Practice with Pandas, Numpy, Scikit-learn

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 200)
y = np.sin(x)

fig, ax = plt.subplots()
ax.plot(x, y)
plt.show()

Following this guide for python

import pandas as pd # setting pd as alias

mydataset = { #  table data
  'cars': ["BMW", "Volvo", "Ford"],
  'passings': [3, 7, 2]
}

a = [1, 7, 2]

myarray = pd.Series(a)

mytable = pd.DataFrame(mydataset) # assigning dataset as table to be printed

print(myarray)

print(myarray[0]) # indexing

print(mytable)

0    1
1    7
2    2
dtype: int64
1
    cars  passings
0    BMW         3
1  Volvo         7
2   Ford         2

import pandas as pd # how to print csv file

df = pd.read_csv('data.csv')

print(df)

df = pd.read_json('data.json') # how to read json file

print(df.to_string())

NumPy is a library used to manage arrays

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

# arrays can have any # of dimensions <= 0

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

print(arr)

print(arr.ndim) # ndim checks dimensions of array

print(arr[0, 1]) # index order: row then column

[1 2 3 4 5]
[[[1 2 3]
  [4 5 6]]

 [[1 2 3]
  [4 5 6]]]
3
[4 5 6]