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Tuesday, May 11, 2021

Matplotlib Tutorial

Matplotlib:

It is a library to create graph plotting in data science.

Import Matplotlib:

Import matplotlib

This line used to import matplotlib.

Find Version:

import matplolib

print(matplotlib.__version__)

This line used to find matplotlib version.

Pyplot:

Most commonly used utility is pyplot.

Import Pyplot:

import matplotlib. pyplot as plt

This line used to import the pyplot module.

Example:

import matplotlib.pyplot as plt

       import numpy as np
       xpoints = np.array([0, 5])
       ypoints = np.array([0, 100])
     plt.plot(xpoints, ypoints)
       plt.show()

Output:

Set Position:

Example:

import matplotlib.pyplot as plt

       import numpy as np
     xpoints = np.array([1, 5])
       ypoints = np.array([4, 10])
       plt.plot(xpoints, ypoints, 'o')

plt.show()

Output:

It shows only the position of the x,y points by using plt.plot(xpoints, ypoints, 'o')

Multiple Points:

import matplotlib.pyplot as plt

           import numpy as np
         xpoints = np.array([2,4,7,9])
           ypoints = np.array([6,3,8,1])
           plt.plot(xpoints, ypoints)
           plt.show()

Output:

Default X Points:

import matplotlib.pyplot as plt

           import numpy as np
           ypoints = np.array([3, 8, 1, 10, 5, 7])
                      plt.plot(ypoints)
           plt.show()

Output:

Markers:

import matplotlib.pyplot as plt

       import numpy as np
       ypoints = np.array([1,4,6,3])
       plt.plot(ypoints, marker = 'o')

plt.show()

Output:

Point the position by the markers code plt.plot(ypoints, marker = 'o')

Marker own symbol:

import matplotlib.pyplot as plt

       import numpy as np
       ypoints = np.array([1,4,6,3])
       plt.plot(ypoints, marker = '*')

plt.show()

Output:

Line Style:

import matplotlib.pyplot as plt

       import numpy as np
     ypoints = np.array([3, 8, 1, 10])
       plt.plot(ypoints, linestyle = 'dotted')
       plt.show()

Output:

We have more line style like this dashed(), dashdot().

Line Color:

import matplotlib.pyplot as plt

       import numpy as np
       ypoints = np.array([3, 8, 1, 10])
       plt.plot(ypoints, color = 'pink')
       plt.show()

Output:

we can use ,ore colors by using plt.plot(ypoints, color = 'pink')

Line Width:

       import matplotlib.pyplot as plt
       import numpy as np
       ypoints = np.array([3, 8, 1, 10])
       plt.plot(ypoints, linewidth = '25.0')
       plt.show()

Output:

Labels:

        import numpy as np
       import matplotlib.pyplot as plt
       x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
       y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])
       plt.plot(x, y)
       plt.title("Income")
       plt.xlabel("Average ")
       plt.ylabel("Days")
       plt.show()

Output:

We can add labels like this.

Grid Lines:

import sys

import matplotlib

matplotlib.use('Agg')

import numpy as np

import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])

y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.title("Income")

plt.xlabel("Average")

plt.ylabel("Days")

plt.plot(x, y)

plt.grid()

plt.show()

Output:

We can add horizontal and vertical lines only by using plt.grid(axis = 'x'),

plt.grid(axis = 'y')

Bar Chart:

import matplotlib.pyplot as plt

       import numpy as np
       x = np.array(["A", "B", "C", "D"])
       y = np.array([3, 8, 1, 10])
       plt.bar(x,y)
       plt.show()

Output:

Histograms:

import sys

import matplotlib

matplotlib.use('Agg')

import matplotlib.pyplot as plt

import numpy as np

x = np.random.normal(150,50,230)

plt.hist(x)

plt.show()

Output:

Pie Chart:

       import matplotlib.pyplot as plt
       import numpy as np
       y = np.array([35, 25, 25, 15])
       plt.pie(y)
       plt.show()

Output:

Sunday, May 9, 2021

Pandas Tutorial

Pandas:

Working with Data sets and Arrays for analyze, clean, and manipulating Data.

Import Pandas:

Coding

Import pandas as pd

Example

Checking pandas version

Coding

import pandas as pd

print(pd.__version__)

This coding used to know about our pandas versions.

Data Frames:

Example 1:

Coding

import pandas as pd

mydataset = { 'TVs': ["Sony", "Samsung", "LG"], 'Passings': [3, 7, 2]

}

myvar = pd.DataFrame(mydataset)

print(myvar)

Output:

Tvs Passings

0 Sony 3

1 Samsung 7

2 LG 2

In this example we set the data's into data frames

pd.DataFrame(mydataset) Used to set data frames

Find Location:

Coding

print(df.loc[1])

Output:

TVs Samsung

            Passings          7
            Name: 1, dtype: object

Read CSV File:

                We use csv files or excel, sheets for the data analysis.

                 These files having all the details of the project what we done.

                Store big data's into this csv files.

Example:

               import pandas as pd

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

print(df.to_string())

Output:

Tvs Passings

0 Sony 3

1 Samsung 7

2 LG 2

3 Panasonic 4

4 Mi 6

5 Sansui 5

In this Example we read the csv file by using pd.read_csv('example.csv').

