Understanding Variables in Python 3.0 for Beginners

 

Here are different ways to create a dataframe in PYTHON USING PANDAS LIBRARY 

code

In [2]:

# from a Python lis

# Import the library - in this case pandas
import pandas as pd 
  
# create and initialize a list of multiple lists 
multilist = [['george', 2], ['peter', 25], ['alice', 24]] 
  
# Create DataFrame 
df = pd.DataFrame(multilist, columns = ['First Name', 'Age']) 
  
# print (show) dataframe. 
df 

Out[2]:

	First Name	Age
0	george	2
1	peter	25
2	alice	24

In [3]:

# Method #2: Creating DataFrame from dict of an narray/lists

# To create DataFrame from a python dictionary of narray/list, all the narray must be of same length. 
# If index is passed then the length index should be equal to the length of arrays. If no index is passed, then by default, index will 
# be range(n) where n is the array length.

In [3]:

# DataFrame from dict narray / lists  
# Addresses by default 
  
import pandas as pd 
  
# intialise data of lists. 
data = {'Last Name':['George', 'Peter', 'Alice', 'John'], 
        'Age':[33, 54, 9, 82],
       'School':['VT', 'NYC', 'UCLA', 'GM']} 
  
# Create DataFrame 
df = pd.DataFrame(data) 
  
# Print the output. 
df 

Out[3]:

	Last Name	Age	School
0	George	33	VT
1	Peter	54	NYC
2	Alice	9	UCLA
3	John	82	GM

In [6]:

Here we create an indexes DataFrame using arrays.

In [8]:

# pandas DataFrame with indexed by DataFrame using arrays. 

import pandas as pd 
  
# initialise data of lists. 
data = {' Last Name':['George', ')Peter', 'Alice', 'John'], 
        'Points':[39, 55,22, 95],
       'School':['VT', 'NYC','UCLA','GM']} 
  
# Creates pandas DataFrame. 
df = pd.DataFrame(data, index =['Position1', 
                                'Position2', 
                                'Position3', 
                                'Position4']) 
  
# print (show) the data 
df 

Out[8]:

	Name	marks	School
rank1	Tom	99	VT
rank2	Jack	98	NYC
rank3	nick	95	NYC
rank4	juli	90	UCLA

In [9]:

# Create a Dataframe from list of dictionaries (multiple dictionaries)

# Pandas DataFrame can be created by passing lists of dictionaries as a input data. 
# By default dictionary keys are consider the columns columns and the values are the rows.

In [4]:

# Pandas DataFrame by lists of dictionaries. 
import pandas as pd 
  
# Initialise data to lists. 
data = [{'a': 10, 'b': 100, 'c':300}, 
        {'a':20, 'b': 200, 'c': 400}] 
  
# Creates DataFrame. 
df = pd.DataFrame(data) 
  
# Print (show) the data 
df 

Out[4]:

	a	b	c
0	10	100	300
1	20	200	400

In [11]:

# Let's generate pandas DataFrame by passing lists of dictionaries and row indexes.

In [7]:

# Pandas DataFrame by passing lists of Dictionaries and row indices. 
import pandas as pd 
  
# Intitialise data of lists  
dataone = [{'b':6, 'c':8}, {'a':100, 'b':200, 'c':300}] 
  
# Creates padas DataFrame by passing Lists of dictionaries and row index. 
df = pd.DataFrame(dataone, index = ['first', 'second']) 
  
# Print (show) the data 
df 

Out[7

]:

	b	c	a
first	6	8	NaN
second	200	300	100.0

    

In [13]:

# Another example is to create pandas DataFrame from lists of dictionaries with both row index as well as 
# column index.

In [8]:

# Pandas DataFrame with lists of dictionaries as well as row and column indexes. 
   
import pandas as pd 
   
# Intitialise lists data. 
data = [{'a': 1, 'b': 2}, 
        {'a': 5, 'b': 10, 'c': 20}] 
   
# With two column indices, values same as dictionary keys 
df1 = pd.DataFrame(data, index =['first', 
                                 'second'], 
                   columns =['a', 'b']) 
   
# With two column indices with one index with other name 
df2 = pd.DataFrame(data, index =['first', 
                                 'second'], 
                   columns =['a', 'b1']) 
   
# print (show) first data frame 
print (df1, "\n") 
   
# Print (show) second DataFrame. 
print (df2) 

        a   b
first   1   2
second  5  10 

        a  b1
first   1 NaN
second  5 NaN

In [15]:

# DataFrame using zip() function.

# Two lists can be merged by using list(zip()) function. Now, create the pandas DataFrame by calling 
# pd.DataFrame() function.

In [16]:

# pandas Datadaframe from lists using zip. 
	
import pandas as pd 
	
# List1 
Name = ['tom', 'krish', 'nick', 'juli'] 
	
# List2 
Age = [25, 30, 26, 22] 
	
# get the list of tuples from two lists. 
# and merge them by using zip(). 
list_of_tuples = list(zip(Name, Age)) 
	
# Assign data to tuples. 
list_of_tuples 


# Converting lists of tuples into 
# pandas Dataframe. 
df = pd.DataFrame(list_of_tuples, 
				columns = ['Name', 'Age']) 
	
# Print data. 
df 

Out[16]:

	Name	Age
0	tom	25
1	krish	30
2	nick	26
3	juli	22

In [17]:

# Pandas DataFrame from Dictionaries of series.

# To create DataFrame from Dicts of series, dictionary can be passed to form a DataFrame. The resultant 
# index is the union of all the series of passed indexed.

In [18]:

# Pandas Dataframe from Dicts of series. 

import pandas as pd 

# Intialise data to Dicts of series. 
d = {'one' : pd.Series([10, 20, 30, 40], 
					index =['a', 'b', 'c', 'd']), 
	'two' : pd.Series([10, 20, 30, 40], 
						index =['a', 'b', 'c', 'd'])} 

# creates Dataframe. 
df = pd.DataFrame(d) 

# print the data. 
df 

Out[18]:

	one	two
a	10	10
b	20	20
c	30	30
d	40	40