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Notes NumPyArray Operations

Array OperationsTopic 27 of 777 min read

Comparison & Logical Operations

Compare arrays element by element (==, !=, >, <), combine results with logical_and/or/not, and summarise with any, all and array_equal.

"In which matches did Virat outscore Rohit?", "Did anyone get a duck?", "Is this season's scorecard identical to the backup copy?" — these are yes/no questions asked for every element. NumPy answers them with comparison operators (which give boolean arrays) and logical functions (which combine or summarise them).

Definition

A comparison operation (==, !=, >, >=, <, <=) compares arrays element-wise and returns a boolean array of the same shape. Logical operations (np.logical_and, np.logical_or, np.logical_not, np.logical_xor — or the operators &, |, ~, ^) combine boolean arrays element-wise. any / all reduce a boolean array to a single answer (or one answer per row/column), and np.array_equal tells you whether two whole arrays are identical.

Boolean array — each element is True or False; dtype bool. It can be used as a mask, counted with .sum(), or averaged with .mean() (= fraction of True).
Element-wise logical — np.logical_and(a, b) is True only where both are True at that position.
Reduce — any = "at least one True?", all = "every element True?".

Syntax

python

a == b    a != b    a > b    a >= b    a < b    a <= b     # b: scalar or array

np.logical_and(c1, c2)     # same as c1 & c2
np.logical_or(c1, c2)      # same as c1 | c2
np.logical_not(c1)         # same as ~c1
np.logical_xor(c1, c2)     # same as c1 ^ c2 (exactly one True)

np.any(c, axis=None)       # or c.any()
np.all(c, axis=None)       # or c.all()
np.array_equal(a, b)       # True if same shape AND same elements
np.allclose(a, b)          # True if equal within a small tolerance (floats)

Function	Parameters	Returns	Default
`np.logical_and/or/xor(x1, x2)`	two boolean (or truthy) arrays	boolean array	—
`np.logical_not(x)`	one array	boolean array	—
`np.any(a, axis)`	array, optional axis	bool, or bool array per axis	`axis=None` (whole array)
`np.all(a, axis)`	array, optional axis	bool, or bool array per axis	`axis=None`
`np.array_equal(a1, a2)`	two arrays	one bool	`equal_nan=False`
`np.allclose(a, b, rtol, atol)`	two arrays, tolerances	one bool	`rtol=1e-05, atol=1e-08`

Example — comparisons give boolean arrays

python

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

virat_more = virat > rohit
print("virat > rohit:", virat_more)
print("dtype        :", virat_more.dtype)
print("Matches Virat won :", virat_more.sum())
print("Fraction won      :", virat_more.mean().round(2))
print("Same score?       :", (virat == rohit).any())
print("Virat >= 50       :", virat >= 50)

text

virat > rohit: [ True  True  True False  True False  True  True False  True False  True
 False  True]
dtype        : bool
Matches Virat won : 9
Fraction won      : 0.64
Same score?       : False
Virat >= 50       : [ True False  True False  True False  True  True False  True False  True
 False  True]

How it works: comparing two arrays of the same shape gives one True/False per match. Because True counts as 1, .sum() counts the wins and .mean() gives the fraction.

Example — logical_and, logical_or, logical_not

python

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

both_50 = np.logical_and(virat >= 50, rohit >= 50)
either_duck = np.logical_or(virat == 0, rohit == 0)
virat_not_50 = np.logical_not(virat >= 50)
only_one_50 = np.logical_xor(virat >= 50, rohit >= 50)

print("Both 50+      :", np.where(both_50)[0] + 1)
print("Either a duck :", np.where(either_duck)[0] + 1)
print("Virat below 50:", virat_not_50.sum(), "matches")
print("Exactly one 50+:", only_one_50.sum(), "matches")
print("Same as & ?   :", np.array_equal(both_50, (virat >= 50) & (rohit >= 50)))

text

Both 50+      : [1 3 7]
Either a duck : [2 6]
Virat below 50: 6 matches
Exactly one 50+: 9 matches
Same as & ?   : True

How it works: the np.logical_* functions and the operators &, |, ~, ^ give identical results on boolean arrays. The functions are easier to read and don't need extra parentheses; the operators are shorter. The match numbers come from np.where(...)[0] + 1.

