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**How to crop an array in Python?**

To crop an array in Python, you can use slicing operations. For instance, suppose you possess a 1-dimensional numpy array called "arr," and you aim to extract a subset containing elements ranging from index 3 to index 7. You can achieve this as follows: `cropped_array = arr[3:8]`. This operation generates a fresh array that encompasses only the elements from the original array, spanning indices 3 through 7 (note that the 8th index is not included).

**How do you make a 3d space in Python?**

To make a 3D space in Python, you can use libraries such as Matplotlib, Plotly, and Mayavi. These libraries offer functions for plotting and visualizing 3D data, such as scatter plots, surface plots, and volume renderings. You can plot data in 3D by defining the x, y, and z coordinates of each point and then plotting them in a 3D coordinate system.

**How does Python calculate series?**

In Python, you can compute a series by adding up a sequence of numbers, like a list or a range of values. The built-in sum function facilitates this calculation. Furthermore, for statistical analysis of a series, you can turn to the NumPy library, which offers functions like mean, median, and std. These functions simplify the process of examining and comprehending the underlying patterns within your data.

**How to use Getattribute in Python?**

The getattribute method in Python is used to retrieve the value of a named attribute of an object. This method necessitates two parameters: the object from which you intend to extract the attribute and a string that signifies the name of the desired attribute. It facilitates dynamic access to object attributes, proving beneficial when you need to retrieve an object's attributes dynamically, depending on variable values or specific conditions. The syntax is * object.getattribute(attribute)*.

**How do you fit data in the Gaussian distribution in Python?**

To model data according to a Gaussian distribution in Python, you have several options. One approach is to utilize libraries like scipy.stats, employing the scipy.stats.norm.fit method. This method fits the data to a Gaussian distribution and provides the mean and standard deviation. You can subsequently visualize the data alongside the Gaussian distribution through matplotlib. Alternatively, the numpy library offers another method, allowing you to calculate the data's histogram using numpy.histogram and then fit it to a Gaussian distribution using numpy.polyfit.

**How to return an integer array in python?**

To return an integer array in Python, you can use the numpy library and the "astype" method. This method allows you to convert an existing array into a new array of a specified data type, in this case int.

**How to combine bytes in Python?**

In Python, you can combine bytes using the b notation to denote a byte string and the + operator to concatenate two or more byte strings. To convert a string to a byte string, use the bytes() function and specify the encoding, such as UTF-8. Example: b"Hello" + b"World" will return the combined byte string b'HelloWorld'.

**How to get output as a list in Python?**

In Python, the output can be returned as a list by using the list() function or by putting the elements in square brackets []. The list can be created from any iterable object, such as a string, tuple, or another list. The list() function takes the iterable as an argument and returns the list. For example, list("Hello") returns ['H', 'e', 'l', 'l', 'o'].

**How to plot x/y z in Python?**

Plotting x/y/z in Python can be done using a 3D plotting library such as Matplotlib or Plotly. To plot 3D data in Python, you need to import the appropriate library, create a figure and axis object, and then plot the data. The plot can be customized with different styles, color maps, labels, and other visual elements to represent the data effectively. To plot x, y, and z, you need to specify the 3 dimensions as arrays and then plot them using the plot3D function in the library.

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**Code for:** Reverse Polish Notation

**Solution:**

```
class stack:
def __init__(self):
self.s = []
def pop(self):
top = self.s[0]
self.s = self.s[1:]
return top
def push(self,item):
self.s = [item] + self.s
return self
def empty(self):
return self.s == []
def peak(self):
return self.s[0]
def __str__(self):
return str(self.s)
class calc:
def __init__(self):
self.data = ""
self.work()
def work(self):
stk = stack()
operations = ['+','-','*','/','x^y']
while True:
token = input()
if token=='q':
print('Terminated')
break
if token=='c':
print('Staring new calculation')
stk.empty()
continue
if token not in operations:
stk.push(float(token))
else:
b = stk.pop()
a = stk.pop()
if token == "+": stk.push(a+b)
if token == "-": stk.push(a-b)
if token == "*": stk.push(a*b)
if token == "/": stk.push(a/b)
if token == "x^y": stk.push(a**b)
print(stk.peak())
x = calc()
```

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