In this article, we will explore how to create a Pandas DataFrame using a two-dimensional list in Python.
Pandas is a powerful library for data manipulation and analysis, and the DataFrame is one of its core data
structures.
Creating a DataFrame
To create a DataFrame from a two-dimensional list, we can use the pd.DataFrame() function provided by
Pandas. Let’s take a look at an example:
import pandas as pd data = [['Alice', 25, 'Engineer'], ['Bob', 30, 'Developer'], ['Charlie', 35, 'Manager']] df = pd.DataFrame(data, columns=['Name', 'Age', 'Job']) print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
In Above code, we first import the Pandas library using the import statement. Then, we define our
data as a two-dimensional list called data. Each inner list represents a row in the DataFrame, and
each element within the inner list represents a column value.
We then pass the data list and the column names as arguments to the pd.DataFrame()
function, which creates the DataFrame. Finally, we use the print() function to display the
DataFrame.
Example:
import pandas as pd
# Define the two-dimensional list
data = [['Alice', 25, 'Engineer'],
['Bob', 30, 'Developer'],
['Charlie', 35, 'Manager']]
# Create a DataFrame
df = pd.DataFrame(data, columns=['Name', 'Age', 'Job'])
# Print the DataFrame
print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
Method 1: Using a Dictionary to Define Column Values
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'Job': ['Engineer', 'Developer', 'Manager']}
df = pd.DataFrame(data)
print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
Method 2: Using List Comprehension
import pandas as pd
data = [['Alice', 25, 'Engineer'],
['Bob', 30, 'Developer'],
['Charlie', 35, 'Manager']]
columns = ['Name', 'Age', 'Job']
df = pd.DataFrame([dict(zip(columns, row)) for row in data])
print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
Method 3: Using the from_records() Method
import pandas as pd
data = [['Alice', 25, 'Engineer'],
['Bob', 30, 'Developer'],
['Charlie', 35, 'Manager']]
columns = ['Name', 'Age', 'Job']
df = pd.DataFrame.from_records(data, columns=columns)
print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
Method 4: Using the append() Method
import pandas as pd
data = [['Alice', 25, 'Engineer'],
['Bob', 30, 'Developer'],
['Charlie', 35, 'Manager']]
columns = ['Name', 'Age', 'Job']
df = pd.DataFrame(columns=columns)
for row in data:
df = df.append(pd.Series(row, index=columns), ignore_index=True)
print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
Method 5: Using the DataFrame constructor with zip()
import pandas as pd
data = [['Alice', 25, 'Engineer'],
['Bob', 30, 'Developer'],
['Charlie', 35, 'Manager']]
columns = ['Name', 'Age', 'Job']
df = pd.DataFrame(list(zip(*data)), columns=columns)
print(df)
Output:
Name Age Job 0 Alice 25 Engineer 1 Bob 30 Developer 2 Charlie 35 Manager
Last Updated on May 17, 2023 by admin