Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Creating Pandas Dataframe can be achieved in multiple ways. Let’s see how can we create a Pandas DataFrame from Lists.
Create a Pandas DataFrame from Lists
Python3
import pandas as pd
lst = [ 'Geeks' , 'For' , 'Geeks' , 'is' ,
'portal' , 'for' , 'Geeks' ]
df = pd.DataFrame(lst)
df
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Output:

Dataframe using list with index and column names
Python3
import pandas as pd
lst = [ 'Geeks' , 'For' , 'Geeks' , 'is' , 'portal' , 'for' , 'Geeks' ]
df = pd.DataFrame(lst, index = [ 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , 'g' ],
columns = [ 'Names' ])
df
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Output:

Create a Pandas DataFrame from Lists Using zip() for zipping two lists
Python3
import pandas as pd
lst = [ 'Geeks' , 'For' , 'Geeks' , 'is' , 'portal' , 'for' , 'Geeks' ]
lst2 = [ 11 , 22 , 33 , 44 , 55 , 66 , 77 ]
df = pd.DataFrame( list ( zip (lst, lst2)),
columns = [ 'Name' , 'val' ])
df
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Output:

Creating DataFrame using multi-dimensional list
Python3
import pandas as pd
lst = [[ 'tom' , 25 ], [ 'krish' , 30 ],
[ 'nick' , 26 ], [ 'juli' , 22 ]]
df = pd.DataFrame(lst, columns = [ 'Name' , 'Age' ])
df
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Output:

Using multi-dimensional list with column name and dtype specified.
Python3
import pandas as pd
lst = [[ 'tom' , 'reacher' , 25 ], [ 'krish' , 'pete' , 30 ],
[ 'nick' , 'wilson' , 26 ], [ 'juli' , 'williams' , 22 ]]
df = pd.DataFrame(lst, columns = [ 'FName' , 'LName' , 'Age' ], dtype = float )
df
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Output:

Using lists in dictionary to create dataframe
Python3
import pandas as pd
nme = ["aparna", "pankaj", "sudhir", "Geeku"]
deg = ["MBA", "BCA", "M.Tech", "MBA"]
scr = [ 90 , 40 , 80 , 98 ]
dict = { 'name' : nme, 'degree' : deg, 'score' : scr}
df = pd.DataFrame( dict )
df
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Output:
