Python Pandas & NumPy PDF

Python Pandas & Numpy NumPy NumPy is short for "Numerical Python". NumPy is a Python library used for working with arrays. Installation of NumPy C:\Users\Your Name>pip install --upgrade pip C:\Users\Your Name>pip install numpy Import NumPy import numpy arr = numpy.array([1, 2, 3,...

Python Pandas & Numpy NumPy NumPy is short for "Numerical Python". NumPy is a Python library used for working with arrays. Installation of NumPy C:\Users\Your Name>pip install --upgrade pip C:\Users\Your Name>pip install numpy Import NumPy import numpy arr = numpy.array([1, 2, 3, 4, 5]) print(arr) Output: [1 2 3 4 5] NumPy as np NumPy is usually imported under the np alias. import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr) Output: [1 2 3 4 5] NumPy is used to work with arrays. The array object in NumPy is called ndarray. We can create a NumPy ndarray object by using the array() function. Use a tuple to create a NumPy array: import numpy as np arr = np.array((1, 2, 3, 4, 5)) print(arr) Output: [1 2 3 4 5] 1-D Arrays Create a 1-D array containing the values 1,2,3,4,5: import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr) Output: [1 2 3 4 5] Access Array Elements Get the first element from the following array: import numpy as np arr = np.array([1, 2, 3, 4]) print(arr) Get the second element from the following array. import numpy as np arr = np.array([1, 2, 3, 4]) print(arr) 2-D Arrays Create a 2-D array containing two arrays with the values 1,2,3 and 4,5,6: import numpy as np arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr) Output: [[1 2 3] [4 5 6]] Access Array Elements import numpy as np arr = np.array([[1,2,3,4,5], [6,7,8,9,10]]) print('2nd element on 1st row: ', arr[0, 1]) Output: 2nd element on 1st dim: 2 3-D arrays Create a 3-D array with two 2-D arrays, both containing two arrays with the values 1,2,3 and 4,5,6: import numpy as np arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) print(arr) Output: [[[1 2 3] [4 5 6]] [[1 2 3] [4 5 6]]] Access Array Elements Access the third element of the second array of the first array: import numpy as np arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) print(arr[0, 1, 2]) Output: 6 NumPy Array Slicing Slicing in python means taking elements from one given index to another given index. We pass slice instead of index like this: [start:end]. We can also define the step, like this: [start:end:step]. If we don't pass start its considered 0 If we don't pass end its considered length of array in that dimension If we don't pass step its considered 1 NumPy Array Slicing Slice elements from index 1 to index 5 from the following array: import numpy as np arr = np.array([1, 2, 3, 4, 5, 6, 7]) print(arr[1:5]) [2 3 4 5]  Slice elements from index 4 to the end of the array: import numpy as np arr = np.array([1, 2, 3, 4, 5, 6, 7]) print(arr[4:]) [5 6 7] Slice elements from the beginning to index 4 (not included): import numpy as np arr = np.array([1, 2, 3, 4, 5, 6, 7]) print(arr[:4]) [1 2 3 4] import numpy as np arr = np.array([1, 2, 3, 4, 5, 6, 7]) print(arr[-3:-1]) [5, 6] Return every other element from index 1 to index 5: import numpy as np arr = np.array([1, 2, 3, 4, 5, 6, 7]) print(arr[1:5:2]) [2 4]  From the second element, slice elements from index 1 to index 4 (not included): import numpy as np arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) print(arr[1, 1:4]) [7 8 9] From both elements, slice index 1 to index 4 (not included), this will return a 2-D array: import numpy as np arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) print(arr[0:2, 1:4]) [[2 3 4] [7 8 9]] What is Pandas? Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. Reg.no Std Name Class 101 Ram 3 102 Kumar 2 103 Sam 3 Installation of Pandas Install it using this command: C:\Users\Your Name>pip install --upgrade pip C:\Users\Your Name>pip install pandas DataFrames Data sets in Pandas are usually multi-dimensional tables, called DataFrames. Series is like a column, a DataFrame is the whole table. Example : Create a DataFrame from two Series: import pandas as pd data = { "calories": [420, 380, 390], "duration": [50, 40, 45] } myvar = pd.DataFrame(data) print(myvar) What is a Series? A Pandas Series is like a column in a table. It is a one-dimensional array holding data of any type. Create a simple Pandas Series from a list: import pandas as pd a = [1, 7, 2] myvar = pd.Series(a) print(myvar) 0 1 1 7 2 2 dtype: int64 Import Pandas Pandas is usually imported under the pd alias. import pandas as pd mydataset = { 'cars': ["BMW", "Volvo", "Ford"], 'passings': [3, 7, 2] } myvar = pd.DataFrame(mydataset) print(myvar) cars passings 0 BMW 3 1 Volvo 7 2 Ford 2 Labels If nothing else is specified, the values are labeled with their index number. First value has index 0, second value has index 1 etc. This label can be used to access a specified value. print(myvar) With the index argument, you can name your own labels. Create your own labels: import pandas as pd a = [1, 7, 2] myvar = pd.Series(a, index = ["x", "y", "z"]) print(myvar) Labels You can also use a key/value object, like a dictionary, when creating a Series. Example : Create a simple Pandas Series from a dictionary: import pandas as pd calories = {"day1": 420, "day2": 380, "day3": 390} myvar = pd.Series(calories) print(myvar) Labels To select only some of the items in the dictionary, use the index argument and specify only the items you want to include in the Series. Create a Series using only data from "day1" and "day2": import pandas as pd calories = {"day1": 420, "day2": 380, "day3": 390} myvar = pd.Series(calories, index = ["day1", "day2"]) print(myvar) Access Series element import pandas as pd a = [1, 7, 2] myvar = pd.Series(a) print(myvar) Output: 1  Write a NumPy program to convert a list of numeric values into a one-dimensional NumPy array. Expected Output: Original List: [12.23, 13.32, 100, 36.32] One-dimensional NumPy array: [ 12.23 13.32 100. 36.32] Write a NumPy program to create an array with values ranging from 12 to 38. Expected Output: [12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37]  Write a NumPy program to create an 8x8 matrix and fill it with a checkerboard pattern. Checkerboard pattern: [[0 1 0 1 0 1 0 1].......... [0 1 0 1 0 1 0 1] [1 0 1 0 1 0 1 0]]