Example — any and all (with axis)

python

runs = np.array([
    [72, 45, 88, 31, 64,  0],   # Virat
    [56,  0, 74, 88, 32, 45],   # Rohit
    [45, 32,  0, 67, 18, 54],   # Hardik
])
balls = np.array([
    [48, 32, 55, 24, 41,  0],
    [39,  0, 50, 56, 25, 33],
    [31, 24,  0, 46, 15, 38],
])

print("Any duck in the table?   :", np.any(runs == 0))
print("All scores below 100?    :", np.all(runs < 100))
print("Duck per player (axis=1) :", (runs == 0).any(axis=1))
print("All players 30+ (axis=0) :", (runs >= 30).all(axis=0))

text

Any duck in the table?   : True
All scores below 100?    : True
Duck per player (axis=1) : [ True  True  True]
All players 30+ (axis=0) : [ True False False  True False False]

How it works: without axis, any/all look at the whole array and return one bool. With axis=1 you get one answer per row (per player); with axis=0, one answer per column (per match).

Example — comparing whole arrays

python

a = np.array([72, 45, 88])
b = np.array([72, 45, 88])
c = np.array([72, 45, 89])
print("a == b              :", a == b)
print("array_equal(a, b)   :", np.array_equal(a, b))
print("array_equal(a, c)   :", np.array_equal(a, c))
print("array_equal(a, a[:2]):", np.array_equal(a, a[:2]))

x = np.array([0.1 + 0.2])
print("x == 0.3            :", x == 0.3)
print("allclose(x, 0.3)    :", np.allclose(x, 0.3))

text

a == b              : [ True  True  True]
array_equal(a, b)   : True
array_equal(a, c)   : False
array_equal(a, a[:2]): False
x == 0.3            : [False]
allclose(x, 0.3)    : True

How it works: a == b gives an array of answers; np.array_equal gives one answer and also returns False if the shapes differ. Floats are stored in binary, so 0.1 + 0.2 is 0.30000000000000004; use np.allclose to compare floats with a tolerance.

Example — never use `if` on a whole boolean array

python

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

try:
    if virat > 50:
        print("big scores")
except ValueError as e:
    print("ValueError:", e)

if (virat > 50).any():
    print("At least one score above 50")
if not (virat > 0).all():
    print("Not every score is above 0 (there is a duck)")

text

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
At least one score above 50
Not every score is above 0 (there is a duck)

Comparison — operators vs functions vs Python keywords

Python keyword (single values)	NumPy operator (arrays)	NumPy function
`and`	`&`	`np.logical_and`
`or`	`\|`	`np.logical_or`
`not`	`~`	`np.logical_not`
—	`^`	`np.logical_xor`
`if any(...)`	—	`np.any` / `.any()`
`if all(...)`	—	`np.all` / `.all()`

Common mistakes

❌ if arr == other: → ambiguous truth value. ✅ if np.array_equal(arr, other):.
❌ (a > 1) and (b > 1) with arrays. ✅ np.logical_and(a > 1, b > 1) or (a > 1) & (b > 1).
❌ a > 30 & a < 60 without parentheses. ✅ (a > 30) & (a < 60).
❌ Comparing floats with ==. ✅ np.allclose / np.isclose.
❌ Using ~ on an int array expecting "not" — on ints ~ is bitwise NOT (~5 == -6). ✅ Use it only on boolean arrays, or use np.logical_not.

Interview tip

Q: Difference between a == b and np.array_equal(a, b)? A: a == b compares element-wise and returns a boolean array (and may broadcast). np.array_equal returns a single True only if both arrays have the same shape and all elements are equal — use it in if statements and tests.

