Convert List to DataFrame in Python

In this article, we will discuss how to convert a single or multiple lists to a DataFrame.

Table Of Contents

• Convert list of lists to DataFrame in Pandas
• Convert Lists of tuples to DataFrame in Pandas
• Convert List of lists to DataFrame & set column names and indexes
• Convert List of tuples to DataFrame and skip certain columns
• Convert multiple lists to DataFrame in Pandas

Python’s pandas library provide a constructor of DataFrame to create a Dataframe by passing objects i.e.

pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)

Here data parameter can be a numpy ndarray, lists, dict, or an other DataFrame. Also, columns and index are for column and index labels. Let’s use this to convert lists to dataframe object from lists.

Convert list of lists to DataFrame in Pandas

Suppose we have a list of lists i.e.

# List of lists
students = [ ['jack', 34, 'Sydeny'] ,
             ['Riti', 30, 'Delhi' ] ,
             ['Aadi', 16, 'New York'] ]

Pass this list to DataFrame’s constructor to create a dataframe object i.e.

import pandas as pd

# Creating a DataFrame object from list of lists
dfObj = pd.DataFrame(students) 

# Display the DataFrame
print(dfObj)

Contents of the created DataFrames are as follows,

      0   1         2
0  jack  34    Sydeny
1  Riti  30     Delhi
2  Aadi  16  New York

Convert Lists of tuples to DataFrame in Pandas

Just like list of lists we can pass list of tuples in dataframe constructor to create a dataframe.

Suppose we have a list of tuples i.e.

# List of Tuples
students = [ ('jack', 34, 'Sydeny') ,
             ('Riti', 30, 'Delhi' ) ,
             ('Aadi', 16, 'New York') ]

Pass this list of tuples to DataFrame’s constructor to create a DataFrame object i.e.

import pandas as pd

# Creating a DataFrame object from list of tuple
dfObj = pd.DataFrame(students) 

# Display the DataFrame
print(dfObj)

Contents of the created dataframe is as follows,

      0   1         2
0  jack  34    Sydeny
1  Riti  30     Delhi
2  Aadi  16  New York

Both Column & Index labels are default. But we can also provide them i.e.

Convert List of lists to DataFrame & set column names and indexes

import pandas as pd

# List of lists
students = [ ['jack', 34, 'Sydeny'] ,
             ['Riti', 30, 'Delhi' ] ,
             ['Aadi', 16, 'New York'] ]


# Convert list of tuples to dataframe and
# set column names and indexes
dfObj = pd.DataFrame(students,
                     columns = ['Name' , 'Age', 'City'],
                     index=['a', 'b', 'c']) 


# Display the DataFrame
print(dfObj)

Contents of the created dataframe is as follows,

   Name  Age      City
a  jack   34    Sydeny
b  Riti   30     Delhi
c  Aadi   16  New York

Convert List of tuples to DataFrame and skip certain columns

What of in our list of tuples we have 3 entries in each tuple. What if we want to use 1st and 3rd entry only? Let’s create a dataframe by skipping 2nd entry in tuples i.e.

import pandas as pd

# List of Tuples
students = [ ('jack', 34, 'Sydeny') ,
             ('Riti', 30, 'Delhi' ) ,
             ('Aadi', 16, 'New York') ]


# Create datafrae from student list of tuples
# but skip column 'Age' i.e. only with 2 columns
dfObj = pd.DataFrame.from_records( students,
                                   exclude=['Age'],
                                   columns = ['Name' , 'Age', 'City'],
                                   index=['a', 'b', 'c']) 



# Display the DataFrame
print(dfObj)

Contents of the created dataframe is as follows,

   Name      City
a  jack    Sydeny
b  Riti     Delhi
c  Aadi  New York

This DataFrame has only two columns because we skipped the middle entry from each of the tuple in list.

