10 Different Ways to Create Pandas DataFrame | Python Data Analysis

In this video, we'll explore ten different methods to create a Pandas DataFrame in Python. Pandas is a powerful library for data manipulation and analysis, and understanding these various techniques will boost your data handling skills. Whether you prefer dictionaries, lists, CSV files, or other data sources, we've got you covered! Watch the video to learn how to create DataFrames effortlessly using Pandas. #From a Dictionary: import pandas as pd # Example data in dictionary format data = { 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'London', 'Paris'] } # Create DataFrame from dictionary df = pd.DataFrame(data) print(df) -------------------------------------- 2)From a List of Lists: import pandas as pd # Example data in list of lists format data = [ ['Alice', 25, 'New York'], ['Bob', 30, 'London'], ['Charlie', 35, 'Paris'] ] # Create DataFrame from list of lists df = pd.DataFrame(data, columns=['Name', 'Age', 'City']) print(df) ------------------- 3)From a CSV File: import pandas as pd # Read data from a CSV file df = pd.read_csv('data.csv') print(df) ---------- 4)From a List of Dictionaries: import pandas as pd # Example data in list of dictionaries format data = [ {'Name': 'Alice', 'Age': 25, 'City': 'New York'}, {'Name': 'Bob', 'Age': 30, 'City': 'London'}, {'Name': 'Charlie', 'Age': 35, 'City': 'Paris'} ] # Create DataFrame from list of dictionaries df = pd.DataFrame(data) 5) From a List of Tuples: import pandas as pd # Example data in list of tuples format data = [ ('Alice', 25, 'New York'), ('Bob', 30, 'London'), ('Charlie', 35, 'Paris') ] # Create DataFrame from list of tuples df = pd.DataFrame(data, columns=['Name', 'Age', 'City']) print(df) ----------- 6)From a NumPy Array: import pandas as pd import numpy as np # Example data in NumPy array format data = np.array([ ['Alice', 25, 'New York'], ['Bob', 30, 'London'], ['Charlie', 35, 'Paris'] ]) # Create DataFrame from NumPy array df = pd.DataFrame(data, columns=['Name', 'Age', 'City']) print(df) 7) From a Dictionary of Series: import pandas as pd # Example data in dictionary of Series format data = { 'Name': pd.Series(['Alice', 'Bob', 'Charlie']), 'Age': pd.Series([25, 30, 35]), 'City': pd.Series(['New York', 'London', 'Paris']) } # Create DataFrame from dictionary of Series df = pd.DataFrame(data) print(df) 8)From a NumPy structured array: import pandas as pd import numpy as np # Example data in NumPy structured array format data = np.array([ ('Alice', 25, 'New York'), ('Bob', 30, 'London'), ('Charlie', 35, 'Paris') ], dtype=[('Name', 'U10'), ('Age', int), ('City', 'U10')]) # Create DataFrame from NumPy structured array df = pd.DataFrame(data) print(df) 9) From a database query (using pandas' database connectors): import pandas as pd import sqlite3 # Connect to the SQLite database conn = sqlite3.connect('example.db') # Query data from the database and create DataFrame query = "SELECT * FROM table_name;" df = pd.read_sql(query, conn) print(df)