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df = pd.DataFrame(data, columns=['Name', 'Age', 'City'])
print(df)
Output:
Name Age City
0 1 Mayur 25
1 2 Esha 30
2 3 Vedant 28
u From a dictionary of lists or arrays: You can create a DataFrame from a dictionary where keys are column names
and values are lists or arrays containing the data for each column.

Program 6: To create a DataFrame using dictionary of lists or arrays

import pandas as pd
data = {'Name': ['Yash', 'Rehan', 'Neha'],
'Age': [25, 30, 28],
'City': ['Delhi', 'Mumbai', 'Bengaluru']}
df = pd.DataFrame(data)
print(df)
Output:
Name Age City
0 Yash 25 Delhi
1 Rehan 30 Mumbai
2 Neha 28 Bengaluru
u Using NumPy ndarrays: Creating DataFrames from NumPy ndarrays allows you to leverage the power of both
libraries for efficient data processing and analysis. NumPy provides efficient array computations, while Pandas offers
powerful data manipulation and analysis tools.

Program 7: To create a DataFrame using ndarray

import pandas as pd
import numpy as np
# Using NumPy ndarray
data = np.array([[1, 'Mayur', 25],
[2, 'Esha', 30],
[3, 'Vedant', 28]])
df = pd.DataFrame(data, columns=['ID', 'Name', 'Age'])
print(df)

Output:
ID Name Age
0 1 Mayur 25
1 2 Esha 30

2 3 Vedant 28
u Using a List of Dictionaries: You can create a DataFrame from a list of dictionaries where each dictionary represents
a row and keys represent column names.

200 Touchpad Artificial Intelligence - XI