Key points to remember

Comparison operators (==, !=, >, >=, <, <=) work element-wise and return a boolean array of the same shape.
boolean.sum() counts True values; boolean.mean() gives the fraction that is True.
np.logical_and / logical_or / logical_not / logical_xor combine conditions element-wise (same as & | ~ ^).
np.any asks 'at least one True?', np.all asks 'all True?'; pass axis to get one answer per row or column.
np.array_equal(a, b) returns one bool: True only if shapes AND all elements match.
Use np.allclose (not ==) to compare floating-point arrays.
Never write if arr > x: on an array — use .any() or .all().

Try it yourself

Who scored more, match by match?

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

virat_more = virat > rohit
print(virat_more)
print("Virat outscored Rohit in", virat_more.sum(), "of", virat.size, "matches")
print("Rohit outscored Virat in", (rohit > virat).sum(), "matches")

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

virat_more = virat > rohit
print(virat_more)
print("Virat outscored Rohit in", virat_more.sum(), "of", virat.size, "matches")
print("Rohit outscored Virat in", (rohit > virat).sum(), "matches")

Logical functions and any/all

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

both = np.logical_and(virat >= 50, rohit >= 50)
print("Both scored 50+ in matches:", np.where(both)[0] + 1)
print("Any duck for Rohit?", np.any(rohit == 0))
print("Virat always 10+?", np.all(virat >= 10))

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

both = np.logical_and(virat >= 50, rohit >= 50)
print("Both scored 50+ in matches:", np.where(both)[0] + 1)
print("Any duck for Rohit?", np.any(rohit == 0))
print("Virat always 10+?", np.all(virat >= 10))

array_equal vs ==

import numpy as np

original = np.array([72, 45, 88, 31])
backup = original.copy()
print(original == backup)
print("Identical?", np.array_equal(original, backup))
backup[2] = 87
print("After a typo:", np.array_equal(original, backup))

import numpy as np

original = np.array([72, 45, 88, 31])
backup = original.copy()
print(original == backup)
print("Identical?", np.array_equal(original, backup))
backup[2] = 87
print("After a typo:", np.array_equal(original, backup))

Practice: consistent players

import numpy as np

# 🏏 Your turn: for each player (row), check whether he scored 30+ in EVERY match he batted
# (balls > 0), and whether he scored 80+ in ANY match. Change the limits and Run.
runs = np.array([
    [72, 45, 88, 31, 64,  0],   # Virat
    [56,  0, 74, 88, 32, 45],   # Rohit
    [45, 32,  0, 67, 18, 54],   # Hardik
])
balls = np.array([
    [48, 32, 55, 24, 41,  0],
    [39,  0, 50, 56, 25, 33],
    [31, 24,  0, 46, 15, 38],
])
players = np.array(["Virat", "Rohit", "Hardik"])

always_30 = ((runs >= 30) | (balls == 0)).all(axis=1)   # 'did not bat' doesn't count
any_80 = (runs >= 80).any(axis=1)
print("Always 30+:", players[always_30])
print("Any 80+   :", players[any_80])

import numpy as np

# 🏏 Your turn: for each player (row), check whether he scored 30+ in EVERY match he batted
# (balls > 0), and whether he scored 80+ in ANY match. Change the limits and Run.
runs = np.array([
    [72, 45, 88, 31, 64,  0],   # Virat
    [56,  0, 74, 88, 32, 45],   # Rohit
    [45, 32,  0, 67, 18, 54],   # Hardik
])
balls = np.array([
    [48, 32, 55, 24, 41,  0],
    [39,  0, 50, 56, 25, 33],
    [31, 24,  0, 46, 15, 38],
])
players = np.array(["Virat", "Rohit", "Hardik"])

always_30 = ((runs >= 30) | (balls == 0)).all(axis=1)   # 'did not bat' doesn't count
any_80 = (runs >= 80).any(axis=1)
print("Always 30+:", players[always_30])
print("Any 80+   :", players[any_80])

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Opening notes

Notes NumPyArray Operations

Array OperationsTopic 27 of 777 min read

Comparison & Logical Operations

Compare arrays element by element (==, !=, >, <), combine results with logical_and/or/not, and summarise with any, all and array_equal.

