue=None, palette=None, hue_order=None, hue_norm=None, expand_margins=True, legend=True, a=None, **kwargs)

Answer 37

Plot marginal distributions by drawing ticks along the x and y axes. This function is intended to complement other plots by showing the location of individual observations in an unobstrusive way.

tips = sns.load_dataset("tips")
sns.kdeplot(data=tips, x="total_bill")
sns.rugplot(data=tips, x="total_bill")

sns.scatterplot(data=tips, x="total_bill", y="tip")
sns.rugplot(data=tips, x="total_bill", y="tip")

sns.scatterplot(data=tips, x="total_bill", y="tip")
sns.rugplot(data=tips, x="total_bill", y="tip", height=.1)

sns.scatterplot(data=tips, x="total_bill", y="tip")
sns.rugplot(data=tips, x="total_bill", y="tip", height=-.02, clip_on=False)

diamonds = sns.load_dataset("diamonds")
sns.scatterplot(data=diamonds, x="carat", y="price", s=5)
sns.rugplot(data=diamonds, x="carat", y="price", lw=1, alpha=.005)

seaborn.rugplot

Question 38

seaborn.lineplot(*, x=None, y=None, hue=None, size=None, style=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order=None, units=None, estimator=’mean’, ci=95, n_boot=1000, seed=None, sort=True, err_style=’band’, err_kws=None, legend=’auto’, ax=None, **kwargs)

Answer 38

Draw a line plot with possibility of several semantic groupings. The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets.

sns.lineplot(data=flights_wide)

sns.lineplot(data=fmri, x="timepoint", y="signal", hue="region", style="event")

fmri = sns.load_dataset("fmri")
sns.lineplot( x = "timepoint", y = "signal", data = fmri);

sns.lineplot(data=museum_data["Avila Adobe"])

seaborn.lineplot
seaborn.lineplot

Question 39

seaborn.scatterplot(*, x=None, y=None, hue=None, style=None, size=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, markers=True, style_order=None, x_bins=None, y_bins=None, units=None, estimator=None, ci=95, n_boot=1000, alpha=None, x_jitter=None, y_jitter=None, legend=’auto’, ax=None, **kwargs)

Answer 39

Draw a scatter plot with possibility of several semantic groupings. The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters.

sns.scatterplot(data=tips, x="total_bill", y="tip", hue="time")

sns.scatterplot(data=tips, x="total_bill", y="tip", hue="time", style="time")

seaborn.set(style='whitegrid')
fmri = seaborn.load_dataset("fmri")
seaborn.scatterplot(x="timepoint", y="signal", data=fmri)

seaborn.set(style='whitegrid')
fmri = seaborn.load_dataset("fmri")
seaborn.scatterplot(x="timepoint", y="signal", hue="region", style="event", data=fmri)

seaborn.scatterplot
seaborn.scatterplot

Question 40

seaborn.histplot(data=None, *, x=None, y=None, hue=None, weights=None, stat=’count’, bins=’auto’, binwidth=None, binrange=None, discrete=None, cumulative=False, common_bins=True, common_norm=True, multiple=’layer’, element=’bars’, fill=True, shrink=1, kde=False, kde_kws=None, line_kws=None, thresh=0, pthresh=None, pmax=None, cbar=False, cbar_ax=None, cbar_kws=None, palette=None, hue_order=None, hue_norm=None, color=None, log_scale=None, legend=True, ax=None, **kwargs)

Answer 40

Plot univariate or bivariate histograms to show distributions of datasets. A histogram is a classic visualization tool that represents the distribution of one or more variables by counting the number of observations that fall within disrete bins.

penguins = sns.load_dataset("penguins")
sns.histplot(data=penguins, x="flipper_length_mm")

sns.histplot(data=penguins, y="flipper_length_mm")

np.random.seed(1)
num_var = np.random.randn(1000)
num_var = pd.Series(num_var, name = "Numerical Variable")
sns.histplot(data = num_var, kde = True)

sns.histplot(data = penguins, x = "body_mass_g", kde = True)

seaborn.histplot
seaborn.histplot

Question 41

seaborn.stripplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor=’gray’, linewidth=0, ax=None, **kwargs)

Answer 41

Draw a scatterplot where one variable is categorical.

sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.stripplot(x=tips["total_bill"])

ax = sns.stripplot(x="day", y="total_bill", data=tips)

seaborn.set(style = 'whitegrid')  
tip = seaborn.load_dataset("tips")   
seaborn.stripplot(x="day", y="total_bill", data=tip)
plt.show()

seaborn.stripplot
seaborn.stripplot

Question 42

seaborn.kdeplot(x=None, *, y=None, shade=None, vertical=False, kernel=None, bw=None, gridsize=200, cut=3, clip=None, legend=True, cumulative=False, shade_lowest=None, cbar=False, cbar_ax=None, cbar_kws=None, ax=None, weights=None, hue=None, palette=None, hue_order=None, hue_norm=None, multiple=’layer’, common_norm=True, common_grid=False, levels=10, thresh=0.05, bw_method=’scott’, bw_adjust=1, log_scale=None, color=None, fill=None, data=None, data2=None, warn_singular=True, **kwargs)

