Creating Pandas dataframe using list of lists

In this article, we will explore the Creating Pandas data frame using a list of lists. A Pandas DataFrame is a versatile 2-dimensional labeled data structure with columns that can contain different data types. It is widely utilized as one of the most common objects in the Pandas library. There are various methods for Creating a Pandas data frame using a list of lists, and we will specifically delve into the approach of utilizing a list of lists for this purpose.

Create Pandas Dataframe using list of lists

There are various methods to create a Pandas data frame using a list of lists. Here, we are discussing some generally used methods that are following

• Using pd.DataFrame() Function
• Handling Missing Values
• DataFrame with Different Data Types

Using pd.DataFrame() function

In this example, we will create a list of lists and then pass it to the Pandas DataFrame function. Also, we will add the parameter of columns which will contain the column names.

Python3

# Import pandas library
import pandas as pd

# initialize list of lists
data = [['Geeks', 10], ['for', 15], ['geeks', 20]]

# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age'])

# print dataframe.
print(df)

Output:

    Name  Age
0  Geeks   10
1    for   15
2  geeks   20

Let’s see another example with the same implementation as above.

Python3

# Import pandas library
import pandas as pd

# initialize list of lists
data = [['DS', 'Linked_list', 10], ['DS', 'Stack', 9], ['DS', 'Queue', 7],
        ['Algo', 'Greedy', 8], ['Algo', 'DP', 6], ['Algo', 'BackTrack', 5], ]

# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Category', 'Name', 'Marks'])

# print dataframe.
print(df)

Output:

  Category         Name  Marks
0       DS  Linked_list     10
1       DS        Stack      9
2       DS        Queue      7
3     Algo       Greedy      8
4     Algo           DP      6
5     Algo    BackTrack      5

Handling Missing Values

Below code creates a Pandas DataFrame named df from a list of lists, where missing values represented as None are replaced with NaN. It prints the resulting DataFrame containing information about individuals, including names, ages, and occupations.

Python3

import pandas as pd
import numpy as np

# Creating a DataFrame with missing values from a list of lists
data = [['Geek1', 28, 'Engineer'],
        ['Geek2', None, 'Data Scientist'],
        ['Geek3', 32, None]]

columns = ['Name', 'Age', 'Occupation']

df = pd.DataFrame(data, columns=columns)
df = df.replace({None: np.nan})  # Replacing None with NaN for missing values
print(df)

Output :

          Name      Age    Occuption
0       Geek1      28.0      Engineer
1        Geek2     NaN      Data Scientist   
2       Geek3     32.0        NaN

DataFrame With Different Data Types

Below code creates a Pandas DataFrame from a list of lists, converting the ‘Age’ column to numeric format and handling errors, with the result printed. The ‘Age’ values, initially a mix of numbers and strings, are corrected to numeric format.

Python3

import pandas as pd

# Creating a DataFrame with different data types from a list of lists
data = [['Geek1', 28, 'Engineer'],
        ['Geek2', 25, 'Data Scientist'],
        ['Geek3', '32', 'Manager']]  # Age represented as a string

columns = ['Name', 'Age', 'Occupation']

df = pd.DataFrame(data, columns=columns)
df['Age'] = pd.to_numeric(df['Age'], errors='coerce')  # Convert 'Age' column to numeric, handling errors
print(df)

Output :

    Name   Age      Occupation
0  Geek1  28.0        Engineer
1  Geek2  25.0  Data Scientist
2  Geek3   NaN         Manager

Defining column names using Dataframe.columns() function 

Doing some operations on dataframe like transpose. And also defining the Dataframe without column parameters and using df.columns() for the same.

In this example the below code uses pandas to create a DataFrame from a list of lists, assigns column names (‘Col_1’, ‘Col_2’, ‘Col_3’), prints the original DataFrame, transposes it, and prints the result. Transposing swaps rows and columns in the DataFrame.

Python3

# Import pandas library
import pandas as pd

# initialize list of lists
data = [[1, 5, 10], [2, 6, 9], [3, 7, 8]]

# Create the pandas DataFrame
df = pd.DataFrame(data)

# specifying column names
df.columns = ['Col_1', 'Col_2', 'Col_3']

# print dataframe.
print(df, "\n")

# transpose of dataframe
df = df.transpose()
print("Transpose of above dataframe is-\n", df)

Output :

   Col_1  Col_2  Col_3
0      1      5     10
1      2      6      9
2      3      7      8 
Transpose of above dataframe is-
         0  1  2
Col_1   1  2  3
Col_2   5  6  7
Col_3  10  9  8