Definition

A comparison operation (==, !=, >, >=, <, <=) compares arrays element-wise and returns a boolean array of the same shape. Logical operations (np.logical_and, np.logical_or, np.logical_not, np.logical_xor — or the operators &, |, ~, ^) combine boolean arrays element-wise. any / all reduce a boolean array to a single answer (or one answer per row/column), and np.array_equal tells you whether two whole arrays are identical.

Boolean array — each element is True or False; dtype bool. It can be used as a mask, counted with .sum(), or averaged with .mean() (= fraction of True).
Element-wise logical — np.logical_and(a, b) is True only where both are True at that position.
Reduce — any = "at least one True?", all = "every element True?".

Syntax

python

a == b    a != b    a > b    a >= b    a < b    a <= b     # b: scalar or array

np.logical_and(c1, c2)     # same as c1 & c2
np.logical_or(c1, c2)      # same as c1 | c2
np.logical_not(c1)         # same as ~c1
np.logical_xor(c1, c2)     # same as c1 ^ c2 (exactly one True)

np.any(c, axis=None)       # or c.any()
np.all(c, axis=None)       # or c.all()
np.array_equal(a, b)       # True if same shape AND same elements
np.allclose(a, b)          # True if equal within a small tolerance (floats)

Function	Parameters	Returns	Default
`np.logical_and/or/xor(x1, x2)`	two boolean (or truthy) arrays	boolean array	—
`np.logical_not(x)`	one array	boolean array	—
`np.any(a, axis)`	array, optional axis	bool, or bool array per axis	`axis=None` (whole array)
`np.all(a, axis)`	array, optional axis	bool, or bool array per axis	`axis=None`
`np.array_equal(a1, a2)`	two arrays	one bool	`equal_nan=False`
`np.allclose(a, b, rtol, atol)`	two arrays, tolerances	one bool	`rtol=1e-05, atol=1e-08`

Example — comparisons give boolean arrays

python

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

virat_more = virat > rohit
print("virat > rohit:", virat_more)
print("dtype        :", virat_more.dtype)
print("Matches Virat won :", virat_more.sum())
print("Fraction won      :", virat_more.mean().round(2))
print("Same score?       :", (virat == rohit).any())
print("Virat >= 50       :", virat >= 50)

text

virat > rohit: [ True  True  True False  True False  True  True False  True False  True
 False  True]
dtype        : bool
Matches Virat won : 9
Fraction won      : 0.64
Same score?       : False
Virat >= 50       : [ True False  True False  True False  True  True False  True False  True
 False  True]

How it works: comparing two arrays of the same shape gives one True/False per match. Because True counts as 1, .sum() counts the wins and .mean() gives the fraction.

Example — logical_and, logical_or, logical_not

python

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

both_50 = np.logical_and(virat >= 50, rohit >= 50)
either_duck = np.logical_or(virat == 0, rohit == 0)
virat_not_50 = np.logical_not(virat >= 50)
only_one_50 = np.logical_xor(virat >= 50, rohit >= 50)

print("Both 50+      :", np.where(both_50)[0] + 1)
print("Either a duck :", np.where(either_duck)[0] + 1)
print("Virat below 50:", virat_not_50.sum(), "matches")
print("Exactly one 50+:", only_one_50.sum(), "matches")
print("Same as & ?   :", np.array_equal(both_50, (virat >= 50) & (rohit >= 50)))

text

Both 50+      : [1 3 7]
Either a duck : [2 6]
Virat below 50: 6 matches
Exactly one 50+: 9 matches
Same as & ?   : True

