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Day 23

Data Analitics class

  • To draw a scatter chart we use plt.scatter()
  • To draw a pie chart we use plt.pie()
plt.pie(runs,labels=player,explode=exp,shadow=True,autopct='%1.1%')
plt.grid(color='green')
plt.show()
  • CSV (Comma Seperated Values)
  • Pandas is a liberary used for data processing.
  • To read a csv file in pandas
  • Here data readed by pandas is called as dataframe.
  • Data Frame is a structure developed by pandas it converts any type of data to pandas data frame using read_csv.
import pandas as pd
df = pd.read_csv('com.csv')
df1 = pd.read_csv('http://13.234.66.67/summer19/datasets/com.csv')
  • Now if I need to see the structure of use the function df.info()
df.info()
  • Now If I need to print all the data
print(df)
  • Now If I say I just need top 3 rows then use head function
df.head(3)
  • Now If I say I just need bottom 3 rows then use tail function
df.tail(3)
  • Now if we need a specific column or such you can specify the column name in df
df['Name']
  • For selecting rows and columns we have a function called
df.iloc[0:,0]
  • To get max min avg mean value from a column
df['Salary'].max()
df['Salary'].min()
df['Salary'].mean()
df['Salary'].avg()

NumPy Numerical Python

  • Tensorflow is the advanced version of NumPy.
  • The data would be atleast in two dimensions and can be in 100's of Dimensions.
  • To install numpy we use
pip install numpy
  • To use numpy we import it with a name to short the module name
import numpy as np
  • We can create array in numpy by calling a function only.
a = np.array([1,2,45,35,45])
  • We can print an array just by using the variable name or we can print a specific value also.
print(a)
print(a[0])
[ 2  7  1 99  4]
2
  • We can also perform operations on arrays
a*2
a**2
a+b
  • We can create multidimensional arrays just by adding list brackets inside the list.
x = np.array([[2,5,8],[3,6,1]])
[[2 5 8]
 [3 6 1]]
  • Now we have a full table and now we can access it by using matrix row x column
  • When we use x+7 it basically adds 7 in every element of the array.
  • If I want a specific row just use the 1st row index in array.
x[0]
[2 5 8]
  • We can get the shape size or matrix dimensions by calling the shape() function with the matrix variable.
x.shape()
  • To get specific number of rows we use slicing tool
z[0:5]

Diabetic Data

#from sklearn.datasets import load_diabetes import pandas as pd

data = pd.read_csv('http://13.234.66.67/summer19/datasets/diabetest.csv')

now printing schema of data

data.info()

Describing the data

data.describe()

printing original data top 5 columns

data.head(5)

plot a particular column with count using seaborn

import seaborn as sb sb.countplot(data['Pregnancies'])

sb.countplot(df['Glucose'])

data.hist(figsize=(15,20))

sb.pairplot(data)

Extract Attribute from Data Frame

features = data.iloc[:,0:8].values

Extract Label from Data Frame

label = data.iloc[:,8].values label

label.shape

DAY 15

MACHINE LEARNING

Notes

  • Data is all about business these days
  • combination of science and data to generate and valuabe data to be used in Business is DATA SCIENCE
  • Python cannot process more then 10GB data at once
  • Machine learning is logical analysis of data to accurately do your work
  • AI is a kind of project management (e.g, autodriving of car)
  • Matlab is a mixture of numpy,pandas,seaborn,matplotlib
  • Collection of homogenous values is array
  • slashlinuxcode.blogspot.com use to study about numpy

Data Science

  • In this domain we have:
  1. Bigdata -> domain to store and process , very large amount of data
  2. Machine Learning - > an sorted data is taken from bigdata to be used

Machine Learning

  • A branch in which we teach something to ML and it keeps on learning using logical approach and datasets available to it

  • it will be a communication between user and machine in the language of maths

  • Libraries to be used mostly to deal with data and visualize it :

    1. Numpy
    2. Pandas
    3. SeaBorn
    4. Matplotlib

  • basic data structures used :

    1. list
    2. tuple
    3. for
    4. set

NUMPY

  • Numerical Python or numpy
  • library to use numeric data
  • its advanced version is Tensor Flow
  • stores and process data in the form of array

Data Processing and Visualization

  • its better to represent the data in graphical form rather then numerical values
  • Libraries popularly used for Visualization are:
    1. MPLD3
    2. matplotlib
    3. Seaborn
    4. gnuplot
    5. Dash

MATPLOTLIB

  • its an open source project and we can be contribute in it
  • to delete a module which is imported use
del module-name