Create a Pandas DataFrame from Lists
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:
Table of Content
Create DataFrame from List using Dictionary
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
Convert List to Pandas Dataframe using zip()
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
Create DataFrame from List by Changing Datatype
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
Create DataFrame from List using Multi-dimensional List
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
Create DataFrame from List with Index and Column Names
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
Convert List to Dataframe – FAQs
How to convert a list to a pandas DataFrame?
You can convert a Python list to a Pandas DataFrame using the ‘pd.DataFrame()’ constructor.
import pandas as pd
my_list = [1, 2, 3, 4, 5]
df = pd.DataFrame(my_list, columns=[‘Numbers’])
print(df)
How to make a pandas DataFrame a list?
To convert a Pandas DataFrame to a Python list, you can access the DataFrame values and convert them to a list using ‘.tolist()’.
import pandas as pd
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})
my_list = df.values.tolist()
print(my_list)
How to create a pandas DataFrame from a list of tuples?
You can create a DataFrame from a list of tuples using the ‘pd.DataFrame()’ constructor and specifying column names.
import pandas as pd
data = [(‘Alice’, 25), (‘Bob’, 30), (‘Charlie’, 35)]
df = pd.DataFrame(data, columns=[‘Name’, ‘Age’])
print(df)
How to convert a nested list to a DataFrame?
To convert a nested list (list of lists) to a DataFrame, you can directly pass it to ‘pd.DataFrame()’.
import pandas as pd
nested_list = [[1, ‘Alice’, 25], [2, ‘Bob’, 30], [3, ‘Charlie’, 35]]
df = pd.DataFrame(nested_list, columns=[‘ID’, ‘Name’, ‘Age’])
print(df)
How to list DataFrames into one DataFrame in Pandas?
If you have multiple DataFrames and want to concatenate them row-wise into one DataFrame, you can use ‘pd.concat()’.
import pandas as pd
df1 = pd.DataFrame({‘A’: [1, 2], ‘B’: [3, 4]})
df2 = pd.DataFrame({‘A’: [5, 6], ‘B’: [7, 8]})
df_combined = pd.concat([df1, df2], ignore_index=True)
print(df_combined)
