How do you find the max and min of an array in python?
Python examples to find the largest (or the smallest) item in a collection (e.g. list, set or array) of comparable elements using max() and min() methods. Show
1. Python max() function
1.1. Find largest integer in array>>> nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2] >>> max( nums ) 42 #Max value in array 1.2. Find largest string in array>>> blogName = ["how","to","do","in","java"] >>> max( blogName ) 'to' #Largest value in array 1.3. Find max key or valueA little complex structure. >>> prices = { 'how': 45.23, 'to': 612.78, 'do': 205.55, 'in': 37.20, 'java': 10.75 } >>> max( prices.values() ) 612.78 >>> max( prices.keys() ) #or max( prices ). Default is keys(). 'to' 2. Python min() functionThis function is used to –
2.1. Find lowest integer in array>>> nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2] >>> min( nums ) -4 #Min value in array 2.2. Find smallest string in array>>> blogName = ["how","to","do","in","java"] >>> min( blogName ) 'do' #Smallest value in array 2.3. Find min key or valueA little complex structure. >>> prices = { 'how': 45.23, 'to': 612.78, 'do': 205.55, 'in': 37.20, 'java': 10.75 } >>> min( prices.values() ) 10.75 >>> min( prices.keys() ) #or min( prices ). Default is keys(). 'do' Happy Learning !! Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question. Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the "typical" values in a dataset, but other aggregates are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.). NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and demonstrate some
of them here. As a quick example, consider computing the sum of all values in an array. Python itself can do this using the built-in In [2]: The syntax is quite similar to
that of NumPy's However, because it executes the operation in compiled code, NumPy's version of the operation is computed much more quickly: In [4]: big_array = np.random.rand(1000000) %timeit sum(big_array) %timeit np.sum(big_array) 10 loops, best of 3: 104 ms per loop 1000 loops, best of 3: 442 µs per loop Be careful, though: the Minimum and Maximum¶Similarly, Python has built-in In [5]: min(big_array), max(big_array) Out[5]: (1.1717128136634614e-06, 0.9999976784968716) NumPy's corresponding functions have similar syntax, and again operate much more quickly: In [6]: np.min(big_array), np.max(big_array) Out[6]: (1.1717128136634614e-06, 0.9999976784968716) In [7]: %timeit min(big_array) %timeit np.min(big_array) 10 loops, best of 3: 82.3 ms per loop 1000 loops, best of 3: 497 µs per loop For In [8]: print(big_array.min(), big_array.max(), big_array.sum()) 1.17171281366e-06 0.999997678497 499911.628197 Whenever possible, make sure that you are using the NumPy version of these aggregates when operating on NumPy arrays! Multi dimensional aggregates¶One common type of aggregation operation is an aggregate along a row or column. Say you have some data stored in a two-dimensional array: In [9]: M = np.random.random((3, 4)) print(M) [[ 0.8967576 0.03783739 0.75952519 0.06682827] [ 0.8354065 0.99196818 0.19544769 0.43447084] [ 0.66859307 0.15038721 0.37911423 0.6687194 ]] By default, each NumPy aggregation function will return the aggregate over the entire array: Aggregation functions take an additional argument specifying the axis along which the aggregate is computed. For example, we can find the minimum value within each column by specifying Out[11]: array([ 0.66859307, 0.03783739, 0.19544769, 0.06682827]) The function returns four values, corresponding to the four columns of numbers. Similarly, we can find the maximum value within each row: Out[12]: array([ 0.8967576 , 0.99196818, 0.6687194 ]) The way the axis is specified here can be confusing to users coming from other languages. The Other aggregation functions¶NumPy provides many other aggregation functions, but we won't discuss them in detail here. Additionally,
most aggregates have a The following table provides a list of useful aggregation functions available in NumPy:
We will see these aggregates often throughout the rest of the book. Example: What is the Average Height of US Presidents?¶Aggregates available in NumPy can be extremely useful for summarizing a set of values. As a simple example, let's consider the heights of all US presidents. This data is available in the file president_heights.csv, which is a simple comma-separated list of labels and values: In [13]: !head -4 data/president_heights.csv
order,name,height(cm) 1,George Washington,189 2,John Adams,170 3,Thomas Jefferson,189 We'll use the Pandas package, which we'll explore more fully in Chapter 3, to read the file and extract this information (note that the heights are measured in centimeters). In [14]: import pandas as pd data = pd.read_csv('data/president_heights.csv') heights = np.array(data['height(cm)']) print(heights) [189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183 177 185 188 188 182 185] Now that we have this data array, we can compute a variety of summary statistics: In [15]: print("Mean height: ", heights.mean()) print("Standard deviation:", heights.std()) print("Minimum height: ", heights.min()) print("Maximum height: ", heights.max()) Mean height: 179.738095238 Standard deviation: 6.93184344275 Minimum height: 163 Maximum height: 193 Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values. We may also wish to compute quantiles: In [16]: print("25th percentile: ", np.percentile(heights, 25)) print("Median: ", np.median(heights)) print("75th percentile: ", np.percentile(heights, 75)) 25th percentile: 174.25 Median: 182.0 75th percentile: 183.0 We see that the median height of US presidents is 182 cm, or just shy of six feet. Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in Chapter 4). For example, this code generates the following chart: In [17]: %matplotlib inline import matplotlib.pyplot as plt import seaborn; seaborn.set() # set plot style In [18]: plt.hist(heights) plt.title('Height Distribution of US Presidents') plt.xlabel('height (cm)') plt.ylabel('number'); These aggregates are some of the fundamental pieces of exploratory data analysis that we'll explore in more depth in later chapters of the book. How do you find the max of an array in Python?The max() Function — Find the Largest Element of a List. In Python, there is a built-in function max() you can use to find the largest number in a list. To use it, call the max() on a list of numbers. It then returns the greatest number in that list.
How do you find the maximum and minimum of an array?An efficient approach using single loop: loop Increment by 2. Declare the max and min variables and check for the array size:. If odd, initialize min and max to the first element.. If even, compare the elements and set min to the smaller value and max to the larger value. ... . Now traverse the array and pick elements in pairs.. How do you find the max and min of a NumPy array in Python?numpy. amax() will find the max value in an array, and numpy. amin() does the same for the min value.
What is MAX () and MIN ()?The min is simply the lowest observation, while the max is the highest observation. Obviously, it is easiest to determine the min and max if the data are ordered from lowest to highest. So for our data, the min is 13 and the max is 110.
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