{ "data": { "question": { "questionId": "1183", "questionFrontendId": "1093", "boundTopicId": null, "title": "Statistics from a Large Sample", "titleSlug": "statistics-from-a-large-sample", "content": "
You are given a large sample of integers in the range [0, 255]
. Since the sample is so large, it is represented by an array count
where count[k]
is the number of times that k
appears in the sample.
Calculate the following statistics:
\n\nminimum
: The minimum element in the sample.maximum
: The maximum element in the sample.mean
: The average of the sample, calculated as the total sum of all elements divided by the total number of elements.median
:\n\tmedian
is the middle element once the sample is sorted.median
is the average of the two middle elements once the sample is sorted.mode
: The number that appears the most in the sample. It is guaranteed to be unique.Return the statistics of the sample as an array of floating-point numbers [minimum, maximum, mean, median, mode]
. Answers within 10-5
of the actual answer will be accepted.
\n
Example 1:
\n\n\nInput: count = [0,1,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\nOutput: [1.00000,3.00000,2.37500,2.50000,3.00000]\nExplanation: The sample represented by count is [1,2,2,2,3,3,3,3].\nThe minimum and maximum are 1 and 3 respectively.\nThe mean is (1+2+2+2+3+3+3+3) / 8 = 19 / 8 = 2.375.\nSince the size of the sample is even, the median is the average of the two middle elements 2 and 3, which is 2.5.\nThe mode is 3 as it appears the most in the sample.\n\n\n
Example 2:
\n\n\nInput: count = [0,4,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\nOutput: [1.00000,4.00000,2.18182,2.00000,1.00000]\nExplanation: The sample represented by count is [1,1,1,1,2,2,2,3,3,4,4].\nThe minimum and maximum are 1 and 4 respectively.\nThe mean is (1+1+1+1+2+2+2+3+3+4+4) / 11 = 24 / 11 = 2.18181818... (for display purposes, the output shows the rounded number 2.18182).\nSince the size of the sample is odd, the median is the middle element 2.\nThe mode is 1 as it appears the most in the sample.\n\n\n
\n
Constraints:
\n\ncount.length == 256
0 <= count[i] <= 109
1 <= sum(count) <= 109
count
represents is unique.Compiled with clang 11
using the latest C++ 17 standard.
Your code is compiled with level two optimization (-O2
). AddressSanitizer is also enabled to help detect out-of-bounds and use-after-free bugs.
Most standard library headers are already included automatically for your convenience.
\"], \"java\": [\"Java\", \" OpenJDK 17
. Java 8 features such as lambda expressions and stream API can be used.
Most standard library headers are already included automatically for your convenience.
\\r\\nIncludes Pair
class from https://docs.oracle.com/javase/8/javafx/api/javafx/util/Pair.html.
Python 2.7.12
.
Most libraries are already imported automatically for your convenience, such as array, bisect, collections. If you need more libraries, you can import it yourself.
\\r\\n\\r\\nFor Map/TreeMap data structure, you may use sortedcontainers library.
\\r\\n\\r\\nNote that Python 2.7 will not be maintained past 2020. For the latest Python, please choose Python3 instead.
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\\r\\n\\r\\nFor hash table operations, you may use uthash. \\\"uthash.h\\\" is included by default. Below are some examples:
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Your code is compiled with debug flag enabled (/debug
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Node.js 16.13.2
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Your code is run with --harmony
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lodash.js library is included by default.
\\r\\n\\r\\nFor Priority Queue / Queue data structures, you may use datastructures-js/priority-queue and datastructures-js/queue.
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Go 1.17.6
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