Adding important python libraries codeiiest-dev#3

Author: aasthasingh73

src/MachineLearningAlgorithms/IMPORTANT_PYTHON_LIBRARIES/1.PNG

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+# Important Python Libraries
+
+## Introduction
+
+Python machine learning libraries have grown to become the most preferred language for machine learning algorithm implementations. Let’s have a look at the main Python libraries used for machine learning. 1. Some important properties of the function are:
+
+- [NumPy](./numpy.md)
+- [Matplotlib](./matplotlib.md)
+- [Pandas](./pandas.md)
+- [Scikit-learn](./Scikit-learn.md)
+- [Scipy](./SciPy.md)

src/MachineLearningAlgorithms/IMPORTANT_PYTHON_LIBRARIES/2.PNG

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+# 2. Matplotlib
+
+Matplotlib is one of the most powerful tools for ***data visualization*** in Python.
+
+***Data visualization*** the practice of visualizing data in graphs, icons, presentations and more. It is most commonly used to translate complex data into digestible insights for a non-technical audience.
+
+So basically it allows us to create figures and plots, and makes it very easy to produce static raster or vector files without the need for any GUIs.
+
+**Import Syntax**
+```py
+import matplotlib.pyplot as plt
+```
+
+### **Functional Approach**
+Using the basic Matplotlib command, we can easily create a plot. Let’s plot an example using two arrays x and y :
+
+```py
+#create data for plotting
+x = [0,1,2,3,4,5]
+y = [0,1,4,9,16,25]
+#the default graph style for plot is a line
+plt.plot(x, y)
+#display the graph
+plt.show
+```
+Output : 
+
+![title](Capture.PNG)
+
+Now that we have a plot, let’s go on to name the x-axis, y-axis, and add a title using .xlabel(), .ylabel() and .title() using:
+
+```py
+plt.title('First Plot')
+plt.xlable('x-axis')
+plt.ylable('y-axis')
+```
+
+### **Bar Graphs**
+
+When using a bar graph, the change in code will be from plt.plot() to plot.bar() changes it into a bar chart.
+
+```py
+#create data for plotting
+x = [5,6,3,7,2]
+y = ["A", "B", "C", "D", "E"]
+plt.bar(y,x, color = "green")
+plt.show()
+```
+Output :
+
+![title](1.PNG)
+
+Graph can also be flipped horizontally by adding an "h" after bar.
+```py
+#create data for plotting
+x = [5,6,3,7,2]
+y = ["A", "B", "C", "D", "E"]
+plt.barh(y,x, color = "yellowgreen")
+plt.show()
+```
+Output :
+
+![title](2.PNG)
+
+
+### **Scatter Plot**
+A scatter plot is a diagram where each value in the data set is represented by a dot.
+
+```py
+#create data for plotting
+x_values = [0,1,2,3,4,5]
+squares = [0,1,4,9,16,25]
+plt.scatter(x_values,squares, s=10, color = "pink")
+plt.show()
+```
+Output:
+
+![title](3.PNG)
+
+### **Histograms**
+
+A histogram is basically used to represent data provided in a form of some groups.It is accurate method for the graphical representation of numerical data distribution.It is a type of bar plot where X-axis represents the bin ranges while Y-axis gives information about frequency.
+
+***Bin*** is an argument specific to a histogram and allows the user to customize how many bins they want.
+
+```py
+x = [2,1,6,4,2,4,8,9,4,2,4,10,6,4,5,7,7,3,2,7,5,3,5,9,2,1]
+#plot for a histogram
+plt.hist(x, bins = 10, color='blue', alpha=0.5)
+plt.show()
+```
+Output :
+
+![title](4.PNG)
+
+## References
+- [More on](http://index-of.co.uk/Tutorials/Matplotlib%20for%20Python%20Developers.pdf)

src/MachineLearningAlgorithms/IMPORTANT_PYTHON_LIBRARIES/3.PNG

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+# 1. NumPy
+
+NumPy is a well known general-purpose array-processing package. Used for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions.NumPy is very useful for handling linear algebra, Fourier transforms, and random numbers.
+
+**Import Syntax**
+```py
+import numpy as np
+
+#Creating an array object
+array = np.array([
+    [1, 2, 3, 5, 6],
+    [2, 1, 5, 6, 7]
+])
+```
+
+**Important  Function**
+```py
+#Print array dimension using array.ndim
+print("No. of dimensions of the array: ", array.ndim, end="\n")
+
+#Print array size using array.size
+print("Size of the array: ", array.size, end="\n")
+
+#Print array shape
+print("Shape of the array: ", array.shape)
+```
+Output : 
+```
+No. of dimensions of the array: 2
+Size of the array: 10
+Shape of the array: (2, 5) 
+```
+
+### **Creating NumPy Arrays**
+
+Arrays in NumPy can be created in multiple ways, with various number of Ranks, defining the size of the Array.
+
+```py
+#Creating a rank 1 array by passing one python list
+list = [1, 2, 3, 5, 6]
+array = np.array(list)
+print(array)
+```
+Output :
+```
+[1 2 3 5 6]
+```
+```py
+#Creating a rank 2 array by passing two python lists
+list1 = [1, 2, 3, 5, 6]
+list2 = [3, 1, 4, 5, 1]
+array = np.array(
+    [list1, list2]
+)
+print(array)
+```
+Output :
+```
+[[1 2 3 5 6]
+[3 1 4 5 1]]
+```
+```py
+#Creating a constant value array of complex type using np.full( )
+array = np.full((2, 2), 4, dtype='complex')
+print(array)
+```
+Output :
+```
+[[4.+0.j 4.+0.j ]
+[4.+0.j 4.+0.j ]]
+```
+### **Basic slicing and indexing in a multidimensional array**
+Slicing and indexing in a multidimensional array are a little bit tricky compared to slicing and indexing in a one-dimensional array.
+```py
+array = np.array([
+    [2, 4, 5, 6],
+    [3, 1, 6, 9],
+    [4, 5, 1, 9],
+    [2, 9, 1, 7]
+])
+
+# Slicing and indexing in 4x4 array
+# Print first two rows and first two columns
+print("\n", array[0:2, 0:2])
+
+# Print all rows and last two columns
+print("\n", array[:, 2:4])
+
+# Print all column but middle two rows
+print("\n", array[1:3, :])
+```
+Output:
+```
+[[2 4]
+[3 1]]
+
+[[5 6]
+[6 9]
+[1 9]
+[1 7]]
+
+[[3 1 6 9]
+[4 5 1 9]]
+```
+### **NumPy Operations on Array**
+
+In NumPy, arrays allow various operations that can be performed on a particular array or a combination of Arrays.These operations  include some basic Mathematical operations as well as Unary and Binary operations.
+
+Examples :
+
+```py
+array = np.array([
+    [2, 4, 5],
+    [3, 1, 6]
+])
+
+#Adding 5 to every element in array1
+newArray = array + 5
+print(newArray)
+```
+Output :
+```
+[[ 7 9 10]
+[ 8 6 11]]
+```
+```py
+#Multiplying 2 to every element in array2
+newArray = array * 2
+print(newArray)
+```
+Output:
+```
+[[ 4 8 10]
+[ 6 2 12]]
+```
+## References
+- [More on](https://numpy.org/doc/)