Answer 42

Plot univariate or bivariate distributions using kernel density estimation.

tips = sns.load_dataset("tips")
sns.kdeplot(data=tips, x="total_bill")

sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="stack")

# data x and y axis for seaborn
x= np.random.randn(200)
y = np.random.randn(200)
# Kde for x var
sns.kdeplot(x)

sns.kdeplot(x,y)

seaborn.kdeplot
seaborn.kdeplot

Question 43

seaborn.kdeplot(x=None, *, y=None, shade=None, vertical=False, kernel=None, bw=None, gridsize=200, cut=3, clip=None, legend=True, cumulative=False, shade_lowest=None, cbar=False, cbar_ax=None, cbar_kws=None, ax=None, weights=None, hue=None, palette=None, hue_order=None, hue_norm=None, multiple=’layer’, common_norm=True, common_grid=False, levels=10, thresh=0.05, bw_method=’scott’, bw_adjust=1, log_scale=None, color=None, fill=None, data=None, data2=None, warn_singular=True, **kwargs)

Answer 43

Plot univariate or bivariate distributions using kernel density estimation.

tips = sns.load_dataset("tips")
sns.kdeplot(data=tips, x="total_bill")

sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="stack")

# data x and y axis for seaborn
x= np.random.randn(200)
y = np.random.randn(200)
# Kde for x var
sns.kdeplot(x)

sns.kdeplot(x,y)

seaborn.kdeplot
seaborn.kdeplot

Question 44

seaborn.pairplot(data, *, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind=’scatter’, diag_kind=’auto’, markers=None, height=2.5, aspect=1, corner=False, dropna=False, plot_kws=None, diag_kws=None, grid_kws=None, size=None)

Answer 44

Plot pairwise relationships in a dataset.

penguins = sns.load_dataset("penguins")
sns.pairplot(penguins)

sns.pairplot(penguins, hue="species")

df = seaborn.load_dataset('tips')
seaborn.pairplot(df, hue ='day')
plt.show()

seaborn.pairplot
seaborn.pairplot

Question 45

seaborn.countplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, dodge=True, ax=None, **kwargs)

Answer 45

Show the counts of observations in each categorical bin using bars.

sns.set_theme(style="darkgrid")
titanic = sns.load_dataset("titanic")
ax = sns.countplot(x="class", data=titanic)

ax = sns.countplot(x="class", hue="who", data=titanic)

g = sns.catplot(x="class", hue="who", col="survived", data=titanic, kind="count", height=4, aspect=.7)

df = sns.load_dataset('tips')
sns.countplot(x ='sex', hue = "smoker", data = df)
plt.show()

df = sns.load_dataset('tips')
sns.countplot(y ='sex', hue = "smoker", data = df)
plt.show()

seaborn.countplot
seaborn.countplot

Question 46

set.union()

Answer 46

method returns a set that contains all items from the original set, and all items from the specified set(s). You can specify as many sets you want, separated by commas.

x = {"apple", "banana", "cherry"}
y = {"google", "microsoft", "apple"}
z = x.union(y) 
print(z)

👉 {'cherry', 'banana', 'microsoft', 'apple', 'google'}

set.union

Question 47

set

Answer 47

used to store multiple items in a single variable. список в котором не дубликатов.

thisset = {"apple", "banana", "cherry", "apple"}
print(thisset)
👉 {'banana', 'cherry', 'apple'}

set

Question 48

set.issuperset(other)

Answer 48

позволяет проверить находится ли каждый элемент последовательности other в множестве sets. Метод возвращает True, если множество sets является надмножеством итерируемого объекта other, если нет, то вернет False.

set_x = {'march', 'feb', 'may'}
list_y = ['march', 'dec', 'feb', 'may']

set_x.issuperset(list_y)
👉 False

set_x >= set(list_y)
👉 False

set_x.add('dec')
set_x.issuperset(list_y)
👉 True

set.issuperset

Question 49

set.discard()

Answer 49

method removes the specified item from the set. This method is different from the remove() method, because the remove() method will raise an error if the specified item does not exist, and the discard() method will not.

thisset = {"apple", "banana", "cherry"}
thisset.discard("banana")
print(thisset)
👉 {'apple', 'cherry'}

set.discard

Question 50

set.intersection()

Answer 50

method returns a set that contains the similarity between two or more sets. Метод возвращает новое множество с элементами, общими для множества sets и всех итерируемых объектов other ( - произвольное число позиционных аргументов).

x = {"apple", "banana", "cherry"}
y = {"google", "microsoft", "apple"}
z = x.intersection(y)

print(z)
👉 {‘apple’}

set.intersection
set.intersection