Machine Learning

There are different ways of creating a pandas data frame:

• Creating using list

• Creating using dictionary

• Using arrays

• Using zip()

Creating using list without index:

# Import pandas library 
import pandas as pd 
# initialize list of lists 
data = [['A', 18, 'M'], ['B', 14, 'F'], ['C', 13, 'M']] 
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Gender']) 
print(df) 

Output:

   Name  Age    Gender
0    A   18      M
1    B   14      F
2    C   13      M

Creating using list with index:

# Import pandas library 
import pandas as pd 
# initialize list of lists 
data = [['A', 18, 'M'], ['B', 14, 'F'], ['C', 13, 'M']] 
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Gender'], index = ['row1', 'row2', 'row3']) 
print(df) 

Output:

Name Age Gender row1 A 18 M row2 B 14 F row3 C 13 M

Creating using dictionary:

Creating the data frame using the dictionary, all the array must be of the same length.

import pandas as pd 
# intialise data of lists. 
data = {'Job':['sofware Eng', 'IT consultant', 'Teacher'], 'Age':[27, 29, 30]} 
# Create DataFrame 
df = pd.DataFrame(data) 
# Print the output. 
print(df) 

Output:

             Job  Age
0    sofware Eng   27
1  IT consultant   29
2        Teacher   30

Using arrays:

import pandas as pd 
# initialise data of lists. 
data = {'Name':['A', 'B', 'C', 'D'], 'Grade':['A+','B+', 'A', 'C']} 
# Creates pandas DataFrame. 
df = pd.DataFrame(data, index =['1', '2', '3', '4']) 
print(df) 

Output:

   Name Grade
1    A    A+
2    B    B+
3    C     A
4    D     C

Using zip()

In this merge two lists using list(zip()) function and use.

#using dic
import pandas as pd  
# List1  
Name = ['A', 'B', 'C', 'D']  
# List2  
Age = [22, 18, 32, 27] 
Gender = ['M', 'F', 'M', 'M']
# and merge them by using zip().  
list_tuple = list(zip(Name, Age, Gender))  
# Assign data to tuples.  
list_tuple   
#convert list into the pandas 
df = pd.DataFrame(list_tuple, columns = ['Name', 'Age', 'Gender'])  
# Print data.  
print(df) 

Output:

Name Age Gender 0 A 22 M 1 B 18 F 2 C 32 M 3 D 27 M

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