It will analyse the all data in the csv file and show all the data's.

Read Json File:

Example:

import pandas as pd

df = pd.read_json('Example.json')

print(df.to_string())

Output:

Tvs Passings

0 Sony 3

1 Samsung 7

2 LG 2

3 Panasonic 4

4 Mi 6

5 Sansui 5

In this example we using Json Files.

JavaScript Object Notation(JSON) lightweight format for storing and transporting data.

We can resd the Json files by using pd.read_json('Example.json')

Viewing The Data's

head() Function:

Example:

import pandas as pd

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

print(df.head(3))

Output:

Tvs Passings

0 Sony 3

1 Samsung 7

2 LG 2

head () function used to show first 3 data's of the csv file.

Tail() Function:

import pandas as pd

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

print(df.tail(3))

Output:

3 Panasonic 4

4 Mi 6

5 Sansui 5

In this tail function used show last data's of the csv file.

Cleaning Data:

Data cleaning is the process of remove empty cells, Wrong data's, Wrong format data, Duplicate Data's.

Example:

import pandas as pd

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

       new_df = df.dropna()

       print(new_df.to_string())

dropna() function is used to remove empty cells from our data's.

Wrong format data Correction:

Example:

import pandas as pd

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

   df['Date'] = pd.to_datetime(df['Date'])

   print(df.to_string())

Convert wrong format of data into correct format.

In this exapmle used to convert correct format of date.

Wrong Data Correction:

df.loc[4, 'Duration'] = 8

In this example we correct the data by its location.

Remove Duplicates:

print(df.duplicated())

this line used to find duplicates.

df.drop_duplicates(inplace = True)

this line used to drop duplicates.

Correlation:

df.corr()

this line used to correlate the columns of the datasets.

Numpy Tutorials

1.Array Create

import numpy as np

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

print(a)

Output

[1, 2, 3, 4, 5]

2.2D Array

import numpy as np

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

print(a)

Output

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

3.Array Indexing 1 dimenstional Array

import numpy as np

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

print(a[0])

Output

4.Array Indexing 2D Array

import numpy as np

arr = np.array([[1,2,3], [6,7,8]])

print(a[0, 1])

Output

5.Array Slicing 1D array

import numpy as np

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

print(a[1:2])

Output

6.Array Slicing 1D array Negative Index

import numpy as np

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

print(a[-1])

Output

7.Array Slicing 2D Array

import numpy as np

a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

print(a[0, 2:3])

Output

8.Dtype

import numpy as np

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

print(a.dtype)

Output

int64

9.Dtype String

import numpy as np

a = np.array(['cat', 'dog', 'cherry'])

print(a.dtype)

Output

<U6

10.Copy

import numpy as np

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

x = a.copy()

a[0] = 7

print(a)

print(x)

Output

[7  2  3  4  5]
[1 2 3 4 5]

11.View

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

x = a.view()

a[0] = 7

print(a)

print(x)

Output

[7  2  3  4  5]
[7  2 3 4 5]

12.Array Shape Find

import numpy as np

a = np.array([[1, 2, 3], [5, 6, 7]])

print(a.shape)

Output

(2,3)

13.Array reshape

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

b = a.reshape(2,6)

print(b)

Output

[[ 1  2  3  4  5  6]

[ 7 8 9 10 11 12]]

14. Array For each

import numpy as np

a = np.array([1, 2, 3])

for x in a:

print(x)

Output

1

2

3

15.Array Joining

import numpy as np

a1 = np.array([1, 2, 3])

a2 = np.array([4, 5, 6])

a = np.concatenate((a1, a2))

print(a)

Output

[1 2 3 4 5 6]

16.Array Split

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6])

a1 = np.array_split(a, 3)

print(a1)

Output

[array([1, 2]), array([3, 4]), array([5, 6])]
17.Array Search
import numpy as np

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

x = np.where(a == 1)

print(x)
Output
(array([0]),)
18.Array Sort

import numpy as np

a = np.array([3, 2, 0, 1])

print(np.sort(a))

Output
[0,1,2,3]

19.Data Distribution in Numpy


from numpy import random

x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(20))

print(x)

Output

[7 7 5 7 7 7 5 7 7 7 7 5 3 7 5 7 7 7 5 7]


20.Normal Distribution

from numpy import random

x = random.normal(size=(1,2))

print(x)

Output
[[ 0.9638252  -0.66766091]][[ 0.9638252  -0.66766091]]

21.Binomial Distribution

from numpy import random

x = random.binomial(n=5, p=0.5, size=5)

print(x)

Output

[3,3,2,2,2]

22.Poisson Distribution
from numpy import random

x = random.poisson(lam=2, size=5)

print(x)

Output

[6 1 4 4 2 1 0 1 2 2]
23.chisquare
from numpy import random

x = random.chisquare(df=2, size=(2, 3))

print(x)

Output
[[0.66396936 0.60936515 4.82225736]
 [0.1746385  0.25990412 4.1739717 ]]

Software Training For Free

Tuesday, May 11, 2021

Matplotlib Tutorial

Matplotlib:

Sunday, May 9, 2021

Numpy Tutorials

Python Technical Interview Questions

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