Convert multiple lists to DataFrame in Pandas

Suppose we have 3 different lists and we want to convert them to a DataFrame, with each list as a column. To do that,
zip the lists to create a list of tuples and create a dataframe with this zipped lists i.e.

import pandas as pd

listOfNames =  ['Jack', 'Riti', 'Aadi']
listOfAge   =  [34, 30, 16]
listOfCity  =  ['Sydney', 'Delhi', 'New york']

# Create a zipped list of tuples from above lists
zippedList =  list(zip(listOfNames, listOfAge, listOfCity)) 

# Create a dataframe from zipped list
dfObj = pd.DataFrame(zippedList,
                     columns = ['Name' , 'Age', 'City'],
                     index=['a', 'b', 'c']) 



# Display the DataFrame
print(dfObj)

Contents of the created dataframe is as follows,

   Name  Age      City
a  Jack   34    Sydney
b  Riti   30     Delhi
c  Aadi   16  New york

The Complete example is as follows,

import pandas as pd

students = [['jack', 34, 'Sydeny'] ,
            ['Riti', 30, 'Delhi' ] ,
            ['Aadi', 16, 'New York'] ]
    
print("****Create a Dataframe from list of lists *****")

# Creating a dataframe object from listoftuples
dfObj = pd.DataFrame(students) 

print("Dataframe : " , dfObj, sep='\n')

# List of Tuples
students = [('jack', 34, 'Sydeny') ,
            ('Riti', 30, 'Delhi' ) ,
            ('Aadi', 16, 'New York') ]

print("****Create a Dataframe from list of tuple *****")

# Creating a dataframe object from listoftuples
dfObj = pd.DataFrame(students) 

print("Dataframe : " , dfObj, sep='\n')


print("****Create a Dataframe from list of tuple, also set column names and indexes *****")

#Convert list of tuples to dataframe and set column names and indexes
dfObj = pd.DataFrame(students,
                     columns = ['Name' , 'Age', 'City'],
                     index=['a', 'b', 'c']) 

print("Dataframe : " , dfObj, sep='\n')

print("****Create dataframe from list of tuples and skip certain columns*********")

# Create datafrae from student list but
# skip column 'Age' i.e. only with 2 columns
dfObj = pd.DataFrame.from_records( students,
                                   exclude=['Age'],
                                   columns = ['Name' , 'Age', 'City'],
                                   index=['a', 'b', 'c']) 

print("Dataframe : " , dfObj, sep='\n')

print("***Create dataframe from multiple lists***")

listOfNames =  ['jack', 'Riti', 'Aadi']
listOfAge   =  [34, 30, 16]
listOfCity  =  ['Sydney', 'Delhi', 'New york']

# Create a zipped list of tuples from above lists
zippedList =  list(zip(listOfNames, listOfAge, listOfCity))

print("zippedList = " , zippedList)

# Create a dataframe from zipped list
dfObj = pd.DataFrame(zippedList,
                     columns = ['Name' , 'Age', 'City'],
                     index=['a', 'b', 'c']) 

print("Dataframe : " , dfObj, sep='\n')

Output:

****Create a Dataframe from list of lists *****
Dataframe : 
      0   1         2
0  jack  34    Sydeny
1  Riti  30     Delhi
2  Aadi  16  New York
****Create a Dataframe from list of tuple *****
Dataframe : 
      0   1         2
0  jack  34    Sydeny
1  Riti  30     Delhi
2  Aadi  16  New York
****Create a Dataframe from list of tuple, also set column names and indexes *****
Dataframe : 
   Name  Age      City
a  jack   34    Sydeny
b  Riti   30     Delhi
c  Aadi   16  New York
****Create dataframe from list of tuples and skip certain columns*********
Dataframe : 
   Name      City
a  jack    Sydeny
b  Riti     Delhi
c  Aadi  New York
***Create dataframe from multiple lists***
zippedList =  [('jack', 34, 'Sydney'), ('Riti', 30, 'Delhi'), ('Aadi', 16, 'New york')]
Dataframe : 
   Name  Age      City
a  jack   34    Sydney
b  Riti   30     Delhi
c  Aadi   16  New york

Summary:

We learned about different ways to convert list to a Pandas DataFrame in Python.