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Create a Pandas DataFrame from Lists

Last Updated : 11 Jul, 2025

Converting lists to DataFrames is crucial in data analysis, Pandas enabling you to perform sophisticated data manipulations and analyses with ease.

List to Dataframe Example

# Simple list
data = [1, 2, 3, 4, 5]
# Convert to DataFrame
df = pd.DataFrame(data, columns=['Numbers'])

Here we will discuss different ways to create a Pandas Dataframe from the lists:

Example 1: To convert a list to a Pandas DataFrame, you can use the pd.DataFrame() constructor. This function takes a list as input and creates a DataFrame with the same number of rows and columns as the input list.

# import pandas as pd
import pandas as pd

# list of strings
lst = ['Geeks', 'For', 'Geeks', 'is', 
            'portal', 'for', 'Geeks']

# Calling DataFrame constructor on list
df = pd.DataFrame(lst)
print(df)

Output:

 0
0   Geeks
1     For
2   Geeks
3      is
4  portal
5     for
6   Geeks

Example 2: To use lists in a dictionary to create a Pandas DataFrame, we Create a dictionary of lists and then Pass the dictionary to the pd.DataFrame() constructor. Optionally, we can specify the column names for the DataFrame by passing a list of strings to the columns parameter of the pd.DataFrame() constructor.

# importing pandas as pd 
import pandas as pd 
 
# list of name, degree, score
nme = ["aparna", "pankaj", "sudhir", "Geeku"]
deg = ["MBA", "BCA", "M.Tech", "MBA"]
scr = [90, 40, 80, 98]
 
# dictionary of lists 
dict = {'name': nme, 'degree': deg, 'score': scr} 
   
df = pd.DataFrame(dict)
   
print(df) 

Output:

 name  degree  score
0  aparna     MBA     90
1  pankaj     BCA     40
2  sudhir  M.Tech     80
3   Geeku     MBA     98

To create a Pandas DataFrame from lists using zip(). We can also use the zip() function to zip together multiple lists to create a DataFrame with more columns.

# import pandas as pd
import pandas as pd

# list of strings
lst = ['Geeks', 'For', 'Geeks', 'is', 'portal', 'for', 'Geeks']

# list of int
lst2 = [11, 22, 33, 44, 55, 66, 77]

# Calling DataFrame constructor after zipping
# both lists, with columns specified
df = pd.DataFrame(list(zip(lst, lst2)),
               columns =['Name', 'val'])
print(df)

Output:

Name  val
0   Geeks   11
1     For   22
2   Geeks   33
3      is   44
4  portal   55
5     for   66
6   Geeks   77

To create a Pandas DataFrame using a multi-dimensional list with column names and dtypes specified. By specifying dtypes, we can ensure that the DataFrame is created with the correct data types.

import pandas as pd

# List1 
lst = [['tom', 'reacher', 25], ['krish', 'pete', 30],
       ['nick', 'wilson', 26], ['juli', 'williams', 22]]

# Create DataFrame
df = pd.DataFrame(lst, columns=['FName', 'LName', 'Age'])

# Convert 'Age' column to float
df['Age'] = df['Age'].astype(float)

print(df)

Output:

   FName     LName   Age
0    tom   reacher  25.0
1  krish      pete  30.0
2   nick    wilson  26.0
3   juli  williams  22.0

To create a DataFrame using a multi-dimensional list, you can use the pd.DataFrame() constructor. The pd.DataFrame() constructor takes a list of lists as input and creates a DataFrame with the same number of rows and columns as the input list.

# import pandas as pd
import pandas as pd 
  
# List1 
lst = [['tom', 25], ['krish', 30],
       ['nick', 26], ['juli', 22]]
  
df = pd.DataFrame(lst, columns =['Name', 'Age'])
print(df)

Output:

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

To create a DataFrame using a list with index and column names, you can use the pd.DataFrame() constructor with the index and columns parameters.

# import pandas as pd
import pandas as pd

# list of strings
lst = ['Geeks', 'For', 'Geeks', 'is', 'portal', 'for', 'Geeks']

# Calling DataFrame constructor on list
# with indices and columns specified
df = pd.DataFrame(lst, index =['a', 'b', 'c', 'd', 'e', 'f', 'g'],
                                              columns =['Names'])
print(df)

Output:

Names
a   Geeks
b     For
c   Geeks
d      is
e  portal
f     for
g   Geeks

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