Example — any and all (with axis)

python

runs = np.array([
    [72, 45, 88, 31, 64,  0],   # Virat
    [56,  0, 74, 88, 32, 45],   # Rohit
    [45, 32,  0, 67, 18, 54],   # Hardik
])
balls = np.array([
    [48, 32, 55, 24, 41,  0],
    [39,  0, 50, 56, 25, 33],
    [31, 24,  0, 46, 15, 38],
])

print("Any duck in the table?   :", np.any(runs == 0))
print("All scores below 100?    :", np.all(runs < 100))
print("Duck per player (axis=1) :", (runs == 0).any(axis=1))
print("All players 30+ (axis=0) :", (runs >= 30).all(axis=0))

text

Any duck in the table?   : True
All scores below 100?    : True
Duck per player (axis=1) : [ True  True  True]
All players 30+ (axis=0) : [ True False False  True False False]

Example — comparing whole arrays

python

a = np.array([72, 45, 88])
b = np.array([72, 45, 88])
c = np.array([72, 45, 89])
print("a == b              :", a == b)
print("array_equal(a, b)   :", np.array_equal(a, b))
print("array_equal(a, c)   :", np.array_equal(a, c))
print("array_equal(a, a[:2]):", np.array_equal(a, a[:2]))

x = np.array([0.1 + 0.2])
print("x == 0.3            :", x == 0.3)
print("allclose(x, 0.3)    :", np.allclose(x, 0.3))

text

a == b              : [ True  True  True]
array_equal(a, b)   : True
array_equal(a, c)   : False
array_equal(a, a[:2]): False
x == 0.3            : [False]
allclose(x, 0.3)    : True

Example — never use `if` on a whole boolean array

python

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

try:
    if virat > 50:
        print("big scores")
except ValueError as e:
    print("ValueError:", e)

if (virat > 50).any():
    print("At least one score above 50")
if not (virat > 0).all():
    print("Not every score is above 0 (there is a duck)")

text

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
At least one score above 50
Not every score is above 0 (there is a duck)

Comparison — operators vs functions vs Python keywords

Python keyword (single values)	NumPy operator (arrays)	NumPy function
`and`	`&`	`np.logical_and`
`or`	`\|`	`np.logical_or`
`not`	`~`	`np.logical_not`
—	`^`	`np.logical_xor`
`if any(...)`	—	`np.any` / `.any()`
`if all(...)`	—	`np.all` / `.all()`

Common mistakes

❌ if arr == other: → ambiguous truth value. ✅ if np.array_equal(arr, other):.
❌ (a > 1) and (b > 1) with arrays. ✅ np.logical_and(a > 1, b > 1) or (a > 1) & (b > 1).
❌ a > 30 & a < 60 without parentheses. ✅ (a > 30) & (a < 60).
❌ Comparing floats with ==. ✅ np.allclose / np.isclose.
❌ Using ~ on an int array expecting "not" — on ints ~ is bitwise NOT (~5 == -6). ✅ Use it only on boolean arrays, or use np.logical_not.

Interview tip

Key points to remember

Comparison operators (==, !=, >, >=, <, <=) work element-wise and return a boolean array of the same shape.
boolean.sum() counts True values; boolean.mean() gives the fraction that is True.
np.logical_and / logical_or / logical_not / logical_xor combine conditions element-wise (same as & | ~ ^).
np.any asks 'at least one True?', np.all asks 'all True?'; pass axis to get one answer per row or column.
np.array_equal(a, b) returns one bool: True only if shapes AND all elements match.
Use np.allclose (not ==) to compare floating-point arrays.
Never write if arr > x: on an array — use .any() or .all().

Try it yourself

Who scored more, match by match?

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

virat_more = virat > rohit
print(virat_more)
print("Virat outscored Rohit in", virat_more.sum(), "of", virat.size, "matches")
print("Rohit outscored Virat in", (rohit > virat).sum(), "matches")

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

virat_more = virat > rohit
print(virat_more)
print("Virat outscored Rohit in", virat_more.sum(), "of", virat.size, "matches")
print("Rohit outscored Virat in", (rohit > virat).sum(), "matches")

Logical functions and any/all

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

both = np.logical_and(virat >= 50, rohit >= 50)
print("Both scored 50+ in matches:", np.where(both)[0] + 1)
print("Any duck for Rohit?", np.any(rohit == 0))
print("Virat always 10+?", np.all(virat >= 10))

import numpy as np

virat = np.array([72, 45, 88, 31, 64,  0, 103, 52, 19, 77, 41, 93, 28, 66])
rohit = np.array([56,  0, 74, 88, 32, 45,  61, 17, 90, 38, 72,  5, 83, 49])

both = np.logical_and(virat >= 50, rohit >= 50)
print("Both scored 50+ in matches:", np.where(both)[0] + 1)
print("Any duck for Rohit?", np.any(rohit == 0))
print("Virat always 10+?", np.all(virat >= 10))

array_equal vs ==

import numpy as np

original = np.array([72, 45, 88, 31])
backup = original.copy()
print(original == backup)
print("Identical?", np.array_equal(original, backup))
backup[2] = 87
print("After a typo:", np.array_equal(original, backup))

import numpy as np

original = np.array([72, 45, 88, 31])
backup = original.copy()
print(original == backup)
print("Identical?", np.array_equal(original, backup))
backup[2] = 87
print("After a typo:", np.array_equal(original, backup))

Practice: consistent players

import numpy as np

# 🏏 Your turn: for each player (row), check whether he scored 30+ in EVERY match he batted
# (balls > 0), and whether he scored 80+ in ANY match. Change the limits and Run.
runs = np.array([
    [72, 45, 88, 31, 64,  0],   # Virat
    [56,  0, 74, 88, 32, 45],   # Rohit
    [45, 32,  0, 67, 18, 54],   # Hardik
])
balls = np.array([
    [48, 32, 55, 24, 41,  0],
    [39,  0, 50, 56, 25, 33],
    [31, 24,  0, 46, 15, 38],
])
players = np.array(["Virat", "Rohit", "Hardik"])

always_30 = ((runs >= 30) | (balls == 0)).all(axis=1)   # 'did not bat' doesn't count
any_80 = (runs >= 80).any(axis=1)
print("Always 30+:", players[always_30])
print("Any 80+   :", players[any_80])

import numpy as np

# 🏏 Your turn: for each player (row), check whether he scored 30+ in EVERY match he batted
# (balls > 0), and whether he scored 80+ in ANY match. Change the limits and Run.
runs = np.array([
    [72, 45, 88, 31, 64,  0],   # Virat
    [56,  0, 74, 88, 32, 45],   # Rohit
    [45, 32,  0, 67, 18, 54],   # Hardik
])
balls = np.array([
    [48, 32, 55, 24, 41,  0],
    [39,  0, 50, 56, 25, 33],
    [31, 24,  0, 46, 15, 38],
])
players = np.array(["Virat", "Rohit", "Hardik"])

always_30 = ((runs >= 30) | (balls == 0)).all(axis=1)   # 'did not bat' doesn't count
any_80 = (runs >= 80).any(axis=1)
print("Always 30+:", players[always_30])
print("Any 80+   :", players[any_80])

Revision Flashcards

1 / 6

Tap a card to flip it and see the answer.

Definition

Syntax

Example — comparisons give boolean arrays

Example — logical_and, logical_or, logical_not

Example — any and all (with axis)

Example — comparing whole arrays

Example — never use if on a whole boolean array

Comparison — operators vs functions vs Python keywords

Common mistakes

Interview tip

Try it yourself

Definition

Syntax

Example — comparisons give boolean arrays

Example — logical_and, logical_or, logical_not

Example — any and all (with axis)

Example — comparing whole arrays

Example — never use if on a whole boolean array

Comparison — operators vs functions vs Python keywords

Common mistakes

Interview tip

Try it yourself

Example — never use `if` on a whole boolean array

Example — never use `if` on a whole boolean array